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What It Takes to Think Deeply About Complex Problems

  • Tony Schwartz

how to solve a problem with complex

Three ways to embrace a more nuanced, spacious perspective.

The problems we’re facing often seem as intractable as they do complex. But as Albert Einstein famously observed, “We cannot solve our problems with the same level of thinking that created them.” So what does it take to increase the complexity of our thinking? To cultivate a more nuanced, spacious perspective, start by challenging your convictions. Ask yourself, “What am I not seeing here?” and “What else might be true?” Second, do your most challenging task first every day, when your mind is fresh and before distractions arise. And third, pay attention to how you’re feeling. Embracing complexity means learning to better manage tough emotions like fear and anger.

The problems we’re facing often seem as complex as they do intractable. And as Albert Einstein is often quoted as saying, “We cannot solve our problems with the same level of thinking that created them.” So what does it take to increase the complexity of our thinking?

how to solve a problem with complex

  • Tony Schwartz is the CEO of The Energy Project and the author of The Way We’re Working Isn’t Working . Become a fan of The Energy Project on Facebook .

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35 problem-solving techniques and methods for solving complex problems

Problem solving workshop

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All teams and organizations encounter challenges as they grow. There are problems that might occur for teams when it comes to miscommunication or resolving business-critical issues . You may face challenges around growth , design , user engagement, and even team culture and happiness. In short, problem-solving techniques should be part of every team’s skillset.

Problem-solving methods are primarily designed to help a group or team through a process of first identifying problems and challenges , ideating possible solutions , and then evaluating the most suitable .

Finding effective solutions to complex problems isn’t easy, but by using the right process and techniques, you can help your team be more efficient in the process.

So how do you develop strategies that are engaging, and empower your team to solve problems effectively?

In this blog post, we share a series of problem-solving tools you can use in your next workshop or team meeting. You’ll also find some tips for facilitating the process and how to enable others to solve complex problems.

Let’s get started! 

How do you identify problems?

How do you identify the right solution.

  • Tips for more effective problem-solving

Complete problem-solving methods

  • Problem-solving techniques to identify and analyze problems
  • Problem-solving techniques for developing solutions

Problem-solving warm-up activities

Closing activities for a problem-solving process.

Before you can move towards finding the right solution for a given problem, you first need to identify and define the problem you wish to solve. 

Here, you want to clearly articulate what the problem is and allow your group to do the same. Remember that everyone in a group is likely to have differing perspectives and alignment is necessary in order to help the group move forward. 

Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner. It can be scary for people to stand up and contribute, especially if the problems or challenges are emotive or personal in nature. Be sure to try and create a psychologically safe space for these kinds of discussions.

Remember that problem analysis and further discussion are also important. Not taking the time to fully analyze and discuss a challenge can result in the development of solutions that are not fit for purpose or do not address the underlying issue.

Successfully identifying and then analyzing a problem means facilitating a group through activities designed to help them clearly and honestly articulate their thoughts and produce usable insight.

With this data, you might then produce a problem statement that clearly describes the problem you wish to be addressed and also state the goal of any process you undertake to tackle this issue.  

Finding solutions is the end goal of any process. Complex organizational challenges can only be solved with an appropriate solution but discovering them requires using the right problem-solving tool.

After you’ve explored a problem and discussed ideas, you need to help a team discuss and choose the right solution. Consensus tools and methods such as those below help a group explore possible solutions before then voting for the best. They’re a great way to tap into the collective intelligence of the group for great results!

Remember that the process is often iterative. Great problem solvers often roadtest a viable solution in a measured way to see what works too. While you might not get the right solution on your first try, the methods below help teams land on the most likely to succeed solution while also holding space for improvement.

Every effective problem solving process begins with an agenda . A well-structured workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.

In SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.

The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!

how to solve a problem with complex

Tips for more effective problem solving

Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.

Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!

Clearly define the problem

Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.

This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.

Don’t jump to conclusions

It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.

The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.  

Try different approaches  

Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.

Don’t take it personally 

Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.

You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.

Get the right people in the room

Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!

If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.

Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.

Document everything

The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!

Bring a facilitator 

Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!

Develop your problem-solving skills

It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.

You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!

Design a great agenda

Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.

Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!

In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.

If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.

  • Six Thinking Hats
  • Lightning Decision Jam
  • Problem Definition Process
  • Discovery & Action Dialogue
Design Sprint 2.0
  • Open Space Technology

1. Six Thinking Hats

Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.

Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.

Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.

2. Lightning Decision Jam

Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.

Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.

In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.

From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on. 

By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages. 

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

3. Problem Definition Process

While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design. 

By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.

Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.

This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!

Problem Definition   #problem solving   #idea generation   #creativity   #online   #remote-friendly   A problem solving technique to define a problem, challenge or opportunity and to generate ideas.

4. The 5 Whys 

Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges. 

The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results. 

By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.

The 5 Whys   #hyperisland   #innovation   This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.

5. World Cafe

World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.

World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!

Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold. 

World Cafe   #hyperisland   #innovation   #issue analysis   World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.

6. Discovery & Action Dialogue (DAD)

One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.

With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!

This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.

Discovery & Action Dialogue (DAD)   #idea generation   #liberating structures   #action   #issue analysis   #remote-friendly   DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.

7. Design Sprint 2.0

Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.

Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.

Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.

8. Open space technology

Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.

Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.

Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!

Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.

Open Space Technology   #action plan   #idea generation   #problem solving   #issue analysis   #large group   #online   #remote-friendly   Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation

Techniques to identify and analyze problems

Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.

While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.

We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.

Let’s take a look!

  • The Creativity Dice
  • Fishbone Analysis
  • Problem Tree
  • SWOT Analysis
  • Agreement-Certainty Matrix
  • The Journalistic Six
  • LEGO Challenge
  • What, So What, Now What?
  • Journalists

Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?

Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed. 

Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.  

No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.

Flip It!   #gamestorming   #problem solving   #action   Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.

10. The Creativity Dice

One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed. 

In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.

Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable. 

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

11. Fishbone Analysis

Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.

Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around. 

Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish. 

Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.

Fishbone Analysis   #problem solving   ##root cause analysis   #decision making   #online facilitation   A process to help identify and understand the origins of problems, issues or observations.

12. Problem Tree 

Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them. 

In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.

Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.

Problem tree   #define intentions   #create   #design   #issue analysis   A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.

13. SWOT Analysis

Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.

Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.

Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward. 

SWOT analysis   #gamestorming   #problem solving   #action   #meeting facilitation   The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.

14. Agreement-Certainty Matrix

Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.

The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results. 

If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process. 

Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.

It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.

SQUID   #gamestorming   #project planning   #issue analysis   #problem solving   When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.

16. Speed Boat

To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.

Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.

In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!   

Speed Boat   #gamestorming   #problem solving   #action   Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.

17. The Journalistic Six

Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.

Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.

The Journalistic Six – Who What When Where Why How   #idea generation   #issue analysis   #problem solving   #online   #creative thinking   #remote-friendly   A questioning method for generating, explaining, investigating ideas.

18. LEGO Challenge

Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills. 

The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.

What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO! 

LEGO Challenge   #hyperisland   #team   A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.

19. What, So What, Now What?

If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.

The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems. 

Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.

Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken. 

This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.

W³ – What, So What, Now What?   #issue analysis   #innovation   #liberating structures   You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!

20. Journalists  

Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.

Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.

In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.

Journalists   #vision   #big picture   #issue analysis   #remote-friendly   This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.

Problem-solving techniques for developing solutions 

The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to narrow down to the correct solution.

Use these problem-solving techniques when you want to help your team find consensus, compare possible solutions, and move towards taking action on a particular problem.

  • Improved Solutions
  • Four-Step Sketch
  • 15% Solutions
  • How-Now-Wow matrix
  • Impact Effort Matrix

21. Mindspin  

Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly. 

With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation. 

This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex. 

MindSpin   #teampedia   #idea generation   #problem solving   #action   A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.

22. Improved Solutions

After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result. 

One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution. 

Improved Solutions   #creativity   #thiagi   #problem solving   #action   #team   You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.

23. Four Step Sketch

Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged. 

By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.

Four-Step Sketch   #design sprint   #innovation   #idea generation   #remote-friendly   The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper,  Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint

24. 15% Solutions

Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change. 

Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.

Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.   

It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change. 

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

25. How-Now-Wow Matrix

The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process. 

When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.

Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud. 

How-Now-Wow Matrix   #gamestorming   #idea generation   #remote-friendly   When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.

26. Impact and Effort Matrix

All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice. 

The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.

Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them. 

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

27. Dotmocracy

If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action? 

Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus. 

One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively. 

Dotmocracy   #action   #decision making   #group prioritization   #hyperisland   #remote-friendly   Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.

All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.

Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.

  • Check-in/Check-out
  • Doodling Together
  • Show and Tell
  • Constellations
  • Draw a Tree

28. Check-in / Check-out

Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process.

Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute. 

If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!

Check-in / Check-out   #team   #opening   #closing   #hyperisland   #remote-friendly   Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.

29. Doodling Together  

Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start. 

Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems. 

Doodling Together   #collaboration   #creativity   #teamwork   #fun   #team   #visual methods   #energiser   #icebreaker   #remote-friendly   Create wild, weird and often funny postcards together & establish a group’s creative confidence.

30. Show and Tell

You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.

Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.

By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team! 

Show and Tell   #gamestorming   #action   #opening   #meeting facilitation   Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.

31. Constellations

Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.

Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible. 

Constellations   #trust   #connection   #opening   #coaching   #patterns   #system   Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.

32. Draw a Tree

Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.

Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic. 

Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.

All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.

Draw a Tree   #thiagi   #opening   #perspectives   #remote-friendly   With this game you can raise awarness about being more mindful, and aware of the environment we live in.

Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.

Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.

  • One Breath Feedback
  • Who What When Matrix
  • Response Cards

How do I conclude a problem-solving process?

All good things must come to an end. With the bulk of the work done, it can be tempting to conclude your workshop swiftly and without a moment to debrief and align. This can be problematic in that it doesn’t allow your team to fully process the results or reflect on the process.

At the end of an effective session, your team will have gone through a process that, while productive, can be exhausting. It’s important to give your group a moment to take a breath, ensure that they are clear on future actions, and provide short feedback before leaving the space. 

The primary purpose of any problem-solving method is to generate solutions and then implement them. Be sure to take the opportunity to ensure everyone is aligned and ready to effectively implement the solutions you produced in the workshop.

Remember that every process can be improved and by giving a short moment to collect feedback in the session, you can further refine your problem-solving methods and see further success in the future too.

33. One Breath Feedback

Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round. 

One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them. 

One breath feedback   #closing   #feedback   #action   This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.

34. Who What When Matrix 

Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.

The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward. 

Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved. 

Who/What/When Matrix   #gamestorming   #action   #project planning   With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.

35. Response cards

Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out! 

Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.

Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised. 

Response Cards   #debriefing   #closing   #structured sharing   #questions and answers   #thiagi   #action   It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.

Save time and effort discovering the right solutions

A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?

With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks  to build your agenda. When you make changes or update your agenda, your session  timing   adjusts automatically , saving you time on manual adjustments.

Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.

Explore  how to use SessionLab  to design effective problem solving workshops or  watch this five minute video  to see the planner in action!

how to solve a problem with complex

Over to you

The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of creative exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.

Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you! 

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thank you very much for these excellent techniques

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Certainly wonderful article, very detailed. Shared!

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Your list of techniques for problem solving can be helpfully extended by adding TRIZ to the list of techniques. TRIZ has 40 problem solving techniques derived from methods inventros and patent holders used to get new patents. About 10-12 are general approaches. many organization sponsor classes in TRIZ that are used to solve business problems or general organiztational problems. You can take a look at TRIZ and dwonload a free internet booklet to see if you feel it shound be included per your selection process.

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></center></p><h2>17 Smart Problem-Solving Strategies: Master Complex Problems</h2><ul><li>March 3, 2024</li><li>Productivity</li><li>25 min read</li></ul><p><center><img style=

Struggling to overcome challenges in your life? We all face problems, big and small, on a regular basis.

So how do you tackle them effectively? What are some key problem-solving strategies and skills that can guide you?

Effective problem-solving requires breaking issues down logically, generating solutions creatively, weighing choices critically, and adapting plans flexibly based on outcomes. Useful strategies range from leveraging past solutions that have worked to visualizing problems through diagrams. Core skills include analytical abilities, innovative thinking, and collaboration.

Want to improve your problem-solving skills? Keep reading to find out 17 effective problem-solving strategies, key skills, common obstacles to watch for, and tips on improving your overall problem-solving skills.

Key Takeaways:

  • Effective problem-solving requires breaking down issues logically, generating multiple solutions creatively, weighing choices critically, and adapting plans based on outcomes.
  • Useful problem-solving strategies range from leveraging past solutions to brainstorming with groups to visualizing problems through diagrams and models.
  • Core skills include analytical abilities, innovative thinking, decision-making, and team collaboration to solve problems.
  • Common obstacles include fear of failure, information gaps, fixed mindsets, confirmation bias, and groupthink.
  • Boosting problem-solving skills involves learning from experts, actively practicing, soliciting feedback, and analyzing others’ success.
  • Onethread’s project management capabilities align with effective problem-solving tenets – facilitating structured solutions, tracking progress, and capturing lessons learned.

What Is Problem-Solving?

Problem-solving is the process of understanding an issue, situation, or challenge that needs to be addressed and then systematically working through possible solutions to arrive at the best outcome.

It involves critical thinking, analysis, logic, creativity, research, planning, reflection, and patience in order to overcome obstacles and find effective answers to complex questions or problems.

The ultimate goal is to implement the chosen solution successfully.

What Are Problem-Solving Strategies?

Problem-solving strategies are like frameworks or methodologies that help us solve tricky puzzles or problems we face in the workplace, at home, or with friends.

Imagine you have a big jigsaw puzzle. One strategy might be to start with the corner pieces. Another could be looking for pieces with the same colors. 

Just like in puzzles, in real life, we use different plans or steps to find solutions to problems. These strategies help us think clearly, make good choices, and find the best answers without getting too stressed or giving up.

Why Is It Important To Know Different Problem-Solving Strategies?

Why Is It Important To Know Different Problem-Solving Strategies

Knowing different problem-solving strategies is important because different types of problems often require different approaches to solve them effectively. Having a variety of strategies to choose from allows you to select the best method for the specific problem you are trying to solve.

This improves your ability to analyze issues thoroughly, develop solutions creatively, and tackle problems from multiple angles. Knowing multiple strategies also aids in overcoming roadblocks if your initial approach is not working.

Here are some reasons why you need to know different problem-solving strategies:

  • Different Problems Require Different Tools: Just like you can’t use a hammer to fix everything, some problems need specific strategies to solve them.
  • Improves Creativity: Knowing various strategies helps you think outside the box and come up with creative solutions.
  • Saves Time: With the right strategy, you can solve problems faster instead of trying things that don’t work.
  • Reduces Stress: When you know how to tackle a problem, it feels less scary and you feel more confident.
  • Better Outcomes: Using the right strategy can lead to better solutions, making things work out better in the end.
  • Learning and Growth: Each time you solve a problem, you learn something new, which makes you smarter and better at solving future problems.

Knowing different ways to solve problems helps you tackle anything that comes your way, making life a bit easier and more fun!

17 Effective Problem-Solving Strategies

Effective problem-solving strategies include breaking the problem into smaller parts, brainstorming multiple solutions, evaluating the pros and cons of each, and choosing the most viable option. 

Critical thinking and creativity are essential in developing innovative solutions. Collaboration with others can also provide diverse perspectives and ideas. 

By applying these strategies, you can tackle complex issues more effectively.

Now, consider a challenge you’re dealing with. Which strategy could help you find a solution? Here we will discuss key problem strategies in detail.

1. Use a Past Solution That Worked

Use a Past Solution That Worked

This strategy involves looking back at previous similar problems you have faced and the solutions that were effective in solving them.

It is useful when you are facing a problem that is very similar to something you have already solved. The main benefit is that you don’t have to come up with a brand new solution – you already know the method that worked before will likely work again.

However, the limitation is that the current problem may have some unique aspects or differences that mean your old solution is not fully applicable.

