The elimination by aspects model is a decision-making theory where the process of elimination influences purchasing choices.
The elimination by aspects (EBA) model is based on a mental shortcut – or heuristic – that consumers use to decide on a product or service to purchase.
These decision heuristics are commonly used in purchasing decisions as a way of reducing information overload. However, this means that the resulting decision is often less than optimal.
E-commerce is a major contributor to sub-optimal decisions, where consumers are exposed to large marketplaces with similarly large product ranges. With so many product alternatives available, making the right decision becomes substantially harder.
Aspect
Explanation
Definition of Elimination by Aspects
Elimination by Aspects (EBA) is a decision-making strategy and cognitive process used to make choices among multiple alternatives. It involves systematically evaluating each option based on specific criteria or attributes. At each step, the decision-maker sets a minimum acceptable level for one criterion, and alternatives that do not meet this criterion are eliminated. This process continues iteratively, narrowing down the options by progressively considering additional criteria until a single choice remains. EBA is a structured approach that helps individuals or organizations make complex decisions by breaking them down into smaller, manageable steps. It is particularly useful when faced with choices that involve multiple factors, trade-offs, and the need to prioritize certain aspects of a decision. EBA can be applied in various contexts, including business, personal life, and problem-solving scenarios.
Key Concepts
Several key concepts define the Elimination by Aspects strategy:
– Criterion-Based Evaluation
The central concept is criterion-based evaluation, where decision-makers assess each alternative based on specific criteria or attributes. These criteria are often chosen based on their relevance to the decision at hand. Criterion-based evaluation provides a structured framework for decision-making.
– Minimum Acceptable Level
At each step of the process, a minimum acceptable level is set for one of the criteria. Alternatives that do not meet or exceed this minimum level are eliminated from consideration. The minimum acceptable level helps prioritize criteria and filter out less desirable options.
– Iterative Process
Elimination by Aspects is an iterative process, meaning it involves multiple rounds of evaluation. In each iteration, the decision-maker focuses on a different criterion and eliminates alternatives that do not meet the established minimum. This process continues until a single choice remains. Iterative evaluation ensures a systematic and thorough examination of alternatives.
– Trade-offs and Prioritization
Decision-makers may need to make trade-offs and prioritize certain criteria over others. This reflects the recognition that not all criteria are equally important, and some may have a greater impact on the final choice. Trade-offs and prioritization allow for a more nuanced decision-making process.
Characteristics
The Elimination by Aspects approach is characterized by the following attributes:
– Systematic Decision-Making
EBA is a systematic decision-making method that follows a structured sequence of evaluating alternatives against predefined criteria. This systematic approach ensures that decisions are well-informed and based on specific considerations. Systematic decision-making reduces the influence of biases and emotions.
– Progressive Elimination
Alternatives are progressively eliminated based on the failure to meet the established minimum for each criterion. This step-by-step elimination process narrows down the options and leads to a final choice. Progressive elimination simplifies complex decisions.
– Flexibility in Criteria
The choice of criteria in EBA can be flexible and tailored to the decision context. Decision-makers can adjust the criteria to align with the unique aspects of the decision or adapt them as new information becomes available. Flexibility in criteria allows for adaptability in decision-making.
– Risk Mitigation
By systematically evaluating alternatives against multiple criteria, EBA helps mitigate the risk of making hasty or biased decisions. It encourages a thorough examination of options, reducing the likelihood of unfavorable outcomes. Risk mitigation enhances decision quality.
Examples of Elimination by Aspects
Elimination by Aspects can be applied in various scenarios:
– Product Selection
When choosing between different products or services, individuals or businesses can use EBA to evaluate options based on criteria such as cost, quality, features, and customer reviews.
– Job Candidate Evaluation
Hiring managers can apply EBA to assess job candidates by considering criteria such as qualifications, experience, cultural fit, and references.
– Investment Decision
Investors can use EBA to evaluate investment opportunities, considering factors like potential returns, risk levels, liquidity, and market trends.
– Vendor Selection
Organizations seeking to partner with vendors or suppliers can employ EBA to compare and select vendors based on criteria like pricing, reliability, product quality, and service level agreements.
Benefits and Considerations
The Elimination by Aspects approach offers several benefits and considerations:
– Structured Decision-Making
EBA provides a structured framework for making complex decisions, reducing the likelihood of making impulsive or uninformed choices. It ensures that all relevant criteria are systematically considered.
– Transparency
The step-by-step nature of EBA makes the decision-making process transparent and easy to follow. This transparency can be valuable in group decision-making or when stakeholders need to understand the rationale behind a choice. Transparency fosters consensus and accountability.
– Objective Decision-Making
By relying on predefined criteria and minimum acceptable levels, EBA promotes more objective decision-making. It helps mitigate the influence of personal biases and emotions, leading to more rational choices. Objective decision-making enhances decision quality.
