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Breaking down the 5 decision-making models

Decisions, decisions, decisions: 5 decision-making models.

Making effective decisions is a critical leadership quality . However, settling on the best course of action is often easier said than done. When instinct and reasoning alone aren't enough to pinpoint the best decision out of your available options, it can often be helpful to utilize a decision-making model.

A decision-making model works by walking you through the decision-making process — and there are several such models available for you to choose from.

To help you improve your problem-solving abilities and make better decisions, let's take a look at five proven decision-making models and when you should use them.

Defining decision-making models

Decision-making models are frameworks designed to help you analyze possible solutions to a problem so that you can make the best possible decision. Because different decision-making models take different approaches to this goal, it's important to match the model with your unique situation and leadership style.

Given that only 20% of team members say that their organization excels at decision-making, most organizations and team leaders have a lot of room to improve in this area. If you want to improve your decision-making approach, mastering the five decision-making models is a great place to start.

The 5 main decision-making models

There are five main decision-making models designed to help leaders analyze relevant information and make optimal decisions.

Once again, each of these models takes a unique approach to decision-making, so it is important to choose the model that will work best for you and your unique situation. With that said, let's take an in-depth look at each model and the situations where each one is most applicable.

1) Rational decision-making model

The rational decision-making model involves identifying the criteria that will have the biggest impact on your decision's outcome and then evaluating possible alternatives against those criteria. The steps of the rational decision-making model are:

• Step #1) Define the problem: You'll want to start by identifying the issue you are trying to solve or the goal you are trying to achieve with your decision.
• Step #2) Define criteria: The next step is to define the criteria you are looking for in your decision. For instance, if you are deciding on a new car, you might be looking for criteria such as space, fuel efficiency, and safety.
• Step #3) Weight your criteria: If all of the criteria you define are equally important to you, then you can skip this step. If some factors are more important, you will want to assign a numerical value to your criteria based on how important each factor is.
• Step #4) Generate alternatives: Having defined and weighted the criteria you are looking for, it's time to brainstorm ideas and develop a few alternatives that meet your criteria.
• Step #5) Evaluate your alternatives: For each possible solution you come up with, you should evaluate it against your criteria, giving extra consideration to the criteria you weighted more heavily.
• Step #6) Choose the best alternative: After evaluating all possible alternatives, select the option that best matches your weighted criteria.
• Step #7) Implement the decision: The next to last step in the rational decision-making model is simply putting your decision into practice.
• Step #8) Evaluate your results: It's essential to evaluate your results anytime you make a decision. Looking at your decision from a retrospective point of view can help you decide if you should use the same decision-making process in the future.

When to use this model

The rational decision-making model is best employed when you have numerous options to consider and plenty of time to evaluate them. One example of a scenario where this model might prove useful is choosing a new hire from a pool of candidates.

2) Bounded rationality decision-making model

Sometimes, taking action quickly and choosing a "good enough" option is better than getting bogged down in searching for the best possible solution. The bounded rationality decision-making model dictates that you should limit your options to a manageable set and then choose the first option that meets your criteria rather than conducting an exhaustive analysis of each one. Going with the first option that meets your minimum threshold of requirements is a process known as "satisficing." While this may not be the best process for every decision, a willingness to satisfice can prove valuable when time constraints limit you.

The bounded rationality decision-making model is best employed when time is of the essence. It's the best model to use when inaction is more costly than not making the best decision. For example, suppose your company has encountered an issue causing extended downtime. In that case, you may want to use the bounded rationality decision-making model to quickly identify the first acceptable solution since every minute wasted is costly.

3) Vroom-Yetton decision-making model

The Vroom-Yetton decision-making model presents seven "yes or no" questions for a decision-maker to answer followed by five decision-making styles for them to choose from. It's the most complex decision-making model on our list, requiring decision-makers to utilize a decision tree to arrive at the right decision-making style based on their answers to the model's questions.

Check out this helpful resource for a complete breakdown of the Vroom-Yetton decision-making model and a copy of the decision tree template you will need to use.

The Vroom-Yetton decision-making model was specifically designed for collaborative decision-making and is best employed when you involve multiple team members in the decision-making process. In fact, one of the main objectives of this model is to determine how much weight should be given to the input from a leader's subordinates.

4) Intuitive decision-making model

Have you ever heard that it's often best to go with your gut? While making decisions based only on instinct may not seem like the best idea to those who prefer a more careful and logical approach, there are plenty of instances where going with your gut is the best way forward.

For example, if you don't have much information to consider, instinct may be the only tool for finding the best solution that you have available. Likewise, trusting your instinct can often yield the best results in cases where you are already deeply experienced with the matter at hand since nothing hones instinct better than experience.

The intuitive decision-making model probably shouldn't be the first model you turn to when you need to make a decision, but there are instances where it can be useful. We've mentioned a couple already, including cases where there isn't enough information for you to make a more informed decision and instances where your own experience is more reliable than the available information.

The intuitive decision-making model can also be useful in cases where you don't have a lot of time and need to make a decision quickly.

5) The recognition primed model

The recognition primed model is similar to the intuitive decision-making model in that it relies heavily on the decision-maker's experience and instinct. However, the recognition primed model is a little more structured than intuitive decision-making and includes the following steps:

• Step #1) Analyze available information to identify possible solutions: The first step in the recognition primed model is to brainstorm possible solutions based on your available information.
• Step #2) Run scenarios through your head: For each possible solution, run the scenario through your head and see how it plays out.
• Step #3) Make a decision: The recognition primed model dictates that the solution that leads to the best possible outcome when you visualize it in your mind is the solution that you should choose.

Like the intuitive decision-making model, the recognition primed model works best in instances where:

• You don't have a lot of information available.
• You trust your instinct and experience.
• Time constraints are a factor.

With that said, using this model effectively does require a certain degree of creativity and imagination since you will have to visualize the outcome of each possible solution.

A note on decision-making biases

Anytime you are faced with an important decision, it is essential not to let biases get in your way. Biases might be rooted in prior experiences, but that doesn't inherently mean that they are grounded in facts. In many cases, avoiding biases is also key to making an ethical decision since biases can sometimes cause you to mistreat certain people and their ideas.

Understanding the different biases

Preventing biases from getting in the way of your decision-making skills starts with identifying the types of biases you need to be aware of, including:

• Confirmation bias: Confirmation bias entails favoring or focusing on information that confirms your pre-existing beliefs and ignoring information that runs counter to those beliefs. While it's important to trust your own experience and beliefs, you don't want to subconsciously favor information just because it aligns with what you already believe to be true.
• Availability bias: Information that is easily accessible in your memory often gets undue weight, and this is known as availability bias. One example of availability bias is overestimating the likelihood of an event just because you can remember a similar event happening to you in the past.
• Survivorship bias: Survivorship bias entails focusing only on the solutions that have generated success in the past. While it's important to consider past results, ignoring possible solutions just because they are unproven will place unnecessary constraints on your decision-making process.
• Anchoring bias: Anchoring bias is the tendency to "anchor" yourself to the first piece of information you learn. Information should not get extra weight just because you have known about it for longer, and new information can be equally important to consider.
• Halo effect: The halo effect occurs when positive experiences with or impressions of one aspect of a possible solution cause you to view the entire solution positively. Rather than being blinded by the positives, seek out and consider the negatives as well.

Define your decision-making process with Range

A lot goes into making good decisions, and the decision-making models we've covered in this article can serve as excellent tools for helping you find the best possible solution to any challenge.

No matter which model you go with, communication, collaboration, and organization are key to making good decisions.

With Range, leaders and team members alike are able to effortlessly organize their ideas, communicate back and forth, share important information, and make collaborative decisions. If you want to get started using powerful team management software to organize your decision-making process, sign up for Range today.

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In This Article Expand or collapse the "in this article" section Problem Solving and Decision Making

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Problem Solving and Decision Making by Emily G. Nielsen , John Paul Minda LAST MODIFIED: 26 June 2019 DOI: 10.1093/obo/9780199828340-0246

Problem solving and decision making are both examples of complex, higher-order thinking. Both involve the assessment of the environment, the involvement of working memory or short-term memory, reliance on long term memory, effects of knowledge, and the application of heuristics to complete a behavior. A problem can be defined as an impasse or gap between a current state and a desired goal state. Problem solving is the set of cognitive operations that a person engages in to change the current state, to go beyond the impasse, and achieve a desired outcome. Problem solving involves the mental representation of the problem state and the manipulation of this representation in order to move closer to the goal. Problems can vary in complexity, abstraction, and how well defined (or not) the initial state and the goal state are. Research has generally approached problem solving by examining the behaviors and cognitive processes involved, and some work has examined problem solving using computational processes as well. Decision making is the process of selecting and choosing one action or behavior out of several alternatives. Like problem solving, decision making involves the coordination of memories and executive resources. Research on decision making has paid particular attention to the cognitive biases that account for suboptimal decisions and decisions that deviate from rationality. The current bibliography first outlines some general resources on the psychology of problem solving and decision making before examining each of these topics in detail. Specifically, this review covers cognitive, neuroscientific, and computational approaches to problem solving, as well as decision making models and cognitive heuristics and biases.