The ideal process is to thoroughly analyze the new challenge, identify the key similarities and differences versus the past case, adapt the old solution as needed to align with the current context, and then pilot it carefully before full implementation.

An example is using the same negotiation tactics from purchasing your previous home when putting in an offer on a new house. Key terms would be adjusted but overall it can save significant time versus developing a brand new strategy.

2. Brainstorm Solutions

Brainstorm Solutions

This involves gathering a group of people together to generate as many potential solutions to a problem as possible.

It is effective when you need creative ideas to solve a complex or challenging issue. By getting input from multiple people with diverse perspectives, you increase the likelihood of finding an innovative solution.

The main limitation is that brainstorming sessions can sometimes turn into unproductive gripe sessions or discussions rather than focusing on productive ideation —so they need to be properly facilitated.

The key to an effective brainstorming session is setting some basic ground rules upfront and having an experienced facilitator guide the discussion. Rules often include encouraging wild ideas, avoiding criticism of ideas during the ideation phase, and building on others’ ideas.

For instance, a struggling startup might hold a session where ideas for turnaround plans are generated and then formalized with financials and metrics.

3. Work Backward from the Solution

Work Backward from the Solution

This technique involves envisioning that the problem has already been solved and then working step-by-step backward toward the current state.

This strategy is particularly helpful for long-term, multi-step problems. By starting from the imagined solution and identifying all the steps required to reach it, you can systematically determine the actions needed. It lets you tackle a big hairy problem through smaller, reversible steps.

A limitation is that this approach may not be possible if you cannot accurately envision the solution state to start with.

The approach helps drive logical systematic thinking for complex problem-solving, but should still be combined with creative brainstorming of alternative scenarios and solutions.

An example is planning for an event – you would imagine the successful event occurring, then determine the tasks needed the week before, two weeks before, etc. all the way back to the present.

4. Use the Kipling Method

Use the Kipling Method

This method, named after author Rudyard Kipling, provides a framework for thoroughly analyzing a problem before jumping into solutions.

It consists of answering six fundamental questions: What, Where, When, How, Who, and Why about the challenge. Clearly defining these core elements of the problem sets the stage for generating targeted solutions.

The Kipling method enables a deep understanding of problem parameters and root causes before solution identification. By jumping to brainstorm solutions too early, critical information can be missed or the problem is loosely defined, reducing solution quality.

Answering the six fundamental questions illuminates all angles of the issue. This takes time but pays dividends in generating optimal solutions later tuned precisely to the true underlying problem.

The limitation is that meticulously working through numerous questions before addressing solutions can slow progress.

The best approach blends structured problem decomposition techniques like the Kipling method with spurring innovative solution ideation from a diverse team. 

An example is using this technique after a technical process failure – the team would systematically detail What failed, Where/When did it fail, How it failed (sequence of events), Who was involved, and Why it likely failed before exploring preventative solutions.

5. Try Different Solutions Until One Works (Trial and Error)

Try Different Solutions Until One Works (Trial and Error)

This technique involves attempting various potential solutions sequentially until finding one that successfully solves the problem.

Trial and error works best when facing a concrete, bounded challenge with clear solution criteria and a small number of discrete options to try. By methodically testing solutions, you can determine the faulty component.

A limitation is that it can be time-intensive if the working solution set is large.

The key is limiting the variable set first. For technical problems, this boundary is inherent and each element can be iteratively tested. But for business issues, artificial constraints may be required – setting decision rules upfront to reduce options before testing.

Furthermore, hypothesis-driven experimentation is far superior to blind trial and error – have logic for why Option A may outperform Option B.

Examples include fixing printer jams by testing different paper tray and cable configurations or resolving website errors by tweaking CSS/HTML line-by-line until the code functions properly.

6. Use Proven Formulas or Frameworks (Heuristics)

Use Proven Formulas or Frameworks (Heuristics)

Heuristics refers to applying existing problem-solving formulas or frameworks rather than addressing issues completely from scratch.

This allows leveraging established best practices rather than reinventing the wheel each time.

It is effective when facing recurrent, common challenges where proven structured approaches exist.

However, heuristics may force-fit solutions to non-standard problems.

For example, a cost-benefit analysis can be used instead of custom weighting schemes to analyze potential process improvements.

Onethread allows teams to define, save, and replicate configurable project templates so proven workflows can be reliably applied across problems with some consistency rather than fully custom one-off approaches each time.

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7. Trust Your Instincts (Insight Problem-Solving)

Trust Your Instincts (Insight Problem-Solving)

Insight is a problem-solving technique that involves waiting patiently for an unexpected “aha moment” when the solution pops into your mind.

It works well for personal challenges that require intuitive realizations over calculated logic. The unconscious mind makes connections leading to flashes of insight when relaxing or doing mundane tasks unrelated to the actual problem.

Benefits include out-of-the-box creative solutions. However, the limitations are that insights can’t be forced and may never come at all if too complex. Critical analysis is still required after initial insights.

A real-life example would be a writer struggling with how to end a novel. Despite extensive brainstorming, they feel stuck. Eventually while gardening one day, a perfect unexpected plot twist sparks an ideal conclusion. However, once written they still carefully review if the ending flows logically from the rest of the story.

8. Reverse Engineer the Problem

Reverse Engineer the Problem

This approach involves deconstructing a problem in reverse sequential order from the current undesirable outcome back to the initial root causes.

By mapping the chain of events backward, you can identify the origin of where things went wrong and establish the critical junctures for solving it moving ahead. Reverse engineering provides diagnostic clarity on multi-step problems.

However, the limitation is that it focuses heavily on autopsying the past versus innovating improved future solutions.

An example is tracing back from a server outage, through the cascade of infrastructure failures that led to it finally terminating at the initial script error that triggered the crisis. This root cause would then inform the preventative measure.

9. Break Down Obstacles Between Current and Goal State (Means-End Analysis)

Break Down Obstacles Between Current and Goal State (Means-End Analysis)

This technique defines the current problem state and the desired end goal state, then systematically identifies obstacles in the way of getting from one to the other.

By mapping the barriers or gaps, you can then develop solutions to address each one. This methodically connects the problem to solutions.

A limitation is that some obstacles may be unknown upfront and only emerge later.

For example, you can list down all the steps required for a new product launch – current state through production, marketing, sales, distribution, etc. to full launch (goal state) – to highlight where resource constraints or other blocks exist so they can be addressed.

Onethread allows dividing big-picture projects into discrete, manageable phases, milestones, and tasks to simplify execution just as problems can be decomposed into more achievable components. Features like dependency mapping further reinforce interconnections.

Using Onethread’s issues and subtasks feature, messy problems can be decomposed into manageable chunks.

10. Ask “Why” Five Times to Identify the Root Cause (The 5 Whys)

Ask "Why" Five Times to Identify the Root Cause (The 5 Whys)

This technique involves asking “Why did this problem occur?” and then responding with an answer that is again met with asking “Why?” This process repeats five times until the root cause is revealed.

Continually asking why digs deeper from surface symptoms to underlying systemic issues.

It is effective for getting to the source of problems originating from human error or process breakdowns.

However, some complex issues may have multiple tangled root causes not solvable through this approach alone.

An example is a retail store experiencing a sudden decline in customers. Successively asking why five times may trace an initial drop to parking challenges, stemming from a city construction project – the true starting point to address.

11. Evaluate Strengths, Weaknesses, Opportunities, and Threats (SWOT Analysis)

Evaluate Strengths, Weaknesses, Opportunities, and Threats (SWOT Analysis)

This involves analyzing a problem or proposed solution by categorizing internal and external factors into a 2×2 matrix: Strengths, Weaknesses as the internal rows; Opportunities and Threats as the external columns.

Systematically identifying these elements provides balanced insight to evaluate options and risks. It is impactful when evaluating alternative solutions or developing strategy amid complexity or uncertainty.

The key benefit of SWOT analysis is enabling multi-dimensional thinking when rationally evaluating options. Rather than getting anchored on just the upsides or the existing way of operating, it urges a systematic assessment through four different lenses:

  • Internal Strengths: Our core competencies/advantages able to deliver success
  • Internal Weaknesses: Gaps/vulnerabilities we need to manage
  • External Opportunities: Ways we can differentiate/drive additional value
  • External Threats: Risks we must navigate or mitigate

Multiperspective analysis provides the needed holistic view of the balanced risk vs. reward equation for strategic decision making amid uncertainty.

However, SWOT can feel restrictive if not tailored and evolved for different issue types.

Teams should view SWOT analysis as a starting point, augmenting it further for distinct scenarios.

An example is performing a SWOT analysis on whether a small business should expand into a new market – evaluating internal capabilities to execute vs. risks in the external competitive and demand environment to inform the growth decision with eyes wide open.

12. Compare Current vs Expected Performance (Gap Analysis)

Compare Current vs Expected Performance (Gap Analysis)

This technique involves comparing the current state of performance, output, or results to the desired or expected levels to highlight shortfalls.

By quantifying the gaps, you can identify problem areas and prioritize address solutions.

Gap analysis is based on the simple principle – “you can’t improve what you don’t measure.” It enables facts-driven problem diagnosis by highlighting delta to goals, not just vague dissatisfaction that something seems wrong. And measurement immediately suggests improvement opportunities – address the biggest gaps first.

This data orientation also supports ROI analysis on fixing issues – the return from closing larger gaps outweighs narrowly targeting smaller performance deficiencies.

However, the approach is only effective if robust standards and metrics exist as the benchmark to evaluate against. Organizations should invest upfront in establishing performance frameworks.

Furthermore, while numbers are invaluable, the human context behind problems should not be ignored – quantitative versus qualitative gap assessment is optimally blended.

For example, if usage declines are noted during software gap analysis, this could be used as a signal to improve user experience through design.

13. Observe Processes from the Frontline (Gemba Walk)

Observe Processes from the Frontline (Gemba Walk)

A Gemba walk involves going to the actual place where work is done, directly observing the process, engaging with employees, and finding areas for improvement.

By experiencing firsthand rather than solely reviewing abstract reports, practical problems and ideas emerge.

The limitation is Gemba walks provide anecdotes not statistically significant data. It complements but does not replace comprehensive performance measurement.

An example is a factory manager inspecting the production line to spot jam areas based on direct reality rather than relying on throughput dashboards alone back in her office. Frontline insights prove invaluable.

14. Analyze Competitive Forces (Porter’s Five Forces)

Analyze Competitive Forces (Porter’s Five Forces)

This involves assessing the marketplace around a problem or business situation via five key factors: competitors, new entrants, substitute offerings, suppliers, and customer power.

Evaluating these forces illuminates risks and opportunities for strategy development and issue resolution. It is effective for understanding dynamic external threats and opportunities when operating in a contested space.

However, over-indexing on only external factors can overlook the internal capabilities needed to execute solutions.

A startup CEO, for example, may analyze market entry barriers, whitespace opportunities, and disruption risks across these five forces to shape new product rollout strategies and marketing approaches.

15. Think from Different Perspectives (Six Thinking Hats)

Think from Different Perspectives (Six Thinking Hats)

The Six Thinking Hats is a technique developed by Edward de Bono that encourages people to think about a problem from six different perspectives, each represented by a colored “thinking hat.”

The key benefit of this strategy is that it pushes team members to move outside their usual thinking style and consider new angles. This brings more diverse ideas and solutions to the table.

It works best for complex problems that require innovative solutions and when a team is stuck in an unproductive debate. The structured framework keeps the conversation flowing in a positive direction.

Limitations are that it requires training on the method itself and may feel unnatural at first. Team dynamics can also influence success – some members may dominate certain “hats” while others remain quiet.

A real-life example is a software company debating whether to build a new feature. The white hat focuses on facts, red on gut feelings, black on potential risks, yellow on benefits, green on new ideas, and blue on process. This exposes more balanced perspectives before deciding.

Onethread centralizes diverse stakeholder communication onto one platform, ensuring all voices are incorporated when evaluating project tradeoffs, just as problem-solving should consider multifaceted solutions.

16. Visualize the Problem (Draw it Out)

Visualize the Problem (Draw it Out)

Drawing out a problem involves creating visual representations like diagrams, flowcharts, and maps to work through challenging issues.

This strategy is helpful when dealing with complex situations with lots of interconnected components. The visuals simplify the complexity so you can thoroughly understand the problem and all its nuances.

Key benefits are that it allows more stakeholders to get on the same page regarding root causes and it sparks new creative solutions as connections are made visually.

However, simple problems with few variables don’t require extensive diagrams. Additionally, some challenges are so multidimensional that fully capturing every aspect is difficult.

A real-life example would be mapping out all the possible causes leading to decreased client satisfaction at a law firm. An intricate fishbone diagram with branches for issues like service delivery, technology, facilities, culture, and vendor partnerships allows the team to trace problems back to their origins and brainstorm targeted fixes.

17. Follow a Step-by-Step Procedure (Algorithms)

Follow a Step-by-Step Procedure (Algorithms)

An algorithm is a predefined step-by-step process that is guaranteed to produce the correct solution if implemented properly.

Using algorithms is effective when facing problems that have clear, binary right and wrong answers. Algorithms work for mathematical calculations, computer code, manufacturing assembly lines, and scientific experiments.

Key benefits are consistency, accuracy, and efficiency. However, they require extensive upfront development and only apply to scenarios with strict parameters. Additionally, human error can lead to mistakes.

For example, crew members of fast food chains like McDonald’s follow specific algorithms for food prep – from grill times to ingredient amounts in sandwiches, to order fulfillment procedures. This ensures uniform quality and service across all locations. However, if a step is missed, errors occur.

The Problem-Solving Process

The Problem-Solving Process

The problem-solving process typically includes defining the issue, analyzing details, creating solutions, weighing choices, acting, and reviewing results.

In the above, we have discussed several problem-solving strategies. For every problem-solving strategy, you have to follow these processes. Here’s a detailed step-by-step process of effective problem-solving:

Step 1: Identify the Problem

The problem-solving process starts with identifying the problem. This step involves understanding the issue’s nature, its scope, and its impact. Once the problem is clearly defined, it sets the foundation for finding effective solutions.

Identifying the problem is crucial. It means figuring out exactly what needs fixing. This involves looking at the situation closely, understanding what’s wrong, and knowing how it affects things. It’s about asking the right questions to get a clear picture of the issue. 

This step is important because it guides the rest of the problem-solving process. Without a clear understanding of the problem, finding a solution is much harder. It’s like diagnosing an illness before treating it. Once the problem is identified accurately, you can move on to exploring possible solutions and deciding on the best course of action.

Step 2: Break Down the Problem

Breaking down the problem is a key step in the problem-solving process. It involves dividing the main issue into smaller, more manageable parts. This makes it easier to understand and tackle each component one by one.

After identifying the problem, the next step is to break it down. This means splitting the big issue into smaller pieces. It’s like solving a puzzle by handling one piece at a time. 

By doing this, you can focus on each part without feeling overwhelmed. It also helps in identifying the root causes of the problem. Breaking down the problem allows for a clearer analysis and makes finding solutions more straightforward. 

Each smaller problem can be addressed individually, leading to an effective resolution of the overall issue. This approach not only simplifies complex problems but also aids in developing a systematic plan to solve them.

Step 3: Come up with potential solutions

Coming up with potential solutions is the third step in the problem-solving process. It involves brainstorming various options to address the problem, considering creativity and feasibility to find the best approach.

After breaking down the problem, it’s time to think of ways to solve it. This stage is about brainstorming different solutions. You look at the smaller issues you’ve identified and start thinking of ways to fix them. This is where creativity comes in. 

You want to come up with as many ideas as possible, no matter how out-of-the-box they seem. It’s important to consider all options and evaluate their pros and cons. This process allows you to gather a range of possible solutions. 

Later, you can narrow these down to the most practical and effective ones. This step is crucial because it sets the stage for deciding on the best solution to implement. It’s about being open-minded and innovative to tackle the problem effectively.

Step 4: Analyze the possible solutions

Analyzing the possible solutions is the fourth step in the problem-solving process. It involves evaluating each proposed solution’s advantages and disadvantages to determine the most effective and feasible option.

