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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Behavioral game theory | 5/6 | https://en.wikipedia.org/wiki/Behavioral_game_theory | reference | science, encyclopedia | 2026-05-05T15:10:04.395995+00:00 | kb-cron |
=== Incentives, consequences, and deception === The role of incentives and consequences in decision-making is interesting to behavioral game theorists because it affects rational behavior. Post (2008) analyzed Deal or no Deal contestant behavior in order to reach conclusions about decision-making when stakes are high. Studying the contestant's choices formed the conclusion that, in a sequential game with high stakes, decisions were based on previous outcomes rather than rationality. Players who face a succession of good outcomes, in this case they eliminate the low-value cases from play, or players who face a succession of poor outcomes become less risk averse. This means that players who are having exceptionally good or exceptionally bad outcomes are more likely to gamble and continue playing than average players. The lucky or unlucky players were willing to reject offers of over one hundred percent of the expected value of their case in order to continue playing. This shows a shift from risk avoiding behavior to risk seeking behavior. This study highlights behavioral biases that are not accounted for by traditional game theory. Riskier behavior in unlucky contestants can be attributed to the break-even effect, which states that gamblers will continue to make risky decisions in order to win back money. On the other hand, riskier behavior in lucky contestants can be explained by the house-money effect, which states that winning gamblers are more likely to make risky decisions because they perceive that they are not gambling with their own money. This analysis shows that incentives influence rational choice, especially when players make a series of decisions. Incentives and consequences also play a large role in deception in games. Gneezy (2005) studied deception using a cheap talk sender-receiver game. In this type of game player one receives information about the payouts of option A and option B. Then, player one gives a recommendation to player two about which option to take. Player one can choose to deceive player two, and player two can choose to reject player one's advice. Gneezy found that participants were more sensitive to their gain from lying than to their opponent's loss. He also found that participants were not wholly selfish and cared about how much their opponents lost from their deception, but this effect diminished as their own payout increased. These findings show that decision makers examine both incentives to lie and consequences of lying in order to decide whether or not to lie. In general people are averse to lying, but given the right incentives they tend to ignore consequences. Wang (2009) also used a cheap talk game to study deception in participants with an incentive to deceive. Using eye tracking he found that participants who received information about payoffs focused on their own payoff twice as often as their opponents. This suggests minimal strategic thinking. Further, participants' pupils dilated when they sent a deceiving, and they dilated more when telling a bigger lie. Through these physical cues Wang concluded that deception is cognitively difficult. These findings show that factors such as incentives, consequences, and deception can create irrational decisions and affect the way games unfold. A consequence of the game theory is its lack of use of empirical data to predict outcomes. "game theory will be no substitute for an empirically grounded behavioral theory when we want to predict what people will actually do in a competitive situation" Predicting rational behavior is possible with game theory but it can be improved if the theory is open to adjustment. The predicted result of the game can be improved and long-lasting if the discipline expands its knowledge of behavioral theory. How people act, think, and feel affect their decisions to play a role in this theory.,. Ken Binmore makes an excellent point that when empirically sound data is present, game theory should not hold the final decision outcome. That this is good for trying to understand if the rational decision being made is due to game theory or is just a consistent behavioral decision being made. The field of economics should try to take every step in improving empirical information in that there is little reliance on just a theory. Businesses value game theory, and the economic discipline must improve the strength of game theory by trying to establish an empirical database. Society will be able to advance its knowledge of behavioral game theory just by expanding the economic discipline of data. Alvin E Roth states, "if we do not take steps in the direction of adding a solid empirical base to game theory, but instead continue to rely on game theory primarily for conceptual insights, then it is likely that long before a hundred-year game theory will have experienced sharply diminishing return"