General Overviews

Current research in the area of problem solving and decision making is published in both general and specialized scientific journals. Theoretical and scholarly work is often summarized and developed in full-length books and chapter. These may focus on the subfields of problem solving and decision making or the larger field of thinking and higher-order cognition.

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The SkillsYouNeed Guide to Interpersonal Skills

Making decisions and solving problems are two key areas in life, whether you are at home or at work. Whatever you’re doing, and wherever you are, you are faced with countless decisions and problems, both small and large, every day.

Many decisions and problems are so small that we may not even notice them. Even small decisions, however, can be overwhelming to some people. They may come to a halt as they consider their dilemma and try to decide what to do.

Small and Large Decisions

In your day-to-day life you're likely to encounter numerous 'small decisions', including, for example:

Tea or coffee?

What shall I have in my sandwich? Or should I have a salad instead today?

What shall I wear today?

Larger decisions may occur less frequently but may include:

Should we repaint the kitchen? If so, what colour?

Should we relocate?

Should I propose to my partner? Do I really want to spend the rest of my life with him/her?

These decisions, and others like them, may take considerable time and effort to make.

The relationship between decision-making and problem-solving is complex. Decision-making is perhaps best thought of as a key part of problem-solving: one part of the overall process.

Our approach at Skills You Need is to set out a framework to help guide you through the decision-making process. You won’t always need to use the whole framework, or even use it at all, but you may find it useful if you are a bit ‘stuck’ and need something to help you make a difficult decision.

Decision Making

Effective Decision-Making

This page provides information about ways of making a decision, including basing it on logic or emotion (‘gut feeling’). It also explains what can stop you making an effective decision, including too much or too little information, and not really caring about the outcome.

A Decision-Making Framework

This page sets out one possible framework for decision-making.

The framework described is quite extensive, and may seem quite formal. But it is also a helpful process to run through in a briefer form, for smaller problems, as it will help you to make sure that you really do have all the information that you need.

Problem Solving

Introduction to Problem-Solving

This page provides a general introduction to the idea of problem-solving. It explores the idea of goals (things that you want to achieve) and barriers (things that may prevent you from achieving your goals), and explains the problem-solving process at a broad level.

The first stage in solving any problem is to identify it, and then break it down into its component parts. Even the biggest, most intractable-seeming problems, can become much more manageable if they are broken down into smaller parts. This page provides some advice about techniques you can use to do so.

Sometimes, the possible options to address your problem are obvious. At other times, you may need to involve others, or think more laterally to find alternatives. This page explains some principles, and some tools and techniques to help you do so.

Having generated solutions, you need to decide which one to take, which is where decision-making meets problem-solving. But once decided, there is another step: to deliver on your decision, and then see if your chosen solution works. This page helps you through this process.

‘Social’ problems are those that we encounter in everyday life, including money trouble, problems with other people, health problems and crime. These problems, like any others, are best solved using a framework to identify the problem, work out the options for addressing it, and then deciding which option to use.

This page provides more information about the key skills needed for practical problem-solving in real life.

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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How to Make Great Decisions, Quickly

• Martin G. Moore

It’s a skill that will set you apart.

As a new leader, learning to make good decisions without hesitation and procrastination is a capability that can set you apart from your peers. While others vacillate on tricky choices, your team could be hitting deadlines and producing the type of results that deliver true value. That’s something that will get you — and them — noticed. Here are a few of a great decision:

• Great decisions are shaped by consideration of many different viewpoints. This doesn’t mean you should seek out everyone’s opinion. The right people with the relevant expertise need to clearly articulate their views to help you broaden your perspective and make the best choice.
• Great decisions are made as close as possible to the action. Remember that the most powerful people at your company are rarely on the ground doing the hands-on work. Seek input and guidance from team members who are closest to the action.
• Great decisions address the root cause, not just the symptoms. Although you may need to urgently address the symptoms, once this is done you should always develop a plan to fix the root cause, or else the problem is likely to repeat itself.
• Great decisions balance short-term and long-term value. Finding the right balance between short-term and long-term risks and considerations is key to unlocking true value.
• Great decisions are timely. If you consider all of the elements listed above, then it’s simply a matter of addressing each one with a heightened sense of urgency.

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Like many young leaders, early in my career, I thought a great decision was one that attracted widespread approval. When my colleagues smiled and nodded their collective heads, it reinforced (in my mind, at least) that I was an excellent decision maker.

• MM Martin G. Moore is the founder of Your CEO Mentor and author of No Bullsh!t Leadership and host of the No Bullsh!t Leadership podcast. His purpose is to improve the quality of leaders globally through practical, real world leadership content. For more information, please visit, www.martingmoore.com.

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Critical Thinking Models: A Comprehensive Guide for Effective Decision Making

Critical thinking models are valuable frameworks that help individuals develop and enhance their critical thinking skills . These models provide a structured approach to problem-solving and decision-making by encouraging the evaluation of information and arguments in a logical, systematic manner. By understanding and applying these models, one can learn to make well-reasoned judgments and decisions.

Various critical thinking models exist, each catering to different contexts and scenarios. These models offer a step-by-step method to analyze situations, scrutinize assumptions and biases, and consider alternative perspectives. Ultimately, the goal of critical thinking models is to enhance an individual’s ability to think critically, ultimately improving their reasoning and decision-making skills in both personal and professional settings.

Key Takeaways

• Critical thinking models provide structured approaches for enhancing decision-making abilities
• These models help individuals analyze situations, scrutinize assumptions, and consider alternative perspectives
• The application of critical thinking models can significantly improve one’s reasoning and judgment skills.

Fundamentals of Critical Thinking

Definition and Importance

Critical thinking is the intellectual process of logically, objectively, and systematically evaluating information to form reasoned judgments, utilizing reasoning , logic , and evidence . It involves:

• Identifying and questioning assumptions,
• Applying consistent principles and criteria,
• Analyzing and synthesizing information,
• Drawing conclusions based on evidence.

The importance of critical thinking lies in its ability to help individuals make informed decisions, solve complex problems, and differentiate between true and false beliefs .

Core Cognitive Skills

Several core cognitive skills underpin critical thinking:

• Analysis : Breaking down complex information into smaller components to identify patterns or inconsistencies.
• Evaluation : Assessing the credibility and relevance of sources, arguments, and evidence.
• Inference : Drawing conclusions by connecting the dots between analyzed information.
• Synthesis : Incorporating analyzed information into a broader understanding and constructing one’s argument.
• Logic and reasoning : Applying principles of logic to determine the validity of arguments and weigh evidence.

These skills enable individuals to consistently apply intellectual standards in their thought process, which ultimately results in sound judgments and informed decisions.

Influence of Cognitive Biases

A key aspect of critical thinking is recognizing and mitigating the impact of cognitive biases on our thought processes. Cognitive biases are cognitive shortcuts or heuristics that can lead to flawed reasoning and distort our understanding of a situation. Examples of cognitive biases include confirmation bias, anchoring bias, and availability heuristic.

To counter the influence of cognitive biases, critical thinkers must be aware of their own assumptions and strive to apply consistent and objective evaluation criteria in their thinking process. The practice of actively recognizing and addressing cognitive biases promotes an unbiased and rational approach to problem-solving and decision-making.

The Critical Thinking Process

Stages of Critical Thinking

The critical thinking process starts with gathering and evaluating data . This stage involves identifying relevant information and ensuring it is credible and reliable. Next, an individual engages in analysis by examining the data closely to understand its context and interpret its meaning. This step can involve breaking down complex ideas into simpler components for better understanding.

The next stage focuses on determining the quality of the arguments, concepts, and theories present in the analyzed data. Critical thinkers question the credibility and logic behind the information while also considering their own biases and assumptions. They apply consistent standards when evaluating sources, which helps them identify any weaknesses in the arguments.

Values play a significant role in the critical thinking process. Critical thinkers assess the significance of moral, ethical, or cultural values shaping the issue, argument, or decision at hand. They determine whether these values align with the evidence and logic they have analyzed.

After thorough analysis and evaluation, critical thinkers draw conclusions based on the evidence and reasoning gathered. This step includes synthesizing the information and presenting a clear, concise argument or decision. It also involves explaining the reasoning behind the conclusion to ensure it is well-founded.

Application in Decision Making

In decision making, critical thinking is a vital skill that allows individuals to make informed choices. It enables them to:

• Analyze options and their potential consequences
• Evaluate the credibility of sources and the quality of information
• Identify biases, assumptions, and values that may influence the decision
• Construct a reasoned, well-justified conclusion

By using critical thinking in decision making, individuals can make more sound, objective choices. The process helps them to avoid pitfalls like jumping to conclusions, being influenced by biases, or basing decisions on unreliable data. The result is more thoughtful, carefully-considered decisions leading to higher quality outcomes.

Critical Thinking Models

Critical thinking models are frameworks that help individuals develop better problem-solving and decision-making abilities. They provide strategies for analyzing, evaluating, and synthesizing information to reach well-founded conclusions. This section will discuss four notable models: The RED Model, Bloom’s Taxonomy, Paul-Elder Model, and The Halpern Critical Thinking Assessment.

