Simon’s Satisficing Strategy In A Nutshell

Simon’s satisficing strategy is a decision-making technique where the individual considers various solutions until they find an acceptable option. Satisficing is a portmanteau combining sufficing and satisfying and was created by psychologist Herbert A. Simon. He argued that many individuals make decisions with a satisfactory (and not optimal) solution. Satisfactory decisions are preferred because they achieve an acceptable result and avoid the resource-intensive search for something more optimal.

Simon’s Satisficing StrategySimon’s Satisficing Strategy, introduced by Nobel laureate Herbert A. Simon, is a decision-making approach that suggests people often make choices that are “good enough” or satisfactory rather than seeking the best possible outcome. It acknowledges the limits of human cognitive resources and time constraints in making decisions.
Key ConceptsBounded Rationality: Simon’s theory of bounded rationality posits that individuals have limited cognitive resources and cannot process all available information, leading to satisficing as an efficient strategy. – Satisficing vs. Maximizing: Satisficing involves selecting the first option that meets a predetermined threshold of acceptability, while maximizing seeks the absolute best choice. – Satisfactory Outcomes: Satisficing aims to achieve outcomes that are satisfactory or adequate, rather than optimal.
ExamplesConsumer Choice: When shopping, individuals may choose the first product that meets their needs and is reasonably priced, rather than conducting exhaustive research to find the absolute best product. – Job Hiring: Hiring managers may select a candidate who meets the minimum qualifications and is a good fit, rather than conducting additional interviews to find the “perfect” candidate. – Project Planning: Project managers may opt for a solution that meets project requirements within the available time and budget constraints, even if it’s not the most elaborate option.
ApplicationsEconomics: Simon’s theory has implications for economic decision-making, as it suggests that individuals and firms may not always seek to maximize utility but instead choose options that are satisfactory. – Management: Satisficing is relevant in management decisions, where leaders often need to make choices under time and resource constraints. – User Experience Design: Designers consider satisficing when creating user interfaces to ensure that users can achieve their goals efficiently without exhaustive effort.
ChallengesRisk of Suboptimality: Satisficing can lead to suboptimal outcomes because it doesn’t always produce the best possible result. – Threshold Determination: Setting the threshold for acceptability can be challenging and subjective, varying from person to person or situation to situation. – Resource Constraints: Satisficing may be more prevalent when individuals have limited time, information, or cognitive resources to make decisions.
MitigationDecision Support Tools: Using decision support tools and frameworks can help individuals and organizations make more informed choices while still considering resource constraints. – Iterative Decision-Making: In complex situations, iterative decision-making allows for revisiting and improving decisions over time. – Learning and Experience: Learning from past decisions and experiences can enhance one’s ability to satisfice effectively.
Scientific SignificanceSimon’s theory of satisficing challenges the traditional economic model of rational decision-making, highlighting the importance of realistic, bounded rationality in understanding how people make choices in the real world.
ConclusionSimon’s Satisficing Strategy provides valuable insights into human decision-making processes. By recognizing the limitations of our cognitive resources and the constraints we face, we can make more effective decisions that are satisfactory for our goals and circumstances. While not always leading to optimal outcomes, satisficing is a practical and efficient approach in a world of bounded rationality.

Understanding Simon’s satisficing strategy

Simon is also the father of bounded rationality.

Indeed, humans lack the cognitive resources to make optimal decisions. We have little understanding of outcome probabilities and can rarely evaluate relevant outcomes with sufficient precision. Furthermore, our memories tend to be unreliable.

Given these limitations, a more realistic approach involves logical and reasoned decision making. Simon called this process “bounded rationality”. Here, satisficing individuals make decisions that are based on certain, non-exhaustive criteria.

Satisficing versus maximizing

Satisficing is not exclusively driven by cognitive limitations. It also seeks to maximize utility, or the extent to which a task or choice is pleasant or desirable. 

For many years, behavioral economists assumed that task desirability was linked to how much information the decision-maker had at their disposal. 

But this is untrue. To prove this, consider the key differences between a satisficer and a maximizer:

  1. The satisficer is not attached to the very best outcome. As a result, they experience less regret and higher self-esteem than their maximizing counterparts – who tend to be outcome-dependent perfectionists.
  2. The satisficer can move on after deciding, while the maximizer needlessly expends more time and energy ruminating.
  3. The satisficer does not obsess over other options and is happier for it. Conversely, the maximizer makes decisions based on external comparisons and not on their own needs or pleasure. This tends to make them unhappier.

