Behavioral Economics

Abstract

Behavioral economics is a branch of psychology and economics that studies how real people make economic choices, and why those choices depart systematically from the predictions of rational-agent models. Where standard theory assumes agents who maximize expected utility with unlimited computational power, behavioral economics documents bounded rationality: judgments shaped by heuristics, choices that reverse with framing, valuations anchored to reference points, and preferences that shift as rewards approach in time. Its foundational contribution is prospect theory, which replaces the utility curve with a value function defined over gains and losses, steeper for losses than for gains. The field has grown from a catalogue of anomalies into a predictive science with applications in finance, health, and public policy, though the reliability and size of its effects remain under active scrutiny.

Keywords: prospect theory, loss aversion, bounded rationality, framing, nudge

Classical economics is built on a convenient fiction: homo economicus, an agent with stable preferences, unlimited computing power, and no failures of self-control. This idealization makes for tractable mathematics, and for a long time its predictive successes were taken as license to ignore its psychological implausibility. Behavioral economics is the systematic study of what happens when that fiction is replaced by an accurate account of human cognition. It begins from the observation, formalized by Herbert Simon, that the mind is not an unlimited optimizer but a bounded one, which settles for options that are good enough rather than searching exhaustively for the best (Simon, 1955). The National Library of Medicine's Medical Subject Headings classifies the field under psychology, reflecting that its methods and its central discoveries are psychological, even as its subject matter is the economy.

What Behavioral Economics Is

The rational-agent model that behavioral economics revises is expected utility theory, the standard account of choice under risk. It holds that a decision-maker assigns a utility to each outcome, weights those utilities by their probabilities, and chooses the option with the highest expected utility, with preferences that are complete, transitive, and independent of how the options are described. The theory is normatively compelling: an agent who violates its axioms can, in principle, be led into a sequence of trades that loses money for certain. Yet as a description of behavior it fails in patterned, reproducible ways. People treat a certain outcome as categorically different from a merely probable one, respond to the way a choice is worded, and let sunk costs and arbitrary reference points drive decisions that the axioms say should be irrelevant.

Behavioral economics does not abandon the idea that people pursue their goals. It replaces the assumption of unbounded rationality with bounded rationality: cognition constrained by limited attention, memory, and computation, operating through heuristics that are efficient but occasionally biased. The research program that made this concrete was Amos Tversky and Daniel Kahneman's study of judgment under uncertainty, which showed that intuitive estimates of probability and frequency rest on a small set of mental shortcuts, such as availability and representativeness, whose signatures are predictable errors (Tversky & Kahneman, 1974). The result reframed error itself: a bias is not random noise but a window onto the mechanism that produced it. This is the same logic that connects the field to the broader psychology of judgment and decision making.

Table 1 sets the two accounts side by side. Each row names an assumption of the rational-agent model and the behavioral revision that replaces it, and each revision is the subject of a later section.

Prospect Theory

The theoretical heart of behavioral economics is prospect theory, introduced by Kahneman and Tversky as a descriptive alternative to expected utility (Kahneman & Tversky, 1979). It differs from the standard model in three respects, each grounded in how people actually evaluate outcomes. First, value is defined over changes in wealth, gains and losses relative to a reference point, not over final states. Second, the value function is concave for gains and convex for losses, so that each additional dollar matters less as the stakes grow, the same diminishing sensitivity that governs perception. Third, and most consequentially, the function is steeper for losses than for gains: the pain of losing an amount exceeds the pleasure of gaining it. This asymmetry is loss aversion, and the ratio of the two slopes, the loss-aversion coefficient, is empirically close to two.

Prospect theory also revised how probabilities enter the calculation. The motivating anomaly long predated the theory: Maurice Allais had shown that most people's choices between certain pairs of gambles violate the independence axiom of expected utility, preferring certainty so strongly that the pattern cannot be reconciled with any linear treatment of probability, a result now known as the Allais paradox (Allais, 1953). Prospect theory answers it directly. Rather than weighting outcomes by their stated probabilities, people apply a nonlinear weighting function that overweights small probabilities and underweights moderate to large ones, which explains why the same person buys both lottery tickets and insurance. The 1992 revision, cumulative prospect theory, extended the model to gambles with many outcomes and to uncertainty as well as risk, applying the weighting to cumulative rather than individual probabilities and giving the theory the rank-dependent form that made it tractable for economic modeling (Tversky & Kahneman, 1992). The interactive below manipulates the value function directly, showing how the reference point and the loss-aversion coefficient reshape the evaluation of a gamble.

