fast-and-frugal-trees

Fast-And-Frugal Trees In A Nutshell

Fast-and-frugal trees are classification trees with sequentially ordered cues that aid in decision making. Fast-and-frugal trees (FFTs) are very simple illustrations of heuristic decision making. Each tree is comprised of sequentially ordered cues – or questions. In turn, each cue has two branches according to how the question can be answered:

  1. If the answer to the question is yes, then the branch leads to the next question in the sequence.
  2. If the answer to the question is no, then the branch leads to an exit point in the sequence.

At the final cue in the sequence, both branches lead to an exit point to ensure that a decision is made either way.

AspectExplanation
DefinitionFast-and-Frugal Trees (FFTs) are a class of decision-making models and heuristics used to simplify complex choices and predictions. Developed by Gerd Gigerenzer and his colleagues, FFTs are designed to make decisions quickly and accurately by relying on a limited number of cues or pieces of information. These decision trees are characterized by their simplicity and efficiency, making them valuable tools in situations where cognitive resources are limited or where quick decisions are essential. FFTs are based on the idea that less can be more in decision-making, emphasizing the use of a small number of relevant cues rather than exhaustive data analysis.
Key ConceptsHeuristic Decision-Making: FFTs are part of the family of heuristics, which are mental shortcuts or rules of thumb that help individuals make decisions efficiently. – Limited Information: They rely on a limited set of cues or pieces of information, often just one or two, to arrive at a decision. – Satisficing: FFTs aim to find a “good enough” solution rather than an optimal one, which can save time and cognitive effort. – Adaptation: These decision trees are designed to adapt to the specific context and environment in which they are used, making them flexible tools for decision-making. – Error Management: FFTs take into account the potential for errors and aim to minimize costly mistakes.
CharacteristicsSimplicity: FFTs are intentionally simple and easy to understand, making them accessible to a wide range of users. – Few Cues: They use a small number of cues, often only one or two, to make decisions. – Efficiency: FFTs are designed for quick decision-making, allowing users to arrive at a choice rapidly. – Robustness: These trees are robust to variations in the environment and context, making them adaptable tools. – Bounded Rationality: FFTs acknowledge that individuals have limited cognitive resources and work within these constraints.
ImplicationsRapid Decision-Making: FFTs are valuable in situations where making quick decisions is crucial, such as emergency response, medical diagnoses, or financial trading. – Reduced Cognitive Load: They help reduce the cognitive load on decision-makers by simplifying the decision process. – Error Reduction: FFTs can help minimize errors by focusing on the most relevant cues and avoiding information overload. – Scalability: They can be applied to a wide range of domains and tasks, making them scalable tools for decision support.
AdvantagesSpeed: FFTs excel in making rapid decisions, saving time in situations where time is of the essence. – Simplicity: Their simplicity makes them accessible to individuals with varying levels of expertise. – Reduced Cognitive Load: FFTs relieve decision-makers from the burden of processing extensive information. – Adaptability: They can adapt to different contexts and domains, increasing their versatility. – Error Management: FFTs are designed with error management in mind, helping to minimize costly mistakes.
DrawbacksLimited Accuracy: While FFTs are efficient, they may not always provide the most accurate decisions, as they prioritize speed and simplicity over optimization. – Context Sensitivity: Their performance can be sensitive to the specific context and cues chosen, requiring careful design. – Overgeneralization: In some cases, FFTs may oversimplify complex decisions, leading to suboptimal choices.
ApplicationsMedical Diagnosis: FFTs have been used in medical decision support systems to aid in rapid diagnoses based on a limited set of patient information. – Emergency Response: They are valuable in emergency response scenarios where quick decisions can save lives. – Financial Trading: In the fast-paced world of financial trading, FFTs can help traders make timely decisions. – Consumer Behavior: Businesses use FFTs to predict consumer behavior and tailor marketing strategies.
Use CasesMedical Triage: In a hospital emergency room, FFTs can assist in patient triage by quickly assessing vital signs and symptoms to prioritize care. – Financial Trading: Traders use FFTs to make rapid decisions about buying or selling financial assets based on a few key indicators. – Online Advertising: Digital marketers employ FFTs to predict user preferences and display relevant ads to online consumers. – Emergency Response: Firefighters and first responders may use FFTs to assess rapidly changing situations and make critical decisions on the ground. – Retail Inventory: Retailers can use FFTs to optimize inventory management by quickly identifying which products are likely to sell well based on limited data.

Why is the fast-and-frugal tree useful in business?