Examples of Simon’s satisficing strategy

Consider the consumer who has a leaking pipe in their basement on a weekend. The best solution to this problem is replacing the pipe, but this entails finding a suitable plumber and is an expensive fix. Instead, the consumer chooses to stem the leak with a temporary sealant. While the sealant is by no means a permanent fix, it is satisfactory enough to stem the leak and saves time, money, and energy.

Satisficing has implications for copywriting and web design too. Visitors will tend not to stay on a company site for long unless there are obvious and satisfactory solutions to their problems.

The strategy can also be seen in consumer psychology. When choosing a product such as a pipe sealant, the consumer is looking for the simplest, most readily available option. While more effective solutions exist, they do not come into consideration. 

For example, an office worker might purchase a single piece of accounting software despite there being more benefit in buying the whole suite. A fitness fanatic may purchase a low-quality pair of earphones to use while running, despite several competitor products offering better sound rendition.

Key takeaways

  • Simon’s satisficing strategy is a form of decision making that advocates satisfactory and not optimal solutions.
  • Simon’s satisficing strategy avoids cognitive overload in the often fruitless search for optimal outcomes. These outcomes result in needless expenditure of time, energy, or money.
  • Simon’s satisficing strategy has applications in consumer psychology and user design. Consumers who adopt the strategy tend to be happier and have higher self-esteem than those who opt to maximize the outcomes of decision making.

As highlighted by German psychologist Gerd Gigerenzer in the paper “Heuristic Decision Making,” the term heuristic is of Greek origin, meaning “serving to find out or discover.” More precisely, a heuristic is a fast and accurate way to make decisions in the real world, which is driven by uncertainty.

What is a Heuristic? Beyond biases and the prevailing narrowed vision of the mind

In a 1996 paper entitled “Reasoning the Fast and Frugal Way: Models of Bounded Rationality” psychologists Gerd Gigerenzer and Daniel G. Goldstein highlighted:

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might.

This is a very important concept to start with. Where modern psychologists and theorists of mind manufacture experiments in the lab, those experiments are tied to specific scenarios, that are hardly replicable in the real world.

Why is that? It all starts with a narrow theory of mind.

A narrow definition of rationality

Experiments are manufactured and often based on assumptions around how our minds work. For instance, if a psychologist will label rationality as the ability to optimize during a decision-making process (just like a machine would do) this requires the mind to gather all the possible information to come to a logical decision.

However, in the real world, decisions are made with incomplete information, a high degree of uncertainty and little to no understanding of what’s coming next. Therefore, when the psychologist mutters about the inability of the human brain to understand statistics or logic. In the real world, that means survival.

If surviving means losing some efficiency or avoiding optimization to prevent massive failure, our mind is working as it should.

Risk vs. Uncertainty

Another component that the conventional or prevailing school of thought is the lack of understanding of the domain in which the human mind is operating. That’s a key point to understand the difference between risk and uncertainty.

Risk is computable

Risk is a concept that analysts love. Why? It’s something that can be modeled. Thus, circumscribed to scenarios that have definite rules, like games. You often see in business books how game theory helped businessmen to be successful.

But that is a story crafted in hindsight. Game theory or your skills as a chess player might help you (in impressing others) in normal circumstances (assuming those exist) but they won’t help you much in the real world. Unless you have an alternative toolbox made of heuristics.

Uncertainty is not computable

When financial analysts evaluate risks they fall into the trap of thinking that we can understand the real world by modeling it. The modern approaches to entrepreneurship try to bring this same logic to the business world, with nefast consequences.

When there is a high variability of outcomes, it’s impossible to model the risk. If at all you need a simple set of rules of thumb to avoid the worst-case scenario because if that materializes that will be no risk-model that will help with that.

Indeed the consequences of an uncertain scenario might be too bad for you to actually even see its outcome because survival is at stake.

Unmodeling the real world

When psychological experiments are made in the lab, often times the psychologist starts with a preconceived idea of the human mind and she works her way back to prove it with an experiment.

When that happens experiments are “manufactured” (in many cases unconsciously) to produce a certain result (in short, biases are more a domain applicable to psychologists than of laypeople dealing with real-world uncertainty).

This has come up recently with what is called a Replication Crisis, which as highlighted on Wikipedia:

The replication crisis (or replicability crisis or reproducibility crisis) is, as of 2019, an ongoing methodological crisis in which it has been found that many scientific studies are difficult or impossible to replicate or reproduce. The replication crisis affects the social sciences and medicine most severely.

