Game theory experiments test how real human behavior matches theoretical predictions. They involve carefully designing game structures, player roles, and payoffs to isolate specific variables. Lab settings offer tight control, while field experiments provide real-world context.

Data collection in game theory experiments records player decisions, strategies, and payoffs. Surveys can gather insights into player reasoning. Large sample sizes are crucial for statistical power, with online platforms making recruitment easier. Ensuring data validity and reliability is essential for meaningful results.

Designing experiments for game theory

Experimental design considerations

• Game theory experiments test how closely actual human behavior matches the predictions and assumptions of game theory models
• Experiments can be designed to test specific game types, such as simultaneous or sequential games, perfect or imperfect information games, and cooperative or non-cooperative games
• Experimental design in game theory involves defining the game structure, specifying player roles and available strategies, determining payoffs for each outcome, and establishing the information set and order of play
• Control conditions are essential in game theory experiments to isolate the effects of specific variables on strategic behavior
  • This may involve varying game parameters, player information, or incentive structures while holding other factors constant
• Randomization and counterbalancing techniques are used in game theory experiments to minimize order effects and ensure that any observed differences in behavior are due to the experimental manipulations rather than extraneous factors

Lab vs. field experiments

• Game theory experiments often involve deception to prevent participants from gaming the system or behaving differently than they would in real-world strategic situations
  • Deception must be carefully managed to balance research integrity with the need for valid data
• Experiments can be conducted in lab settings with tight controls or in the field with more naturalistic environments
  • Lab experiments allow for greater precision in manipulating variables (player roles, payoff structures)
  • Field experiments provide more external validity and generalizability (real-world strategic situations, such as auctions or negotiations)

Studying strategic behavior

Data collection methods

• Data collection in game theory experiments typically involves recording player decisions, strategies chosen, and resulting payoffs for each round of play
  • This data can be used to analyze how closely behavior matches game-theoretic predictions and identify deviations from rational play
• Surveys can be used in conjunction with experiments to gather data on player beliefs, expectations, and thought processes during strategic interactions
  • Surveys may ask players to explain their reasoning behind specific decisions or to predict the likely behavior of other players
• Incentivized experiments, where participants' payoffs depend on their performance, are often used in game theory to encourage players to take the game seriously and make decisions as they would in real-world situations with real stakes

Sample size and participant recruitment

• Large sample sizes are important in game theory experiments to ensure statistical power and detect significant effects
  • Researchers may need to recruit hundreds of participants to have sufficient data to draw meaningful conclusions
• Online platforms and crowdsourcing tools (Amazon Mechanical Turk, Prolific) have made it easier to collect large amounts of game theory data from diverse populations
  • However, researchers must be cautious about the quality and reliability of online data and take steps to ensure participant engagement and prevent fraudulent responses

Data validity and reliability

Types of validity

• Internal validity refers to the extent to which an experiment accurately measures what it intends to measure and controls for confounding variables
  • Game theory experiments must be carefully designed to ensure that observed differences in behavior are due to the experimental manipulations and not extraneous factors
• External validity refers to the generalizability of experimental findings to real-world contexts
  • Game theory experiments conducted in highly controlled lab settings may not always translate to more complex and dynamic real-world environments
• Ecological validity assesses how well an experimental task or setting reflects the real-world situation it is meant to study
  • Game theory experiments should strive to create realistic scenarios and incentive structures that mirror the strategic decisions people face in actual social, economic, and political contexts

Reliability and demand effects

• Reliability refers to the consistency and reproducibility of experimental results across different samples, experimenters, and time points
  • Game theory findings should be robust and replicable to instill confidence in their validity
  • Inter-rater reliability is important when coding and categorizing qualitative game theory data, such as player strategies or open-ended survey responses
    • Multiple coders should independently analyze the data and demonstrate high levels of agreement
  • Test-retest reliability can be assessed by having the same participants complete a game theory task or survey at multiple time points and comparing the consistency of their responses
• Demand characteristics refer to the ways in which participants' knowledge of an experiment's purpose can influence their behavior
  • Game theory researchers must be cautious about the information they reveal to participants and use deception when necessary to minimize demand effects

Analyzing experimental data

Descriptive and inferential statistics

• Descriptive statistics, such as means, medians, and standard deviations, can be used to summarize game theory data and identify overall patterns of behavior
  • These statistics can be compared to game-theoretic predictions to assess how closely actual behavior matches expected outcomes
• Inferential statistics, such as t-tests, ANOVAs, and regression analyses, can be used to test hypotheses about the effects of experimental manipulations on game theory outcomes
  • These tests allow researchers to determine whether observed differences between conditions are statistically significant and unlikely to be due to chance
• Non-parametric tests, such as chi-square or Mann-Whitney U tests, may be appropriate for analyzing game theory data that is not normally distributed or does not meet the assumptions of parametric tests

Advanced statistical techniques

• Regression analyses can be used to model the relationships between multiple variables in game theory data, such as how player characteristics, game parameters, or experimental conditions predict strategic behavior or outcomes
• Bayesian statistical methods may be useful for analyzing game theory data when prior information or beliefs are available to inform probability estimates
  • Bayesian analyses can help researchers update their beliefs based on new data and make probabilistic predictions about future outcomes
• Data visualization techniques, such as graphs, plots, and heat maps, are important for exploring and communicating patterns in game theory data
  • Clear and informative visualizations can help researchers identify trends, outliers, and relationships that may not be immediately apparent from raw data or summary statistics