Figure 1. The value function is concave for gains and convex for losses, and steeper on the loss side by the factor λ. An even-money 50/50 bet of +$100 / −$100 has zero expected money, yet whenever λ > 1 its expected value is negative, so a loss-averse chooser turns it down. Lower λ toward 1 and the same bet becomes acceptable; shifting the reference point re-centres what counts as a gain or a loss.

Loss aversion has consequences that reach well beyond the laboratory gamble. It underlies the endowment effect, the tendency to demand more to give up a good than one would pay to acquire it, demonstrated when owners of a mug valued it at roughly twice the price buyers were willing to offer (Kahneman et al., 1990). It produces the status quo bias, a general stickiness of default options, because moving away from any reference state converts forgone gains into felt losses (Kahneman et al., 1991). And it clarified a long-standing puzzle in finance: the equity premium, the large historical excess return of stocks over bonds, is explained if investors evaluate their portfolios frequently and feel each short-run loss acutely, a combination Shlomo Benartzi and Richard Thaler named myopic loss aversion (Benartzi & Thaler, 1995).

The power of these predictions exposes the theory's central unfinished question: what fixes the reference point in the first place? Prospect theory took it as given, usually the status quo, but a reference point that can be anything threatens to make the model unfalsifiable. Botond Kőszegi and Matthew Rabin closed much of this gap by deriving the reference point from a person's rational expectations about outcomes, so that what counts as a loss is defined by what one had expected to receive rather than by an arbitrary baseline, turning reference dependence into a disciplined, predictive theory (Kőszegi & Rabin, 2006).

Heuristics, Framing, and Mental Accounting

Because value attaches to changes from a reference point, the description of a choice, which fixes that reference, can reverse the choice itself. In the best-known demonstration, respondents chose between public-health programs to combat a disease expected to kill 600 people. When the options were framed as lives saved, most preferred the sure option; when the identical outcomes were framed as lives lost, most preferred the gamble, because a certain loss is especially aversive (Tversky & Kahneman, 1981). The framing violated a bedrock axiom of rational choice, invariance: a preference should not depend on how equivalent outcomes are labeled. This sensitivity to description is one of the clearest links between behavioral economics and the broader study of framing effects. The interactive below reconstructs the disease problem and toggles between the two frames.

An outbreak is expected to kill 600 people. Two programs are proposed:

Figure 2. Both programs save 200 in expectation, and the two frames are the same arithmetic. Yet when outcomes are framed as gains people are risk-averse and take the sure survival; when the identical outcomes are framed as losses they turn risk-seeking to avoid the certain death. Flipping the toggle flips the majority choice, the signature preference reversal of framing.

Richard Thaler extended this reference-dependent logic into a systematic account of consumer behavior. People keep mental accounts, treating money as non-fungible depending on where it came from or what it is designated for, so that a windfall is spent more freely than earned income and a loss in one account is not offset against a gain in another (Thaler, 1980). Mental accounting explains why a household will simultaneously carry high-interest credit-card debt and a low-interest savings account, or drive across town to save a few dollars on a small purchase but not on a large one. These are not random errors; they follow from the same value function that generates loss aversion, applied to how outcomes are bracketed and booked.

Not every theorist reads the evidence as a catalogue of irrationality. Gerd Gigerenzer has argued that fast-and-frugal heuristics are not defective approximations to optimization but adaptive tools matched to the structure of real environments, so that a simple rule can outperform a complex model when information is scarce or uncertain, a position known as ecological rationality (Gigerenzer & Gaissmaier, 2011). On this view the heuristics-and-biases program and the ecological-rationality program describe the same machinery from opposite ends: the first emphasizes where shortcuts fail, the second where they succeed. The tension is unresolved and productive, and it keeps the field honest about when a departure from the rational model is a bug and when it is a feature.

A further departure concerns the assumption of pure self-interest. In the ultimatum game, one player proposes how to divide a sum of money and the second either accepts, letting both keep their shares, or rejects, leaving both with nothing. A narrowly self-interested responder should accept any positive offer, yet responders reliably reject offers they judge unfair, and proposers anticipate this by offering close to an even split (Güth et al., 1982). The finding launched the study of social preferences, showing that fairness and reciprocity enter the value people place on outcomes directly, and it is the empirical foundation for the formal models of fairness that later carried social motives into mainstream economic theory.

