🎱Game Theory Unit 12 – Behavioral Game Theory: Bounded Rationality

What's This Unit About?

• Focuses on the concept of bounded rationality, a key aspect of behavioral game theory
• Explores how individuals make decisions in real-world scenarios with limited information, cognitive constraints, and time pressure
• Examines various models and frameworks that attempt to capture bounded rationality in decision-making processes
• Discusses the role of uncertainty and how it influences the choices made by players in a game
• Introduces behavioral biases and heuristics that individuals rely on when making decisions under bounded rationality
• Provides real-world applications of bounded rationality in fields such as economics, psychology, and political science
• Addresses critiques and limitations of the bounded rationality approach in game theory

Key Concepts and Definitions

• Bounded rationality: the idea that individuals make decisions based on limited information, cognitive constraints, and time pressure, rather than perfect rationality
• Satisficing: the process of making decisions that are "good enough" rather than optimal, given the constraints faced by the decision-maker
• Heuristics: mental shortcuts or rules of thumb that individuals use to simplify complex decision-making processes
• Cognitive biases: systematic errors in judgment and decision-making that arise from the use of heuristics and other mental shortcuts
• Uncertainty: the lack of complete information about the outcomes or probabilities associated with different choices in a decision-making scenario
• Adaptive behavior: the ability of individuals to adjust their decision-making strategies based on feedback and experience over time
• Aspiration levels: the minimum level of satisfaction or utility that an individual aims to achieve when making decisions under bounded rationality

Bounded Rationality Basics

• Bounded rationality challenges the assumption of perfect rationality in traditional game theory
• Recognizes that individuals have limited cognitive abilities, time, and information when making decisions
• Suggests that people use simplified models of the world to make decisions, rather than considering all possible options and outcomes
• Emphasizes the role of satisficing, where individuals aim to make decisions that are "good enough" given their constraints
• Highlights the importance of adaptive behavior, as individuals learn from their experiences and adjust their decision-making strategies over time
• Introduces the concept of aspiration levels, which represent the minimum level of satisfaction or utility that individuals aim to achieve
• Argues that the use of heuristics and mental shortcuts is a natural response to the complexity of real-world decision-making scenarios

Models of Bounded Rationality

• Simon's Satisficing Model: proposes that individuals search for alternatives until they find one that meets their aspiration level, rather than seeking the optimal solution
  • Introduces the concept of search costs and the trade-off between the quality of a decision and the time and effort required to make it
• Gigerenzer's Fast and Frugal Heuristics: suggests that individuals use simple, easy-to-compute heuristics to make decisions under time pressure and limited information
  • Examples include the recognition heuristic (choosing the most familiar option) and the take-the-best heuristic (focusing on the most important cue)
• Kahneman and Tversky's Prospect Theory: describes how individuals make decisions under risk and uncertainty, focusing on the role of reference points and loss aversion
  • Explains the tendency for people to overweight small probabilities and underweight large probabilities
• Selten's Aspiration Adaptation Theory: proposes that individuals adjust their aspiration levels based on their experiences and the feedback they receive from the environment
  • Suggests that people are more likely to take risks when their current performance falls below their aspiration level
• Rubinstein's Modeling Bounded Rationality: introduces a formal framework for incorporating bounded rationality into game-theoretic models
  • Allows for the analysis of strategic interactions between players with limited cognitive abilities and information-processing capacities

Decision-Making Under Uncertainty

• Uncertainty refers to situations where individuals lack complete information about the outcomes or probabilities associated with different choices
• Bounded rationality becomes particularly relevant in the presence of uncertainty, as individuals must rely on heuristics and mental shortcuts to make decisions
• Ambiguity aversion: the tendency for individuals to prefer options with known probabilities over those with unknown probabilities, even when the expected value is the same
• Ellsberg Paradox: demonstrates how individuals violate the axioms of expected utility theory when faced with ambiguity
• Uncertainty can lead to the use of decision-making strategies that prioritize robustness and adaptability over optimality
• Individuals may engage in information-seeking behavior to reduce uncertainty, but this process is subject to cognitive limitations and search costs
• The presence of uncertainty can also influence the formation and updating of beliefs, as individuals may rely on biased or incomplete information to make judgments

Behavioral Biases and Heuristics

• Anchoring: the tendency for individuals to rely heavily on the first piece of information they receive (the "anchor") when making judgments or estimates
• Availability heuristic: the tendency to overestimate the likelihood of events that are easily remembered or come to mind quickly
• Confirmation bias: the tendency to seek out and interpret information in a way that confirms one's preexisting beliefs or hypotheses
• Framing effect: the way in which a decision problem is presented (or "framed") can influence the choices made by individuals
  • Example: presenting a policy in terms of lives saved vs. lives lost can lead to different preferences
• Overconfidence: the tendency for individuals to overestimate their own abilities, knowledge, or chances of success
• Representativeness heuristic: the tendency to judge the likelihood of an event based on how closely it resembles a typical or representative case
• Status quo bias: the tendency to prefer the current state of affairs and resist change, even when better alternatives are available

Applications in Real-World Scenarios

• Behavioral finance: applies insights from bounded rationality to understand investor behavior and market anomalies
  • Explains phenomena such as the disposition effect (holding losing investments too long and selling winning investments too soon) and the equity premium puzzle (the higher-than-expected returns on stocks relative to bonds)
• Consumer choice: bounded rationality can help explain why consumers make suboptimal decisions, such as failing to switch to cheaper or better products
  • Examples include the endowment effect (overvaluing items one owns) and the sunk cost fallacy (continuing to invest in a losing proposition because of past investments)
• Organizational decision-making: bounded rationality can shed light on how managers and firms make decisions under uncertainty and cognitive constraints
  • Helps explain the use of simple rules and heuristics in strategic decision-making, as well as the persistence of suboptimal practices
• Public policy: insights from bounded rationality can inform the design of policies and interventions that account for human cognitive limitations
  • Examples include the use of defaults (automatic enrollment in retirement savings plans) and the framing of information (presenting risks in terms of frequencies rather than probabilities)
• Negotiation and conflict resolution: bounded rationality can help explain why parties in a negotiation may fail to reach mutually beneficial agreements
  • Highlights the role of cognitive biases, such as the fixed-pie perception (assuming that gains for one party necessarily come at the expense of the other) and reactive devaluation (undervaluing concessions made by the other party)

Critiques and Limitations

• Some argue that the concept of bounded rationality is too broad and lacks a clear, unified definition
• The emphasis on heuristics and biases may overlook the adaptive value of these strategies in many real-world contexts
• Bounded rationality models often lack the precision and predictive power of traditional game-theoretic models
• The focus on individual decision-making may neglect the role of social and institutional factors in shaping behavior
• Empirical testing of bounded rationality models can be challenging, as it requires capturing complex cognitive processes and measuring elusive constructs
• Critics argue that the bounded rationality approach may lead to an overly pessimistic view of human decision-making capabilities
• There is a risk of attributing all deviations from perfect rationality to cognitive limitations, rather than considering alternative explanations (such as emotions or social norms)