Fast-and-frugal trees are particularly useful when decisions need to be made quickly. They are well suited to binary classification problems – or problems with elements occupying two possible outcomes.

FFTs have been trialed in emergency room scenarios to help physicians triage patients. During a peer-reviewed study, a classification tree of just three cues enabled doctors to diagnose and then direct patients to either a regular nursing bed or the coronary care unit. 

Cues were based on historical acute heart disease data, allowing high-risk patients to be identified quickly and accurately. In fact, the process was so accurate that it was a better predictor of heart disease than the clinical judgment of the physicians themselves.

Constructing fast-and-frugal trees

There are several ways to construct fast-and-frugal trees.

In the emergency room example, physicians had historical data on factors that lead to acute heart disease. Chest pain was one such predisposition, leading to the creation of a cue entitled “Chest pain chief symptom?” with a yes or no answer.

Although FFTs were designed to be simple, they have nonetheless been adapted by using more complex methods. Primarily, this is seen in FFTs that are constructed using a zig-zag algorithm.

Here, the tree is created using positive and negative cue validity. This validity is defined as the proportion of cases with a positive/negative outcome in all cases with a positive/negative cue value. Typically, the first – or “root” – cue of a zig-zag analysis tree is the cue with the greatest positive (or negative) validity. This ensures that the most significant positive and negative decisions are made first. 

Typically, cue validities are determined by using counts and ratios. In more advanced scenarios, they must be estimated using elements of probability theory such as conditional independence. Zig-zag decision trees enhance the already strong fundamentals of FFTs. Given that the tree can be completed with pen and paper, the accuracy of a zig-zag tree is as high as using a logistic regression model.

Fast-and-frugal tree applications

As noted, FFTs are useful in any situation requiring fast and accurate decisions or risk assessment. 

Beyond medical applications, these trees have been used in the military to identify enemy threats and also in courtrooms to decide whether to bail or jail a defendant.

In recent times, fast-and-frugal trees have also shone a light on how customer management decisions are made. Retail banking sales managers who embodied fast, frugal, and adaptive decision making were able to better anticipate client needs.