Part of this trend is in the use of statistical tools that are not proper for real-world analyses, and the fact that research sometimes turns into an attention-driven activity. As pointed out by Noah Smith in Bloombergs’ Why ‘Statistical Significance’ Is Often Insignificant:”

In psychology, in medicine, and in some fields of economics, large and systematic searches are discovering that many findings in the literature are spurious. John Ioannidis, professor of medicine and health research at Stanford University, goes so far as to say that “most published research findings are false,”  including those in economics. The  tendency research journals have of publishing anything with p-values lower than 5 percent — the arbitrary value referred to as “statistical significance” — is widely suspected as a culprit.

To be sure, this is not to say those experiments aren’t valid. Worse than that, in some instances, they carry from the beginning assumptions about the psyche of the subjects that are biased themselves.

In short, the biases that we all talk about nowadays, especially in the business world, in reality, might easily be explained with a theory of mind that goes beyond the conventional definition of rationality.

This definition starts by thinking of our mind as an easily tricked machine, that due to its survival mechanisms isn’t well-adapted anymore to modern times. Thus, it can easily fall prey to dozens if not hundreds of biases that affect our daily lives.

That is we see anywhere today in business publications massive lists of cognitive biases that make us more “aware.”

Heuristics: dirt and quick? Not really!

As highlighted in Heuristic Decision Making:”

The goal of making judgments more accurately by ignoring information is new. It goes beyond the classical assumption that a heuristic trades off some accuracy for less effort.

The main perspective for which heuristics have been studied and communicated to a mass business audience is through the fact that by definition a heuristic is quick and dirty. In short, our error-prone mind generates biases because we use heuristics that made us sacrifice efficiency for speed in the face of a sort la lazy mechanism of the mind.

According to this view, the mind might ignore important information in an efficiency-driven way, almost like it was optimizing for computing power. 

In reality, the mind might have learned that ignoring useless information is a more effective survival mechanism in that specific context. Therefore, focusing on one key data point is way more reliable than taking more information. This completely changes the paradigm.

Where a lazy-driven mind avoids too much information because it’s not computably able to process it (thus sacrificing efficiency for speed almost like it was a computer). In a new paradigm, where heuristics and rules of thumbs become central as a necessary filtering mechanism of the mind that learns ho to ignore useless and irrelevant information.

In short, what matters is the outcome of the action, not the process neither the motivation that drives the process.

Conflict of interests, marketing, and manipulation

New media have enabled companies to communicate at large scale. When this communication is done right we can call it marketing. When that’s done wrong we can call it a conflict of interest or at worst manipulation.

Thus, many of what we call biases are also the consequence of the way the message gets framed to us. In short, it’s like playing a game where one player has to trick the other. As the other player learns the tricks of the first player, new strategies need to be found.

One there is a gap between the trickster and the tricked a bias might emerge as a better ability of the trickster.

Blind faith in technology

While planning a trip back to the city I live in, I was thinking to postpone the trip due to bad weather. While consulting my GPS which optimizes for shorter routes (not certainly for the beauty of the landscape or chances of survival) I risked to get to the end of the trip underwater.

In short, the GPS was giving me the time to destination with a bit of delay but without necessarily mentioning that I was getting there by risking to be flooded!

This blind faith in technology isn’t due to our inability to deal with it. Rather with the way these technologies are framed. When technology is built to optimize, and when it is marketed so that you believe that optimization is what matters in any context (optimization works in narrow ordinary situations) you end up relying too much on it.

The central problem with a two-system thinking model

Theories proposed by psychologists like Kahneman and Tversky have become central in the business world. The book Thinking, Fast And Slow has become a business bible and indeed that is a great read.

Yet the assumptions underlying these theories stand on a hypothetical optimization process humans should follow when making a decision. As highlighted in the paper Heuristic Decision Making:”

As Kahneman (2003) explained in his Nobel Memorial Lecture: “Our research attempted to obtain a map of bounded rationality, by exploring the systematic biases that separate the beliefs that people have and the choices they make from the optimal beliefs and choices assumed in rational-agent models”

This view might start with a wrong definition and interpretation of bounded rationality formulated by Simon. Bounded rationality is not about systematic biases, it’s about decision-making in the real world, which is unpredictable.