The RED Model

The RED Model stands for Recognize Assumptions, Evaluate Arguments, and Draw Conclusions. It emphasizes the importance of questioning assumptions, weighing evidence, and reaching logical conclusions.

• Recognize Assumptions: Identify and challenge assumptions that underlie statements, beliefs, or arguments.
• Evaluate Arguments: Assess the validity and reliability of evidence to support or refute claims.
• Draw Conclusions: Make well-reasoned decisions based on available information and sound reasoning.

The RED Model helps individuals become more effective problem solvers and decision-makers by guiding them through the critical thinking process ^(source) .

Bloom’s Taxonomy

Bloom’s Taxonomy is a hierarchical model that classifies cognitive skills into six levels of complexity. These levels are remembering, understanding, applying, analyzing, evaluating, and creating. By progressing through these levels, individuals can develop higher-order thinking skills.

• Remembering: Recall information or facts.
• Understanding: Comprehend the meaning of ideas, facts, or problems.
• Applying: Use knowledge in different situations.
• Analyzing: Break down complex topics or problems into sub-parts.
• Evaluating: Assess the quality, relevance, or credibility of information, ideas, or solutions.
• Creating: Combine elements to form a new whole, generate new ideas, or solve complex issues.

Paul-Elder Model

The Paul-Elder Model introduces the concept of “elements of thought,” focusing on a structured approach to critical thinking. This model promotes intellectual standards, such as clarity, accuracy, and relevance. It consists of three stages:

• Critical Thinking: Employ the intellectual standards to problem-solving and decision-making processes.
• Elements of Thought: Consider purpose, question at issue, information, interpretation and inference, concepts, assumptions, implications, and point of view.
• Intellectual Traits: Develop intellectual traits, such as intellectual humility, intellectual empathy, and intellectual perseverance.

This model fosters a deeper understanding and appreciation of critical thinking ^(source) .

The Halpern Critical Thinking Assessment

The Halpern Critical Thinking Assessment is a standardized test developed by Diane Halpern to assess critical thinking skills. The evaluation uses a variety of tasks to measure abilities in core skill areas, such as verbal reasoning, argument analysis, and decision making. Pearson, a leading publisher of educational assessments, offers this test as a means to assess individuals’ critical thinking skills ^(source) .

These four critical thinking models can be used as frameworks to improve and enhance cognitive abilities. By learning and practicing these models, individuals can become better equipped to analyze complex information, evaluate options, and make well-informed decisions.

Evaluating Information and Arguments

In this section, we will discuss the importance of evaluating information and arguments in the process of critical thinking, focusing on evidence assessment, logic and fallacies, and argument analysis.

Evidence Assessment

Evaluating the relevance, accuracy, and credibility of information is a vital aspect of critical thinking. In the process of evidence assessment, a thinker should consider the following factors:

• Source reliability : Research and understand the expertise and credibility of the source to ensure that biased or inaccurate information is not being considered.
• Currency : Check the date of the information to make sure it is still relevant and accurate in the present context.
• Objectivity : Analyze the information for potential bias and always cross-reference it with other credible sources.

When practicing critical thinking skills, it is essential to be aware of your own biases and make efforts to minimize their influence on your decision-making process.

Logic and Fallacies

Logic is crucial for deconstructing and analyzing complex arguments, while identifying and avoiding logical fallacies helps maintain accurate and valid conclusions. Some common fallacies to watch out for in critical thinking include:

• Ad Hominem : Attacking the person making the argument instead of addressing the argument itself.
• Strawman : Misrepresenting an opponent’s argument to make it easier to refute.
• False Dilemma : Presenting only two options when there may be multiple viable alternatives.
• Appeal to Authority : Assuming a claim is true simply because an authority figure supports it.

Being aware of these fallacies enables a thinker to effectively evaluate the strength of an argument and make sound judgments accordingly.

Argument Analysis

Analyzing an argument is the process of evaluating its structure, premises, and conclusion while determining its validity and soundness. To analyze an argument, follow these steps:

• Identify the premises and conclusion : Determine the main point is being argued, how it is related and substance of the argument.
• Evaluate the validity : Assess whether the conclusion logically follows from the premises and if the argument’s structure is sound.
• Test the soundness : Evaluate the truth and relevance of the premises. This may require verifying the accuracy of facts and evidence, as well as assessing the reliability of sources.
• Consider counter-arguments : Identify opposing viewpoints and counter-arguments, and evaluate their credibility to gauge the overall strength of the original argument.

By effectively evaluating information and arguments, critical thinkers develop a solid foundation for making well-informed decisions and solving problems.

Enhancing Critical Thinking

Strategies for improvement.

To enhance critical thinking, individuals can practice different strategies, including asking thought-provoking questions, analyzing ideas and observations, and being open to different perspectives. One effective technique is the Critical Thinking Roadmap , which breaks critical thinking down into four measurable phases: execute, synthesize, recommend, and communicate. It’s important to use deliberate practice in these areas to develop a strong foundation for problem-solving and decision-making. In addition, cultivating a mindset of courage , fair-mindedness , and empathy will support critical thinking development.

Critical Thinking in Education

In the field of education, critical thinking is an essential component of effective learning and pedagogy. Integrating critical thinking into the curriculum encourages student autonomy, fosters innovation, and improves student outcomes. Teachers can use various approaches to promote critical thinking, such as:

• Employing open-ended questions to stimulate ideas
• Incorporating group discussions or debates to facilitate communication and evaluation of viewpoints
• Assessing and providing feedback on student work to encourage reflection and improvement
• Utilizing real-world scenarios and case studies for practical application of concepts

Developing a Critical Thinking Mindset

To truly enhance critical thinking abilities, it’s important to adopt a mindset that values integrity , autonomy , and empathy . These qualities help to create a learning environment that encourages open-mindedness, which is key to critical thinking development. To foster a critical thinking mindset:

• Be curious : Remain open to new ideas and ask questions to gain a deeper understanding.
• Communicate effectively : Clearly convey thoughts and actively listen to others.
• Reflect and assess : Regularly evaluate personal beliefs and assumptions to promote growth.
• Embrace diversity of thought : Welcome different viewpoints and ideas to foster innovation.

Incorporating these approaches can lead to a more robust critical thinking skillset, allowing individuals to better navigate and solve complex problems.

Critical Thinking in Various Contexts

The workplace and beyond.

Critical thinking is a highly valued skill in the workplace, as it enables employees to analyze situations, make informed decisions, and solve problems effectively. It involves a careful thinking process directed towards a specific goal. Employers often seek individuals who possess strong critical thinking abilities, as they can add significant value to the organization.

In the workplace context, critical thinkers are able to recognize assumptions, evaluate arguments, and draw conclusions, following models such as the RED model . They can also adapt their thinking to suit various scenarios, allowing them to tackle complex and diverse problems.

Moreover, critical thinking transcends the workplace and applies to various aspects of life. It empowers an individual to make better decisions, analyze conflicting information, and engage in constructive debates.

Creative and Lateral Thinking

Critical thinking encompasses both creative and lateral thinking. Creative thinking involves generating novel ideas and solutions to problems, while lateral thinking entails looking at problems from different angles to find unique and innovative solutions.

Creative thinking allows thinkers to:

• Devise new concepts and ideas
• Challenge conventional wisdom
• Build on existing knowledge to generate innovative solutions

Lateral thinking, on the other hand, encourages thinkers to:

• Break free from traditional thought patterns
• Combine seemingly unrelated ideas to create unique solutions
• Utilize intuition and intelligence to approach problems from a different perspective

Both creative and lateral thinking are essential components of critical thinking, allowing individuals to view problems in a holistic manner and generate well-rounded solutions. These skills are highly valued by employers and can lead to significant personal and professional growth.

In conclusion, critical thinking is a multifaceted skill that comprises various thought processes, including creative and lateral thinking. By embracing these skills, individuals can excel in the workplace and in their personal lives, making better decisions and solving problems effectively.

Overcoming Challenges

Recognizing and addressing bias.

Cognitive biases and thinking biases can significantly affect the process of critical thinking . One of the key components of overcoming these challenges is to recognize and address them. It is essential to be aware of one’s own beliefs, as well as the beliefs of others, to ensure fairness and clarity throughout the decision-making process. To identify and tackle biases, one can follow these steps:

• Be self-aware : Understand personal beliefs and biases, acknowledging that they may influence the interpretation of information.
• Embrace diverse perspectives : Encourage open discussions and invite different viewpoints to challenge assumptions and foster cognitive diversity.
• Reevaluate evidence : Continuously reassess the relevance and validity of the information being considered.

By adopting these practices, individuals can minimize the impact of biases and enhance the overall quality of their critical thinking skills.

Dealing with Information Overload

In today’s world, information is abundant, and it can become increasingly difficult to demystify and make sense of the available data. Dealing with information overload is a crucial aspect of critical thinking. Here are some strategies to address this challenge:

• Prioritize information : Focus on the most relevant and reliable data, filtering out unnecessary details.
• Organize data : Use tables, charts, and lists to categorize information and identify patterns more efficiently.
• Break down complex information : Divide complex data into smaller, manageable segments to simplify interpretation and inferences.