Examples of the fast-and-frugal tree in various contexts

  • Emergency Room Triage: As mentioned in the description, fast-and-frugal trees have been used in emergency rooms to quickly triage patients. For instance, doctors might ask a series of simple questions about the patient’s symptoms (e.g., “Is the patient experiencing chest pain?”) to determine whether the patient needs immediate care in the coronary care unit or can be directed to a regular nursing bed.
  • Military Threat Identification: In military applications, fast-and-frugal trees can be used to identify potential enemy threats. Soldiers on the field might be trained to ask a series of yes-or-no questions about suspicious activities or behavior to quickly assess whether there is a potential threat.
  • Courtroom Decision Making: In the legal system, fast-and-frugal trees have been used to aid in decision-making processes. For instance, judges might use a series of sequential cues to decide whether a defendant should be granted bail or held in custody based on the likelihood of flight risk or danger to the community.
  • Customer Management in Retail Banking: Retail banking sales managers can utilize fast-and-frugal trees to better anticipate client needs. By asking a sequence of questions about a customer’s financial situation and preferences, the manager can efficiently recommend suitable products or services.
  • Product Quality Assurance: Manufacturing companies might use fast-and-frugal trees to quickly assess the quality of products. By asking a series of yes-or-no questions related to specific product defects or issues, inspectors can efficiently determine whether a product meets the required standards.
  • Risk Assessment in Insurance: Insurers can employ fast-and-frugal trees to assess risk quickly and accurately. For example, when evaluating an applicant for life insurance, a series of simple questions about the applicant’s health and lifestyle could help determine the appropriate coverage and premiums.
  • Marketing and Advertising: Fast-and-frugal trees can aid marketers in segmenting their target audience. By asking key questions about consumer preferences or behaviors, marketers can quickly identify relevant customer segments for specific products or campaigns.
  • Product Recommendations in E-commerce: Online retailers can use fast-and-frugal trees to recommend products to customers based on their preferences and past behavior. By asking a series of questions about their interests, previous purchases, and browsing history, the system can efficiently suggest relevant products.
  • Medical Diagnosis: Fast-and-frugal trees can assist doctors in quickly diagnosing medical conditions. For instance, in the case of diagnosing influenza, a series of questions about symptoms like fever, cough, and body aches can lead to a rapid decision on whether to prescribe antiviral medication.
  • Quality Control in Manufacturing: Manufacturing plants can employ fast-and-frugal trees to assess the quality of products on the assembly line. Inspectors can ask questions about product specifications and defects to decide whether an item meets quality standards.
  • Customer Service Chatbots: Chatbots in customer service can use fast-and-frugal trees to address customer inquiries efficiently. By asking a series of questions, the chatbot can quickly narrow down the problem and provide relevant solutions.
  • Financial Risk Assessment: Banks and financial institutions can use fast-and-frugal trees to evaluate the creditworthiness of loan applicants. Questions about income, credit history, and outstanding debts can help determine whether to approve a loan.
  • Fraud Detection: In the world of cybersecurity, fast-and-frugal trees can be applied to identify potentially fraudulent activities. By asking questions related to transaction patterns and account behavior, the system can flag suspicious actions for further investigation.
  • Supply Chain Management: Fast-and-frugal trees can aid in supply chain decision-making. For instance, in inventory management, a series of questions about demand, lead times, and storage costs can guide decisions on ordering and stock levels.
  • Environmental Impact Assessment: Environmental consultants can use fast-and-frugal trees to assess the environmental impact of construction projects. Questions about project location, size, and potential impacts on ecosystems can help determine whether a project should proceed.
  • Agricultural Pest Control: Farmers can employ fast-and-frugal trees to decide on pest control measures for crops. Questions about the type of pest, crop stage, and weather conditions can lead to informed decisions about pesticide application.
  • Educational Assessment: Teachers and educators can use fast-and-frugal trees to identify students who may need additional support. Questions about academic performance, behavior, and attendance can help determine intervention strategies.
  • Traffic Management: Traffic control centers can utilize fast-and-frugal trees to make real-time decisions during traffic incidents. Questions about the location and severity of the incident can guide decisions on diverting traffic or dispatching emergency services.
  • Restaurant Menu Optimization: Restaurant owners can use fast-and-frugal trees to optimize their menus. By asking questions about customer preferences and dietary restrictions, they can design menus that cater to a wide range of tastes.
  • Energy Consumption Reduction: Businesses can employ fast-and-frugal trees to reduce energy consumption. Questions about building occupancy, lighting, and HVAC usage can lead to energy-saving recommendations.
  • Human Resources: HR departments can use fast-and-frugal trees for employee onboarding and benefits selection. Questions about employee preferences and needs can help tailor benefit packages.
  • Real Estate Investment: Real estate investors can use fast-and-frugal trees to assess potential properties. Questions about location, property type, and rental income can guide investment decisions.
  • Epidemiological Studies: Epidemiologists can use fast-and-frugal trees to identify risk factors for diseases. Questions about lifestyle, exposure to toxins, and genetic factors can inform public health interventions.

Key takeaways:

  • Fast-and-frugal trees are heuristic models that are useful for tasks where binary decisions or classifications need to be made.
  • Fast-and-frugal trees are made of sequentially ordered cues, otherwise known as questions. Each cue has a binary answer, with one answer leading to a subsequent question and the other leading to an exit point.
  • Fast-and-frugal trees are simple and effective decision-making tools. However, the decision-making process is enhanced by using historical data and aspects of probability theory.

Key Highlights:

  • Fast-and-Frugal Trees (FFTs): Simple classification trees aiding decision-making with sequentially ordered cues.
  • Cues in FFTs: Each cue has two branches: “yes” leads to the next question, “no” leads to an exit point.
  • Usefulness in Business: Ideal for quick decisions and binary classification problems.
  • Medical Application: Successfully used in emergency room triage, outperforming physicians’ clinical judgment.
  • Construction: FFTs can be built using simple methods or zig-zag algorithms for more complexity.
  • Applications: Used in military threat identification, courtroom decision-making, customer management, and product quality assurance.
  • Risk Assessment: Applied in insurance for efficient evaluation of applicants.
  • Marketing and Advertising: Aid in audience segmentation and product recommendations in e-commerce.
  • Enhanced Decision Making: FFTs are effective when combined with historical data and probability theory.

Connected Thinking Frameworks

Convergent vs. Divergent Thinking

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

Biases

biases
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
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
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
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

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

occams-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

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

antifragility
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

ergodicity
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
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
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
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

einstellung-effect
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

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

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

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

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

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

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

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

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

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.

Heuristic

heuristic
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

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

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

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

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

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

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

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
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
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

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

goodharts-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

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

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

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

Bandwagon Effect

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

moores-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
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
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

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

groupthink
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.

Stereotyping

stereotyping
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

murphys-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

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
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
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
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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