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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Behavioral game theory | 4/6 | https://en.wikipedia.org/wiki/Behavioral_game_theory | reference | science, encyclopedia | 2026-05-05T15:10:04.395995+00:00 | kb-cron |
=== Learning === Learning models are a way of explaining and predicting strategic decisions in behavioral game theory. More specifically, they aim to explain how player's choices may change when given the chance to learn about their opponents or the game. There are three different types of learning models. The first is reinforcement learning. Reinforcement learning suggests that if a player received a high reward from choosing a certain behavior or strategy, then that player would be more inclined to use the same strategy again. If a particular strategy has not been used before however, then the strategy would not appear to be more or less appealing to the player. Another learning model is belief learning. Belief learning assumes that players often remember their opponents previous strategies in games, and will henceforth change their own strategies based on their opponents past behavior. Lastly, experience weighted attraction learning uses a mixture of belief learning and reinforcement learning in its model. This model accounts for the strategies and payoffs that have been played and unplayed. The experience weighted attraction learning framework posits that people learn from past experiences as well as by questioning what they could've done differently. Furthermore, it also believes that players evaluate their past rewards half as much as their actual rewards.
=== Beliefs === Beliefs about other people in a decision-making game are expected to influence ones ability to make rational choices. However, beliefs of others can also cause experimental results to deviate from equilibrium, utility-maximizing decisions. In an experiment by Costa-Gomez (2008) participants were questioned about their first order beliefs about their opponent's actions prior to completing a series of normal-form games with other participants. Participants complied with Nash Equilibrium only 35% of the time. Further, participants only stated beliefs that their opponents would comply with traditional game theory equilibrium 15% of the time. This means participants believed their opponents would be less rational than they really were. The results of this study show that participants do not choose the utility-maximizing action and they expect their opponents to do the same. Also, the results show that participants did not choose the utility-maximizing action that corresponded to their beliefs about their opponent's action. While participants may have believed their opponent was more likely to make a certain decision, they still made decisions as if their opponent was choosing randomly. Another study that examined participants from the TV show Deal or No Deal found divergence from rational choice. Participants were more likely to base their decisions on previous outcomes when progressing through the game. Risk aversion decreased when participants' expectations were not met within the game. For example, a subject that experienced a string of positive outcomes was less likely to accept the deal and end the game. The same was true for a subject that experienced primarily negative outcomes early in the game.
=== Social cooperation === Social behavior and cooperation with other participants are two factors that are not modeled in traditional game theory, but are often seen in an experimental setting. The evolution of social norms has been neglected in decision-making models, but these norms influence the ways in which real people interact with one another and make choices. One tendency is for a person to be a strong reciprocator. This type of person enters a game with the predisposition to cooperate with other players. They will increase their cooperation levels in response to cooperation from other players and decrease their cooperation levels, even at their own expense, to punish players who do not cooperate. This is not payoff-maximizing behavior, as a strong reciprocator is willing to reduce their payoff in order to encourage cooperation from others. Rational choice theory has limitations in interactive decision making, and it is also difficult to accurately predict human behavior in social cooperation. Behavioral games not only require players to make rational choices, but also require players to be able to predict the decisions of other players in their interactions. In game experiments, rational choice conflicts with individual decision making, and individual behavior may be able to achieve greater gains than rational choice. Rational choice theory has limitations and uncertainties for social interaction decisions, so the predicted results are not the same as the experimental results. Dufwenberg and Kirchsteiger (2004) developed a model based on reciprocity called the sequential reciprocity equilibrium. This model adapts traditional game theory logic to the idea that players reciprocate actions in order to cooperate. The model had been used to more accurately predict experimental outcomes of classic games such as the prisoner's dilemma and the centipede game. Rabin (1993) also created a fairness equilibrium that measures altruism's effect on choices. He found that when a player is altruistic to another player the second player is more likely to reciprocate that altruism. This is due to the idea of fairness. Fairness equilibriums take the form of mutual maximum, where both players choose an outcome that benefits both of them the most, or mutual minimum, where both players choose an outcome that hurts both of them the most. These equilibriums are also Nash equilibriums, but they incorporate the willingness of participants to cooperate and play fair.