After coming up with potential solutions, the next step is to analyze them. This means looking closely at each idea to see how well it solves the problem. You weigh the pros and cons of every solution.

Consider factors like cost, time, resources, and potential outcomes. This analysis helps in understanding the implications of each option. It’s about being critical and objective, ensuring that the chosen solution is not only effective but also practical.

This step is vital because it guides you towards making an informed decision. It involves comparing the solutions against each other and selecting the one that best addresses the problem.

By thoroughly analyzing the options, you can move forward with confidence, knowing you’ve chosen the best path to solve the issue.

Step 5: Implement and Monitor the Solutions

Implementing and monitoring the solutions is the final step in the problem-solving process. It involves putting the chosen solution into action and observing its effectiveness, making adjustments as necessary.

Once you’ve selected the best solution, it’s time to put it into practice. This step is about action. You implement the chosen solution and then keep an eye on how it works. Monitoring is crucial because it tells you if the solution is solving the problem as expected. 

If things don’t go as planned, you may need to make some changes. This could mean tweaking the current solution or trying a different one. The goal is to ensure the problem is fully resolved. 

This step is critical because it involves real-world application. It’s not just about planning; it’s about doing and adjusting based on results. By effectively implementing and monitoring the solutions, you can achieve the desired outcome and solve the problem successfully.

Why This Process is Important

Following a defined process to solve problems is important because it provides a systematic, structured approach instead of a haphazard one. Having clear steps guides logical thinking, analysis, and decision-making to increase effectiveness. Key reasons it helps are:

  • Clear Direction: This process gives you a clear path to follow, which can make solving problems less overwhelming.
  • Better Solutions: Thoughtful analysis of root causes, iterative testing of solutions, and learning orientation lead to addressing the heart of issues rather than just symptoms.
  • Saves Time and Energy: Instead of guessing or trying random things, this process helps you find a solution more efficiently.
  • Improves Skills: The more you use this process, the better you get at solving problems. It’s like practicing a sport. The more you practice, the better you play.
  • Maximizes collaboration: Involving various stakeholders in the process enables broader inputs. Their communication and coordination are streamlined through organized brainstorming and evaluation.
  • Provides consistency: Standard methodology across problems enables building institutional problem-solving capabilities over time. Patterns emerge on effective techniques to apply to different situations.

The problem-solving process is a powerful tool that can help us tackle any challenge we face. By following these steps, we can find solutions that work and learn important skills along the way.

Key Skills for Efficient Problem Solving

Key Skills for Efficient Problem Solving

Efficient problem-solving requires breaking down issues logically, evaluating options, and implementing practical solutions.

Key skills include critical thinking to understand root causes, creativity to brainstorm innovative ideas, communication abilities to collaborate with others, and decision-making to select the best way forward. Staying adaptable, reflecting on outcomes, and applying lessons learned are also essential.

With practice, these capacities will lead to increased personal and team effectiveness in systematically addressing any problem.

 Let’s explore the powers you need to become a problem-solving hero!

Critical Thinking and Analytical Skills

Critical thinking and analytical skills are vital for efficient problem-solving as they enable individuals to objectively evaluate information, identify key issues, and generate effective solutions. 

These skills facilitate a deeper understanding of problems, leading to logical, well-reasoned decisions. By systematically breaking down complex issues and considering various perspectives, individuals can develop more innovative and practical solutions, enhancing their problem-solving effectiveness.

Communication Skills

Effective communication skills are essential for efficient problem-solving as they facilitate clear sharing of information, ensuring all team members understand the problem and proposed solutions. 

These skills enable individuals to articulate issues, listen actively, and collaborate effectively, fostering a productive environment where diverse ideas can be exchanged and refined. By enhancing mutual understanding, communication skills contribute significantly to identifying and implementing the most viable solutions.

Decision-Making

Strong decision-making skills are crucial for efficient problem-solving, as they enable individuals to choose the best course of action from multiple alternatives. 

These skills involve evaluating the potential outcomes of different solutions, considering the risks and benefits, and making informed choices. Effective decision-making leads to the implementation of solutions that are likely to resolve problems effectively, ensuring resources are used efficiently and goals are achieved.

Planning and Prioritization

Planning and prioritization are key for efficient problem-solving, ensuring resources are allocated effectively to address the most critical issues first. This approach helps in organizing tasks according to their urgency and impact, streamlining efforts towards achieving the desired outcome efficiently.

Emotional Intelligence

Emotional intelligence enhances problem-solving by allowing individuals to manage emotions, understand others, and navigate social complexities. It fosters a positive, collaborative environment, essential for generating creative solutions and making informed, empathetic decisions.

Leadership skills drive efficient problem-solving by inspiring and guiding teams toward common goals. Effective leaders motivate their teams, foster innovation, and navigate challenges, ensuring collective efforts are focused and productive in addressing problems.

Time Management

Time management is crucial in problem-solving, enabling individuals to allocate appropriate time to each task. By efficiently managing time, one can ensure that critical problems are addressed promptly without neglecting other responsibilities.

Data Analysis

Data analysis skills are essential for problem-solving, as they enable individuals to sift through data, identify trends, and extract actionable insights. This analytical approach supports evidence-based decision-making, leading to more accurate and effective solutions.

Research Skills

Research skills are vital for efficient problem-solving, allowing individuals to gather relevant information, explore various solutions, and understand the problem’s context. This thorough exploration aids in developing well-informed, innovative solutions.

Becoming a great problem solver takes practice, but with these skills, you’re on your way to becoming a problem-solving hero. 

How to Improve Your Problem-Solving Skills?

How to Improve Your Problem-Solving Skills

Improving your problem-solving skills can make you a master at overcoming challenges. Learn from experts, practice regularly, welcome feedback, try new methods, experiment, and study others’ success to become better.

Learning from Experts

Improving problem-solving skills by learning from experts involves seeking mentorship, attending workshops, and studying case studies. Experts provide insights and techniques that refine your approach, enhancing your ability to tackle complex problems effectively.

To enhance your problem-solving skills, learning from experts can be incredibly beneficial. Engaging with mentors, participating in specialized workshops, and analyzing case studies from seasoned professionals can offer valuable perspectives and strategies. 

Experts share their experiences, mistakes, and successes, providing practical knowledge that can be applied to your own problem-solving process. This exposure not only broadens your understanding but also introduces you to diverse methods and approaches, enabling you to tackle challenges more efficiently and creatively.

Improving problem-solving skills through practice involves tackling a variety of challenges regularly. This hands-on approach helps in refining techniques and strategies, making you more adept at identifying and solving problems efficiently.

One of the most effective ways to enhance your problem-solving skills is through consistent practice. By engaging with different types of problems on a regular basis, you develop a deeper understanding of various strategies and how they can be applied. 

This hands-on experience allows you to experiment with different approaches, learn from mistakes, and build confidence in your ability to tackle challenges.

Regular practice not only sharpens your analytical and critical thinking skills but also encourages adaptability and innovation, key components of effective problem-solving.

Openness to Feedback

Being open to feedback is like unlocking a secret level in a game. It helps you boost your problem-solving skills. Improving problem-solving skills through openness to feedback involves actively seeking and constructively responding to critiques. 

This receptivity enables you to refine your strategies and approaches based on insights from others, leading to more effective solutions. 

Learning New Approaches and Methodologies

Learning new approaches and methodologies is like adding new tools to your toolbox. It makes you a smarter problem-solver. Enhancing problem-solving skills by learning new approaches and methodologies involves staying updated with the latest trends and techniques in your field. 

This continuous learning expands your toolkit, enabling innovative solutions and a fresh perspective on challenges.

Experimentation

Experimentation is like being a scientist of your own problems. It’s a powerful way to improve your problem-solving skills. Boosting problem-solving skills through experimentation means trying out different solutions to see what works best. This trial-and-error approach fosters creativity and can lead to unique solutions that wouldn’t have been considered otherwise.

Analyzing Competitors’ Success

Analyzing competitors’ success is like being a detective. It’s a smart way to boost your problem-solving skills. Improving problem-solving skills by analyzing competitors’ success involves studying their strategies and outcomes. Understanding what worked for them can provide valuable insights and inspire effective solutions for your own challenges. 

Challenges in Problem-Solving

Facing obstacles when solving problems is common. Recognizing these barriers, like fear of failure or lack of information, helps us find ways around them for better solutions.

Fear of Failure

Fear of failure is like a big, scary monster that stops us from solving problems. It’s a challenge many face. Because being afraid of making mistakes can make us too scared to try new solutions. 

How can we overcome this? First, understand that it’s okay to fail. Failure is not the opposite of success; it’s part of learning. Every time we fail, we discover one more way not to solve a problem, getting us closer to the right solution. Treat each attempt like an experiment. It’s not about failing; it’s about testing and learning.

Lack of Information

Lack of information is like trying to solve a puzzle with missing pieces. It’s a big challenge in problem-solving. Because without all the necessary details, finding a solution is much harder. 

How can we fix this? Start by gathering as much information as you can. Ask questions, do research, or talk to experts. Think of yourself as a detective looking for clues. The more information you collect, the clearer the picture becomes. Then, use what you’ve learned to think of solutions. 

Fixed Mindset

A fixed mindset is like being stuck in quicksand; it makes solving problems harder. It means thinking you can’t improve or learn new ways to solve issues. 

How can we change this? First, believe that you can grow and learn from challenges. Think of your brain as a muscle that gets stronger every time you use it. When you face a problem, instead of saying “I can’t do this,” try thinking, “I can’t do this yet.” Look for lessons in every challenge and celebrate small wins. 

Everyone starts somewhere, and mistakes are just steps on the path to getting better. By shifting to a growth mindset, you’ll see problems as opportunities to grow. Keep trying, keep learning, and your problem-solving skills will soar!

Jumping to Conclusions

Jumping to conclusions is like trying to finish a race before it starts. It’s a challenge in problem-solving. That means making a decision too quickly without looking at all the facts. 

How can we avoid this? First, take a deep breath and slow down. Think about the problem like a puzzle. You need to see all the pieces before you know where they go. Ask questions, gather information, and consider different possibilities. Don’t choose the first solution that comes to mind. Instead, compare a few options. 

Feeling Overwhelmed

Feeling overwhelmed is like being buried under a mountain of puzzles. It’s a big challenge in problem-solving. When we’re overwhelmed, everything seems too hard to handle. 

How can we deal with this? Start by taking a step back. Breathe deeply and focus on one thing at a time. Break the big problem into smaller pieces, like sorting puzzle pieces by color. Tackle each small piece one by one. It’s also okay to ask for help. Sometimes, talking to someone else can give you a new perspective. 

Confirmation Bias

Confirmation bias is like wearing glasses that only let you see what you want to see. It’s a challenge in problem-solving. Because it makes us focus only on information that agrees with what we already believe, ignoring anything that doesn’t. 

How can we overcome this? First, be aware that you might be doing it. It’s like checking if your glasses are on right. Then, purposely look for information that challenges your views. It’s like trying on a different pair of glasses to see a new perspective. Ask questions and listen to answers, even if they don’t fit what you thought before.

Groupthink is like everyone in a group deciding to wear the same outfit without asking why. It’s a challenge in problem-solving. It means making decisions just because everyone else agrees, without really thinking it through. 

How can we avoid this? First, encourage everyone in the group to share their ideas, even if they’re different. It’s like inviting everyone to show their unique style of clothes. 

Listen to all opinions and discuss them. It’s okay to disagree; it helps us think of better solutions. Also, sometimes, ask someone outside the group for their thoughts. They might see something everyone in the group missed.

Overcoming obstacles in problem-solving requires patience, openness, and a willingness to learn from mistakes. By recognizing these barriers, we can develop strategies to navigate around them, leading to more effective and creative solutions.

What are the most common problem-solving techniques?

The most common techniques include brainstorming, the 5 Whys, mind mapping, SWOT analysis, and using algorithms or heuristics. Each approach has its strengths, suitable for different types of problems.

What’s the best problem-solving strategy for every situation?

There’s no one-size-fits-all strategy. The best approach depends on the problem’s complexity, available resources, and time constraints. Combining multiple techniques often yields the best results.

How can I improve my problem-solving skills?

Improve your problem-solving skills by practicing regularly, learning from experts, staying open to feedback, and continuously updating your knowledge on new approaches and methodologies.

Are there any tools or resources to help with problem-solving?

Yes, tools like mind mapping software, online courses on critical thinking, and books on problem-solving techniques can be very helpful. Joining forums or groups focused on problem-solving can also provide support and insights.

What are some common mistakes people make when solving problems?

Common mistakes include jumping to conclusions without fully understanding the problem, ignoring valuable feedback, sticking to familiar solutions without considering alternatives, and not breaking down complex problems into manageable parts.

Final Words

Mastering problem-solving strategies equips us with the tools to tackle challenges across all areas of life. By understanding and applying these techniques, embracing a growth mindset, and learning from both successes and obstacles, we can transform problems into opportunities for growth. Continuously improving these skills ensures we’re prepared to face and solve future challenges more effectively.

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Solving Complex Problems: Structured Thinking, Design Principles, and AI

Sang-Gook Kim

Download the Course Schedule

How do you solve important, large-scale challenges with evolving and contradictory constraints? In this 5-day course, transform your approach to large-scale problem solving, from multi-stakeholder engineering projects to the online spread of misinformation. Alongside engineers and leaders from diverse industries, you’ll explore actionable innovative frameworks for assessing, communicating, and implementing complex systems—and significantly increase your likelihood of success.

THIS COURSE MAY BE TAKEN INDIVIDUALLY OR AS PART OF THE  PROFESSIONAL CERTIFICATE PROGRAM IN INNOVATION & TECHNOLOGY  OR THE  PROFESSIONAL CERTIFICATE PROGRAM IN DESIGN & MANUFACTURING .

how to solve a problem with complex

Engineering projects with shifting goals. Inefficient national healthcare systems. The online spread of misinformation. Every day, professionals are tasked with addressing major challenges that present opportunities for great triumph—or significant failure. How do you approach an important, large-scale challenge with evolving and contradictory constraints? Is the solution a new technology, a new policy, or something else altogether? In our new course Solving Complex Problems: Structured Thinking, Design Principles, and AI , you’ll acquire core principles that will change the way you approach and solve large-scale challenges—increasing your likelihood of success. Over the course of five days, you will explore proven design principles, heuristic-based insights, and problem-solving approaches, and learn how to persuasively present concepts and system architectures to stakeholders. Methods utilize recent developments in AI and Big Data, as well as innovative strategies from MIT Lincoln Laboratory that have been successfully applied to large and complex national defense systems. By taking part in interactive lectures and hands-on projects, you will learn to think through and leverage important steps, including problem abstraction, idea generation, concept development and refinement, system-level thinking, and proposal generation. Alongside an accomplished group of global peers, you will explore the strategies and frameworks you need to implement large-scale systems that can have a significant positive impact—and minimize the probability of failure.

Certificate of Completion from MIT Professional Education  

Solving Complex Problems cert image

  • Approach and solve large and complex problems.
  • Assess end-to-end processes and associated challenges, in order to significantly increase the likelihood of success in developing more complex systems.
  • Implement effective problem-solving techniques, including abstracting the problem, idea generation, concept development and refinement, system-level thinking, and proposal generation.
  • Utilize system-level thinking skills to evaluate, refine, down select, and evaluate best ideas and concepts.
  • Apply the Axiomatic Design methodology to a broad range of applications in manufacturing, product design, software, and architecture.
  • Generate and present proposals that clearly articulate innovative ideas, clarify the limits of current strategies, define potential customers and impact, and outline a success-oriented system development and risk mitigation plan.
  • Effectively communicate ideas and persuade others, and provide valuable feedback.
  • Confidently develop and execute large-scale system concepts that will drive significant positive impact.

Edwin F. David Head of the Engineering Division, MIT Lincoln Laboratory

Jonathan E. Gans Group Leader of the Systems and Architectures Group, MIT Lincoln Laboratory

Robert T-I. Shin Principal Staff in the Intelligence, Surveillance, and Reconnaissance (ISR) and Tactical Systems Division, MIT Lincoln Laboratory Director, MIT Beaver Works

This course is appropriate for professionals who design or manage complex systems with shifting needs and goals. It is also well suited to those who want to improve the quality and performance of their operations and decision-making in a large-scale system environment. Potential participants include engineers, group leaders, and senior managers in government and industries including automotive, aerospace, semiconductors, engineering, manufacturing, healthcare, bio-medical, finance, architecture, public policy, education, and military.