By implementing these techniques, individuals can effectively manage information overload, enabling them to process and analyze data more effectively, leading to better decision-making.

In conclusion, overcoming challenges such as biases and information overload is essential in the pursuit of effective critical thinking. By recognizing and addressing these obstacles, individuals can develop clarity and fairness in their thought processes, leading to well-informed decisions and improved problem-solving capabilities.

Measuring Critical Thinking

Assessment tools and criteria.

There are several assessment tools designed to measure critical thinking, each focusing on different aspects such as quality, depth, breadth, and significance of thinking. One example of a widely used standardized test is the Watson-Glaser Critical Thinking Appraisal , which evaluates an individual’s ability to interpret information, draw conclusions, and make assumptions. Another test is the Cornell Critical Thinking Tests Level X and Level Z , which assess an individual’s critical thinking skills through multiple-choice questions.

Furthermore, criteria for assessing critical thinking often include precision, relevance, and the ability to gather and analyze relevant information. Some assessors utilize the Halpern Critical Thinking Assessment , which measures the application of cognitive skills such as deduction, observation, and induction in real-world scenarios.

The Role of IQ and Tests

It’s important to note that intelligence quotient (IQ) tests and critical thinking assessments are not the same. While IQ tests aim to measure an individual’s cognitive abilities and general intelligence, critical thinking tests focus specifically on one’s ability to analyze, evaluate, and form well-founded opinions. Therefore, having a high IQ does not necessarily guarantee strong critical thinking skills, as critical thinking requires additional mental processes beyond basic logical reasoning.

To build and enhance critical thinking skills, individuals should practice and develop higher-order thinking, such as critical alertness, critical reflection, and critical analysis. Using a Critical Thinking Roadmap , such as the four-phase framework that includes execution, synthesis, recommendation, and the ability to apply, individuals can continuously work to improve their critical thinking abilities.

Frequently Asked Questions

What are the main steps involved in the paul-elder critical thinking model.

The Paul-Elder Critical Thinking Model is a comprehensive framework for developing critical thinking skills. The main steps include: identifying the purpose, formulating questions, gathering information, identifying assumptions, interpreting information, and evaluating arguments. The model emphasizes clarity, accuracy, precision, relevance, depth, breadth, logic, and fairness throughout the critical thinking process. By following these steps, individuals can efficiently analyze and evaluate complex ideas and issues.

Can you list five techniques to enhance critical thinking skills?

Here are five techniques to help enhance critical thinking skills:

• Ask open-ended questions : Encourages exploration and challenges assumptions.
• Engage in active listening: Focus on understanding others’ viewpoints before responding.
• Reflect on personal biases: Identify and question any preconceived notions or judgments.
• Practice mindfulness: Develop self-awareness and stay present in the moment.
• Collaborate with others: Exchange ideas and learn from diverse perspectives.

What is the RED Model of critical thinking and how is it applied?

The RED Model of critical thinking consists of three key components: Recognize Assumptions, Evaluate Arguments, and Draw Conclusions. To apply the RED Model, begin by recognizing and questioning underlying assumptions, being aware of personal biases and stereotypes. Next, evaluate the strengths and weaknesses of different arguments, considering evidence, logical consistency, and alternative explanations. Lastly, draw well-reasoned conclusions that are based on the analysis and evaluation of the information gathered.

How do the ‘3 C’s’ of critical thinking contribute to effective problem-solving?

The ‘3 C’s’ of critical thinking – Curiosity, Creativity, and Criticism – collectively contribute to effective problem-solving. Curiosity allows individuals to explore various perspectives and ask thought-provoking questions, while Creativity helps develop innovative solutions and unique approaches to challenges. Criticism, or the ability to evaluate and analyze ideas objectively, ensures that the problem-solving process remains grounded in logic and relevance.

What characteristics distinguish critical thinking from creative thinking?

Critical thinking and creative thinking are two complementary cognitive skills. Critical thinking primarily focuses on analyzing, evaluating, and reasoning, using objectivity and logical thinking. It involves identifying problems, assessing evidence, and drawing sound conclusions. Creative thinking, on the other hand, is characterized by the generation of new ideas, concepts, and approaches to solve problems, often involving imagination, originality, and out-of-the-box thinking.

What are some recommended books to help improve problem-solving and critical thinking skills?

There are several books that can help enhance problem-solving and critical thinking skills, including:

• “Thinking, Fast and Slow” by Daniel Kahneman: This book explores the dual process theory of decision-making and reasoning.
• “The 5 Elements of Effective Thinking” by Edward B. Burger and Michael Starbird: Offers practical tips and strategies for improving critical thinking skills.
• “Critique of Pure Reason” by Immanuel Kant: A classic philosophical work that delves into the principles of reason and cognition.
• “Mindware: Tools for Smart Thinking” by Richard E. Nisbett: Presents a range of cognitive tools to enhance critical thinking and decision-making abilities.
• “The Art of Thinking Clearly” by Rolf Dobelli: Explores common cognitive biases and errors in judgment that can affect critical thinking.

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7 important steps in the decision making process

The decision making process is a method of gathering information, assessing alternatives, and making a final choice with the goal of making the best decision possible. In this article, we detail the step-by-step process on how to make a good decision and explain different decision making methodologies.

We make decisions every day. Take the bus to work or call a car? Chocolate or vanilla ice cream? Whole milk or two percent?

There's an entire process that goes into making those tiny decisions, and while these are simple, easy choices, how do we end up making more challenging decisions? 

At work, decisions aren't as simple as choosing what kind of milk you want in your latte in the morning. That’s why understanding the decision making process is so important. 

What is the decision making process?

The decision making process is the method of gathering information, assessing alternatives, and, ultimately, making a final choice. 

Decision-making tools for agile businesses

In this ebook, learn how to equip employees to make better decisions—so your business can pivot, adapt, and tackle challenges more effectively than your competition.

The 7 steps of the decision making process

Step 1: identify the decision that needs to be made.

When you're identifying the decision, ask yourself a few questions: 

What is the problem that needs to be solved?

What is the goal you plan to achieve by implementing this decision?

How will you measure success?

These questions are all common goal setting techniques that will ultimately help you come up with possible solutions. When the problem is clearly defined, you then have more information to come up with the best decision to solve the problem.

Step 2: Gather relevant information

​Gathering information related to the decision being made is an important step to making an informed decision. Does your team have any historical data as it relates to this issue? Has anybody attempted to solve this problem before?

It's also important to look for information outside of your team or company. Effective decision making requires information from many different sources. Find external resources, whether it’s doing market research, working with a consultant, or talking with colleagues at a different company who have relevant experience. Gathering information helps your team identify different solutions to your problem.

Step 3: Identify alternative solutions

This step requires you to look for many different solutions for the problem at hand. Finding more than one possible alternative is important when it comes to business decision-making, because different stakeholders may have different needs depending on their role. For example, if a company is looking for a work management tool, the design team may have different needs than a development team. Choosing only one solution right off the bat might not be the right course of action. 

Step 4: Weigh the evidence

This is when you take all of the different solutions you’ve come up with and analyze how they would address your initial problem. Your team begins identifying the pros and cons of each option, and eliminating alternatives from those choices.

There are a few common ways your team can analyze and weigh the evidence of options:

Pros and cons list

SWOT analysis

Decision matrix

Step 5: Choose among the alternatives

The next step is to make your final decision. Consider all of the information you've collected and how this decision may affect each stakeholder. 

Sometimes the right decision is not one of the alternatives, but a blend of a few different alternatives. Effective decision-making involves creative problem solving and thinking out of the box, so don't limit you or your teams to clear-cut options.

One of the key values at Asana is to reject false tradeoffs. Choosing just one decision can mean losing benefits in others. If you can, try and find options that go beyond just the alternatives presented.

Step 6: Take action

Once the final decision maker gives the green light, it's time to put the solution into action. Take the time to create an implementation plan so that your team is on the same page for next steps. Then it’s time to put your plan into action and monitor progress to determine whether or not this decision was a good one. 

Step 7: Review your decision and its impact (both good and bad)

Once you’ve made a decision, you can monitor the success metrics you outlined in step 1. This is how you determine whether or not this solution meets your team's criteria of success.

Here are a few questions to consider when reviewing your decision:

Did it solve the problem your team identified in step 1? 

Did this decision impact your team in a positive or negative way?

Which stakeholders benefited from this decision? Which stakeholders were impacted negatively?

If this solution was not the best alternative, your team might benefit from using an iterative form of project management. This enables your team to quickly adapt to changes, and make the best decisions with the resources they have. 

Types of decision making models

While most decision making models revolve around the same seven steps, here are a few different methodologies to help you make a good decision.

​Rational decision making models

This type of decision making model is the most common type that you'll see. It's logical and sequential. The seven steps listed above are an example of the rational decision making model. 

When your decision has a big impact on your team and you need to maximize outcomes, this is the type of decision making process you should use. It requires you to consider a wide range of viewpoints with little bias so you can make the best decision possible. 