Intertemporal Choice and Self-Control

A second major departure from the standard model concerns choices that play out over time. Rational agents are assumed to discount future rewards exponentially, at a constant rate, which keeps their preferences consistent: an option preferred today is still preferred when both its costs and benefits recede equally into the future. Real preferences do not behave this way. People discount the near future far more steeply than the distant future, a pattern captured by hyperbolic discounting, so that a smaller-sooner reward can be preferred over a larger-later one only when the sooner reward is nearly at hand (Laibson, 1997). The result is dynamic inconsistency: plans made calmly for the future are overturned when the moment of temptation arrives.

This present bias, formalized by Ted O'Donoghue and Matthew Rabin as a tendency to give disproportionate weight to immediate costs and benefits, is the engine behind procrastination, undersaving, and failures of self-control, and it distinguishes people who are naive about their own bias from those sophisticated enough to plan around it (O'Donoghue & Rabin, 1999). A comprehensive review by Shane Frederick, George Loewenstein, and O'Donoghue showed that the single discount rate of textbook models cannot accommodate the evidence, and that time preference is better understood as a bundle of distinct psychological motives (Frederick et al., 2002). The interactive below contrasts exponential and hyperbolic discounting and shows how the hyperbolic curve produces a preference reversal as time passes.

Figure 3. Both rewards are discounted toward the present as the vantage point (red line) slides forward. Under exponential discounting the two present-value curves never cross, so the preference is constant. Under hyperbolic discounting they do cross: from a distance the larger-later $120 wins, but as the sooner reward comes within reach its present value shoots up and overtakes, and the chooser reverses to the smaller-sooner $100, the hallmark of present bias.

The steepness of discounting is not merely a laboratory curiosity but a stable individual difference with clinical weight. A meta-analysis across psychiatric conditions found that steep delay discounting is elevated transdiagnostically, in addictions, mood disorders, and more, suggesting that excessive devaluation of the future is a process cutting across diagnostic categories rather than a symptom of any one (Amlung et al., 2019).

Neuroeconomics and Mechanism

If preferences depart from the rational model in patterned ways, those patterns should have a physical basis in the brain, and neuroeconomics set out to find it. An early and influential result used brain imaging to test whether present bias reflects a single valuation system or a competition between two. Samuel McClure and colleagues reported that choices involving an immediately available reward preferentially engaged limbic and paralimbic regions associated with the dopamine system, while all intertemporal choices engaged prefrontal and parietal regions, a dissociation consistent with two interacting systems rather than one uniform discounter (McClure et al., 2004). The finding gave a neural interpretation to the tug-of-war between patience and temptation, and connected economic choice to the brain's reward processing circuitry.

A broader framework followed, treating value-based decision making as a sequence of computational stages, representing the options, valuing them, selecting an action, and learning from the outcome, each with candidate neural substrates (Rangel et al., 2008). This decomposition let economic constructs such as expected value, risk, and reference dependence be mapped onto measurable brain signals, turning abstract preference parameters into quantities with neural correlates. Reviewing the maturing field, Cary Frydman and Colin Camerer argued that neuroscience does more than localize known effects: measures of neural and physiological state can improve predictions of financial behavior and adjudicate between competing psychological models that make identical choice predictions (Frydman & Camerer, 2016). The mechanistic turn also drew on the psychology of dual-process theory, the idea that fast, automatic processes and slow, deliberate ones jointly govern judgment.

Applications, Debates, and Reliability

The policy application that carried behavioral economics into public life is the nudge: a change in the choice architecture, the way options are presented, that steers behavior predictably without forbidding any option or materially changing incentives. Defaulting employees into a pension plan, for instance, dramatically raises participation precisely because the status quo bias and present bias that the field documents make the default sticky. Ralph Hertwig and Till Grüne-Yanoff distinguished this steering approach from an alternative they call boosting, which aims to build people's own competence to decide well rather than to engineer their environment, and argued that the two rest on different assumptions about human rationality and different ethics of intervention (Hertwig & Grüne-Yanoff, 2017).

