Expected Utility

Expected Utility is a decision-making concept that combines the value and probability of outcomes. It involves calculating expected values based on utility functions. Rational decision-makers may exhibit risk aversion or seeking behavior. Critics highlight deviations from strict rationality, leading to alternative theories like Prospect Theory. It finds applications in economics, finance, and public policy.

Understanding Expected Utility:

What is Expected Utility?

Expected utility is a fundamental concept in economics and decision theory that provides a framework for making choices in situations of uncertainty. It seeks to understand how individuals evaluate and compare different options by considering both their potential outcomes and the probability of those outcomes occurring. Expected utility theory assumes that individuals make rational decisions by maximizing their expected utility.

Key Elements of Expected Utility Theory:

  1. Utility: Utility represents the satisfaction or well-being an individual derives from a particular outcome or choice. It is a subjective measure and can vary from person to person and situation to situation.
  2. Outcomes: Expected utility theory considers the various possible outcomes associated with each choice. Outcomes can be positive (gains) or negative (losses).
  3. Probabilities: The theory incorporates probabilities to quantify the likelihood of each outcome occurring. These probabilities reflect the individual’s beliefs or assessments.
  4. Expected Utility: Expected utility is a mathematical calculation that combines the utility of each outcome with its associated probability. It represents the average or expected satisfaction an individual can anticipate from a particular choice.

Why Expected Utility Matters:

Understanding expected utility is crucial because it provides a systematic and rational approach to decision-making in situations of uncertainty. Recognizing the significance of this concept, its benefits, and its limitations is essential for individuals, economists, and policymakers seeking to make informed choices and evaluate the rationality of decisions.

The Impact of Decision-Making under Uncertainty:

  • Risk and Uncertainty: Many real-life decisions involve some level of risk and uncertainty, such as investment choices, career decisions, and healthcare options.
  • Rational Decision-Making: Expected utility theory offers a framework for rational decision-making by helping individuals weigh the potential gains and losses of each option.

Benefits of Expected Utility Theory:

  • Consistency: The theory promotes consistency in decision-making by providing a structured approach for comparing and selecting options.
  • Risk Management: Expected utility theory assists in managing risk by quantifying the potential outcomes and their likelihood.

Challenges in Expected Utility Theory:

  • Subjectivity: Utility and probability assessments can be highly subjective, leading to variations in decision outcomes among individuals.
  • Information Gaps: Making accurate probability assessments can be challenging when there is limited information or ambiguity about potential outcomes.
  • Violations of Rationality: Empirical studies have shown that people often deviate from the predictions of expected utility theory, suggesting that human decision-making may not always align with strict rationality.

Challenges in Implementing Expected Utility Theory:

Implementing expected utility theory effectively can be challenging, particularly due to the subjectivity of utility and probability assessments and the complexities of real-life decision scenarios. Recognizing and addressing these challenges is vital for responsible and practical decision-making.

Subjectivity of Utility:

  • Interpersonal Variations: Different individuals may assign different utilities to the same outcome, making it challenging to compare decisions across individuals.
  • Temporal Variations: An individual’s utility for a particular outcome may change over time or in different circumstances.

Probability Assessments:

  • Limited Information: In many cases, individuals have limited information or experience uncertainty about the probabilities of potential outcomes.
  • Overconfidence: Individuals may overestimate their ability to accurately assess probabilities, leading to suboptimal decisions.

Real-Life Complexity:

  • Multiple Factors: Real-life decisions often involve multiple factors and trade-offs beyond simple utility and probability assessments.
  • Emotional Influences: Emotional factors, such as fear, regret, or excitement, can affect decision-making and may not align with expected utility calculations.

Behavioral Deviations:

  • Behavioral Economics Findings: Empirical research in behavioral economics has uncovered systematic deviations from the predictions of expected utility theory, indicating that people do not always make decisions strictly based on expected utility.
  • Prospect Theory: Prospect theory, proposed by Daniel Kahneman and Amos Tversky, suggests that people tend to be risk-averse for gains and risk-seeking for losses, which contradicts the assumptions of expected utility theory.

Expected Utility Theory in Action:

To understand expected utility theory better, let’s explore how it can be applied in real-life scenarios and what it reveals about human decision-making.

Investment Decisions:

  • Scenario: An individual is deciding between two investment opportunities: one with a higher potential return but higher risk and another with a lower potential return but lower risk.
  • Expected Utility Theory in Action:
    • Utility Assessment: The individual assesses the utility they would derive from the potential gains and losses associated with each investment.
    • Probability Assessment: They estimate the probabilities of various outcomes, such as market fluctuations and economic conditions.
    • Expected Utility Calculation: Using expected utility calculations, they compare the two investment opportunities and choose the one that maximizes their expected satisfaction.

Medical Treatment Choice:

  • Scenario: A patient diagnosed with a serious medical condition must decide between two treatment options: one with a higher chance of success but more side effects and another with a lower chance of success but fewer side effects.
  • Expected Utility Theory in Action:
    • Utility Assessment: The patient considers the utility of potential health improvements and side effects for each treatment.
    • Probability Assessment: They estimate the probabilities of treatment success and the likelihood of experiencing side effects.
    • Expected Utility Calculation: Using expected utility calculations, they make an informed choice based on maximizing their expected well-being.