Computer Requirements

A laptop with PowerPoint is required.

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  • Front Psychol

Complex Problem Solving: What It Is and What It Is Not

Dietrich dörner.

1 Department of Psychology, University of Bamberg, Bamberg, Germany

Joachim Funke

2 Department of Psychology, Heidelberg University, Heidelberg, Germany

Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems. Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems.

Succeeding in the 21st century requires many competencies, including creativity, life-long learning, and collaboration skills (e.g., National Research Council, 2011 ; Griffin and Care, 2015 ), to name only a few. One competence that seems to be of central importance is the ability to solve complex problems ( Mainzer, 2009 ). Mainzer quotes the Nobel prize winner Simon (1957) who wrote as early as 1957:

The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problem whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality. (p. 198)

The shift from well-defined to ill-defined problems came about as a result of a disillusion with the “general problem solver” ( Newell et al., 1959 ): The general problem solver was a computer software intended to solve all kind of problems that can be expressed through well-formed formulas. However, it soon became clear that this procedure was in fact a “special problem solver” that could only solve well-defined problems in a closed space. But real-world problems feature open boundaries and have no well-determined solution. In fact, the world is full of wicked problems and clumsy solutions ( Verweij and Thompson, 2006 ). As a result, solving well-defined problems and solving ill-defined problems requires different cognitive processes ( Schraw et al., 1995 ; but see Funke, 2010 ).

Well-defined problems have a clear set of means for reaching a precisely described goal state. For example: in a match-stick arithmetic problem, a person receives a false arithmetic expression constructed out of matchsticks (e.g., IV = III + III). According to the instructions, moving one of the matchsticks will make the equations true. Here, both the problem (find the appropriate stick to move) and the goal state (true arithmetic expression; solution is: VI = III + III) are defined clearly.

Ill-defined problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear. For example: The goal state for solving the political conflict in the near-east conflict between Israel and Palestine is not clearly defined (living in peaceful harmony with each other?) and even if the conflict parties would agree on a two-state solution, this goal again leaves many issues unresolved. This type of problem is called a “complex problem” and is of central importance to this paper. All psychological processes that occur within individual persons and deal with the handling of such ill-defined complex problems will be subsumed under the umbrella term “complex problem solving” (CPS).

Systematic research on CPS started in the 1970s with observations of the behavior of participants who were confronted with computer simulated microworlds. For example, in one of those microworlds participants assumed the role of executives who were tasked to manage a company over a certain period of time (see Brehmer and Dörner, 1993 , for a discussion of this methodology). Today, CPS is an established concept and has even influenced large-scale assessments such as PISA (“Programme for International Student Assessment”), organized by the Organization for Economic Cooperation and Development ( OECD, 2014 ). According to the World Economic Forum, CPS is one of the most important competencies required in the future ( World Economic Forum, 2015 ). Numerous articles on the subject have been published in recent years, documenting the increasing research activity relating to this field. In the following collection of papers we list only those published in 2010 and later: theoretical papers ( Blech and Funke, 2010 ; Funke, 2010 ; Knauff and Wolf, 2010 ; Leutner et al., 2012 ; Selten et al., 2012 ; Wüstenberg et al., 2012 ; Greiff et al., 2013b ; Fischer and Neubert, 2015 ; Schoppek and Fischer, 2015 ), papers about measurement issues ( Danner et al., 2011a ; Greiff et al., 2012 , 2015a ; Alison et al., 2013 ; Gobert et al., 2015 ; Greiff and Fischer, 2013 ; Herde et al., 2016 ; Stadler et al., 2016 ), papers about applications ( Fischer and Neubert, 2015 ; Ederer et al., 2016 ; Tremblay et al., 2017 ), papers about differential effects ( Barth and Funke, 2010 ; Danner et al., 2011b ; Beckmann and Goode, 2014 ; Greiff and Neubert, 2014 ; Scherer et al., 2015 ; Meißner et al., 2016 ; Wüstenberg et al., 2016 ), one paper about developmental effects ( Frischkorn et al., 2014 ), one paper with a neuroscience background ( Osman, 2012 ) 1 , papers about cultural differences ( Güss and Dörner, 2011 ; Sonnleitner et al., 2014 ; Güss et al., 2015 ), papers about validity issues ( Goode and Beckmann, 2010 ; Greiff et al., 2013c ; Schweizer et al., 2013 ; Mainert et al., 2015 ; Funke et al., 2017 ; Greiff et al., 2017 , 2015b ; Kretzschmar et al., 2016 ; Kretzschmar, 2017 ), review papers and meta-analyses ( Osman, 2010 ; Stadler et al., 2015 ), and finally books ( Qudrat-Ullah, 2015 ; Csapó and Funke, 2017b ) and book chapters ( Funke, 2012 ; Hotaling et al., 2015 ; Funke and Greiff, 2017 ; Greiff and Funke, 2017 ; Csapó and Funke, 2017a ; Fischer et al., 2017 ; Molnàr et al., 2017 ; Tobinski and Fritz, 2017 ; Viehrig et al., 2017 ). In addition, a new “Journal of Dynamic Decision Making” (JDDM) has been launched ( Fischer et al., 2015 , 2016 ) to give the field an open-access outlet for research and discussion.

This paper aims to clarify aspects of validity: what should be meant by the term CPS and what not? This clarification seems necessary because misunderstandings in recent publications provide – from our point of view – a potentially misleading picture of the construct. We start this article with a historical review before attempting to systematize different positions. We conclude with a working definition.

Historical Review

The concept behind CPS goes back to the German phrase “komplexes Problemlösen” (CPS; the term “komplexes Problemlösen” was used as a book title by Funke, 1986 ). The concept was introduced in Germany by Dörner and colleagues in the mid-1970s (see Dörner et al., 1975 ; Dörner, 1975 ) for the first time. The German phrase was later translated to CPS in the titles of two edited volumes by Sternberg and Frensch (1991) and Frensch and Funke (1995a) that collected papers from different research traditions. Even though it looks as though the term was coined in the 1970s, Edwards (1962) used the term “dynamic decision making” to describe decisions that come in a sequence. He compared static with dynamic decision making, writing:

  • simple  In dynamic situations, a new complication not found in the static situations arises. The environment in which the decision is set may be changing, either as a function of the sequence of decisions, or independently of them, or both. It is this possibility of an environment which changes while you collect information about it which makes the task of dynamic decision theory so difficult and so much fun. (p. 60)

The ability to solve complex problems is typically measured via dynamic systems that contain several interrelated variables that participants need to alter. Early work (see, e.g., Dörner, 1980 ) used a simulation scenario called “Lohhausen” that contained more than 2000 variables that represented the activities of a small town: Participants had to take over the role of a mayor for a simulated period of 10 years. The simulation condensed these ten years to ten hours in real time. Later, researchers used smaller dynamic systems as scenarios either based on linear equations (see, e.g., Funke, 1993 ) or on finite state automata (see, e.g., Buchner and Funke, 1993 ). In these contexts, CPS consisted of the identification and control of dynamic task environments that were previously unknown to the participants. Different task environments came along with different degrees of fidelity ( Gray, 2002 ).

According to Funke (2012) , the typical attributes of complex systems are (a) complexity of the problem situation which is usually represented by the sheer number of involved variables; (b) connectivity and mutual dependencies between involved variables; (c) dynamics of the situation, which reflects the role of time and developments within a system; (d) intransparency (in part or full) about the involved variables and their current values; and (e) polytely (greek term for “many goals”), representing goal conflicts on different levels of analysis. This mixture of features is similar to what is called VUCA (volatility, uncertainty, complexity, ambiguity) in modern approaches to management (e.g., Mack et al., 2016 ).

In his evaluation of the CPS movement, Sternberg (1995) compared (young) European approaches to CPS with (older) American research on expertise. His analysis of the differences between the European and American traditions shows advantages but also potential drawbacks for each side. He states (p. 301): “I believe that although there are problems with the European approach, it deals with some fundamental questions that American research scarcely addresses.” So, even though the echo of the European approach did not enjoy strong resonance in the US at that time, it was valued by scholars like Sternberg and others. Before attending to validity issues, we will first present a short review of different streams.

Different Approaches to CPS

In the short history of CPS research, different approaches can be identified ( Buchner, 1995 ; Fischer et al., 2017 ). To systematize, we differentiate between the following five lines of research:

  • simple (a) The search for individual differences comprises studies identifying interindividual differences that affect the ability to solve complex problems. This line of research is reflected, for example, in the early work by Dörner et al. (1983) and their “Lohhausen” study. Here, naïve student participants took over the role of the mayor of a small simulated town named Lohhausen for a simulation period of ten years. According to the results of the authors, it is not intelligence (as measured by conventional IQ tests) that predicts performance, but it is the ability to stay calm in the face of a challenging situation and the ability to switch easily between an analytic mode of processing and a more holistic one.
  • simple (b) The search for cognitive processes deals with the processes behind understanding complex dynamic systems. Representative of this line of research is, for example, Berry and Broadbent’s (1984) work on implicit and explicit learning processes when people interact with a dynamic system called “Sugar Production”. They found that those who perform best in controlling a dynamic system can do so implicitly, without explicit knowledge of details regarding the systems’ relations.
  • simple (c) The search for system factors seeks to identify the aspects of dynamic systems that determine the difficulty of complex problems and make some problems harder than others. Representative of this line of research is, for example, work by Funke (1985) , who systematically varied the number of causal effects within a dynamic system or the presence/absence of eigendynamics. He found, for example, that solution quality decreases as the number of systems relations increases.
  • simple (d) The psychometric approach develops measurement instruments that can be used as an alternative to classical IQ tests, as something that goes “beyond IQ”. The MicroDYN approach ( Wüstenberg et al., 2012 ) is representative for this line of research that presents an alternative to reasoning tests (like Raven matrices). These authors demonstrated that a small improvement in predicting school grade point average beyond reasoning is possible with MicroDYN tests.
  • simple (e) The experimental approach explores CPS under different experimental conditions. This approach uses CPS assessment instruments to test hypotheses derived from psychological theories and is sometimes used in research about cognitive processes (see above). Exemplary for this line of research is the work by Rohe et al. (2016) , who test the usefulness of “motto goals” in the context of complex problems compared to more traditional learning and performance goals. Motto goals differ from pure performance goals by activating positive affect and should lead to better goal attainment especially in complex situations (the mentioned study found no effect).

To be clear: these five approaches are not mutually exclusive and do overlap. But the differentiation helps to identify different research communities and different traditions. These communities had different opinions about scaling complexity.

The Race for Complexity: Use of More and More Complex Systems

In the early years of CPS research, microworlds started with systems containing about 20 variables (“Tailorshop”), soon reached 60 variables (“Moro”), and culminated in systems with about 2000 variables (“Lohhausen”). This race for complexity ended with the introduction of the concept of “minimal complex systems” (MCS; Greiff and Funke, 2009 ; Funke and Greiff, 2017 ), which ushered in a search for the lower bound of complexity instead of the higher bound, which could not be defined as easily. The idea behind this concept was that whereas the upper limits of complexity are unbound, the lower limits might be identifiable. Imagine starting with a simple system containing two variables with a simple linear connection between them; then, step by step, increase the number of variables and/or the type of connections. One soon reaches a point where the system can no longer be considered simple and has become a “complex system”. This point represents a minimal complex system. Despite some research having been conducted in this direction, the point of transition from simple to complex has not been identified clearly as of yet.

Some years later, the original “minimal complex systems” approach ( Greiff and Funke, 2009 ) shifted to the “multiple complex systems” approach ( Greiff et al., 2013a ). This shift is more than a slight change in wording: it is important because it taps into the issue of validity directly. Minimal complex systems have been introduced in the context of challenges from large-scale assessments like PISA 2012 that measure new aspects of problem solving, namely interactive problems besides static problem solving ( Greiff and Funke, 2017 ). PISA 2012 required test developers to remain within testing time constraints (given by the school class schedule). Also, test developers needed a large item pool for the construction of a broad class of problem solving items. It was clear from the beginning that MCS deal with simple dynamic situations that require controlled interaction: the exploration and control of simple ticket machines, simple mobile phones, or simple MP3 players (all of these example domains were developed within PISA 2012) – rather than really complex situations like managerial or political decision making.

As a consequence of this subtle but important shift in interpreting the letters MCS, the definition of CPS became a subject of debate recently ( Funke, 2014a ; Greiff and Martin, 2014 ; Funke et al., 2017 ). In the words of Funke (2014b , p. 495):

  • simple  It is funny that problems that nowadays come under the term ‘CPS’, are less complex (in terms of the previously described attributes of complex situations) than at the beginning of this new research tradition. The emphasis on psychometric qualities has led to a loss of variety. Systems thinking requires more than analyzing models with two or three linear equations – nonlinearity, cyclicity, rebound effects, etc. are inherent features of complex problems and should show up at least in some of the problems used for research and assessment purposes. Minimal complex systems run the danger of becoming minimal valid systems.

Searching for minimal complex systems is not the same as gaining insight into the way how humans deal with complexity and uncertainty. For psychometric purposes, it is appropriate to reduce complexity to a minimum; for understanding problem solving under conditions of overload, intransparency, and dynamics, it is necessary to realize those attributes with reasonable strength. This aspect is illustrated in the next section.

Importance of the Validity Issue

The most important reason for discussing the question of what complex problem solving is and what it is not stems from its phenomenology: if we lose sight of our phenomena, we are no longer doing good psychology. The relevant phenomena in the context of complex problems encompass many important aspects. In this section, we discuss four phenomena that are specific to complex problems. We consider these phenomena as critical for theory development and for the construction of assessment instruments (i.e., microworlds). These phenomena require theories for explaining them and they require assessment instruments eliciting them in a reliable way.

The first phenomenon is the emergency reaction of the intellectual system ( Dörner, 1980 ): When dealing with complex systems, actors tend to (a) reduce their intellectual level by decreasing self-reflections, by decreasing their intentions, by stereotyping, and by reducing their realization of intentions, (b) they show a tendency for fast action with increased readiness for risk, with increased violations of rules, and with increased tendency to escape the situation, and (c) they degenerate their hypotheses formation by construction of more global hypotheses and reduced tests of hypotheses, by increasing entrenchment, and by decontextualizing their goals. This phenomenon illustrates the strong connection between cognition, emotion, and motivation that has been emphasized by Dörner (see, e.g., Dörner and Güss, 2013 ) from the beginning of his research tradition; the emergency reaction reveals a shift in the mode of information processing under the pressure of complexity.

The second phenomenon comprises cross-cultural differences with respect to strategy use ( Strohschneider and Güss, 1999 ; Güss and Wiley, 2007 ; Güss et al., 2015 ). Results from complex task environments illustrate the strong influence of context and background knowledge to an extent that cannot be found for knowledge-poor problems. For example, in a comparison between Brazilian and German participants, it turned out that Brazilians accept the given problem descriptions and are more optimistic about the results of their efforts, whereas Germans tend to inquire more about the background of the problems and take a more active approach but are less optimistic (according to Strohschneider and Güss, 1998 , p. 695).

The third phenomenon relates to failures that occur during the planning and acting stages ( Jansson, 1994 ; Ramnarayan et al., 1997 ), illustrating that rational procedures seem to be unlikely to be used in complex situations. The potential for failures ( Dörner, 1996 ) rises with the complexity of the problem. Jansson (1994) presents seven major areas for failures with complex situations: acting directly on current feedback; insufficient systematization; insufficient control of hypotheses and strategies; lack of self-reflection; selective information gathering; selective decision making; and thematic vagabonding.

The fourth phenomenon describes (a lack of) training and transfer effects ( Kretzschmar and Süß, 2015 ), which again illustrates the context dependency of strategies and knowledge (i.e., there is no strategy that is so universal that it can be used in many different problem situations). In their own experiment, the authors could show training effects only for knowledge acquisition, not for knowledge application. Only with specific feedback, performance in complex environments can be increased ( Engelhart et al., 2017 ).