Fast, frugal, yet accurate

Another key concept to internalize to deeply understand this alternative view of bounded rationality is the concept of ecological rationality. Ecological rationality looks for strategies that are better suited for a specific environment and context.

The key point here is that there is no best strategy, or optimization strategy because that would not be possible in a large world made of uncertainty.

Therefore, the rules of thumb we might be able to use for each circumstance will help us take advantage of the structure of the environment we operate within.

Thus in this sort of decision-making process, it is like we do create a small world but highly adapted to context and circumstance, which is the opposite of what classic theories of rationality do, assuming that our mind works in a vacuum, or in a sort of free-context reality.

The two sides of Bounded rationality

Bounded rationality is a concept attributed to Herbert Simon, an economist and political scientist interested in decision-making and how we make decisions in the real world. In fact, he believed that rather than optimizing (which was the mainstream view in the past decades) humans follow what he called satisficing.

Based on what we have said so far, let’s look again at the concept of bounded rationality. According to the definition given by his father, Simon, bounded rationality has two main sides:

  • ecological
  • and cognitive

It’s ecological because “the mind is adapted for real-world environments.” Therefore, on the one side, the mind makes decisions based on the structure of the environment. And on the other side, there is the computational capability of the decision-maker (cognitive side).

As highlighted by Gerd Gigerenzer and Wolfgang Gaissmaier in Heuristic Decision Making modern psychologists have focused their attention on the latter (the cognitive side).

More precisely, the focus on the cognitive side has produced the misunderstanding that as the human mind has limited ability to process information, it produces a set of irreparable biases.

Part of this misunderstanding might be given by the fact that those presumably simple heuristics that the mind uses to solve real-world problems are not sophisticated enough to look interesting to the norms of classical rationality.

The importance of Ecological Rationality

Once you understand the other side of rationality, not the cognitive, but the ecological, it changes everything.

In an ecological rationality sense, less-is-more becomes a powerful heuristic to rely on in many of the real-world scenarios.

Less-is-more is about ignoring cues that not only make us worse decision-makers. It also means that after a certain point more information leads to worse decisions, even when the costs of acquiring that information are zero. 

Redefining biases

In the conventional view, a bias is a cognitive error the mind makes, which is due to our lack of understanding of the real world driven by classic rationality. In the alternative way to look at bounded rationality in a decision-making process, it needs to balance out bias and flexibility to produce overall an inference which is more effective than a system that has no biases at all!

In that scenario, less information, ignoring a big chunk of noisy information and make “biased decisions” might lead to better decision-making.

Building an adaptive toolbox for entrepreneurs

Once you understand all the principles highlighted above, you start tinkering with simple algorithms, that we can call heuristics, extremely useful for the businessman who doesn’t want to fall trap of complex thinking for the sake of it.

The FourWeekMBA analysis and study into this adaptive toolbox has just started, and we’ll be looking more and more into a set of simple heuristics to use in different contexts, by starting from when it makes sense to use them in the first place.

There are a few contexts in the business world where gathering more information, data and complex models can indeed help build a successful company (like at an operational level). But there are many other places (strategy and vision) where those complex systems not only do not work but are harmful.

For the sake of having a better toolbox for directing your business in the right direction, we’ll continue our investigation!

Case Studies

Examples Across Various Scenarios:

1. Simon’s Satisficing Strategy:

  • Job Hunt: A recent graduate takes the first job offer they receive instead of waiting for a potentially better offer, prioritizing immediate employment over the optimal job fit.
  • House Purchase: A couple buys a house that meets most of their criteria instead of continuing a lengthy search for the “perfect” home.

2. Understanding Bounded Rationality:

  • Shopping: Due to cognitive overload from numerous choices, a shopper buys a popular brand of cereal instead of comparing the nutritional value of every option.
  • Voting: A voter chooses a candidate based on party affiliation rather than researching each candidate’s policies in depth.

3. Satisficing vs. Maximizing:

  • Car Buying: A satisficer might buy a car that meets their basic needs and is within their budget. In contrast, a maximizer might spend months researching to find the best car in the market, ensuring it has the best features and value for money.

4. Examples of Satisficing:

  • Tech Purchase: A person buys a smartphone that meets their immediate needs instead of waiting for the latest model with advanced features due to budget constraints.
  • Dining: A group chooses a restaurant nearby that’s decent enough rather than spending hours searching for the best-rated restaurant in the city.