Career Decisions:

  • Scenario: A recent graduate is choosing between two job offers: one with a higher salary but longer working hours and another with a lower salary but a better work-life balance.
  • Expected Utility Theory in Action:
    • Utility Assessment: The graduate assesses the utility they would derive from salary, work hours, work-life balance, and career growth prospects.
    • Probability Assessment: They consider factors such as job stability and future career opportunities.
    • Expected Utility Calculation: Using expected utility calculations, they weigh the trade-offs and choose the job offer that maximizes their expected job satisfaction and overall well-being.

Environmental Policy:

  • Scenario: Policymakers are deciding whether to implement a new environmental policy that aims to reduce greenhouse gas emissions.
  • Expected Utility Theory in Action:
    • Utility Assessment: Policymakers consider the utility derived from reducing environmental impact, economic implications, and public opinion.
    • Probability Assessment: They estimate the probabilities of policy success, economic costs, and social acceptance.
    • Expected Utility Calculation: Using expected utility calculations, policymakers make an informed decision by maximizing expected societal well-being while considering trade-offs and risks.


In conclusion, expected utility theory offers a structured framework for rational decision-making under uncertainty, providing individuals and organizations with a systematic approach to evaluate and compare different options. Recognizing the significance of expected utility theory, understanding its benefits, and addressing its limitations is essential for responsible and informed decision-making in various aspects of life.

Case Studies

Economic Decision-Making:

Investment Choices: An individual decides between two investment opportunities. Option A offers a guaranteed 5% return, while Option B has a 50% chance of a 10% return and a 50% chance of no return. Expected Utility theory helps assess which option maximizes the individual’s utility based on their risk preferences.

Consumer Choices:

Product Selection: A consumer is choosing between two smartphones. Smartphone X has better features but is more expensive, while Smartphone Y is cheaper but has fewer features. The consumer’s expected utility depends on their preferences for features and price.


Portfolio Diversification: An investor constructs a portfolio by allocating funds to different assets, such as stocks and bonds. Expected Utility theory can guide the investor in choosing the portfolio mix that balances risk and return according to their utility function.

Public Policy:

Environmental Regulation: Government policymakers must decide on emission reduction targets for a specific industry. Expected Utility theory can help assess the expected costs and benefits of different regulatory approaches, factoring in environmental outcomes and economic impacts.


Medical Treatment Choice: A patient with a serious illness must choose between two treatment options. Treatment A has a lower chance of success but is less invasive, while Treatment B has a higher chance of success but is riskier. The patient’s choice depends on their expected utility and risk tolerance.


Insurance Premium Selection: When choosing between insurance plans, individuals consider not only the premium cost but also the expected utility associated with potential future claims. A lower premium plan may have higher deductibles and lower expected payouts.

Environmental Conservation:

Wildlife Conservation: Conservationists deciding between two conservation projects evaluate the expected utility of each. Project X aims to protect a critically endangered species, while Project Y focuses on restoring a degraded ecosystem. Expected Utility theory helps prioritize projects based on their environmental impact and cost-effectiveness.


College Selection: High school students deciding which college to attend weigh factors like tuition costs, location, academic reputation, and potential future earnings. Expected Utility theory can assist in evaluating these factors to make an informed choice.

Key Highlights

  • Decision-Making Framework: Expected Utility Theory is a foundational framework in economics and decision theory used to model how individuals make choices under uncertainty.
  • Utility Function: It introduces the concept of a utility function, which represents an individual’s preferences and quantifies the satisfaction or happiness derived from different outcomes.
  • Expected Value: The theory focuses on the concept of expected value, which calculates the average value of an uncertain outcome by multiplying each possible outcome by its probability and summing them.
  • Risk Aversion: Expected Utility Theory accounts for individuals’ risk attitudes. Risk-averse individuals prefer certain outcomes over risky ones with the same expected value.
  • Concave Utility Functions: It assumes that individuals have concave (diminishing marginal) utility functions, meaning that they are risk-averse for gains and risk-seeking for losses.
  • Comparative Analysis: The theory enables individuals to compare different choices by calculating their expected utilities, allowing them to select the option that maximizes expected utility.
  • Applications: Expected Utility Theory is widely used in economics, finance, psychology, and various fields to model and analyze decision-making in diverse scenarios, from investment choices to public policy.
  • Limitations: Critics of the theory argue that it may not accurately capture all aspects of human decision-making, especially when individuals exhibit behaviors inconsistent with the assumptions of rationality and risk aversion.
  • Normative Framework: While it provides a normative approach to decision-making, it may not always align with how individuals actually make decisions, as human behavior often deviates from the predictions of the theory.
  • Foundation for Behavioral Economics: Expected Utility Theory laid the groundwork for the development of behavioral economics, which explores deviations from rational decision-making and incorporates psychological insights into decision models.

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).

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.

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.

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.


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.

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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