@booklet {einavhart-rorciagvabw2012,
	title = {Reversal of Risky Choice in a Good Versus a Bad World},
	howpublished = {Discussion Papers},
	year = {2012},
	month = {8},
	abstract = {In many situations one has to choose between risky alternatives, knowing only one{\textquoteright}s past experience with those alternatives. Such decisions can be made in more - or less - benevolent settings or {\textquoteright}worlds{\textquoteright}. In a {\textquoteright}good world{\textquoteright}, high payoffs are more frequent than low payoffs, and vice versa in a {\textquoteright}bad world{\textquoteright}. In two studies, we explored whether the world influences choice behavior: Whether people behave differently in a {\textquoteright}good{\textquoteright} versus a {\textquoteright}bad{\textquoteright} world. Subjects made repeated, incentivized choices between two gambles, one riskier than the other, neither offering a sure amount. The gambles were held equivalent in terms of their expected value, differing only in variance. Worlds were manipulated both between- and within-subject: In Study 1, each subject experienced one world - good, bad or mediocre; in Study 2, each subject experienced both a good and a bad world. We examine the aggregate pattern of behavior (average choice frequencies), and the dynamics of behavior across time. We observed significant differences in the aggregate pattern: In a good world, subjects tended to choose the riskier alternative, and vice versa in a bad world. The pattern of the dynamics, i.e., the transitions from round to round, were best explained by a reaction to the counterfactual reward: When the unchosen alternative yielded a better payoff, the tendency to subsequently choose it was higher. We compared these two patterns to the predictions of three types of models: Reinforcement learning, regret-based and disappointment-based models. Behavior was in line only with the predictions of regret-based models.},
	url = {/files/dp619.pdf},
	author = {Einav Hart, Yaakov Kareev, , Judith Avrahami}
}