5. Heuristics and Ecological Rationality:

  • Travel: A tourist in a new city chooses a restaurant with a queue outside, assuming it’s good, rather than checking every restaurant review.
  • Investment: An investor follows a rule of thumb, like investing in well-known blue-chip companies instead of analyzing every stock in detail.

6. Conflict of Interests and Manipulation:

  • Advertising: A person chooses a product because of a celebrity endorsement, influenced by marketing tactics, rather than the product’s actual merits.
  • Sales: A salesperson highlights only the positive aspects of a product, framing the decision context, and manipulating the customer into making a purchase.

7. The Two Sides of Bounded Rationality:

  • Emergency Decisions: In a crisis, a person might prioritize immediate safety (ecological) over evaluating all possible escape routes (cognitive).
  • Investment Decisions: An investor might rely on market trends (ecological) rather than detailed financial analysis (cognitive) due to time constraints.

8. Redefining Biases:

  • Risk Aversion: A person might avoid risky investments as a protective mechanism, even if they have the potential for high returns. This bias can be seen as an adaptive tool to prevent significant losses.

9. Building an Adaptive Toolbox:

  • Business Negotiations: An entrepreneur might use a rule of always countering the first offer in negotiations, ensuring they don’t settle for less.
  • Product Launch: A business owner might follow a heuristic of testing a new product in a smaller market before a full-scale launch, ensuring any issues are addressed beforehand.

Key Highlights

  • Simon’s Satisficing Strategy: A decision-making technique where individuals settle for an acceptable option rather than pursuing the optimal solution to avoid resource-intensive searches.
  • Understanding Bounded Rationality: Humans lack cognitive resources to make optimal decisions, leading to bounded rationality. Satisficing is an example of bounded rationality.
  • Satisficing vs. Maximizing: Satisficers accept satisfactory outcomes, experience less regret, and avoid unnecessary rumination compared to maximizers who seek the best possible results.
  • Examples of Satisficing: Consumers may opt for a temporary fix instead of the best solution to a problem due to cost, time, or effort constraints.
  • Heuristics and Ecological Rationality: Heuristics are fast and accurate decision-making methods driven by uncertainty. Ecological rationality emphasizes decision-making in real-world environments.
  • Conflict of Interests and Manipulation: Biases can arise from marketing and manipulation, framing the decision-making context and affecting outcomes.
  • The Two Sides of Bounded Rationality: Bounded rationality involves both ecological (environment-based) and cognitive (computational capability) aspects.
  • Redefining Biases: Biases can be seen as adaptive tools, balancing bias and flexibility to enhance overall decision-making.
  • Building an Adaptive Toolbox: Entrepreneurs can develop a toolbox of simple heuristics to make effective decisions in various business contexts.


  • Reasoning the Fast and Frugal Way: Models of Bounded Rationality, Gerd Gigerenzer and Daniel G. Goldstein, Max Planck Institute for Psychological Research and University of Chicago, Psychological Review Copyright 1996 by the American Psychological Association, Inc. 1996, Vol. 103. No. 4, 650-669
  • Heuristic Decision Making, Gerd Gigerenzer and Wolfgang Gaissmaier, Annu. Rev. Psychol. 2011. 62:451–82
  • Simon, Herbert, 1983. “On the Behavioral and Rational Foundation of Economic Theory,” Working Paper Series 115, Research Institute of Industrial Economics.
  • Simon, Herbert A., 1978. “Rational Decision-Making in Business Organizations,” Nobel Prize in Economics documents 1978-1, Nobel Prize Committee.

Read Next: Heuristics, Biases.

Other business resources:

Read Next: Heuristics, Biases.

Connected Thinking Frameworks

Convergent vs. Divergent Thinking

Convergent thinking occurs when the solution to a problem can be found by applying established rules and logical reasoning. Whereas divergent thinking is an unstructured problem-solving method where participants are encouraged to develop many innovative ideas or solutions to a given problem. Where convergent thinking might work for larger, mature organizations where divergent thinking is more suited for startups and innovative companies.

Critical Thinking

Critical thinking involves analyzing observations, facts, evidence, and arguments to form a judgment about what someone reads, hears, says, or writes.


The concept of cognitive biases was introduced and popularized by the work of Amos Tversky and Daniel Kahneman in 1972. Biases are seen as systematic errors and flaws that make humans deviate from the standards of rationality, thus making us inept at making good decisions under uncertainty.

Second-Order Thinking

Second-order thinking is a means of assessing the implications of our decisions by considering future consequences. Second-order thinking is a mental model that considers all future possibilities. It encourages individuals to think outside of the box so that they can prepare for every and eventuality. It also discourages the tendency for individuals to default to the most obvious choice.