These four phenomena illustrate why the type of complexity (or degree of simplicity) used in research really matters. Furthermore, they demonstrate effects that are specific for complex problems, but not for toy problems. These phenomena direct the attention to the important question: does the stimulus material used (i.e., the computer-simulated microworld) tap and elicit the manifold of phenomena described above?

Dealing with partly unknown complex systems requires courage, wisdom, knowledge, grit, and creativity. In creativity research, “little c” and “BIG C” are used to differentiate between everyday creativity and eminent creativity ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). Everyday creativity is important for solving everyday problems (e.g., finding a clever fix for a broken spoke on my bicycle), eminent creativity changes the world (e.g., inventing solar cells for energy production). Maybe problem solving research should use a similar differentiation between “little p” and “BIG P” to mark toy problems on the one side and big societal challenges on the other. The question then remains: what can we learn about BIG P by studying little p? What phenomena are present in both types, and what phenomena are unique to each of the two extremes?

Discussing research on CPS requires reflecting on the field’s research methods. Even if the experimental approach has been successful for testing hypotheses (for an overview of older work, see Funke, 1995 ), other methods might provide additional and novel insights. Complex phenomena require complex approaches to understand them. The complex nature of complex systems imposes limitations on psychological experiments: The more complex the environments, the more difficult is it to keep conditions under experimental control. And if experiments have to be run in labs one should bring enough complexity into the lab to establish the phenomena mentioned, at least in part.

There are interesting options to be explored (again): think-aloud protocols , which have been discredited for many years ( Nisbett and Wilson, 1977 ) and yet are a valuable source for theory testing ( Ericsson and Simon, 1983 ); introspection ( Jäkel and Schreiber, 2013 ), which seems to be banned from psychological methods but nevertheless offers insights into thought processes; the use of life-streaming ( Wendt, 2017 ), a medium in which streamers generate a video stream of think-aloud data in computer-gaming; political decision-making ( Dhami et al., 2015 ) that demonstrates error-proneness in groups; historical case studies ( Dörner and Güss, 2011 ) that give insights into the thinking styles of political leaders; the use of the critical incident technique ( Reuschenbach, 2008 ) to construct complex scenarios; and simulations with different degrees of fidelity ( Gray, 2002 ).

The methods tool box is full of instruments that have to be explored more carefully before any individual instrument receives a ban or research narrows its focus to only one paradigm for data collection. Brehmer and Dörner (1993) discussed the tensions between “research in the laboratory and research in the field”, optimistically concluding “that the new methodology of computer-simulated microworlds will provide us with the means to bridge the gap between the laboratory and the field” (p. 183). The idea behind this optimism was that computer-simulated scenarios would bring more complexity from the outside world into the controlled lab environment. But this is not true for all simulated scenarios. In his paper on simulated environments, Gray (2002) differentiated computer-simulated environments with respect to three dimensions: (1) tractability (“the more training subjects require before they can use a simulated task environment, the less tractable it is”, p. 211), correspondence (“High correspondence simulated task environments simulate many aspects of one task environment. Low correspondence simulated task environments simulate one aspect of many task environments”, p. 214), and engagement (“A simulated task environment is engaging to the degree to which it involves and occupies the participants; that is, the degree to which they agree to take it seriously”, p. 217). But the mere fact that a task is called a “computer-simulated task environment” does not mean anything specific in terms of these three dimensions. This is one of several reasons why we should differentiate between those studies that do not address the core features of CPS and those that do.

What is not CPS?

Even though a growing number of references claiming to deal with complex problems exist (e.g., Greiff and Wüstenberg, 2015 ; Greiff et al., 2016 ), it would be better to label the requirements within these tasks “dynamic problem solving,” as it has been done adequately in earlier work ( Greiff et al., 2012 ). The dynamics behind on-off-switches ( Thimbleby, 2007 ) are remarkable but not really complex. Small nonlinear systems that exhibit stunningly complex and unstable behavior do exist – but they are not used in psychometric assessments of so-called CPS. There are other small systems (like MicroDYN scenarios: Greiff and Wüstenberg, 2014 ) that exhibit simple forms of system behavior that are completely predictable and stable. This type of simple systems is used frequently. It is even offered commercially as a complex problem-solving test called COMPRO ( Greiff and Wüstenberg, 2015 ) for business applications. But a closer look reveals that the label is not used correctly; within COMPRO, the used linear equations are far from being complex and the system can be handled properly by using only one strategy (see for more details Funke et al., 2017 ).

Why do simple linear systems not fall within CPS? At the surface, nonlinear and linear systems might appear similar because both only include 3–5 variables. But the difference is in terms of systems behavior as well as strategies and learning. If the behavior is simple (as in linear systems where more input is related to more output and vice versa), the system can be easily understood (participants in the MicroDYN world have 3 minutes to explore a complex system). If the behavior is complex (as in systems that contain strange attractors or negative feedback loops), things become more complicated and much more observation is needed to identify the hidden structure of the unknown system ( Berry and Broadbent, 1984 ; Hundertmark et al., 2015 ).

Another issue is learning. If tasks can be solved using a single (and not so complicated) strategy, steep learning curves are to be expected. The shift from problem solving to learned routine behavior occurs rapidly, as was demonstrated by Luchins (1942) . In his water jar experiments, participants quickly acquired a specific strategy (a mental set) for solving certain measurement problems that they later continued applying to problems that would have allowed for easier approaches. In the case of complex systems, learning can occur only on very general, abstract levels because it is difficult for human observers to make specific predictions. Routines dealing with complex systems are quite different from routines relating to linear systems.

What should not be studied under the label of CPS are pure learning effects, multiple-cue probability learning, or tasks that can be solved using a single strategy. This last issue is a problem for MicroDYN tasks that rely strongly on the VOTAT strategy (“vary one thing at a time”; see Tschirgi, 1980 ). In real-life, it is hard to imagine a business manager trying to solve her or his problems by means of VOTAT.

What is CPS?

In the early days of CPS research, planet Earth’s dynamics and complexities gained attention through such books as “The limits to growth” ( Meadows et al., 1972 ) and “Beyond the limits” ( Meadows et al., 1992 ). In the current decade, for example, the World Economic Forum (2016) attempts to identify the complexities and risks of our modern world. In order to understand the meaning of complexity and uncertainty, taking a look at the worlds’ most pressing issues is helpful. Searching for strategies to cope with these problems is a difficult task: surely there is no place for the simple principle of “vary-one-thing-at-a-time” (VOTAT) when it comes to global problems. The VOTAT strategy is helpful in the context of simple problems ( Wüstenberg et al., 2014 ); therefore, whether or not VOTAT is helpful in a given problem situation helps us distinguish simple from complex problems.

Because there exist no clear-cut strategies for complex problems, typical failures occur when dealing with uncertainty ( Dörner, 1996 ; Güss et al., 2015 ). Ramnarayan et al. (1997) put together a list of generic errors (e.g., not developing adequate action plans; lack of background control; learning from experience blocked by stereotype knowledge; reactive instead of proactive action) that are typical of knowledge-rich complex systems but cannot be found in simple problems.

Complex problem solving is not a one-dimensional, low-level construct. On the contrary, CPS is a multi-dimensional bundle of competencies existing at a high level of abstraction, similar to intelligence (but going beyond IQ). As Funke et al. (2018) state: “Assessment of transversal (in educational contexts: cross-curricular) competencies cannot be done with one or two types of assessment. The plurality of skills and competencies requires a plurality of assessment instruments.”

There are at least three different aspects of complex systems that are part of our understanding of a complex system: (1) a complex system can be described at different levels of abstraction; (2) a complex system develops over time, has a history, a current state, and a (potentially unpredictable) future; (3) a complex system is knowledge-rich and activates a large semantic network, together with a broad list of potential strategies (domain-specific as well as domain-general).

Complex problem solving is not only a cognitive process but is also an emotional one ( Spering et al., 2005 ; Barth and Funke, 2010 ) and strongly dependent on motivation (low-stakes versus high-stakes testing; see Hermes and Stelling, 2016 ).

Furthermore, CPS is a dynamic process unfolding over time, with different phases and with more differentiation than simply knowledge acquisition and knowledge application. Ideally, the process should entail identifying problems (see Dillon, 1982 ; Lee and Cho, 2007 ), even if in experimental settings, problems are provided to participants a priori . The more complex and open a given situation, the more options can be generated (T. S. Schweizer et al., 2016 ). In closed problems, these processes do not occur in the same way.

In analogy to the difference between formative (process-oriented) and summative (result-oriented) assessment ( Wiliam and Black, 1996 ; Bennett, 2011 ), CPS should not be reduced to the mere outcome of a solution process. The process leading up to the solution, including detours and errors made along the way, might provide a more differentiated impression of a person’s problem-solving abilities and competencies than the final result of such a process. This is one of the reasons why CPS environments are not, in fact, complex intelligence tests: research on CPS is not only about the outcome of the decision process, but it is also about the problem-solving process itself.

Complex problem solving is part of our daily life: finding the right person to share one’s life with, choosing a career that not only makes money, but that also makes us happy. Of course, CPS is not restricted to personal problems – life on Earth gives us many hard nuts to crack: climate change, population growth, the threat of war, the use and distribution of natural resources. In sum, many societal challenges can be seen as complex problems. To reduce that complexity to a one-hour lab activity on a random Friday afternoon puts it out of context and does not address CPS issues.

Theories about CPS should specify which populations they apply to. Across populations, one thing to consider is prior knowledge. CPS research with experts (e.g., Dew et al., 2009 ) is quite different from problem solving research using tasks that intentionally do not require any specific prior knowledge (see, e.g., Beckmann and Goode, 2014 ).

More than 20 years ago, Frensch and Funke (1995b) defined CPS as follows:

  • simple  CPS occurs to overcome barriers between a given state and a desired goal state by means of behavioral and/or cognitive, multi-step activities. The given state, goal state, and barriers between given state and goal state are complex, change dynamically during problem solving, and are intransparent. The exact properties of the given state, goal state, and barriers are unknown to the solver at the outset. CPS implies the efficient interaction between a solver and the situational requirements of the task, and involves a solver’s cognitive, emotional, personal, and social abilities and knowledge. (p. 18)

The above definition is rather formal and does not account for content or relations between the simulation and the real world. In a sense, we need a new definition of CPS that addresses these issues. Based on our previous arguments, we propose the following working definition:

  • simple  Complex problem solving is a collection of self-regulated psychological processes and activities necessary in dynamic environments to achieve ill-defined goals that cannot be reached by routine actions. Creative combinations of knowledge and a broad set of strategies are needed. Solutions are often more bricolage than perfect or optimal. The problem-solving process combines cognitive, emotional, and motivational aspects, particularly in high-stakes situations. Complex problems usually involve knowledge-rich requirements and collaboration among different persons.

The main differences to the older definition lie in the emphasis on (a) the self-regulation of processes, (b) creativity (as opposed to routine behavior), (c) the bricolage type of solution, and (d) the role of high-stakes challenges. Our new definition incorporates some aspects that have been discussed in this review but were not reflected in the 1995 definition, which focused on attributes of complex problems like dynamics or intransparency.

This leads us to the final reflection about the role of CPS for dealing with uncertainty and complexity in real life. We will distinguish thinking from reasoning and introduce the sense of possibility as an important aspect of validity.

CPS as Combining Reasoning and Thinking in an Uncertain Reality

Leading up to the Battle of Borodino in Leo Tolstoy’s novel “War and Peace”, Prince Andrei Bolkonsky explains the concept of war to his friend Pierre. Pierre expects war to resemble a game of chess: You position the troops and attempt to defeat your opponent by moving them in different directions.

“Far from it!”, Andrei responds. “In chess, you know the knight and his moves, you know the pawn and his combat strength. While in war, a battalion is sometimes stronger than a division and sometimes weaker than a company; it all depends on circumstances that can never be known. In war, you do not know the position of your enemy; some things you might be able to observe, some things you have to divine (but that depends on your ability to do so!) and many things cannot even be guessed at. In chess, you can see all of your opponent’s possible moves. In war, that is impossible. If you decide to attack, you cannot know whether the necessary conditions are met for you to succeed. Many a time, you cannot even know whether your troops will follow your orders…”

In essence, war is characterized by a high degree of uncertainty. A good commander (or politician) can add to that what he or she sees, tentatively fill in the blanks – and not just by means of logical deduction but also by intelligently bridging missing links. A bad commander extrapolates from what he sees and thus arrives at improper conclusions.

Many languages differentiate between two modes of mentalizing; for instance, the English language distinguishes between ‘thinking’ and ‘reasoning’. Reasoning denotes acute and exact mentalizing involving logical deductions. Such deductions are usually based on evidence and counterevidence. Thinking, however, is what is required to write novels. It is the construction of an initially unknown reality. But it is not a pipe dream, an unfounded process of fabrication. Rather, thinking asks us to imagine reality (“Wirklichkeitsfantasie”). In other words, a novelist has to possess a “sense of possibility” (“Möglichkeitssinn”, Robert Musil; in German, sense of possibility is often used synonymously with imagination even though imagination is not the same as sense of possibility, for imagination also encapsulates the impossible). This sense of possibility entails knowing the whole (or several wholes) or being able to construe an unknown whole that could accommodate a known part. The whole has to align with sociological and geographical givens, with the mentality of certain peoples or groups, and with the laws of physics and chemistry. Otherwise, the entire venture is ill-founded. A sense of possibility does not aim for the moon but imagines something that might be possible but has not been considered possible or even potentially possible so far.

Thinking is a means to eliminate uncertainty. This process requires both of the modes of thinking we have discussed thus far. Economic, political, or ecological decisions require us to first consider the situation at hand. Though certain situational aspects can be known, but many cannot. In fact, von Clausewitz (1832) posits that only about 25% of the necessary information is available when a military decision needs to be made. Even then, there is no way to guarantee that whatever information is available is also correct: Even if a piece of information was completely accurate yesterday, it might no longer apply today.

Once our sense of possibility has helped grasping a situation, problem solvers need to call on their reasoning skills. Not every situation requires the same action, and we may want to act this way or another to reach this or that goal. This appears logical, but it is a logic based on constantly shifting grounds: We cannot know whether necessary conditions are met, sometimes the assumptions we have made later turn out to be incorrect, and sometimes we have to revise our assumptions or make completely new ones. It is necessary to constantly switch between our sense of possibility and our sense of reality, that is, to switch between thinking and reasoning. It is an arduous process, and some people handle it well, while others do not.

If we are to believe Tuchman’s (1984) book, “The March of Folly”, most politicians and commanders are fools. According to Tuchman, not much has changed in the 3300 years that have elapsed since the misguided Trojans decided to welcome the left-behind wooden horse into their city that would end up dismantling Troy’s defensive walls. The Trojans, too, had been warned, but decided not to heed the warning. Although Laocoön had revealed the horse’s true nature to them by attacking it with a spear, making the weapons inside the horse ring, the Trojans refused to see the forest for the trees. They did not want to listen, they wanted the war to be over, and this desire ended up shaping their perception.

The objective of psychology is to predict and explain human actions and behavior as accurately as possible. However, thinking cannot be investigated by limiting its study to neatly confined fractions of reality such as the realms of propositional logic, chess, Go tasks, the Tower of Hanoi, and so forth. Within these systems, there is little need for a sense of possibility. But a sense of possibility – the ability to divine and construe an unknown reality – is at least as important as logical reasoning skills. Not researching the sense of possibility limits the validity of psychological research. All economic and political decision making draws upon this sense of possibility. By not exploring it, psychological research dedicated to the study of thinking cannot further the understanding of politicians’ competence and the reasons that underlie political mistakes. Christopher Clark identifies European diplomats’, politicians’, and commanders’ inability to form an accurate representation of reality as a reason for the outbreak of World War I. According to Clark’s (2012) book, “The Sleepwalkers”, the politicians of the time lived in their own make-believe world, wrongfully assuming that it was the same world everyone else inhabited. If CPS research wants to make significant contributions to the world, it has to acknowledge complexity and uncertainty as important aspects of it.

