The attraction effect and other decoy effects are often understood as anomalies and modeled as departures from rational inference. We show how these decoy effects may arise from simple Bayesian updating. Our new model, the Bayesian probit, has the same parameters as the standard multinomial probit model: each choice alternative is associated with a Gaussian random variable. Unlike the standard probit and any random utility model, however, the Bayesian probit can jointly accommodate similarity effects, the attraction effect, and the compromise effect. We provide a new definition of revealed similarity from observed choice behavior, and show that in the Bayesian probit revealed similarity is captured by signal correlation. We also show that signal averages capture revealed preference; and that signal precision captures the decision maker's familiarity with the options. This link of parameters to observable choice behavior facilitates measurement and provides a useful tool for discrete choice applications.
This paper studies preferences over menus of alternatives. A preference is monotonic when every menu is at least as good as any of its subsets. The main result is that any numerical representation for a monotonic preference can be written in minimax form. A minimax representation suggests a decision maker who faces uncertainty about her own future tastes and who exhibits an extreme form of ambiguity aversion with respect to this subjective uncertainty. Applying the main result in a setting with a finite number of alternatives leads to a natural weakening of the seminal characterization of preference for flexibility introduced by Kreps (1979). This new characterization clarifies the consequences of his last axiom, ordinal submodularity. While the remaining axioms are equivalent to the existence of a (weakly) increasing aggregator of second period maximal utilities, ordinal submodularity holds if and only if this aggregator can be taken to be strictly increasing. * I am especially indebted to Faruk Gul for his guidance and encouragement. I am grateful to Wolfgang Pesendorfer, and to Daniel Gottlieb and Justinas Pelenis for helpful comments and discussions. I also thank
We introduce random evolving lotteries to study preference for non‐instrumental information. Each period, the agent enjoys a flow payoff from holding a lottery that will resolve at the terminal date. We provide a representation theorem for non‐separable risk consumption preferences and use it to characterize agents' attitude to non‐instrumental information. To address applications, we characterize peak‐trough utilities that aggregate trajectories of flow utilities linearly but, in addition, put weight on the best (peak) and worst (trough) lotteries along each path. We show that the model is consistent with recent experimental evidence on attitudes to information, including a preference for gradual arrival of good news and the ostrich effect, that is, decision makers' tendency to prefer information after good news to information after bad news.
This paper studies preferences over menus of alternatives. A preference is monotonic when every menu is at least as good as any of its subsets. The main result is that any numerical representation for a monotonic preference can be written in minimax form. A minimax representation suggests a decision maker who faces uncertainty about her own future tastes and who exhibits an extreme form of ambiguity aversion with respect to this subjective uncertainty. Applying the main result in a setting with a finite number of alternatives leads to a natural weakening of the seminal characterization of preference for flexibility introduced by Kreps (1979). This new characterization clarifies the consequences of his last axiom, ordinal submodularity. While the remaining axioms are equivalent to the existence of a (weakly) increasing aggregator of second period maximal utilities, ordinal submodularity holds if and only if this aggregator can be taken to be strictly increasing. * I am especially indebted to Faruk Gul for his guidance and encouragement. I am grateful to Wolfgang Pesendorfer, and to Daniel Gottlieb and Justinas Pelenis for helpful comments and discussions. I also thank
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