The field's rapid success provoked a necessary reckoning with the robustness of its findings. When David Gal and Derek Rucker challenged loss aversion directly, arguing that many demonstrations are fragile, context-dependent, or artifacts of design, they forced a sharper statement of when the effect holds (Gal & Rucker, 2018). The reply, from Kristian Mrkva and colleagues, granted that loss aversion has moderators, it grows with stakes and varies with individual and cultural factors, while showing that the core asymmetry is real and that reports of its death were exaggerated (Mrkva et al., 2020). This is the field correcting itself in public, and it extends to prospect theory as a whole: a large multi-laboratory effort replicated the theory's canonical patterns of risk preference across nineteen countries, establishing that the basic phenomena generalize even as their parameters vary (Ruggeri et al., 2020).

That reappraisal is part of the wider replication movement in the social sciences, which tested whether landmark experimental results reproduce. A systematic replication of social-science studies published in Nature and Science found that a substantial fraction did replicate, though at smaller effect sizes than originally reported, and that researchers could partly predict which would survive (Camerer et al., 2018). The message for behavioral economics is not that its foundations are unsound but that its effect sizes must be measured, not assumed, a discipline the field has increasingly adopted.

Current Directions

The most active current work is quantitative rather than conceptual: pinning down how large the field's signature effects actually are, and under what conditions. The clearest example is a comprehensive meta-analysis of loss aversion by Alexander Brown, Taisuke Imai, Ferdinand Vieider, and Camerer, which synthesized hundreds of estimates and found a mean loss-aversion coefficient in the neighborhood of the classic value of two, while documenting wide variation across studies, populations, and elicitation methods (Brown et al., 2024). Rather than asking whether loss aversion exists, such work asks how much, for whom, and measured how, the questions of a mature science.

Three further fronts are open. The moderator program, extending the Gal-Rucker debate, is charting the boundary conditions of loss aversion and other anomalies, replacing single universal constants with structured accounts of when effects strengthen or vanish. The methodological front continues to press replication, pre-registration, and larger, more diverse samples, correcting the field's early reliance on small studies and narrow populations. And the mechanistic front, building on neuroeconomics, is testing whether physiological and neural measures can improve out-of-sample prediction of real financial and health behavior, moving the field from explaining choices after the fact toward forecasting them. Across all three, the direction is the same: from a catalogue of ways the rational model fails toward a quantitative, mechanistic account of how choices are actually made.

Discussion

Behavioral economics occupies an unusual position among the sciences of choice: it began as a critique and matured into a research program without ever settling the question its critique raised. The rational-agent model it revises is not simply wrong; it remains the normative benchmark against which every documented anomaly is measured, and the field's central constructs — reference dependence, loss aversion, present bias — are defined precisely as departures from it. This is a strength and a vulnerability at once. It gives the field a rigorous scaffold, but it ties every result to a baseline whose own descriptive adequacy was never the point.

The deepest unresolved issues are conceptual rather than empirical. Prospect theory's reference point, sharpened by the expectations-based account of Kőszegi and Rabin, remains the field's central free parameter: a theory that predicts loss aversion is only as disciplined as its account of what counts as a loss. The debate between the heuristics-and-biases and ecological-rationality traditions is similarly unclosed, because the two camps disagree less about the phenomena than about whether a systematic deviation from optimization should be read as a defect or an adaptation. And the replication reckoning has shown that the field's effects are real but smaller and more context-dependent than its early confidence implied, which shifts the burden from demonstrating that an anomaly exists to specifying the conditions under which it holds.

What behavioral economics has established beyond serious dispute is that description-independent, time-consistent, computationally unbounded choice is not how humans actually decide, and that the departures are systematic enough to model and to exploit. The open frontier is whether those models can move from explanation to prediction — from accounting for choices after they are made to forecasting them out of sample — and whether the mechanistic constructs of neuroeconomics can give the behavioral parameters a causal, rather than merely descriptive, footing. On that question the field is still, productively, unfinished.

