Testing Uncertainty Preferences Experimentally
Behavioral economics delves into how psychological factors influence economic decisions. A key area of study is how individuals perceive and react to uncertainty, often referred to as their 'uncertainty preferences' or 'risk preferences'. Experimentally testing these preferences allows economists to move beyond theoretical assumptions and observe real-world behavior.
Understanding Uncertainty Preferences
Uncertainty preferences describe an individual's attitude towards outcomes that are not known with certainty. These preferences are typically categorized into three main types:
- Risk Aversion: Preferring a certain outcome over a gamble with the same expected value.
- Risk Neutrality: Indifferent between a certain outcome and a gamble with the same expected value.
- Risk Seeking: Preferring a gamble over a certain outcome with the same expected value.
Experimental methods are crucial for measuring individual uncertainty preferences.
Economists design controlled experiments where participants make choices between gambles and certain payoffs. By observing these choices, researchers can infer an individual's underlying preferences.
The core of experimental testing involves presenting participants with a series of choices. These choices typically pit a certain monetary amount against a probabilistic outcome (a gamble). For instance, a participant might be asked to choose between receiving 100 and a 50% chance of receiving $0. By systematically varying the amounts and probabilities, researchers can map out an individual's utility function, which mathematically represents their preferences over wealth under uncertainty.
Common Experimental Designs
Several experimental designs are commonly used to elicit uncertainty preferences. These methods aim to isolate and measure attitudes towards risk and uncertainty in a controlled environment.
| Method | Description | Key Measurement |
|---|---|---|
| Choice Tasks (Lotteries) | Participants choose between a certain amount and a gamble with specified probabilities and payoffs. | Risk aversion/seeking coefficient |
| Bisection Method | Participants indicate indifference points between two lotteries or between a lottery and a certain amount. | Certainty equivalent |
| Bidding Games | Participants bid on lotteries, revealing their willingness to pay for them. | Subjective value of uncertain outcomes |
Eliciting Risk Preferences: A Deeper Dive
The most straightforward method involves presenting participants with a series of binary choices. Each choice typically involves a certain payoff versus a probabilistic payoff (a lottery). For example:
Choice 1: Receive 20 and a 50% chance of $0.
Choice 2: Receive 30 and a 50% chance of $0.
By observing which option a participant chooses across a range of such gambles, researchers can infer their level of risk aversion. A risk-averse individual will tend to choose the certain payoff for lower amounts but may switch to the lottery as the potential payoff increases significantly. This pattern helps in estimating parameters of utility functions, such as the coefficient of relative risk aversion (CRRA).
The concept of a utility function is central to understanding risk preferences. A utility function maps outcomes (like monetary amounts) to a level of 'utility' or satisfaction. For risk-averse individuals, the utility function is concave, meaning that each additional dollar provides less additional utility than the previous one. This diminishing marginal utility of wealth explains why people prefer a certain 0 or 100). The concavity means the utility of the expected value (utility of 0 and utility of $200).
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Challenges and Considerations
While experimental methods are powerful, they are not without challenges. These include ensuring participants understand the probabilistic nature of the choices, controlling for framing effects (how choices are presented), and accounting for individual differences in cognitive abilities or numeracy. Furthermore, preferences can be context-dependent and may vary based on the domain (e.g., financial risk vs. health risk).
The 'certainty effect' is a well-documented phenomenon where people tend to overvalue a sure outcome compared to a probabilistic one, even if the probabilistic outcome has a higher expected value.
Empirical Testing and Behavioral Theories
Experimental findings on uncertainty preferences have significantly informed and challenged traditional economic theories. For instance, the observation of widespread risk aversion led to the development of Expected Utility Theory (EUT). However, anomalies observed in experiments, such as the Allais Paradox, highlighted limitations of EUT and spurred the development of alternative models like Prospect Theory, which better accounts for how people actually make decisions under uncertainty, including the role of reference points and loss aversion.
Risk Aversion, Risk Neutrality, and Risk Seeking.
To observe real-world behavior and infer individual attitudes towards risk and uncertainty, moving beyond theoretical assumptions.
Learning Resources
Provides a comprehensive overview of Expected Utility Theory, a foundational concept in understanding decision-making under risk.
The seminal paper introducing Prospect Theory, which offers a descriptive model of decision-making under risk that deviates from Expected Utility Theory.
An accessible introduction to behavioral economics, covering key concepts and experimental approaches.
A video lecture introducing the field of experimental economics and its methodologies.
A blog post discussing various methods for eliciting risk preferences in experimental settings.
A comprehensive reference work covering a wide range of topics in experimental economics, including decision-making under uncertainty.
Lecture notes from an MIT course on microeconomic theory, detailing decision-making under uncertainty and risk preferences.
Explains the concept of risk aversion and how utility functions are used to model it.
A blog post discussing the role of experimental design in advancing behavioral economics.
Explains the Allais Paradox, a famous example that challenged the validity of Expected Utility Theory.