Lateral Thinking

Lateral thinking is a business strategy that involves approaching a problem from a different direction. The strategy attempts to remove traditionally formulaic and routine approaches to problem-solving by advocating creative thinking, therefore finding unconventional ways to solve a known problem. This sort of non-linear approach to problem-solving, can at times, create a big impact.

Bounded Rationality

Bounded rationality is a concept attributed to Herbert Simon, an economist and political scientist interested in decision-making and how we make decisions in the real world. In fact, he believed that rather than optimizing (which was the mainstream view in the past decades) humans follow what he called satisficing.

Dunning-Kruger Effect

The Dunning-Kruger effect describes a cognitive bias where people with low ability in a task overestimate their ability to perform that task well. Consumers or businesses that do not possess the requisite knowledge make bad decisions. What’s more, knowledge gaps prevent the person or business from seeing their mistakes.

Occam’s Razor

Occam’s Razor states that one should not increase (beyond reason) the number of entities required to explain anything. All things being equal, the simplest solution is often the best one. The principle is attributed to 14th-century English theologian William of Ockham.

Lindy Effect

The Lindy Effect is a theory about the ageing of non-perishable things, like technology or ideas. Popularized by author Nicholas Nassim Taleb, the Lindy Effect states that non-perishable things like technology age – linearly – in reverse. Therefore, the older an idea or a technology, the same will be its life expectancy.


Antifragility was first coined as a term by author, and options trader Nassim Nicholas Taleb. Antifragility is a characteristic of systems that thrive as a result of stressors, volatility, and randomness. Therefore, Antifragile is the opposite of fragile. Where a fragile thing breaks up to volatility; a robust thing resists volatility. An antifragile thing gets stronger from volatility (provided the level of stressors and randomness doesn’t pass a certain threshold).


Ergodicity is one of the most important concepts in statistics. Ergodicity is a mathematical concept suggesting that a point of a moving system will eventually visit all parts of the space the system moves in. On the opposite side, non-ergodic means that a system doesn’t visit all the possible parts, as there are absorbing barriers

Systems Thinking

Systems thinking is a holistic means of investigating the factors and interactions that could contribute to a potential outcome. It is about thinking non-linearly, and understanding the second-order consequences of actions and input into the system.

Vertical Thinking

Vertical thinking, on the other hand, is a problem-solving approach that favors a selective, analytical, structured, and sequential mindset. The focus of vertical thinking is to arrive at a reasoned, defined solution.

Metaphorical Thinking

Metaphorical thinking describes a mental process in which comparisons are made between qualities of objects usually considered to be separate classifications.  Metaphorical thinking is a mental process connecting two different universes of meaning and is the result of the mind looking for similarities.

Maslow’s Hammer

Maslow’s Hammer, otherwise known as the law of the instrument or the Einstellung effect, is a cognitive bias causing an over-reliance on a familiar tool. This can be expressed as the tendency to overuse a known tool (perhaps a hammer) to solve issues that might require a different tool. This problem is persistent in the business world where perhaps known tools or frameworks might be used in the wrong context (like business plans used as planning tools instead of only investors’ pitches).

Peter Principle

The Peter Principle was first described by Canadian sociologist Lawrence J. Peter in his 1969 book The Peter Principle. The Peter Principle states that people are continually promoted within an organization until they reach their level of incompetence.

Straw Man Fallacy

The straw man fallacy describes an argument that misrepresents an opponent’s stance to make rebuttal more convenient. The straw man fallacy is a type of informal logical fallacy, defined as a flaw in the structure of an argument that renders it invalid.

Google Effect

The Google effect is a tendency for individuals to forget information that is readily available through search engines. During the Google effect – sometimes called digital amnesia – individuals have an excessive reliance on digital information as a form of memory recall.

Streisand Effect

The Streisand Effect is a paradoxical phenomenon where the act of suppressing information to reduce visibility causes it to become more visible. In 2003, Streisand attempted to suppress aerial photographs of her Californian home by suing photographer Kenneth Adelman for an invasion of privacy. Adelman, who Streisand assumed was paparazzi, was instead taking photographs to document and study coastal erosion. In her quest for more privacy, Streisand’s efforts had the opposite effect.

Compromise Effect

Single-attribute choices – such as choosing the apartment with the lowest rent – are relatively simple. However, most of the decisions consumers make are based on multiple attributes which complicate the decision-making process. The compromise effect states that a consumer is more likely to choose the middle option of a set of products over more extreme options.

