Behavioral economics - Core Theories of Decision Making

Behavioral Economics: Bounded Rationality and Prospect Theory Introduction Traditional economic theory assumes that people make decisions rationally—that they carefully consider all available information, calculate expected utilities perfectly, and maximize their satisfaction. However, real-world decision-making is messier than this ideal. Two major frameworks have reshaped how economists and psychologists understand human choices: bounded rationality and prospect theory. Together, they explain why people often deviate from the perfectly rational model, and more importantly, they help predict how people actually make decisions under uncertainty and with limited information. Bounded Rationality What is Bounded Rationality? Herbert A. Simon coined the term bounded rationality to capture a simple but profound insight: human decision-making is constrained by cognitive limitations, the time available to decide, and the complexity of the problems we face. Rather than assuming people are perfectly rational agents with unlimited computational power, Simon argued that people are limited in their ability to process information and think through all possible alternatives. Bounded rationality replaces the classical assumption of unlimited rational optimization with a more realistic model. People don't—and can't—consider every possible option and calculate which one maximizes their utility. Instead, they work within the boundaries imposed by their minds and their circumstances. The Satisficing Principle Given these limitations, how do people actually make decisions? The answer is through satisficing: accepting a solution that is "good enough" rather than searching endlessly for the optimal one. Think of it this way: imagine you're job hunting. The perfectly rational decision-maker would evaluate every possible job in the economy, compare them all to determine which offers the highest utility, and then accept that one. But this is obviously impractical. Instead, you search through job postings, and once you find a position that meets your criteria—good salary, interesting work, reasonable commute—you accept it. This job may not be the absolute best job available anywhere, but it satisfies your needs, and you've saved yourself months or years of searching. The key insight is that satisficing reduces the costs of deliberation and search. By accepting a satisfactory solution, you avoid the enormous burden of finding the optimal one. This is not a flaw in human reasoning—it's actually quite rational given that information gathering and decision-making themselves consume time and cognitive effort. Applications in Organizations and Policy The bounded rationality framework has important implications beyond individual decisions. Cyert and March studied how firms actually make decisions and found that organizations function as coalitions of different groups (managers, workers, shareholders) with partly conflicting goals. Rather than maximizing profits in some abstract sense, firms pursue satisficing goals—they aim for "satisfactory" profit levels, market share, and growth. This explains why firms don't constantly restructure themselves seeking maximum efficiency; instead, they settle into relatively stable patterns that work adequately. In the realm of public policy and choice design, Richard Thaler and Cass Sunstein have developed the concept of "nudges"—ways of structuring choices to accommodate bounded rationality and guide people toward better decisions. A famous example: placing healthier foods at eye level in cafeterias increases healthy eating without forbidding any choices. This recognizes that people have limited attention and willpower, so the architecture of choice matters enormously. Rather than expecting people to be perfectly rational calculators, smart choice design works with human limitations rather than against them. Prospect Theory Overview and Historical Foundation While bounded rationality explains how people cope with complexity and limited time, prospect theory (developed by Daniel Kahneman and Amos Tversky in 1979) explains something more specific: how people evaluate choices involving risk and uncertainty. Prospect theory describes the actual psychological processes people use when facing decisions like "Should I buy insurance?" or "Should I accept a gamble?" Kahneman and Tversky discovered through experiments that people systematically violate the predictions of expected utility theory—the standard economic model. Their key insight was that losses loom much larger than gains. A person who loses $100 feels worse than they'd feel good about winning $100. This asymmetry is not just a small deviation from rational behavior; it's profound and shapes countless decisions. The Two-Stage Process Prospect theory proposes that people evaluate risky choices in two stages: Editing Stage: First, people simplify the problem. They might round numbers, ignore small probability differences, or frame the problem in terms of gains or losses relative to a reference point. This is where cognitive shortcuts do important work, reflecting bounded rationality. For example, if you're considering buying insurance, you might simplify the calculation to "How much would losing my house cost me?" rather than computing precise expected values. Evaluation Stage: Next, people assess the simplified alternatives using a special value function that differs markedly from the standard economic model. This is where the key behavioral insights emerge. Reference Dependence and Loss Aversion The foundation of prospect theory is reference dependence: outcomes are not evaluated in absolute terms but relative to a reference point. The reference point is typically the status quo—what you currently have or expect. An outcome above this reference point is experienced as a gain; an outcome below it is experienced as a loss. Critically, the same change in wealth feels completely different depending on whether it's framed as a gain or a loss. The most important empirical finding is loss aversion: losses are weighted roughly 2.25 times more heavily than equivalent gains in terms of their emotional and motivational impact. If you'd feel 10 units of distress losing $100, you'd only feel about 4.4 units of joy gaining $100. This explains many puzzling behaviors. For instance, investors often hold losing investments too long, hoping to break even (avoiding the loss), even when selling and reinvesting elsewhere would maximize their long-term wealth. The pain of accepting the loss weighs heavier than the rational calculation suggests it should. Probability Weighting and Diminishing Sensitivity Prospect theory also describes how people weight probabilities—and here too, they deviate systematically from rationality. Probability weighting refers to how people transform objective probabilities into subjective weights that guide their decisions. People tend to overweight small probabilities and underweight large ones, creating an inverse-S shaped weighting function. For example, the difference between a 1% and 2% probability feels very large (doubling!), so people overweight these small probabilities. This is why people buy lottery tickets (small probability of large gain) or excessive insurance against rare disasters. Conversely, the difference between a 98% and 99% probability feels relatively small, so people underweight the difference. This is why people are often casual about risks when probabilities are already high. The second important principle is diminishing sensitivity: the subjective value difference between outcomes declines as the absolute amounts get larger. The difference between gaining $0 and $100 feels enormous, but the difference between gaining $1,000 and $1,100 feels minimal, even though both represent a 10% increase. Mathematically, this makes the value function concave for gains (each additional dollar is worth less) and convex for losses (each additional loss hurts less as the absolute amount grows larger). Importantly, the value function is steeper for losses than for gains. This steepness for losses is another manifestation of loss aversion: changes near the reference point (the boundary between gains and losses) matter most. <extrainfo> Cumulative Prospect Theory Kahneman and Tversky later refined their framework in cumulative prospect theory (1992), which removed the editing phase and reformulated probability weighting to apply cumulatively across outcomes ranked by value. This technical refinement resolved some mathematical inconsistencies in the original theory, but the core insights about reference dependence, loss aversion, probability weighting, and diminishing sensitivity remain unchanged and equally important for understanding behavior. </extrainfo> Connecting the Concepts Bounded rationality and prospect theory work together to explain real economic behavior. Bounded rationality explains why people use simplified decision processes—they have limited cognitive capacity and must make choices quickly. Prospect theory explains how people make those simplified calculations, revealing systematic patterns in how they evaluate gains, losses, and probabilities. Together, these frameworks show that departures from perfect rationality are not random errors but predictable, systematic biases that follow psychological principles. This understanding has revolutionized economics, finance, and public policy, making it possible to design better choices and predict behavior more accurately.