Intuitive decision making models

This type of decision making model is dictated not by information or data, but by gut instincts. This form of decision making requires previous experience and pattern recognition to form strong instincts.

This type of decision making is often made by decision makers who have a lot of experience with similar kinds of problems. They have already had proven success with the solution they're looking to implement. 

Creative decision making model

The creative decision making model involves collecting information and insights about a problem and coming up with potential ideas for a solution, similar to the rational decision making model. 

The difference here is that instead of identifying the pros and cons of each alternative, the decision maker enters a period in which they try not to actively think about the solution at all. The goal is to have their subconscious take over and lead them to the right decision, similar to the intuitive decision making model. 

This situation is best used in an iterative process so that teams can test their solutions and adapt as things change.

Track key decisions with a work management tool

Tracking key decisions can be challenging when not documented correctly. Learn more about how a work management tool like Asana can help your team track key decisions, collaborate with teammates, and stay on top of progress all in one place.

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Introduction

Practitioners in various domains are often faced with ill-structured problems. For example, teachers devise lesson plans that consider learners’ prior knowledge, curriculum guidelines, and classroom management strategies. Similarly, engineers must develop products that meet safety standards, yet achieve project guidelines that meet client needs. Given the types of problems that practitioners face in everyday decision-making, educators have increasingly begun to adopt inquiry-based learning, which better exposes learners to the types of issues faced within a domain (Hung et al., 2019; Koehler & Vilarinho-Pereira, 2021). This instructional approach includes multiple changes to the educational experience when compared to the teacher-centric classroom approach (Reigeluth & Carr-Chellman, 2009). As opposed to a didactic strategy to instruction, students take ownership of their learning and generate questions among their peers, while teachers serve as facilitators (Lazonder & Harmsen, 2016; Loyens & Rikers, 2011; Savery, 2009). The central focus of these strategies also includes ill-structured cases that are similar to the types of problems practitioners face. The complexity of these problems often consists of interconnected variables (latent, salient) and multiple perspectives, so there is rarely a single predetermined solution that satisfies all options (Ifenthaler, 2014). Additionally, these problems are challenging because they include multiple criteria for evaluation (Jonassen, 2011b; Ju & Choi, 2017), which makes it challenging to definitively determine when a ‘right’ answer has been achieved.

There are a number of skillsets needed for problem-solving instructional strategies, such as the inquiry process (Glazewski & Hmelo-Silver, 2018), collaboration (Koehler & Vilarinho-Pereira, 2021), and argumentation (Noroozi et al., 2017). Another important element of problem-solving includes decision-making; that is, the process by which individuals make choices as they resolve the ill-structured case. Understanding decision-making is important because individuals engage in a myriad of choices throughout the problem representation and solution generation phases of problem-solving (Ge et al., 2016). Moreover, learners must engage in multiple and interconnected decisions as they select evidence and determine causal chains during various stages of problem-solving (Shin & Jeong, 2021). The decision-making process is also closely linked with failure and the iterative choices needed to overcome errors in the problem-solving cycles (Schank et al., 1999; Sinha & Kapur, 2021). As such, decision-making is key for learners’ agency as they engage in self-directed learning and take ownership of ill-structured cases.

Despite its importance, the field of learning design only minimally addresses theories and models specifically associated with decision-making. The decision-making processes required for inquiry-based learning necessitates a more in-depth analysis because it is foundational to problem-solving as individuals weigh evidence, make strategic choices amidst an array of variables, and causal reasoning. In addition, an advanced understanding of this skill set would allow educators to develop systems that leverage specific decision-making strategies within design. Based on this gap, we survey broad decision-making paradigms (normative, descriptive, and prescriptive), along with case-based decision-making theory (Gilboa & Schmeidler, 1995; Kolodner, 1991). For each category, we then proffer an example that instantiates the theory. Finally, the article concludes with implications for practice.

Literature Review

Inquiry-based learning is an instructional strategy that affords learners with agency as they solve ill-structured problems. Although variations exist (problem-based learning, project-based learning, case-based instruction), the strategy often situates a contextual case to the learners that is representative of the domain (Lazonder & Harmsen, 2016; Loyens & Rikers, 2011). When compared with teacher-centric approaches where the instructor acts as the ‘sage on the stage’ (Reigeluth & Carr-Chellman, 2009), students in inquiry-based learning engage in a variety of learning actions in the problem representation and solution generation stage. The former necessitates learners define the problem, identify variables, and determine the underlying causal mechanisms of the issue (Delahunty et al., 2020; Ertmer & Koehler, 2018). Solution generation requires learners propose a way to resolve the issue, along with supporting evidence (Ge et al., 2016). This latter stage also includes how learners test out a solution and iterate based on the degree to which their approach meets its goals. As learners engage in these tasks, they must remedy knowledge gaps and work with their peers to reconcile different perspectives. Beyond just retention of facts, learners also engage in information seeking (Belland et al., 2020), question generation (Olney et al., 2012), causal reasoning (Giabbanelli & Tawfik, 2020; Shin & Jeong, 2021), argumentation (Ju & Choi, 2017; Noroozi & Hatami, 2019), and other higher-order thinking skills.

Another important aspect of inquiry-based learning also includes decision-making, which describes the choices learners select as they understand the problem and move towards its resolution. To that end, various theories and models that explicate the nuances of problem-solving have implicitly referenced decision-making. When describing the solution generation stage, Jonassen (1997) asserts that learners’ “resulting mental model of the problem will support the learner's decision and justify the chosen solution” (p. 81). Ge et al. (2016) proposed a conceptual model of self-regulated learning in ill-structured problem-solving in which “students not only must make informed decisions and select the most viable against alternative solutions, but also must support their decisions with defensible and cogent arguments” (p. 4). In terms of encountered failure during problem-solving, Kapur (2008) explains how students must “decide on the criteria for decision making or general parameters for solutions” (p. 391) during criteria development. Indeed, these foundation theories and models of problem-solving highlight the importance of decision-making in various aspects of inquiry-based learning.

Despite its importance, very little understanding is known within the learning design field about the specific decision-making processes inherent within problem-solving. Instead, there is a large body of literature dedicated to strategic approaches to self-directed learning (Xie et al., 2019), collaboration (Radkowitsch et al., 2020), and others. However, specific attention is needed towards decision-making to understand how learners seek out information, weigh evidence, and make choices as they engage in problem-solving. A review of theories argues for three distinct overarching theoretical paradigms of decision-making (Schwartz & Bergus, 2008): normative, descriptive, and prescriptive. There is also a related body of literature around case-based decision-making theory (Gilboa & Schmeidler, 1995), which describes how prior experiences are used to inform choices for new problems. Below we define the theory and related literature, along with a design example that instantiates the decision-making approach.

Outline of Decision-Making Theories and Constructs

Normative Decision-Making

Normative decision-making theoretical foundations.

Normative decision-making describes how learners make choices based on the following: (a) perceived subjective utility and (b) probability (Gati & Kulcsár, 2021). The former focuses on the values of each outcome, especially in terms of how the individual assesses expected benefits and costs associated with one’s goals and preferences. Alternatively, probability describes the degree to which individuals perceive that a selected action will lead to a specific outcome. Hence, a key assumption - and potential criticism - of normative decision-making is that individuals are logically consistent as they make choices under the constraints of rationality, which has been called into question.

Another important element of normative decision-making includes ‘compensatory models’; that is, how the benefits of an alternative outweigh the disadvantages. The most common compensatory model described in the literature is multi-attribute utility theory (MAUT), which is used to account for decision-making amidst multiple criteria (Jansen, 2011). MAUT thus aligns well with ill-structured problem-solving because it assumes that choices are made amongst a variety of competing alternatives. In a conservation example, one might select a green energy alternative to reduce carbon emissions, but it may be disruptive to the existing energy sources (e.g., fossil fuels) and raise costs in the short term. In the context of medicine, a surgery might ultimately resolve an issue, but it poses a risk for post-procedure infections and other complications. As individuals consider each alternative, MAUT is a way of “measuring the decision-maker’s values separately for a set of influential attributes and by weighting these by the relative importance of these attributes as perceived by the decision-maker” (Jansen, 2011, p. 101). MAUT component of normative decision-making specifically argues individuals progress in the following five steps (Von Winterfeldt & Edwards, 1993): 

• Individuals explicate the various alternatives and salient attributes associated with each choice.
• Each alternative is evaluated separately based on each attribute in terms of the following: complete (all essential aspects are addressed), operational (attributes can be meaningfully used), decomposable (deconstructing aspects of evaluation as to simplify evaluation process), non-redundant (remove duplicates of aspects), and minimal (keep a number of attributes focused and central to the problem).
• Individuals assign relative weights to each attribute
• Individuals sum the aggregate weight to evaluate each alternative.
• Individuals make a final choice.

Rather than pursue a less than optimal selection, MAUT argues that “they [individuals] strive to choose the most beneficial alternative and obtain all information relevant to the decision, and they are capable of considering all possible outcomes of the choice, estimating the value of each alternative and aggregating these values into a composite variable” (Gati et al., 2019, p. 123). Another characteristic is how individuals select the factors and assess the degree to which they can be compensated. Some individuals (e.g., expert, novice) may weigh a specific factor differently, even if the other aspects align with their desired outcomes. Given that individuals are not always rational and consistent in decision-making, some argue that the normative decision-making model is not truly representative of how individuals actually engage in everyday problem-solving (Gati et al., 2019; Jansen, 2011; Schwartz & Bergus, 2008). 