Butterfly Effect

In business, the butterfly effect describes the phenomenon where the simplest actions yield the largest rewards. The butterfly effect was coined by meteorologist Edward Lorenz in 1960 and as a result, it is most often associated with weather in pop culture. Lorenz noted that the small action of a butterfly fluttering its wings had the potential to cause progressively larger actions resulting in a typhoon.

IKEA Effect

The IKEA effect is a cognitive bias that describes consumers’ tendency to value something more if they have made it themselves. That is why brands often use the IKEA effect to have customizations for final products, as they help the consumer relate to it more and therefore appending to it more value.

Ringelmann Effect 

Ringelmann Effect
The Ringelmann effect describes the tendency for individuals within a group to become less productive as the group size increases.

The Overview Effect

The overview effect is a cognitive shift reported by some astronauts when they look back at the Earth from space. The shift occurs because of the impressive visual spectacle of the Earth and tends to be characterized by a state of awe and increased self-transcendence.

House Money Effect

The house money effect was first described by researchers Richard Thaler and Eric Johnson in a 1990 study entitled Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice. The house money effect is a cognitive bias where investors take higher risks on reinvested capital than they would on an initial investment.


As highlighted by German psychologist Gerd Gigerenzer in the paper “Heuristic Decision Making,” the term heuristic is of Greek origin, meaning “serving to find out or discover.” More precisely, a heuristic is a fast and accurate way to make decisions in the real world, which is driven by uncertainty.

Recognition Heuristic

The recognition heuristic is a psychological model of judgment and decision making. It is part of a suite of simple and economical heuristics proposed by psychologists Daniel Goldstein and Gerd Gigerenzer. The recognition heuristic argues that inferences are made about an object based on whether it is recognized or not.

Representativeness Heuristic

The representativeness heuristic was first described by psychologists Daniel Kahneman and Amos Tversky. The representativeness heuristic judges the probability of an event according to the degree to which that event resembles a broader class. When queried, most will choose the first option because the description of John matches the stereotype we may hold for an archaeologist.

Take-The-Best Heuristic

The take-the-best heuristic is a decision-making shortcut that helps an individual choose between several alternatives. The take-the-best (TTB) heuristic decides between two or more alternatives based on a single good attribute, otherwise known as a cue. In the process, less desirable attributes are ignored.

Bundling Bias

The bundling bias is a cognitive bias in e-commerce where a consumer tends not to use all of the products bought as a group, or bundle. Bundling occurs when individual products or services are sold together as a bundle. Common examples are tickets and experiences. The bundling bias dictates that consumers are less likely to use each item in the bundle. This means that the value of the bundle and indeed the value of each item in the bundle is decreased.

Barnum Effect

The Barnum Effect is a cognitive bias where individuals believe that generic information – which applies to most people – is specifically tailored for themselves.

Anchoring Effect

The anchoring effect describes the human tendency to rely on an initial piece of information (the “anchor”) to make subsequent judgments or decisions. Price anchoring, then, is the process of establishing a price point that customers can reference when making a buying decision.

Decoy Effect

The decoy effect is a psychological phenomenon where inferior – or decoy – options influence consumer preferences. Businesses use the decoy effect to nudge potential customers toward the desired target product. The decoy effect is staged by placing a competitor product and a decoy product, which is primarily used to nudge the customer toward the target product.

Commitment Bias

Commitment bias describes the tendency of an individual to remain committed to past behaviors – even if they result in undesirable outcomes. The bias is particularly pronounced when such behaviors are performed publicly. Commitment bias is also known as escalation of commitment.

First-Principles Thinking

First-principles thinking – sometimes called reasoning from first principles – is used to reverse-engineer complex problems and encourage creativity. It involves breaking down problems into basic elements and reassembling them from the ground up. Elon Musk is among the strongest proponents of this way of thinking.

Ladder Of Inference

The ladder of inference is a conscious or subconscious thinking process where an individual moves from a fact to a decision or action. The ladder of inference was created by academic Chris Argyris to illustrate how people form and then use mental models to make decisions.

Goodhart’s Law

Goodhart’s Law is named after British monetary policy theorist and economist Charles Goodhart. Speaking at a conference in Sydney in 1975, Goodhart said that “any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Goodhart’s Law states that when a measure becomes a target, it ceases to be a good measure.