Key Researchers

Colin F. Camerer (b. 1959). Caltech behavioral economist who founded behavioral game theory and helped establish neuroeconomics, using brain imaging to trace the computations behind value-based choice and leading large-scale replication efforts. Wikipedia - Google Scholar - ORCID - Faculty

Gerd Gigerenzer (b. 1947). Psychologist at the Max Planck Institute for Human Development who developed the ecological-rationality program, arguing that fast-and-frugal heuristics are adaptive tools rather than mere biases. Wikipedia - Google Scholar - ORCID - Faculty

Daniel Kahneman (1934–2024). Psychologist who, with Tversky, created the heuristics-and-biases program and prospect theory; awarded the 2002 Nobel Memorial Prize in Economic Sciences for integrating psychology into economic science. Wikipedia - Google Scholar - Britannica

Botond Kőszegi (b. 1973). Economist who, with Rabin, gave reference-dependent preferences a rigorous theoretical foundation by deriving reference points from rational expectations. Wikipedia - Google Scholar - ORCID - Faculty

David I. Laibson (b. 1966). Harvard economist who formalized quasi-hyperbolic discounting, showing how present bias generates self-control problems in saving and consumption. Wikipedia - Google Scholar - Faculty

George Loewenstein (b. 1955). Carnegie Mellon economist and psychologist, a founder of behavioral economics and neuroeconomics, known for work on intertemporal choice and the visceral, affective drivers of decisions. Wikipedia - Google Scholar - ORCID - Faculty

Ted O'Donoghue (b. 20th c.). Cornell economist who, with Rabin, modeled present-biased preferences and the distinction between naive and sophisticated responses to one's own self-control problems. Google Scholar - Wikidata - Faculty

Matthew Rabin (b. 1963). Harvard economist who built formal psychological-economics models of fairness, reference dependence, and present bias, bringing psychological realism into mainstream theory. Wikipedia - Google Scholar - Faculty

Herbert A. Simon (1916–2001). Polymath who introduced bounded rationality and satisficing, the intellectual seed of behavioral economics; a Nobel laureate in economics and a Turing Award winner in artificial intelligence. Wikipedia - Britannica - Faculty

Cass R. Sunstein (b. 1954). Harvard legal scholar who, with Thaler, developed nudge theory and libertarian paternalism, translating behavioral findings into a framework for policy. Wikipedia - Google Scholar - ORCID - Faculty

Richard H. Thaler (b. 1945). Chicago economist who developed mental accounting, documented the endowment effect, and co-created nudge theory; awarded the 2017 Nobel Memorial Prize in Economic Sciences. Wikipedia - Google Scholar - Faculty

Amos Tversky (1937–1996). Cognitive psychologist and Kahneman's collaborator, co-creator of the heuristics-and-biases program and prospect theory, whose work reshaped the study of judgment under uncertainty. Wikipedia - Britannica

Worked Example

Prospect theory predicts that a fair gamble, one with zero expected monetary value, will still be rejected, because losses are weighted more heavily than gains. Consider a coin flip that wins $100 on heads and loses $100 on tails. Its expected monetary value is zero, so a risk-neutral expected-utility agent is indifferent. A loss-averse agent is not.

Take the standard value function with diminishing-sensitivity exponent α = 0.88 and loss-aversion coefficient λ = 2.25, the parameters Tversky and Kahneman estimated in 1992. The subjective value of the gain is v($100) = 100^0.88 = 57.5. The subjective value of the loss is v(−$100) = −2.25 × 100^0.88 = −129.5, more than twice as large in magnitude. Weighting each outcome by one-half, the gamble is worth 0.5 × 57.5 + 0.5 × (−129.5) = −36.0 in value units. Because this is negative, the agent declines a bet that a rational actor finds fair.

The same arithmetic answers the inverse question: how large must the winning prize be before the gamble becomes attractive? Setting the value of the gamble to zero and solving for the gain G gives G = (λ × 100^0.88)^(1/0.88) = $251. Under the simpler assumption of linear value with loss aversion alone (α = 1), the break-even prize is exactly λ × $100 = $225. Either way the prediction is striking and confirmed in the laboratory: people typically require a potential gain of roughly $200 to $250 before they will accept the risk of losing $100. The factor of about two between the amount that must be won and the amount at risk is loss aversion made quantitative.

Frequently Asked Questions

Is behavioral economics the same as behavioral finance? No, though they overlap heavily. Behavioral economics is the general study of psychologically realistic economic choice; behavioral finance is its application to financial markets specifically, explaining phenomena such as the equity premium, bubbles, and investor overtrading using the same tools of loss aversion, mental accounting, and biased belief formation.

Does behavioral economics claim people are irrational? Not exactly. It claims people are boundedly rational: they pursue their goals with limited attention, memory, and computation, using heuristics that work well in many settings but produce systematic errors in others. Some researchers, notably in the ecological-rationality tradition, argue that many so-called biases are in fact adaptive responses to real-world uncertainty.