For more than 40 years, CPS has been a new subject of psychological research. During this time period, the initial emphasis on analyzing how humans deal with complex, dynamic, and uncertain situations has been lost. What is subsumed under the heading of CPS in modern research has lost the original complexities of real-life problems. From our point of view, the challenges of the 21st century require a return to the origins of this research tradition. We would encourage researchers in the field of problem solving to come back to the original ideas. There is enough complexity and uncertainty in the world to be studied. Improving our understanding of how humans deal with these global and pressing problems would be a worthwhile enterprise.

Author Contributions

JF drafted a first version of the manuscript, DD added further text and commented on the draft. JF finalized the manuscript.

Authors Note

After more than 40 years of controversial discussions between both authors, this is the first joint paper. We are happy to have done this now! We have found common ground!

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank the Deutsche Forschungsgemeinschaft (DFG) for the continuous support of their research over many years. Thanks to Daniel Holt for his comments on validity issues, thanks to Julia Nolte who helped us by translating German text excerpts into readable English and helped us, together with Keri Hartman, to improve our style and grammar – thanks for that! We also thank the two reviewers for their helpful critical comments on earlier versions of this manuscript. Finally, we acknowledge financial support by Deutsche Forschungsgemeinschaft and Ruprecht-Karls-Universität Heidelberg within their funding programme Open Access Publishing .

1 The fMRI-paper from Anderson (2012) uses the term “complex problem solving” for tasks that do not fall in our understanding of CPS and is therefore excluded from this list.

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Unit 16: Complex numbers

About this unit.

This topic covers:

  • Adding, subtracting, multiplying, & dividing complex numbers
  • Complex plane

Absolute value & angle of complex numbers

  • Polar coordinates of complex numbers

What are the imaginary numbers?

  • Intro to the imaginary numbers (Opens a modal)
  • Powers of the imaginary unit (Opens a modal)
  • Simplifying roots of negative numbers (Opens a modal)
  • i as the principal root of -1 (Opens a modal)
  • Powers of the imaginary unit 4 questions Practice
  • Simplify roots of negative numbers 4 questions Practice

What are the complex numbers?

  • Intro to complex numbers (Opens a modal)
  • Classifying complex numbers (Opens a modal)
  • Parts of complex numbers 4 questions Practice
  • Classify complex numbers 4 questions Practice

The complex plane

  • Plotting numbers on the complex plane (Opens a modal)
  • The complex plane (Opens a modal)
  • Plot numbers on the complex plane 4 questions Practice

Adding & subtracting complex numbers

  • Adding complex numbers (Opens a modal)
  • Subtracting complex numbers (Opens a modal)
  • Add & subtract complex numbers 4 questions Practice
  • Graphically add & subtract complex numbers 4 questions Practice

Multiplying complex numbers

  • Multiplying complex numbers (Opens a modal)
  • Complex number operations review (Opens a modal)
  • Multiply complex numbers (basic) 4 questions Practice
  • Multiply complex numbers 4 questions Practice

Complex conjugates & dividing complex numbers

  • Intro to complex number conjugates (Opens a modal)
  • Complex number conjugates (Opens a modal)
  • Dividing complex numbers (Opens a modal)
  • Dividing complex numbers review (Opens a modal)
  • Complex number conjugates 4 questions Practice
  • Divide complex numbers 4 questions Practice
  • Absolute value of complex numbers (Opens a modal)
  • Absolute value & angle of complex numbers (Opens a modal)
  • Complex number absolute value & angle review (Opens a modal)
  • Modulus (absolute value) of complex numbers 4 questions Practice
  • Angle of complex numbers 4 questions Practice
  • Complex numbers from absolute value & angle 4 questions Practice

Distance & midpoint of complex numbers

  • Distance & midpoint of complex numbers (Opens a modal)
  • Distance of complex numbers 4 questions Practice
  • Midpoint of complex numbers 4 questions Practice

Polar form of complex numbers

  • Polar & rectangular forms of complex numbers (Opens a modal)
  • Complex number forms review (Opens a modal)
  • Complex number polar form intuition (Opens a modal)
  • Polar & rectangular forms of complex numbers 4 questions Practice

Multiplying & dividing complex numbers in polar form

  • Dividing complex numbers: polar & exponential form (Opens a modal)
  • Visualizing complex number multiplication (Opens a modal)
  • Powers of complex numbers (Opens a modal)
  • Complex number equations: x³=1 (Opens a modal)
  • Visualizing complex number powers (Opens a modal)
  • Complex number polar form review (Opens a modal)
  • Multiply & divide complex numbers in polar form 4 questions Practice
  • Powers of complex numbers 4 questions Practice

Challenging complex number problems

  • Challenging complex numbers problem (1 of 3) (Opens a modal)
  • Challenging complex numbers problem (2 of 3) (Opens a modal)
  • Challenging complex numbers problem (3 of 3) (Opens a modal)
  • Challenging complex numbers problem: complex determinant (Opens a modal)

SoftwareDominos

how to solve a problem with complex

Complex Problems: What Does the Nature of the Problem Tell Us About Its Solution

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1. Overview

In The 7 Timeless Steps to Guide You Through Complex Problem Solving , we discussed a generic approach that could be systematically applied to solving complex problems. Since not all problems are complex, and many gradations of complexity exist, it is probably a good idea to start by defining what complex problem-solving involves and what categories of problems are most suitable to tackle using that approach. For this reason, understanding complex problems made the top position on the list.

Practically all living organisms deal with complex problems, from single-celled amebas to societies of Homo sapiens , and surprisingly, the solution-creation process can be very similar, at least on the conceptual level. This article will elaborate on this point further, articulating the terminology and ideas often associated with how complex adaptive systems solve complex problems. More specifically, we will answer the following questions:

This article is part of a series on complex problem-solving. The list below will guide you through the different subtopics.

Complex Problem-Solving Guide in 7 Steps

The 7 Timeless Steps to Guide You Through Complex Problem Solving

The Nature of Complex Problems

What Does the Nature of the Problem Tell Us About Its Solution

Gaussian Distributions vs Power Laws

Your Ultimate Guide to Making Sense of Natural and Social Phenomena

Complex Problem-Solving in Groups

An Exploratory Overview of ProbleSolving Processes in Groups

The Power of Critical Thinking

An Essential Guide for Personal and Professional Development

Group-Decision Making

6 Modes That Tell Us How Teams Decide

3. An Intuitive Definition of Complex Problems

We all intuitively grasp the characteristics of challenging problems, at least at their fundamental levels. For instance, we can promptly recognize that fixing a faulty washing machine is relatively simple. First, we need basic technical skills to identify the faulty part. Next, we would read the code on the back, order a spare part, and finally replace it.

In simple problems, there is no uncertainty around the root cause or the solution.

On the other hand, deciding whether or not to accept a job offer is anything but simple. Firstly, you will never have sufficient information to make an optimal decision . Secondly, you cannot predict the consequences of such a decision. Finally, whichever choice you make will change your worldview, rendering any forecasts you have made of the future almost instantly obsolete.

The following characteristics distinguish complex problems.

So, what are complex problems?

Complex problems — an intuitive guide.

Non-triviality

Complex problems generally admit non-trivial solutions. In addition to strong field expertise and solid analytical skills, they require a high cognitive load to formulate.

Uncertainty

Solutions to complex problems cannot be guaranteed as the behaviour of the system to which the solution is applied is always unpredictable.

Diagnosing complex problems is especially challenging because consensus on facts, root causes, and solutions can be difficult to obtain, especially in large groups.

3. Challenges of Working With Complex Problems

Experts like Nassim Taleb, Gerd Gigerenzer, and Daniel Kahnemann insist that solving complex problems is relatively easy once we understand which tools to apply. In their view, failures come from applying engineering methods like optimization rather than intuition , heuristics, biases, imitation, and many other techniques refined over millennia of evolution and accumulated wisdom.

4. Complex Problems in the Literature

Experts have extensively researched topics associated with intuition, cognitive psychology , risk management , organisational behaviour , and decision-making under uncertainty. This has left us with a rich body of knowledge popularized by best-selling authors such as Daniel Kahneman and Nassim Taleb, which will be reviewed next.

4.1 Fooled by Randomness (Taleb, 2001)

Fooled by Randomness is one of Taleb’s best-selling books , and its central story revolves around the hidden role of chance in our lives. In Taleb’s view, we grossly and routinely overestimate our capabilities to forecast future events ( the turkey problem ) and cope with that failure through mechanisms like the narrative fallacy and our ability to reconstruct past events based on new information.

how to solve a problem with complex

Key takeaways from Fooled By Randomness

  • In social , financial, economic, and political systems, Gaussian distributions mislead at best by providing a comforting but shifting ground for modelling events.
  • Power laws like Pareto’s provide more suitable models for examining complex systems .
  • Time-tested heuristics, formulated through a long knowledge acquisition and refinement period, are more valuable for decision-making under uncertainty than optimisation techniques, which require a well-behaved underlying model (such as the Gaussian).

4.2 Thinking, Fast and Slow (Kahneman, 2011)

Thinking, Fast and Slow is a best-selling book by Daniel Kahneman popularizing his work in cognitive psychology about the mechanism and efficiency of human judgment and decision-making under conditions of uncertainty. His original idea revolves around modelling the human mind as two systems, which he refers to as System 1 and System 2.

how to solve a problem with complex

Key takeaways from Thinking, Fast and Slow

  • Systems 1 and 2 perfectly cover our decision-making needs for simple and complex problems.
  • System 1 is fast and inexpensive, allowing us to make critical decisions with imperfect or unreliable data .
  • System 1 relies on heuristics and biases to compensate for unreliable information and processing time.
  • System 2 is slow and expensive but more accurate, allowing us to make decisions requiring a high cognitive load and processing larger amounts of information.

4.3 Process Consultation (Schein, 1969)

Professor Edgar Schein is a leading authority in organizational behaviour, culture , and psychology. His short but insightful book Process Consultation: Its Role in Organizational Development dedicates a full chapter to group problem-solving and decision-making. Schein explores how leaders and their groups tackle complex problems in this chapter.

how to solve a problem with complex

Key takeaways from Process Consultation

  • Group problem-solving presents challenges and dynamics that differ from those of individuals and is a subject in its own right.
  • In both cases, events that cause tension and anxiety trigger a solution-finding process that culminates in applying changes to the environment or the individual or group’s interactions with it.
  • However, problem formulation, solution creation and implementation differ significantly between the two cases.

5. The Information Sufficiency Problem

5.1 how much data is enough.

During the Newtonian age, physicists believed that once the initial conditions of a physical system were precisely determined, its future evolution could be predicted with arbitrary precision. For example, the laws of dynamics allow us to calculate the infinite trajectory of a point mass given its initial position and velocity.

What happens when the system consists of innumerable particles, each with a different initial speed and position? For practical reasons, we substitute the individual particles with a unit of volume where its macro properties can be calculated by averaging over its constituent particles. For example, instead of registering the speed and position of every molecule in a gas container, we substitute those numbers with temperature and pressure calculated on a coarse-grained subvolume. This coarse-graining allows us to explore the system’s physical properties without drowning in data.

5.2 The Rise of Statistical Mechanics and Probabilistic Models

The coarse-graining method and the impracticality of precise calculations on the molecular level gave rise to statistical mechanics , which Boltzmann and others pioneered. Under statistical mechanics, physical systems are governed by the laws of thermodynamics. The second law of thermodynamics is the most famous, dictating that a system’s entropy (or disorder) must always increase.

The practical advantages of using coarse-graining came at a cost, as a probabilistic model replaced the classic view of deterministic evolution. In this new paradigm, a physical system is predisposed to evolve into one of numerous states. We can only predict the probability that it will be in a given future state, but we can never be sure which one.

But all is not lost. Even with the probabilistic model, we can still calculate a system’s future state and create contingency plans for each scenario. We might even be able to influence the outcome by applying pressure on known system levers. This assumption forms the basis of Strategic Choice Theory .

Strategic choice theory, in the realm of organizational theory, emphasizes the influence of leaders and decision-makers on an organization’s direction. It contrasts with earlier views that saw organizations solely responding to external forces.

how to solve a problem with complex

Managing Probabilistic Systems

In probabilistic models, we assume that the system’s future states are well-defined and their probabilities are calculable. Given this information, adequate planning and optimization processes can be applied to maximize a specific utility function.

5.3 Probabilistic Models Cannot Account for Innovation

Any physical, chemical, or biological system that shows innovation cannot, by definition, be analyzed using probability models, as the latter assumes all future states are static and knowable in advance. Also, the probabilities for reaching any of those states are either fixed or vary according to well-specified rules.

Therefore, probabilistic models are not good enough to predict the future behaviour of human systems. This also spells trouble for Strategic Choice Theory, which relies on simple causal relationships between leaders’ interventions and desired consequences to achieve progress or resolve conflicts.

If a system can produce novel behaviour, it is unpredictable and, therefore, hard to manage. Ecologies of living organisms can only be understood through complexity theory and managed by principles that consider that.

Complex systems presenting complex problems will never offer sufficient information, and managers must make choices under uncertain conditions.

Even if we consider every atom (or elementary particle) in the universe , we still would not be able to predict the rich diversity of phenomena (including biodiversity on Earth) that we currently observe. Quantum mechanics and symmetry breaking ensure enough randomness is injected into the system to produce rich but unpredictable results.

The same applies when we try to understand the source of consciousness in our brains. Would it help to incorporate every neuron and synapse in a gigantic mathematical model? Even if this becomes practical someday, experts seem to believe that emerging consciousness in the inanimate matter is far away.

In summary, there seems to be a hard limit on how much useful information, in principle and practice, can be gleaned by observing a complex system .

6. Problem Classification

6.1 maximizing utility functions.

Problems can present themselves in many different ways. However, we are interested in those characterized by a utility function.

A utility function is a concept primarily used in economics, decision theory, and game theory to represent an individual’s preferences over different outcomes or states of the world. It assigns a numerical (or utility) value to each possible outcome or combination, reflecting the individual’s subjective satisfaction or preference associated with those outcomes.

How Are Utility Functions Used?

how to solve a problem with complex

Here are some key points about utility functions:

Using utility functions, people can compare complex options involving chance or risk and make decisions based on their preferences and risk tolerance.

6.2 Ordered, Chaotic, Complex, and Random Systems

Imagine that you have the following problem. You are required to configure an air conditioning system for a data centre. The system is composed of two machines: a cooling engine and a computer connected to it. The computer has temperature and humidity sensors and various switches and dials that allow operators to set control parameters such as maximum temperature or humidity.

how to solve a problem with complex

The engineer setting up the system must configure it to minimize power consumption while keeping the room at a given temperature and humidity level. The only issue is that the system does not have an operations guide, and the engineer has to figure out how to set it up using trial and error.

Four scenarios are possible: Ordered, Random, Complex, and Chaotic.

Ordered Systems

  • Changes in the switches or dials produce a clear response in the cooling machine.
  • Although some settings may impact others, the engineer can, through trial and error, understand the relationship between the controls and the outcomes.
  • Ordered systems have direct causal relationships and hard constraints between their components.
  • Problems in an ordered system can be resolved through the relationships between control parameters and the utility function.
  • Computers, watches, and washing machines are examples of such systems.

Random Systems

  • Changes in the switches or dials produce different responses every time. There seems to be no correlation between the settings and the outcomes.
  • Random systems have no causal links and no constraints between their components.
  • Random systems present problems that cannot be resolved; they are, by definition, unmanageable.
  • A reward system based on rolling two dice is a random system.

Chaotic Systems

  • Small changes in the switches or dials produce wild responses. Although the system appears random and unpredictable, it shows regular behavioural patterns over the long term.
  • Chaotic systems have causal links and hard constraints between their components in addition to non-linear dynamics.
  • Chaotic systems are also challenging to manage. However, causes can be linked to effects, and regularities can be leveraged.
  • The weather, a turbulent water flow , and three bodies rotating around each other in gravitational fields are examples of chaotic systems.