Normative decision-making theoretical application

Normative decision-making approaches applied to learning design make choices and probabilities salient to the learner, such as in the case of learner dashboards (Valle et al., 2021) or heuristics. Arguably, the most common application of decision-making in learning technologies for inquiry-based learning includes simulations, which situate individuals within an authentic context and posit a series of choices, and allow them to model choices (Liu et al., 2021). Systems that especially exhibit normative decision-making often consist of the following: (a) encourages learners to consider what is currently known about the phenomena vs. what knowledge the decision-makers lack, (b) makes probability associated with a choice clear, and (c) observes the outcomes of the decision.

One example of normative decision-making applied to design includes The Wildlife Module/Wildfire Explorer project developed by Concord Consortium. In this environment, learners are tasked with lowering wildfire risk in terms of fires and other natural hazards (see Figure 1). The decision-making is especially focused on choices around terrain and weather conditions, which add to or limit the amount of risk that is posed to each town. As learners make decisions, the interface allows individuals to manipulate variables and thus observe how certain choices will result in higher benefits relative to others. For instance, reducing the amount of brush in the area will better prevent wildfire when compared with cutting fire lines. In another instance, they explore how dry terrain and 30 mile per hour (MPH) winds would increase the potential wildfire risk of an area. The learning environment thus instantiates aspects of normative decision-making as learners select the parameters and discern its effects on the wildfire within the region.

Wildlife Module/Wildfire Explorer as Applying Normative Decision-Making

Descriptive Decision-Making

Descriptive decision-making theoretical foundations.

Whereas the normative decision-making approaches assume individuals make rational decisions that maximize choices, descriptive decision-making illustrates the gap between optimal decision-making and how people actually make choices (Gati et al., 2019). Although it is sometimes criticized for the lack of clarity, there are some elements of descriptive decision-making that have emerged. One key component includes satisficing, which posits that individuals attempt to make decisions based on how choices are maximized and meet specific goals. As outlined in the seminal work by Simon (1972), individuals aspire to engage in complex rational selections; however, humans have limited cognitive resources available to process the information available during decision-making. Because choices for ill-structured problems often have competing alternatives, individuals settle for decisions that meet some kind of determined threshold for acceptance in light of a given set of defined criteria. The theory further argues individuals will likely choose the first option that satisfices the desire; so while the final selection may be satisficing, it may not necessarily be the best and most rational decision (Gati et al., 2019). This is especially true in ill-structured problems that include multiple perspectives and constraints that make an ideal solution difficult. Rather, individuals instead strive for a viable choice that can be justified in light of multiple criteria and constraints.

Descriptive decision-making theoretical application

One example includes the EstemEquity project (Gish-Lieberman et al., 2021), which is a learning environment designed to address attrition rates for women of color in STEM through mentorship strategies aimed at building self-efficacy. Because the dynamics of mentorship can be difficult, the system relies heavily on decision-making and reflection upon choice outcomes (see Figure 2). The first steps of a scenario outline a common mentor/mentee challenge, such as a mentee frustrated because she feels as though the mentor is not listening to her underlying problem as she navigates higher education in pursuit of her STEM career. The learning environment then poses two choices that would resolve the issue. Although no single solution will fully remedy the ill-structured mentorship challenge, they must make value judgments about the criteria for success and the degree to which their decision meets the requirements. Based on the goals, the learning environment provides feedback as to how the choice satisfices given their determined threshold of optimal mentor and mentee relationships.

EstemEquity as Applying Descriptive Decision-Making

Prescriptive Decision-Making

Prescriptive decision-making theoretical foundations.

The aforementioned approaches highlight how individuals engage in sense-making as they make a selection among latent and salient variables. To better support ideal decision-making, the prescriptive approach is concerned with providing overt aids to make the best decisions (Divekar et al., 2012). Moreover, prescriptive decision-making “bridges the gap between descriptive observations of the way people make choices and normative guidelines for how they should make choices” (Keller, 1989, p. 260). Prescriptive decision-making thus provides explicit guidelines for making better decisions while taking into consideration human limitations. For example, physicians may use a heuristic that outlines a specific medication based on symptoms and patient characteristics (e.g., height, weight, age). Similarly, a mental health counselor may select a certain intervention approach when a client presents certain behavioral characteristics. In doing so, prescriptive decision-making outlines a series of “if-then” scenarios and details the ideal choice; that is, the pragmatic benefit of the decision to be made given a set of certain circumstances (Gati et al., 2019).

There are multiple challenges and benefits to the prescriptive approach to decision-making. In terms of the former, some question the degree to which a single set of heuristics can be applied across multiple ill-structured problems with varying degrees of nuance. That said, the prescriptive approach has gained traction in the ‘big data’ era, which compiles a considerable amount of information to make it actionable for the individual. An emerging subset of the field includes prescriptive analytics, especially in the business domain (Lepenioti et al., 2020). Beyond just presenting information, prescriptive analytics distinguishes itself because it provides the optimal solution based on input and data-mining strategies from various sources (Poornima & Pushpalatha, 2020). As theorists and practitioners look to align analytics with prescriptive decision-making, Frazzetto et al., (2019) argues: 

If the past has been understood (descriptive analytics; ‘DA’), and predictions about the future are available (predictive analytics; ‘PDA’), then it is possible to actively suggest (prescribe) a best option for adapting and shaping the plans according to the predicted future (p. 5).

Prescriptive decision-making theoretical application

Prescriptive decision-making approaches arguably are most used in adaptive tutoring systems, which outline a series of “if-then” steps based on learners’ interactions. ElectronixTutor is an adaptive system that helps learners understand electrical engineering principles within a higher educational context (see Figure 3). Rather than allowing the learner to navigate as desired or make ad-hoc selections, the recommender system leverages user input from completed lessons to prescribe the optimal lesson choice that best furthers their electrical engineering knowledge. For example, after successful completion on the “Series and Parallel Circuit” (the “if”), the system prescribes that the learner advance to the next “Amplifier” lessons (the “then”) because the system has determined that as the next stage of the learning trajectory. When a learner inputs the correct decision, they are prompted with the optimal selection the system deems as best advances their learning. Alternatively, a wrong selection constrains the choices for the learner and reduces the complexity of the process to a few select decisions. In doing so, the adaptive system implements artificial intelligence to prescribe the optimal path the learner should take based on the previous input from the learner (Hampton & Graesser, 2019).

Autotutor as Applying Prescriptive Decision-Making

Case-Based Decision-Making Theory

Case-based decision-making theoretical foundations.

The literature suggests case-based decision-making theory (CBDMT) is another problem-solving approach individuals employ within domain practice (Gilboa & Schmeidler, 1995). The premise behind CBDMT is that individuals recall previous experiences which are similar to the extant issue and select the solution that yielded a successful resolution (Huang & Pape, 2020; Pape & Kurtz, 2013). These cases are often referred to as ‘repeated choice problems’ whereby individuals see available actions as similar between the new problem and prior experiences (Ossadnik et al., 2013). According to the theory, memory is a set of cases that consists of the following constructs: problem, a potential act chosen in the problem, and ensuing consequence. Specifically, “the memory contains the information required by the decision-maker to evaluate an act, which is specific to the problem” (Ossadnik et al., 2013, p. 213). A key element in a case-based approach to decision-making includes the problem features, the assigned weights of said features, and observed consequences as a reference point for the new problem (Bleichrodt et al., 2017).

The CBDMT approach is similar to the normative approach to decision-making in that it describes how learners make a summative approach to decision-making; however, it differs in that it explicates how one leverages prior experience to calculate these values. Moreover, the value of a case for decision-making is evaluated through a comparison of related acts of other known issues when the new problem is assessed by the individual. Specifically, Gilboa and Schmeidler (1995) propose: “Each act is evaluated by the sum of the utility levels that resulted from using this act in past cases, each weighted by the similarity of that past case to the problem at hand” (p. 605). In this instance, utility refers to the benefits of the decision being made and the forecasting of outcomes (Grosskopf et al., 2015; Lovallo et al., 2012). The individual compares the new case to a previous case and then selects the decision with the highest utility outcome. As one gains expertise, CBDMT proffers one can “combine variations in memory with variations in sets of choice alternatives, leading to generalized versions” (Bleichrodt et al., 2017, p. 127) 

Case-based decision-making theoretical application

Because novices lack prior experiences, one might argue it may be difficult to apply CBDMT in learning design. However, the most often applied approach is by leveraging narratives as a form of vicarious experience (Jonassen, 2011a). In one example by Rong et al. (2020), veterinary students are asked to solve ill-structured problems about how to treat animals that go through various procedures. As part of the main problem to solve, learners must take into consideration the animal’s medical history, height, weight, and a variety of other characteristics. To engender learners’ problem-solving, the case profiles multiple decision points, and later asks the learners to make their own choice and justify its selection. Decision-making is supported through expert cases, which serve as vicarious memory and encourage the learners to transfer the lessons learned towards the main problem to solve (Figure 4). In doing so, the exemplars serve as key decision-making aids as novices navigate the complexity of the ill-structured problem.