Six Thinking Hats Model

The Six Thinking Hats model was created by psychologist Edward de Bono in 1986, who noted that personality type was a key driver of how people approached problem-solving. For example, optimists view situations differently from pessimists. Analytical individuals may generate ideas that a more emotional person would not, and vice versa.

Mandela Effect

The Mandela effect is a phenomenon where a large group of people remembers an event differently from how it occurred. The Mandela effect was first described in relation to Fiona Broome, who believed that former South African President Nelson Mandela died in prison during the 1980s. While Mandela was released from prison in 1990 and died 23 years later, Broome remembered news coverage of his death in prison and even a speech from his widow. Of course, neither event occurred in reality. But Broome was later to discover that she was not the only one with the same recollection of events.

Crowding-Out Effect

The crowding-out effect occurs when public sector spending reduces spending in the private sector.

Bandwagon Effect

The bandwagon effect tells us that the more a belief or idea has been adopted by more people within a group, the more the individual adoption of that idea might increase within the same group. This is the psychological effect that leads to herd mentality. What in marketing can be associated with social proof.

Moore’s Law

Moore’s law states that the number of transistors on a microchip doubles approximately every two years. This observation was made by Intel co-founder Gordon Moore in 1965 and it become a guiding principle for the semiconductor industry and has had far-reaching implications for technology as a whole.

Disruptive Innovation

Disruptive innovation as a term was first described by Clayton M. Christensen, an American academic and business consultant whom The Economist called “the most influential management thinker of his time.” Disruptive innovation describes the process by which a product or service takes hold at the bottom of a market and eventually displaces established competitors, products, firms, or alliances.

Value Migration

Value migration was first described by author Adrian Slywotzky in his 1996 book Value Migration – How to Think Several Moves Ahead of the Competition. Value migration is the transferal of value-creating forces from outdated business models to something better able to satisfy consumer demands.

Bye-Now Effect

The bye-now effect describes the tendency for consumers to think of the word “buy” when they read the word “bye”. In a study that tracked diners at a name-your-own-price restaurant, each diner was asked to read one of two phrases before ordering their meal. The first phrase, “so long”, resulted in diners paying an average of $32 per meal. But when diners recited the phrase “bye bye” before ordering, the average price per meal rose to $45.


Groupthink occurs when well-intentioned individuals make non-optimal or irrational decisions based on a belief that dissent is impossible or on a motivation to conform. Groupthink occurs when members of a group reach a consensus without critical reasoning or evaluation of the alternatives and their consequences.


A stereotype is a fixed and over-generalized belief about a particular group or class of people. These beliefs are based on the false assumption that certain characteristics are common to every individual residing in that group. Many stereotypes have a long and sometimes controversial history and are a direct consequence of various political, social, or economic events. Stereotyping is the process of making assumptions about a person or group of people based on various attributes, including gender, race, religion, or physical traits.

Murphy’s Law

Murphy’s Law states that if anything can go wrong, it will go wrong. Murphy’s Law was named after aerospace engineer Edward A. Murphy. During his time working at Edwards Air Force Base in 1949, Murphy cursed a technician who had improperly wired an electrical component and said, “If there is any way to do it wrong, he’ll find it.”

Law of Unintended Consequences

The law of unintended consequences was first mentioned by British philosopher John Locke when writing to parliament about the unintended effects of interest rate rises. However, it was popularized in 1936 by American sociologist Robert K. Merton who looked at unexpected, unanticipated, and unintended consequences and their impact on society.

Fundamental Attribution Error

Fundamental attribution error is a bias people display when judging the behavior of others. The tendency is to over-emphasize personal characteristics and under-emphasize environmental and situational factors.

Outcome Bias

Outcome bias describes a tendency to evaluate a decision based on its outcome and not on the process by which the decision was reached. In other words, the quality of a decision is only determined once the outcome is known. Outcome bias occurs when a decision is based on the outcome of previous events without regard for how those events developed.

Hindsight Bias

Hindsight bias is the tendency for people to perceive past events as more predictable than they actually were. The result of a presidential election, for example, seems more obvious when the winner is announced. The same can also be said for the avid sports fan who predicted the correct outcome of a match regardless of whether their team won or lost. Hindsight bias, therefore, is the tendency for an individual to convince themselves that they accurately predicted an event before it happened.

Read Next: BiasesBounded RationalityMandela EffectDunning-Kruger EffectLindy EffectCrowding Out EffectBandwagon Effect.

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