– Time and Resource Requirements
EBA can be time-consuming, especially when evaluating numerous alternatives against multiple criteria. Decision-makers need to allocate sufficient time and resources to complete the process thoroughly. Time and resource requirements should be considered in decision planning.
To understand how the elimination by aspects model works, consider it as a process.
First, a consumer browses a list of products with one attribute they deem essential. In the EBA model, attributes are simply product features. Products that do not contain this essential feature are excluded as potential purchases. A consumer looking to purchase a new car, for example, may deem that safety is the most important feature – thereby excluding any manufacturer except Volvo.
With the shortened list of potential purchases, the consumer repeats the first step but with a different attribute. For example, the consumer may then search Volvo’s entire range for a car capable of accommodating four children.
At this point, the range of potential cars has been shortened further. But the consumer also desires a safe, family-sized Volvo that has excellent fuel economy.
Although many consumers may stop after three or four essential attributes, many others will continue until a single, “best fit” product remains
Ultimately, each product in the EBA model is judged based on its assortment or combination of product features. There is less emphasis on the product as a whole, provided that the product has the essential features a consumer desires.
Potential downsides to the elimination by aspects model
The elimination by aspects model is based on heuristic decision making as a means of bypassing information overload. As a result, the product a consumer eventually decides to purchase will be satisfactory – but not optimal.
This is because the model is non-compensatory, meaning that highly desirable attributes cannot compensate for less than desirable attributes. In the case of a new Volvo, cars were excluded in the third step based on fuel economy. However, a car slightly less economical on fuel may have delivered other benefits in reliability, servicing cost, and warranty. That is, benefits that would have compensated for the slight reduction in fuel economy.
Key takeaways
The elimination by aspects model is a mental shortcut that consumers use to make purchasing decisions when a large range of products are present.
In the elimination by aspects model, a consumer progressively shortens a list of potential products according to whether they contain the right combination of desired features.
The elimination by aspects model gives a satisfactory but non-optimal result. This is because the model does not make allowances for a desirable attribute being able to compensate for a non-desired attribute.
Key highlights of the Elimination by Aspects (EBA) model:
Definition and Concept: The Elimination by Aspects (EBA) model is a decision-making theory that influences purchasing choices. It’s a heuristic, or mental shortcut, used by consumers to decide on a product or service to buy.
Decision Heuristics and Information Overload: Consumers use decision heuristics like EBA to simplify complex purchasing decisions, reducing information overload. However, this might lead to suboptimal decisions due to the exclusion of potentially valuable information.
E-commerce Impact: E-commerce platforms with vast product ranges contribute to suboptimal decisions, as consumers face difficulty in evaluating numerous alternatives.
Process of EBA:
Consumers start by identifying a crucial attribute (product feature) they want in a product.
Products lacking this essential feature are eliminated from consideration.
The process is repeated for different attributes, further narrowing down the choices.
Consumers may continue until a single “best fit” product remains based on their prioritized attributes.
Non-Compensatory Nature: EBA is non-compensatory, meaning highly desirable attributes cannot compensate for less desirable ones. This leads to a satisfactory but not necessarily optimal choice.
Example Scenario: If a consumer is looking for a family-sized, safe car with excellent fuel economy, they may eliminate options based on each of these attributes in successive stages.
Shortcomings of EBA:
EBA leads to satisficing (finding a satisfactory option) rather than optimizing (finding the best possible option).
It does not allow for attributes compensating for each other, potentially excluding better options.
Non-Optimal Outcomes: While EBA simplifies decision-making, it may prevent consumers from considering trade-offs between attributes, leading to missing out on better choices.
Value of EBA: EBA helps consumers manage information overload and quickly narrow down options in a complex marketplace.
Trade-Offs and Compromises: The EBA model can be seen as a process of prioritizing attributes and making trade-offs based on preferences.
Balancing Attributes: While EBA helps consumers eliminate options, it’s essential to recognize that some attributes might balance each other and create value in unique ways.
Application in E-commerce: EBA is particularly relevant in online shopping scenarios where consumers face a wide range of choices and need to efficiently make decisions.
Non-Optimal, Yet Practical: EBA provides practicality and efficiency in decision-making but might not lead to the best possible outcome.
Conclusion: The EBA model is a cognitive tool that helps consumers navigate the complexities of decision-making, especially in situations where a large number of options are available. However, it’s important to acknowledge its limitations in terms of achieving optimal choices.