Video Exemplars as Applying Case-Based Decision-Making Theory

Discussion and Implications for Design

Theorists of education have often discussed ways to foster various elements of ill-structured problem-solving, including problem representation (Ge et al., 2016), information-seeking (Glazewski & Hmelo-Silver, 2018), question generation (Olney et al., 2012), and others. While this has undoubtedly advanced the field of learning design, we argue decision-making is an equally foundational aspect of problem-solving that requires further attention. Despite its importance, there is very little discourse as to the nuances of decision-making within learning design and how each perspective impacts the problem-solving process. A further explication of these approaches would allow educators and designers to better support learners as they engage in inquiry-based learning and similar instructional strategies that engender complex problemsolving. To address this gap, this article introduces and discusses the application of the following decision-making paradigms: normative, descriptive, prescriptive, and CBDMT.

The above theoretical paradigms have implications for how these theories align with other design approaches of learning systems. In many instances, scaffolds are designed to support specific aspects of problem solving. Some systems are designed to support the collaborative process that occurs during inquiry-based learning (Noroozi et al., 2017), while other scaffolds outline the argumentation process (Malogianni et al., 2021). Alternatively, learning environments may embed prior narratives to model how practitioners solve problems (Tawfik et al., 2020). While each of these theories supports a critical aspect of problem solving, there are opportunities to further refine these learning systems by more directly supporting the decision-making process. For example, one way to align these design strategies and normative decision-making theories would be to outline the different choices and probabilities of expected outcomes. A learning system might embed supports that outline alternative perspectives or reflection questions, but could also include scaffolds that explicate optimal solution paths as it applies a prescriptive decision-making approach. In doing so, designers can simultaneously support various aspects of ill-structured problem solving.

There are also implications as it relates to the expert-novice continuum, which is often cited as a critical component of problem-solving (Jonassen, 2011a; Kim & Hannafin, 2008). Indeed, a body of rich literature has described differences as experts and novices identify variables within ill-structured problems (Jacobson, 2001; Wolff et al., 2021) and define the problem-space within contexts (Ertmer & Koehler, 2018; Hmelo-Silver, 2013). Whereas many post-hoc artifacts have documented outcomes that describe how novices grow during inquiry-based learning (e.g., concept map, argumentation scores), less is known about in situ decision-making processes and germane design strategies novice learners engage in when they are given problem-solving cases. For example, it may be that novices might benefit more from a prescriptive decision-making design strategy given the inherent complexity and challenges of cognitive load presented within an inquiry-based learning module. Alternatively, one might argue simulation learning environments designed for normative decision-making would make the variables more explicit, and thus better aid learners in their choice selection when presented with a case. The simulation approach often employed for normative decision-making might also allow for iterative decision-making, which may be especially advantageous for novices that are newly exposed to the domain. A further understanding of these decision-making approaches allows educators and designers to better support learners and develop systems that emphasize this higher-order learning skillset.

As learners engage in information-seeking during problem-solving, it follows that a choice is made based on the synthetization of multiple different sources (Glazewski & Hmelo-Silver, 2018). Future explorations around information seeking and decision-making would yield important insights for problem solving in multiple respects. For instance, the normative decision-making approach argues individuals assign values to various attributes and use this assessment to make a selection. As learners engage in inquiry-based learning, designers can use understanding of normative approaches to determine how individuals search for information to satisfice an opinion, use this to assess the probability of an action, and the resulting choice. From a descriptive decision-making approach, learners weigh various information sources as they seek out an answer that satisfices. Finally, a case-based decision-making theory approach may find learners search for information and related weights for the following: problem (q ∈ Q), a potential act chosen in the problem (a ∈ A), and ensuing consequence (r ∈ R). Although the design of inquiry-based learning environments often overlooks the intersection of information-seeking approaches and decision-making, a better understanding of the role of theory would aid designers as they construct learning environments that support this aspect of problem solving.

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Dialectical Models of Deliberation, Problem Solving and Decision Making

• Published: 13 September 2019
• Volume 34 , pages 163–205, ( 2020 )

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• Douglas Walton   ORCID: orcid.org/0000-0003-0728-1370 1 ,
• Alice Toniolo 2 &
• Timothy J. Norman 3  

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Hamblin distinguished between formal and descriptive dialectic. Formal normative models of deliberation dialogue have been strongly emphasized as argumentation frameworks in computer science. But making such models of deliberation applicable to real natural language examples has reached a point where the descriptive aspect needs more interdisciplinary work. The new formal and computational models of deliberation dialogue that are being built in computer science seem to be closely related to some already existing and very well established computing technologies such as problem solving and decision making, but whether or how dialectical argumentation can be helpful to support these systems remains an open question. The aim of this paper is to examine some real examples of argumentation that seem to hover on the borderlines between deliberation, problem solving and decision making.

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Acknowledgements

Thanks are due to the Social Sciences and Humanities Research Council of Canada for Insight Grant 435-2012-0104: The Carneades Argumentation System. Part of this work was supported by the Scottish Informatics and Computer Science Alliance.

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Walton, D., Toniolo, A. & Norman, T.J. Dialectical Models of Deliberation, Problem Solving and Decision Making. Argumentation 34 , 163–205 (2020). https://doi.org/10.1007/s10503-019-09497-9

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Common Problem-Solving Models & How to Use Them

Problem – solving models are step-by-step processes that provide a framework for addressing challenges. Problems arise in every facet of life. From work. to home. to friends and family, problems and conflicts can make life difficult and interfere with our physical and mental well-being. Understanding how to approach problems when they arise and implementing problem-solving techniques can make the journey through a problem less onerous on ourselves and those around us.

By building a structured problem-solving process, you can begin to build muscle memory by repeatedly practicing the same approach, and eventually, you may even begin to find yourself solving complex problems . Building a problem-solving model for each of the situations where you may encounter a problem can give you a path forward, even when the most difficult of problems arise.

This article will explore the concept of problem-solving models and dive into examples of such models and how to use them. It will also outline the benefits of implementing a problem-solving model in each area of life and why these problem-solving methods can have a large impact on your overall well-being. The goal of this article is to help you identify effective problem-solving strategies and develop critical thinking to generate solutions for any problem that comes your way.

Problem-Solving Model Defined

The first step in creating a problem-solving plan is to understand what we mean when we say problem-solving models. A problem-solving model is a step-by-step process that helps a team identify and effectively solve problems that they may encounter. This problem-solving approach gives the team the muscle memory and guide to address a conflict and resolve disputes quickly and effectively.

There are common problem-solving models that many teams have implemented, but there is also the freedom to shape a method to fit the needs of a specific situation. These models often rely on various problem-solving techniques to identify the root cause of the issue and find the best solution. This article will explore some common problem-solving models as well as general problem-solving techniques to help a team engage with and solve problems effectively.

Benefits of Implementing Problem-Solving Models

Before we discuss the exact models for problem-solving, it can be helpful to discuss why problem-solving models are beneficial in the first place. There are a variety of benefits to having a plan in place when a problem arises, but a few important benefits are listed below.

Guide Posts

When a team encounters a problem and has a guide for how to approach and solve the problem, it can be a relief to know that they have a process to fall back on when the issue cannot be resolved quickly from the beginning. A problem-solving strategy will serve as a guide for the parties to know which steps to take next and how to identify the appropriate solution.

It can also clarify when the issue needs to stay within the team, and when the issue needs to be escalated to someone in a position with more authority. It can also help the entire team solve complex problems without creating an issue out of the way the team solves the problem. It gives the team a blueprint to work from and encourages them to find a good solution.

Creative Solutions That Last

When the team or family has a way to fall back on to solve a problem, it takes some of the pressure off of coming up with the process and allows the parties to focus on identifying the relevant information and coming up with various potential solutions to the issue. By using a problem-solving method, the parties can come up with different solutions and find common ground with the best solution. This can be stifled if the team is too focused on figuring out how to solve the problem.

Additionally, the solutions that the parties come up with through problem-solving tools will often address the root cause of the issue and stop the team from having to revisit the same problem over and over again. This can lead to overall productivity and well-being and help the team continue to output quality work. By encouraging collaboration and creativity, a problem-solving technique will often keep solving problems between the parties moving forward and possibly even address them before they show up.

Common Models to Use in the Problem-Solving Process

Several models can be applied to a complex problem and create possible solutions. These range from common and straightforward to creative and in-depth to identify the most effective ways to solve a problem. This section will discuss and break down the problem-solving models that are most frequently used.

Standard Problem-Solving Process

When you search for a problem-solving technique, chances are you will find the standard model for saving problems. This model identifies and uses several important steps that will often be used in other models as well, so it can be helpful to begin the model-building process with an understanding of this model as a base. Other models often draw from this process and adapt one or more of the steps to help create additional options. Each of these steps works to accomplish a specific goal in furtherance of a solution.