What is the single most important idea in the field? Loss aversion, the finding that losses loom larger than equivalent gains, is the most consequential single result. It anchors prospect theory and explains the endowment effect, the status quo bias, and myopic loss aversion in financial markets. Its typical magnitude is a weighting ratio of about two to one.

How does behavioral economics differ from standard economics? Standard economics assumes agents maximize expected utility with stable, description-independent preferences and unlimited computational power. Behavioral economics keeps the idea of goal-directed choice but replaces those assumptions with reference-dependent value, nonlinear probability weighting, present-biased time preference, and heuristic judgment.

What is a nudge? A nudge is a change in the way choices are presented, the choice architecture, that predictably alters behavior without forbidding options or significantly changing economic incentives. Automatically enrolling employees in a retirement plan while letting them opt out is the canonical example; it exploits the stickiness of defaults.

Is loss aversion a real and reliable effect? The core asymmetry is robust, but its size depends on context, stakes, and how it is measured. A vigorous debate beginning in 2018 questioned its universality; subsequent meta-analysis confirmed a mean coefficient near the classic value of two while documenting substantial variation and genuine moderators.

What is present bias? Present bias is the tendency to overweight immediate costs and benefits relative to future ones, producing preferences that reverse as a reward draws near. It is modeled by hyperbolic or quasi-hyperbolic discounting and explains procrastination, undersaving, and other failures of self-control that a constant discount rate cannot.

How is behavioral economics tested in the brain? Neuroeconomics uses brain imaging and physiological measures to identify the neural systems that compute value, weigh risk, and resolve conflicts between immediate and delayed rewards. Findings such as the differential engagement of limbic and prefrontal systems in immediate versus delayed choice give present bias a mechanistic interpretation.

Glossary

Allais paradox.

A pattern of choices between gambles that violates the independence axiom of expected utility, driven by an overweighting of certainty; the motivating anomaly for nonlinear probability weighting.

Anchoring.

The tendency for an initial value, even an arbitrary or irrelevant one, to bias subsequent numerical judgments and valuations toward it.

Bounded rationality.

Herbert Simon's concept that decision-makers are constrained by limited information, attention, and computation, and so satisfice rather than optimize.

Choice architecture.

The design of the environment in which choices are presented, including defaults, ordering, and framing, which influences decisions without changing the options themselves.

Decision making.

The cognitive process of selecting a course of action among alternatives, the core behavior behavioral economics seeks to describe.

Delay discounting.

The devaluation of a reward as the delay to receiving it increases; steep discounting indicates a strong preference for immediate reward.

Dual-process theory.

The view that judgment arises from a fast, automatic system and a slow, deliberate system operating together.

Endowment effect.

The tendency to value a good more highly once one owns it, so that the selling price demanded exceeds the buying price offered; a consequence of loss aversion.

Expected utility theory.

The normative model of choice under risk in which an agent maximizes probability-weighted utility over outcomes.

Framing effect.

A change in preference caused solely by how logically equivalent options are described, as in the gains-versus-losses wording of a decision.

Heuristic.

A mental shortcut that reduces a complex judgment to a simpler operation, efficient in general but prone to systematic bias.

Hyperbolic discounting.

A pattern of time preference in which near-term delays are discounted far more steeply than distant ones, producing preference reversals over time.

Loss aversion.

The finding that losses are weighted more heavily than equivalent gains, by a factor of roughly two; the central asymmetry of prospect theory.

Mental accounting.

The tendency to treat money as non-fungible, sorting it into separate notional accounts by source or purpose and evaluating gains and losses within each.

Nudge.

An adjustment to choice architecture that steers behavior in a predictable direction without forbidding options or changing incentives materially.

Present bias.

The disproportionate weighting of immediate costs and benefits relative to future ones, the engine of procrastination and undersaving.

Prospect theory.

The descriptive theory of choice under risk in which value is defined over gains and losses from a reference point, with a steeper slope for losses.

Reference point.

The baseline, often the status quo or an expectation, against which outcomes are coded as gains or losses.

Satisficing.

Choosing the first option that meets an acceptability threshold rather than searching for the optimum; Simon's alternative to maximization.

Ultimatum game.

A two-player bargaining experiment in which responders reject unfair offers despite the cost to themselves; the foundational demonstration of social preferences.

References