Complex Systems

  • Changes in the switches or dials produce different responses every time. Slight correlations can be measured between the settings and the outcomes.
  • Complex systems have indirect causal links and loose constraints between their components.
  • Complex systems come in two varieties: adaptive and non-adaptive.
  • An example of a non-adaptive complex system is the Brusselator . An example of a complex adaptive system is a microbiome.
  • Complex systems present problems that can be resolved through heuristics, safe-to-fail experimentation, and managing in the present rather than towards a desirable future state.

7. Small Worlds, Optimization, and Unknown Unknowns

7.1 leonard j. savage’s “small world”.

Leonard Jimmie Savage (1917-1971) was an American statistician and economist who significantly contributed to statistics , decision theory, and econometrics.

“Savage’s Small World” refers to a thought experiment proposed by the statistician and economist Leonard Jimmie Savage. This concept is often cited in discussions about subjective probability and decision theory.

Decision-Making in a Simple World

L. J. Savage’s “Small World”

  • In Savage’s Small World, imagine a small society where everyone knows each other’s preferences, capabilities, and the outcomes of their decisions.
  • Within this world, individuals can communicate freely and exchange information about their beliefs, desires, and experiences.
  • In such a setting, decision-making becomes more transparent and informed, as individuals can access comprehensive knowledge about each other’s perspectives and choices.

The significance of Savage’s Small World lies in its implications for decision theory. It illustrates an idealized scenario where uncertainty is minimized, and individuals have perfect knowledge about the consequences of their actions. In reality, however, decision-makers often face uncertainty and incomplete information, prompting probabilistic reasoning and subjective judgment.

By contrasting Savage’s Small World with the complexities of real-world decision-making, Savage highlighted the importance of subjective probability for navigating uncertainty and making rational choices. Subjective probability allows individuals to express their beliefs and uncertainty in a formal framework, facilitating reasoned decision-making even when complete information is lacking.

7.2 Optimization Techniques

Optimization techniques can be effectively applied in a Small World scenario where all outcomes and probabilities can be precisely computed beforehand. This is because decision-makers have complete knowledge of the system, allowing them to accurately assess the consequences of their actions and choose the optimal course of action based on predetermined criteria.

In contrast, in the real world, uncertainty, complexity, and incomplete information often make it challenging to compute outcomes and probabilities beforehand precisely. As a result, optimization techniques may not be as effective, as they rely on accurate information to generate optimal solutions. Decision-makers must contend with uncertainty and imperfect knowledge, which can lead to suboptimal outcomes even when applying optimisation techniques.

One example where optimization relies on known outcomes and their probabilities is in the context of inventory management.

In inventory management, a retailer determines the optimal inventory level for each product to minimize costs while ensuring that customer demand is met. In this case, the utility function represents the retailer’s objective, which typically involves minimizing inventory holding costs and stockouts.

Optimisation Process in Inventory Management

Here’s a rigorous breakdown of the optimization process:

  • 1 – Identify outcomes and probabilities :
  • 2- Define the utility function :
  • 3- Formulate the optimization problem :
  • 4- Solve the optimization problem :

By incorporating known outcomes (demand scenarios) and their probabilities into the utility function and using optimization techniques, retailers can manage their inventory effectively, minimizing costs while ensuring customer satisfaction and maintaining adequate product availability.

7.3 Optimisation in Complex Worlds

Optimization techniques may encounter challenges in complex situations, particularly those governed by power laws (see discussion on Gaussian Distributions vs Power Laws: Your Ultimate Guide to Making Sense of Natural and Social Phenomena and their impact on our understanding of complex natural phenomena), due to several reasons:

Practical challenges of estimating model parameters in power laws versus Gaussians

Comparing the practical difficulties of estimating model parameters such as mean and variance in power laws versus Gaussians:

  • Mean estimation
  • Variance estimation

7.4 Unknown Unknowns

The concept of “ unknown unknowns” refers to phenomena or factors that are not only unknown but also unknowable.

In Savage’s “Small World,” which represents an idealized scenario where decision-makers have perfect knowledge of outcomes and their probabilities, the concept of “unknown unknowns” highlights the limitations of this idealization. In a Small World, nothing new ever happens, and there can be no “Unknown Unknowns”.

In contrast, complex adaptive systems constantly display emergent behaviour; patterns that could not have been anticipated. A leader managing such a system cannot list all possible outcomes, let alone assign each probability.

8. Subjectivity and the Role of the Observer

In decision-making, a leader’s subjective experience contrasts with their role as an objective observer. For example, in systems thinking and cybernetics, the leader must diagnose problems based on data and evidence and formulate a logical and rational solution.

A leader working on complex problems in a social group is an integral part of the system. As we have seen in previous sections, the leader is unable to gather sufficient information about the system in principle and practice, and whatever data they gather will be coloured by their subjective experience

Systems Thinking in a Nuntshell

Systems Thinking is a holistic approach to understanding complex systems by examining their interconnectedness, interdependencies, and dynamics. It views the leader’s role in an organization as crucial for effective strategy formulation and decision-making by emphasizing the following principles:

  • Holistic Perspective :
  • Interconnectedness :
  • Feedback Loops :

Systems Thinking addresses the paradox of the leader being part of the system being managed by acknowledging the leader’s dual role as both a participant within the system and an external observer guiding its direction. Several key principles help resolve this conflict:

Exploring problem-solving reveals that not all problems are created equal. Distinguishing between simple and complex problems reveals the underlying nature of the systems they belong to. Simple problems are typically found within ordered systems, whereas complex problems are inherent to complex systems. These systems extend beyond biological ecologies to encompass social groups and organizations, where intricate interactions and emergent behaviours define their complexity.

One defining characteristic of complex systems is their governance by power laws, rendering traditional optimization techniques ineffective. Unlike in ordered systems, where linear solutions may suffice, complex systems defy such neat categorizations. Applying optimization strategies proves futile due to the non-linear, unpredictable dynamics governed by power laws.

Heuristics emerge as promising alternatives to optimization in navigating the labyrinth of complex systems. These intuitive, rule-of-thumb approaches allow for adaptive decision-making, acknowledging complex systems’ inherent uncertainty and non-linearity.

10. References

  • Thinking, Fast and Slow — by Daniel Kahneman , 2011
  • Fooled by Randomness — by Nassim Nicholas Taleb , 2001
  • Process Consultation — by Edgar Schein , 1969
  • The Quark and the Jaguar — by Murray Gell-Mann , 1994

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how to solve a problem with complex

Breaking Down Complex Math Problems: A Step-by-Step Guide

Understanding complex math problems is essential for students, teachers, and individuals keen on applying math in daily life or at work. Here’s a step-by-step guide to help you dissect and tackle these seemingly daunting equations or problems.

Step 1: Understand the Problem

Before anything else, understand what the problem is asking. To do this, you’ll need to:

  • Read the problem carefully, making sure to consider all the provided information.
  • Identify what you need to find (this will often be clearly stated in the problem).
  • Pinpoint any vocabulary or concepts that you may not understand and look them up.

Let’s take the problem, “If the sum of two consecutive numbers is 27, what are the numbers?” The goal here is to identify the two numbers. The term ‘consecutive’ is vital; it tells us that if one number is n, the next is n+1.

Step 2: Devise a Plan

Creating a plan involves deciding the methods or formulas to use to solve the problem. It may also include deciding the order in which to perform certain operations or steps.

For our problem, we know that consecutive numbers can be represented as n and n+1. The problem tells us their sum is 27. So, we set up the equation: n + (n + 1) = 27.

Step 3: Carry Out the Plan

At this point, we execute the plan we devised in step 2. Here, we solve the equation:

  • Combine like terms: 2n + 1 = 27
  • Subtract 1 from both sides: 2n = 26
  • Divide both sides by 2: n = 13

After we found n, we also need to find n+1, which equals 14. So, the two consecutive numbers are 13 and 14.

Step 4: Check Your Work

Checking the work ensures that the answer is reasonable and fits the criteria of the problem. Here, we check by substituting the numbers back into the original problem. Is 13 + 14 equal to 27? Yes, so we know our solution is correct.

Step 5: Reflect on Your Work

Reflection involves thinking about how you approached the problem, what strategies worked, what didn’t, and why. Reflecting helps improve your problem-solving skills for future problems.

Now let’s try a more complex problem:

“In a basketball game, if Player A scored twice as many points as Player B, and together they scored 54 points, how many points did each player score?”

We are looking for the points each player scored. We know that Player A scored twice as many points as Player B and that they scored 54 points together.

Let’s represent Player B’s score as ‘x’. Given that Player A scored twice as many points, we can represent his score as ‘2x’. Their combined score is 54 points. So, we can create the equation: x + 2x = 54.

Solve the equation:

Combine like terms: 3x = 54

Divide both sides by 3: x = 18

Player B scored 18 points. Since Player A scored twice as many points, he scored 36 points.

Substitute the numbers back into the original problem. Does 18 (Player B’s points) + 36 (Player A’s points) equal 54? Yes, so our solution is correct.

Just as with the simpler problem, reflect on your work. Understanding your own process can make tackling similar problems easier in the future.

In conclusion, breaking down complex math problems doesn’t have to be intimidating. By taking the time to understand the problem, devise a plan, carry out that plan, and then check and reflect on your work, you can effectively solve complex math problems. And remember, practice makes perfect! The more problems you solve, the better you will become.

Now, let’s take a look at a complex word problem that involves quadratic equations – a critical concept in algebra.

“A rectangle has a length that is 2 more than 3 times the width. The area of the rectangle is 75 square units. What are the dimensions of the rectangle?”

Our task is to find the width and length of the rectangle. We know from the problem that:

  • The length is 2 more than 3 times the width. If we represent the width as ‘w’, then the length is ‘3w + 2’.
  • The area of the rectangle is 75. The formula for the area of a rectangle is ‘width * length’.

We have two equations and two unknowns, which is a good start. Let’s set up our equations:

  • L = 3w + 2 (from the first piece of information)
  • L * w = 75 (from the second piece of information)

We can substitute the first equation into the second one because they both equal ‘L’.

This results in a quadratic equation:

(3w + 2) * w = 75 3w^2 + 2w = 75 3w^2 + 2w - 75 = 0

The next step is to solve this quadratic equation. Since it’s a quadratic equation, we can use the quadratic formula:

w = [-b ± sqrt(b^2 - 4ac)] / (2a)

In our equation, a = 3, b = 2, and c = -75. Plug those values into the formula:

w = [-2 ± sqrt((2)^2 - 4*3*(-75))] / (2*3) w = [-2 ± sqrt(4 + 900)] / 6 w = [-2 ± sqrt(904)] / 6

The square root of 904 is approximately 30.07. So the two possible values for w are:

w = [-2 + 30.07] / 6 = 4.68 (approximately) w = [-2 - 30.07] / 6 = -5.35 (approximately)

Since we can’t have a negative width, we discard the second solution. So the width is approximately 4.68 units.

Plug the value of w into the first equation to find the length:

L = 3*4.68 + 2 = 16.04 (approximately)

We should check our answers. The area of the rectangle is width times length, which should be 75:

4.68 * 16.04 = 75.1 (approximately)

The slight difference is due to the rounding. So, our solution is correct.

Consider how we approached this problem. We took the given information, translated it into mathematical equations, and then solved those equations. Recognizing that the problem was a quadratic equation allowed us to use the quadratic formula, which is a common method for solving such problems.

As you can see, with the right approach, even complex math problems can be broken down into manageable steps. This process requires practice, but over time, it will become second nature.

If you are a Class 7 CBSE student looking to practice class 7 math online for better marks, you can visit LearnTheta. It’s an online platform designed to aid you in achieving effective preparation.

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Complex Numbers in Optimization Toolbox Solvers

Generally, Optimization Toolbox™ solvers do not accept or handle objective functions or constraints with complex values. However, the least-squares solvers lsqcurvefit , lsqnonlin , and lsqlin , and the fsolve solver can handle these objective functions under the following restrictions:

The objective function must be analytic in the complex function sense (for details, see Nevanlinna and Paatero [1] ). For example, the function f ( z ) = Re( z ) –  i Im( z ) is not analytic, but the function f ( z ) = exp( z ) is analytic. This restriction automatically holds for lsqlin .

There must be no constraints, not even bounds. Complex numbers are not well ordered, so it is not clear what “bounds” might mean. When there are problem bounds, nonlinear least-squares solvers disallow steps leading to complex values.

Do not set the FunValCheck option to 'on' . This option immediately halts a solver when the solver encounters a complex value.

Do not use the 'interior-point' algorithm of lsqcurvefit or lsqnonlin . That algorithm is primarily for handling constraints, and has not been validated to work with complex values.

The problem-based approach does not support complex values in the following: an objective function, nonlinear equalities, and nonlinear inequalities. If a function calculation has a complex value, even as an intermediate value, the final result might be incorrect.

The least-squares solvers and fsolve try to minimize the squared norm of a vector of function values. This makes sense even in the presence of complex values.

If you have a non-analytic function or constraints, split the real and imaginary parts of the problem. For an example, see Fit a Model to Complex-Valued Data .

To get the best (smallest norm) solution, try setting a complex initial point. For example, solving 1 +  x 4  = 0 fails if you use a real start point:

However, if you use a complex initial point, fsolve succeeds:

[1] Nevanlinna, Rolf, and V. Paatero. Introduction to Complex Analysis . Addison-Wesley, 1969.

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Top 100+ SQL Interview Questions and Practice Exercises

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Table of Contents

Review Your SQL Knowledge

Practice regularly, familiarize yourself with the testing platform, prepare for different types of questions, additional tips, explore 55+ general sql interview questions, practice, practice, practice, …, sql cheat sheet, data analysis in sql, window functions, common table expressions, advanced sql, good luck with your interview.

Are you gearing up for a SQL interview? This article is packed with over 100 SQL interview questions and practical exercises, organized by topic, to help you prepare thoroughly and approach your interview with confidence.

SQL is essential for many jobs, like data analysis, data science, software engineering, data engineering, testing, and many others. Preparing well for a SQL interview is crucial, no matter what role you're aiming for.

Searching for a new job can be really stressful, whether you're choosing to switch, have been laid off, or are looking for your first job. That's why being well-prepared is essential.

In this article, I've gathered over 100 SQL interview questions and exercises. These questions are spread across various articles published at LearnSQL.com. I have organized the articles by topic. Feel free to explore only the topics related to your specific job. I've also included tips to help you prepare for your interview.

SQL Interview Preparation Tips

Start preparing for your SQL interview well in advance. Once you're invited to an interview (Congratulations!), ask your recruiter what to expect and what is the format of the interview. For the SQL part you can usually expect coding exercises on an automated testing platform, a take-home assignment, or a whiteboard session.

The key to performing well in a SQL interview is practice. You'll likely be nervous, so the more familiar you are with SQL, the more instinctive your responses will become. Practice a variety of SQL problems so that querying becomes second nature to you.

If your interview involves using a specific coding platform, try to get comfortable with it beforehand. Many platforms offer a demo or practice session, so take advantage of this feature to familiarize yourself with the interface. This familiarity can help reduce stress and improve your performance during the actual interview.

Illustration: Person during an interview

  • Coding Platform Questions: Whether during the interview or as a take-home task, make sure you understand the typical questions and problems that might appear on these platforms. Practice solving similar problems under timed conditions.
  • Whiteboard Interviews: Be ready to write code in pseudocode and discuss your thought process. Focus on explaining the concepts and logic behind your solutions more than the exact syntax, which demonstrates a deeper understanding of the problem-solving process.
  • Review Key SQL Concepts: Make sure you're comfortable with all fundamental SQL operations such as joins, subqueries, window functions, and aggregation. Also, review more advanced topics if the job role demands it.
  • Mock Interviews: Consider doing mock interviews with friends or mentors to simulate the interview environment. This practice can help you manage time and stress effectively.
  • Rest Well: Ensure you're well-rested before the interview day; a clear mind will help you think and perform better.

By incorporating these strategies into your preparation, you can approach your SQL interview with confidence and increase your chances of success.

Begin by refreshing your SQL knowledge, particularly if you haven't used it in a while. In this section we have collected some resources to assist you.