Related Framework
Description
When to Apply
Elimination by Aspects Model
The Elimination by Aspects Model, proposed by Tversky, offers a decision-making strategy where options are evaluated based on specific attributes or aspects, and choices are eliminated sequentially if they do not meet a minimum threshold for each aspect. This iterative process continues until only one option remains, which is then selected as the final choice. The model emphasizes the importance of attribute comparisons and gradual elimination to simplify complex decisions and facilitate the selection of the most desirable option. Understanding the Elimination by Aspects Model can guide individuals in structuring decision-making processes, prioritizing decision criteria, and making informed choices across various contexts.
When making complex decisions or evaluating multiple alternatives, applying the Elimination by Aspects Model can improve decision clarity and enhance option evaluation by systematically comparing attributes, thus simplifying decision processes and facilitating optimal choices in personal decisions, professional settings, or organizational planning, ultimately empowering decision-makers and supporting effective decision outcomes through structured decision analysis and systematic option evaluation.
Expected Utility Theory
Expected Utility Theory, developed by von Neumann and Morgenstern, posits that individuals make decisions by weighing the potential outcomes of different choices and selecting the option with the highest expected utility, where utility represents the subjective value or satisfaction derived from each outcome. The theory considers both the probabilities of different outcomes and individuals’ preferences or risk attitudes to determine rational decision-making. Understanding Expected Utility Theory can assist individuals in assessing decision alternatives, considering potential gains and losses, and making choices that align with their preferences and goals.
When evaluating decision alternatives or assessing risk preferences, applying Expected Utility Theory can improve decision rationality and enhance risk management by incorporating outcome probabilities and individual preferences, thus facilitating informed choices and mitigating decision uncertainty in investment decisions, policy-making processes, or strategic planning, ultimately empowering decision-makers and supporting rational decision outcomes through quantitative decision analysis and subjective utility assessment.
Prospect Theory
Prospect Theory, developed by Kahneman and Tversky, challenges the assumptions of Expected Utility Theory by suggesting that individuals’ decision-making is influenced by psychological biases and heuristics. The theory posits that people evaluate potential gains and losses relative to a reference point and exhibit risk aversion for gains but risk-seeking behavior for losses, a phenomenon known as loss aversion. Prospect Theory also highlights the influence of framing effects on decision preferences, where the presentation of options can significantly impact decision outcomes. Understanding Prospect Theory can help individuals recognize cognitive biases, such as loss aversion and framing effects, and adjust decision strategies accordingly to make more rational and adaptive choices.
When addressing decision biases or navigating framing effects, applying Prospect Theory can improve decision awareness and enhance choice consistency by recognizing cognitive biases and adjusting decision strategies, thus mitigating irrational choices and promoting adaptive decision-making in financial planning, marketing campaigns, or policy interventions, ultimately empowering decision-makers and supporting rational decision processes through behavioral economics insights and psychological decision analysis.
Multi-Attribute Utility Theory
Multi-Attribute Utility Theory (MAUT) extends Expected Utility Theory by considering decisions involving multiple attributes or criteria, each with varying importance weights and utility functions. MAUT provides a structured framework for decision analysis, where decision-makers evaluate alternatives based on their performance across different criteria and calculate an overall utility score for each option. The theory enables individuals to systematically assess complex decision problems, prioritize decision criteria, and compare alternatives objectively. Understanding Multi-Attribute Utility Theory can support individuals in making informed decisions, especially in situations involving trade-offs among multiple attributes or conflicting objectives.
When evaluating multi-criteria decisions or assessing trade-offs, applying Multi-Attribute Utility Theory can improve decision consistency and enhance option comparison by incorporating multiple decision criteria and weighting attribute importance, thus facilitating systematic decision analysis and supporting objective choice evaluation in project selection, product design, or resource allocation, ultimately empowering decision-makers and optimizing decision outcomes through structured decision frameworks and comprehensive decision analysis.
Decision Trees
Decision Trees provide a visual representation of decision alternatives and possible outcomes, allowing decision-makers to evaluate choices based on decision criteria and uncertainty factors. Decision Trees facilitate structured decision analysis by illustrating sequential decision points, possible outcomes, and associated probabilities, enabling individuals to assess decision alternatives and their potential consequences systematically. Understanding Decision Trees can assist individuals in modeling decision problems, identifying decision pathways, and analyzing decision uncertainties to make informed and strategic choices.
When modeling decision problems or evaluating uncertain outcomes, utilizing Decision Trees can improve decision visualization and enhance risk assessment by depicting decision pathways and probability distributions, thus facilitating decision analysis and supporting strategic planning in business strategy, project management, or risk assessment, ultimately empowering decision-makers and informing decision strategies through visual decision representation and quantitative risk analysis.
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.
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 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 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 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.
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 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.
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 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, 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 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, 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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The Barnum Effect is a cognitive bias where individuals believe that generic information – which applies to most people – is specifically tailored for themselves.
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.
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 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 – 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.
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 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.
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.
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.
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 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 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 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.
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 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.”
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 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 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 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.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