Define the Problem

The first step in addressing a problem is to create a clear definition of the issue at hand. This will often require the team to communicate openly and honestly to place parameters around the issue. As the team defines the problem, it will be clear what needs to be solved and what pieces of the conflict are ancillary to the major issue. It helps to find the root causes of the issue and begin a process to address that rather than the symptoms of the problem. The team can also create a problem statement, which outlines the parameters of the problem and what needs to be fixed.

In addition to open and honest communication, other techniques can help to identify the root cause and define the problem. This includes a thorough review of the processes and steps that are currently used in the task and whether any of those steps are directly or indirectly causing the problem.

This includes reviewing how tasks are done, how communication is shared, and the current partners and team members that work together to identify if any of those are part of the issue. It is also the time to identify if some of the easy fixes or new tools would solve the problem and what the impact would be.

It is also important to gain a wide understanding of the problem from all of the people involved. Many people will have opinions on what is going on, but it is also important to understand the facts over the opinions that are affecting the problem. This can also help you identify if the problem is arising from a boundary or standard that is not being met or honored. By gathering data and understanding the source of the problem, the process of solving it can begin.

Generate Solutions

The next step in the basic process is to generate possible solutions to the problem. At this step, it is less important to evaluate how each of the options will play out and how they may change the process and more important to identify solutions that could address the issue. This includes solutions that support the goals of the team and the task, and the team can also identify short and long-term solutions.

The team should work to brainstorm as many viable solutions as possible to give them the best options to consider moving forward. They cannot pick the first solution that is proposed and consider it a successful problem-solving process.

Evaluate and Select

After a few good options have been identified, the next step is to evaluate the options and pick the most viable option that also supports the goals of the team or organization. This includes looking at each of the possible solutions and determining how they would either encourage or hinder the goals and standards of the team. These should evaluated without bias toward the solution proposed or the person putting forward the solution. Additionally, the team should consider both actual outcomes that have happened in the past and predicted instances that may occur if the solution is chosen.

Each solution should be evaluated by considering if the solution would solve the current problem without causing additional issues, the willingness of the team to buy in and implement the solution, and the actual ability of the team to implement the solution.

Participation and honesty from all team members will make the process go more smoothly and ensure that the best option for everyone involved is selected. Once the team picks the option they would like to use for the specific problem, they should clearly define what the solution is and how it should be implemented. There should also be a strategy for how to evaluate the effectiveness of the solution.

Implement the Solution and Follow Up

Once a solution is chosen, a team will often assume that the work of solving problems is complete. However, the final step in the basic model is an important step to determine if the matter is resolved or if additional options are needed. After the solution has been implemented by the team, the members of the team must provide feedback and identify any potential obstacles that may have been missed in the decision-making process.

This encourages long-term solutions for the problem and helps the team to continue to move forward with their work. It also gives the team a sense of ownership and an example of how to evaluate an idea in the future.

If the solution is not working the way that it should, the team will often need to adapt the option, or they may get to the point where they scrap the option and attempt another. Solving a problem is not always a linear process, and encouraging reform and change within the process will help the team find the answer to the issues that they face.

GROW Method

Another method that is similar to the standard method is the G.R.O.W. method. This method has very similar steps to the standard method, but the catchiness of the acronym helps a team approach the problem from the same angle each time and work through the method quickly.

The first step in the method is to identify a goal, which is what the “g” stands for in “grow.” To establish a goal, the team will need to look at the issues that they are facing and identify what they would like to accomplish and solve through the problem-solving process. The team will likely participate in conversations that identify the issues that they are facing and what they need to resolve.

The next step is to establish the current reality that the group is facing. This helps them to determine where they currently are and what needs to be done to move them forward. This can help the group establish a baseline for where they started and what they would like to change.

The next step is to find any obstacles that may be blocking the group from achieving their goal. This is where the main crux of the issues that the group is facing will come out. This is also helpful in giving the group a chance to find ways around these obstacles and toward a solution.

Way Forward

After identifying the obstacles and potential ways to avoid them, the group will then need to pick the best way to move forward and approach their goal together. Here, they will need to create steps to move forward with that goal.

Divide and Conquer

Another common problem-solving method is the divide-and-conquer method. Here, instead of the entire team working through each step of the process as a large group, they split up the issue into smaller problems that can be solved and have individual members or small groups work through the smaller problems. Once each group is satisfied with the solution to the problem, they present it to the larger group to consider along with the other options.

This process can be helpful if there is a large team attempting to solve a large and complex problem. It is also beneficial because it can be used in teams with smaller, specialized teams within it because it allows each smaller group to focus on what they know best.

However, it does encourage the parties to shy away from collaboration on the overall issue, and the different solutions that each proposes may not be possible when combined and implemented.

For this reason, it is best to use this solution when approaching complex problems with large teams and the ability to combine several problem-solving methods into one.

Six Thinking Hats

The Six Thinking Hats theory is a concept designed for a team with a lot of differing conflict styles and problem-solving techniques. This method was developed to help sort through the various techniques that people may use and help a team find a solution that works for everyone involved. It helps to organize thinking and lead the conversation to the best possible solution.

Within this system, there are six different “hats” that identify with the various aspects of the decision-making process: the overall process, idea generation, intuition and emotions, values, information gathering, and caution or critical thinking. The group agrees to participate in the process by agreeing on which of the hats the group is wearing at a given moment. This helps set parameters and expectations around what the group is attempting to achieve at any moment.

This system is particularly good in a group with different conflict styles or where people have a hard time collecting and organizing their thoughts. It can be incredibly beneficial for complex problems with many moving parts. It can also help groups identify how each of the smaller sections relates to the big picture and help create new ideas to answer the overall problem.

However, it can derail if the group focuses too heavily or for too long on one of the “hats.” The group should ensure that they have a facilitator to guide them through the process and ensure that each idea and section is considered adequately.

Trial and Error

The trial and error process takes over the evaluation and selection process and instead chooses to try out each of the alternatives to determine what the best option would be. It allows the team to gather data on each of the options and how they apply practically. It also provides the ability for the team to have an example of each possible answer to help a decision-maker determine what the best option is.

Problem-solving methods that focus on trial and error can be helpful when a team has a simple problem or a lot of time to test potential solutions, gather data, and determine an answer to the issue.

It can also be helpful when the team has a sense of the best guess for a solution but wants to test it out to determine if the data supports that option, or if they have several viable options and would like to identify the best one. However, it can be incredibly time-consuming to test each of the options and evaluate how they went. Time can often be saved by evaluating each option and selecting the best to test.

Other Problem-Solving Skills

In addition to the methods outlined above, other problem-solving skills can be used regardless of the model that is used. These techniques can round out the problem-solving process and help address either specific steps in the overall method or alter the step in some way to help it fit a specific situation.

Ask Good Questions

One of the best ways to work through any of the problem-solving models is to ask good questions. This will help the group find the issue at the heart of the problem and address that issue rather than the symptoms. The best questions will also help the group find viable solutions and pick the solution that the group can use to move forward. The more creative the questions , the more likely that they will produce innovative solutions.

Take a Step Back

Occasionally, paying attention to a problem too much can give the group tunnel vision and harm the overall processes that the group is using. Other times, the focus can lead to escalations in conflict. When this happens, it can be helpful to set aside the problem and give the group time to calm down. Once they have a chance to reconsider the options and how they apply, they can approach the issue with a new sense of purpose and determination. This can lead to additional creative solutions that may help the group find a new way forward.

Final Thoughts

Problem-solving can be a daunting part of life. However, with a good problem-solving method and the right techniques, problems can be addressed well and quickly. Applying some of these options outlined in this article can give you a head start in solving your next problem and any others that arise.

To learn more about problem-solving models, problem-solving activities, and more, contact ADR Times !

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Reconstructive Paradigms: A Problem-Solving Approach in Complex Tissue Defects

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• 1 "Carol Davila" University of Medicine and Pharmacy Bucharest, 050474 București, Romania.
• 2 Clinic of Plastic Surgery and Reconstructive Microsurgery, Clinical Emergency Hospital Bucharest, 011602 București, Romania.
• 3 Clinic of Plastic Surgery and Reconstructive Microsurgery, Central Military Universitary Emergency Hospital "Carol Davila", 010825 București, Romania.
• PMID: 38541953
• PMCID: PMC10971357
• DOI: 10.3390/jcm13061728

The field of plastic surgery is continuously evolving, with faster-emerging technologies and therapeutic approaches, leading to the necessity of establishing novel protocols and solving models. Surgical decision-making in reconstructive surgery is significantly impacted by various factors, including the etiopathology of the defect, the need to restore form and function, the patient's characteristics, compliance and expectations, and the surgeon's expertise. A broad surgical armamentarium is currently available, comprising well-established surgical procedures, as well as emerging techniques and technologies. Reconstructive surgery paradigms guide therapeutic strategies in order to reduce morbidity, mortality and risks while maximizing safety, patient satisfaction and properly restoring form and function. The paradigms provide researchers with formulation and solving models for each unique problem, assembling complex entities composed of theoretical, practical, methodological and instrumental elements.

Keywords: decision-making; paradigm; reconstructive surgery; therapeutic strategies.

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