Our "SQL Basics" course is perfect for beginners or anyone needing a brief review. It covers both basic and intermediate SQL topics. In this course, you will actively write SQL code in various exercises, which will help you grow more confident in your SQL skills as you advance.

Illustration: SQL Basics course

After you have refreshed the basics, check out these articles filled with SQL interview questions to help you prepare:

  • Complete SQL Practice for Interviews — includes 16 SQL interview questions with practical exercises.
  • 16 SQL Interview Questions for Business Analysts — SQL interview questions tailored for analysts.
  • 8 Common Entry Level SQL Developer Interview Questions — great for beginners.
  • Top 15 SQL Interview Questions in 2021 — a compilation of recent and relevant questions.

After refreshing your SQL skills, it’s important to keep practicing. Interviews can be stressful, and even straightforward topics can become challenging under pressure. The more you practice, the more confidently you can handle questions and problem-solving during an interview.

Here are some practice resources we recommend:

  • SQL Practice track – This series includes 10 comprehensive SQL practice courses to sharpen your skills, perfect for those looking for hands-on practice. Key courses in this track include:
  • SQL Practice Set – Provides a range of exercises across various SQL topics and databases.
  • SQL Practice: A Store – Specifically designed for data analysts, this course offers practical SQL tasks using a database from an online store.
  • SQL Practice: Blog & Traffic Data – Perfect for marketers and data analysts, this course focuses on analyzing traffic data from a pet store blog.

You can find many SQL practice materials and premium resources in Your Guide to SQL Practice at LearnSQL.com .

Lastly, we recommend our SQL Basics Cheat Sheet . It is a quick reference guide that covers basic SQL syntax. Keep it handy as you review your SQL knowledge and practice your skills.

Page 1 of SQL Basics Cheat Sheet

Explore 50+ Specific SQL Topic Interview Questions

After you have refreshed your basic SQL knowledge, you might notice certain topics that are trickier for you or more relevant to your specific job role. In this section we've compiled resources that help you prepare for interview questions on specific SQL topics.

JOINs are a fundamental SQL construction used to combine data from multiple tables. They are also an essential topic at any SQL interview.

In our article The Top 10 SQL JOIN Interview Questions with Answers we've gathered the 10 most common questions about SQL JOINs that you might encounter in interviews. For each question we give you a detailed answer that will highlight what the interviewer is looking for in each question.

If you want to practice SQL JOINs, we recommend our interactive SQL JOINs course . It focuses on exercises specifically about SQL JOINs and contains 93 practice exercises to help you get confidence in your joining skills.

Additionally, we recommend Your Complete Guide to SQL JOINs , a comprehensive article that covers the basic knowledge of SQL JOINs, with additional articles and other resources on our platform.

The GROUP BY clause, paired with aggregate functions, is fundamental in SQL for calculating statistics like counts, averages, and sums from your data. This topic is essential for any SQL interview.

Our article Top 9 SQL GROUP BY Interview Questions provides a collection of the most frequently asked interview questions about GROUP BY . Each question includes a detailed answer, making sure you're prepared to discuss these topics during an interview.

If you are looking for an intermediate-level practice of GROUP BY topics, we recommend our Creating Basic SQL Reports course. It offers 100 exercises that focus on nuances of GROUP BY that can be asked about during an interview. It’s a hands-on course where you write your own SQL queries to help you better understand the issues and commit them to memory.

Furthermore, our article GROUP BY and Aggregate Functions: A Complete Overview gives a thorough explanation of GROUP BY and aggregate functions. This comprehensive guide is an excellent resource to round out your study, ensuring you have a robust understanding of how these functions work and how they can be applied in various scenarios.

We know that many of our users work specifically in the domain of data analysis. For these users, we have prepared an article 25 SQL Interview Questions for Data Analysts , which collects common SQL interview questions that can be asked for a role of data analyst. The article covers intermediate and advanced topics, like CTEs or window functions.

Window functions are an advanced SQL topic. Window functions are particularly useful when writing complex reports in SQL. For this reason, they are essential in data analysis and will come up in any data analysis interview.

Our article Top 10 SQL Window Functions Interview Questions contains the most common interview questions you might encounter regarding window functions. Each question has a detailed answer and links to further resources to help you dive deeper into each topic.

For those looking to refresh their knowledge through practice, we recommend our specialized courses:

  • Window Functions – Covers the entire syntax of SQL window functions through interactive, hands-on exercises, making it ideal for those new to window functions or needing a refresher.
  • Window Functions Practice Set - Aimed at those already familiar with window functions, this course provides additional practice to help refine your skills and prepare for more complex interview questions.

Additionally, we recommend our Window Functions Cheat Sheet , a handy quick reference guide for window functions. For a more thorough review, SQL Window Functions Guide is a comprehensive article that covers the basics of window functions with links to additional resources.

Common Table Expressions, or CTEs, is another advanced topic crucial for SQL interviews. CTEs help you organize and manage long and complex queries, make writing complex reports easier, and help you query hierarchical structures through recursive queries.

Our article Top 5 SQL CTE Interview Questions compiles essential CTE-related questions you're likely to face in interviews.in an article. Each question in the article is paired with a detailed answer to help you understand what is the most important in each response.

We also recommend our interactive Recursive Queries course that covers the syntax of CTEs through practice. The course is designed to teach the syntax and use of CTEs, including recursive CTEs, through hands-on exercises.

Finally, check out these articles to help you get ready for an advanced SQL interview:

  • How to Prepare for an Advanced SQL Interview
  • Top 27 Advanced SQL Interview Questions with Answers
  • 15 Tricky SQL Interview Questions for Experienced Users

We also suggest our Advanced SQL Practice track, which is an online series of SQL practice courses designed for advanced users.

In this article we have gathered over 100 SQL interview questions and 20 additional resources compiled here to ensure you're thoroughly prepared. To further enhance your preparation, we recommend our All Forever SQL Package . It provides access to all our current and future courses in a single purchase, making it an excellent investment for your ongoing SQL education and interview readiness.

Sign up for free at LearnSQL.com and explore our SQL courses offer . Each month, we offer one of our courses—typically a practical, hands-on course—for free . This gives you a perfect opportunity to try out our resources without any commitment and see how they can help you succeed in your SQL interview. Take advantage of these offers to boost your confidence and sharpen your SQL skills effectively.

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Computer Science > Machine Learning

Title: combinatorial optimization with policy adaptation using latent space search.

Abstract: Combinatorial Optimization underpins many real-world applications and yet, designing performant algorithms to solve these complex, typically NP-hard, problems remains a significant research challenge. Reinforcement Learning (RL) provides a versatile framework for designing heuristics across a broad spectrum of problem domains. However, despite notable progress, RL has not yet supplanted industrial solvers as the go-to solution. Current approaches emphasize pre-training heuristics that construct solutions but often rely on search procedures with limited variance, such as stochastically sampling numerous solutions from a single policy or employing computationally expensive fine-tuning of the policy on individual problem instances. Building on the intuition that performant search at inference time should be anticipated during pre-training, we propose COMPASS, a novel RL approach that parameterizes a distribution of diverse and specialized policies conditioned on a continuous latent space. We evaluate COMPASS across three canonical problems - Travelling Salesman, Capacitated Vehicle Routing, and Job-Shop Scheduling - and demonstrate that our search strategy (i) outperforms state-of-the-art approaches on 11 standard benchmarking tasks and (ii) generalizes better, surpassing all other approaches on a set of 18 procedurally transformed instance distributions.

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iOS 18’s Big AI Update Could Automatically Solve Math Problems for You

Like predictive text, but for math equations.

how to solve a problem with complex

WWDC 2024 hype season is upon us! We’re a little over a week away from Apple’s annual developer conference, where the company is expected to announce new versions of all of its platforms, including iOS 18 .

All rumors point to iOS 18 being the biggest update to the iPhone software in years, with generative AI as the headlining act. We’ve already heard of some obvious AI updates, like smart summaries, photo retouching, and automatic replies . However, a new report from AppleInsider suggests there’s even more AI in store. The Apple-focused publication says iOS 18 will get a “Catch Up” feature where Siri can provide an overview of recent notifications, cross-device media controls where you can activate Siri on one device to control another, and even the ability to create and edit images within iMessage using generative AI.

With all these leaked AI features, it might be easy to overlook perhaps the most underrated one: AI that can automatically solve math problems.

Apple Calculator app

The Calculator functionality is going to be integrated into the Notes app.

No Need to Open Up the Calculator App

According to AppleInsider , a new feature called “Keyboard Math Predictions” will detect and automatically solve math equations that are entered as text. Think of this like your iPhone’s predictive text that finishes your sentences for you, but for math problems. On top of that, the Notes app will get a crash course in math, with the ability to recognize math equations and offer solutions with help from the Calculator app’s integration. Apple is also reportedly working on a way to generate graphs within the Notes app.

Apple’s AI isn’t the only one that can handle some math thrown its way. OpenAI previously announced GPT-4o, and the AI chatbot made easy work of an equation looking to solve for x. GPT-4o can even act as more of a personal tutor for more involved problems.

Apple teaser for WWDC 2024

WWDC 2024 will finally reveal all the AI things that Apple has been working on.

Big AI Reveal on June 10

Fortunately, we only have to wait until Apple kicks off its WWDC 2024 on June 10 to see what’s in store with iOS 18. The software update won’t officially arrive until the fall, but Apple will share installable developer and public betas shortly after WWDC if you’re willing to deal with bugs and such throughout the summer.

With Apple hopping on the wave, generative AI chatbots are proving that they can handle math just as well as they can understand natural language. You could argue that AI-assisted math could make us all dumber, but that’s also what people said about calculators and computers. Sure, technology has made some people lazier, but it’s also helped save us time and solve even more complex problems. Adding AI that can solve math into the iPhone will only strengthen its purpose as do-it-all device.

how to solve a problem with complex

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  1. Solving Equations With Complex Numbers

  2. Complex Numbers

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  6. Simultaneous equations with complex numbers (Advanced Mathematics)

COMMENTS

  1. What It Takes to Think Deeply About Complex Problems

    And third, pay attention to how you're feeling. Embracing complexity means learning to better manage tough emotions like fear and anger. The problems we're facing often seem as complex as they ...

  2. How To Solve Complex Problems

    A synthesis definition. By pulling the main themes of these definitions together, we can get a sense of what complex problem-solvers must do: Gain a better understanding of the phenomena of a complex problem or mess. Use a discipline-agnostic approach in order to develop deliberate interventions.

  3. 35 problem-solving techniques and methods for solving complex problems

    Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems. Problem-solving strategies can live and die on whether people are ...

  4. 17 Smart Problem-Solving Strategies: Master Complex Problems

    Step 1: Identify the Problem. The problem-solving process starts with identifying the problem. This step involves understanding the issue's nature, its scope, and its impact. Once the problem is clearly defined, it sets the foundation for finding effective solutions.

  5. How to solve complex problems

    The best way to solve it is to consider a 1x2 bar first, then a 1x3, a 2x3, etc. The lesson behind this, is that if you have to solve a complex problem, you will want to cut it in the smallest pieces as possible, until reaching the most elementary ones, and then expand them little by little to understand the overall problem.

  6. Complex Problem-Solving: Definition and Steps

    Complex problem solving is a series of observations and informed decisions used to find and implement a solution to a problem. Beyond finding and implementing a solution, complex problem solving also involves considering future changes to circumstance, resources and capabilities that may affect the trajectory of the process and success of the ...

  7. The 7 Timeless Steps to Guide You Through Complex Problem Solving

    The seven steps to solving complex problems are listed below. We will go through them in great detail in the following sections. The 7 steps to creative solutions. Step 1: Understand the nature of complex problems. Step 2: Identifying and defining the problem.

  8. Complex numbers

    Level up on all the skills in this unit and collect up to 900 Mastery points! Welcome to the world of imaginary and complex numbers. We'll learn what imaginary and complex numbers are, how to perform arithmetic operations with them, represent them graphically on the complex plane, and apply these concepts to solve quadratic equations in new ways.

  9. Solving Complex Problems: Structured Thinking, Design Principles, and AI

    Approach and solve large and complex problems. Assess end-to-end processes and associated challenges, in order to significantly increase the likelihood of success in developing more complex systems. Implement effective problem-solving techniques, including abstracting the problem, idea generation, concept development and refinement, system ...

  10. How to Solve Complex Problems: A Step-by-Step Guide

    Here are some more detailed steps you can follow to help solve complex problems: Define the problem clearly: The first step in solving any problem is to define it clearly and understand exactly what you are trying to solve. Gather all the necessary information and identify the root cause of the problem. This may involve conducting research ...

  11. The Six Systems Thinking Steps to Solve Complex Problems

    Step 5: Going Deeper into the Issues. After defining the problem and the system structure, this step tends to understand the underlying problems through clarifying four items: the purpose of the system (what we want), the mental models, the large system, and personal role in the situation. Set 6: Plan an Intervention.

  12. Complex Problem Solving: What It Is and What It Is Not

    Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems.

  13. Solving Complex Problems Specialization

    To solve complex problems, whether it is the challenge of developing a new product, or Einstein's task of trying to explain how gravity worked - and literally everything else in between - it is not enough to take the problem and apply already existing skills. The skill that has always led to big breakthroughs in any field or industry is the ...

  14. Complex numbers

    Unit test. Test your understanding of Complex numbers with these NaN questions. Start test. This topic covers: - Adding, subtracting, multiplying, & dividing complex numbers - Complex plane - Absolute value & angle of complex numbers - Polar coordinates of complex numbers.

  15. How To Best Approach Complex Problems

    When you reach a good-enough-for-now idea, end the meeting and try it. Often teams reach this point in 30 minutes and then spend the next couple of hours trying to perfect it, because they're ...

  16. Six Tips For Solving Complex Problems

    Here are some things you can do to improve your ability to solve complex problems: 1. Always be learning. Prepare your mind to be a bit faster and deal with things in a better way by constantly ...

  17. Effective Strategies for Complex Problem Solving

    Here's how you can effectively approach complex problems. Powered by AI and the LinkedIn community. 1. Define Clearly. 2. Break it Down. 3. Prioritize Parts. 4.

  18. Complex Problems: What Does the Nature of the Problem Tell Us About Its

    1. Overview. In The 7 Timeless Steps to Guide You Through Complex Problem Solving, we discussed a generic approach that could be systematically applied to solving complex problems.Since not all problems are complex, and many gradations of complexity exist, it is probably a good idea to start by defining what complex problem-solving involves and what categories of problems are most suitable to ...

  19. Complex Math Problem Solving: Your Step-by-Step Guide

    In conclusion, breaking down complex math problems doesn't have to be intimidating. By taking the time to understand the problem, devise a plan, carry out that plan, and then check and reflect on your work, you can effectively solve complex math problems. And remember, practice makes perfect! The more problems you solve, the better you will ...

  20. complex equations

    Two Complex solutions result from the form ( x - h)² = k when k < 0 ( k is negative) Solve ( x - 5)² = - 25. Taking root of both sides, we get x - 5 = ! 5 i, so x = 5 ! 5 i which are conjugates. x = 5 + 5 i or x = 5 - 5 i. . We learned 3 ways to solve Quadratic equations when we were restricted to Real solutions.

  21. Complex Equations Calculator

    Free complex equations calculator - solve complex equations step-by-step

  22. How to Solve Complex Problems: Test Solutions with Simple Models

    In this implementation, we decided to approach the problem at stage 2. The system diagram below shows search working in two phases. First, the flyer items are retrieved.

  23. Complex Numbers in Optimization Toolbox Solvers

    If you have a non-analytic function or constraints, split the real and imaginary parts of the problem. For an example, see Fit a Model to Complex-Valued Data. To get the best (smallest norm) solution, try setting a complex initial point. For example, solving 1 + x 4 = 0 fails if you use a real start point:

  24. Innovative Solutions for the Kadomtsev ...

    NIM is a promising approach to solving complex mathematical problems, and its effectiveness and efficiency are highlighted through its application to the PKP equation. The results obtained through the use of NIM are compared to the exact solutions of the PKP equation, and it is found that the NIM approach provides results that are in close ...

  25. Top 100+ SQL Interview Questions and Practice Exercises

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