We propose a class of multiple-prior representations of preferences under ambiguity, where the belief the decision-maker (DM) uses to evaluate an uncertain prospect is the outcome of a game played by two conflicting forces, Pessimism and Optimism. The model does not restrict the sign of the DM’s ambiguity attitude, and we show that it provides a unified framework through which to characterize different degrees of ambiguity aversion, and to represent the co-existence of negative and positive ambiguity attitudes within individuals as documented in experiments. We prove that our baseline representation, dual-self expected utility (DSEU), yields a novel representation of the class of invariant biseparable preferences (Ghirardato, Maccheroni, and Marinacci, 2004), which drops uncertainty aversion from maxmin expected utility (Gilboa and Schmeidler, 1989). Extensions of DSEU allow for more general departures from independence.
Charities routinely send “thank you letters” and small gifts to express gratitude to donors but seek to defray these costs by making additional asks for donations and/or engagement. But the “ask for more” can backfire if potential donors perceive persuasive intent in the expression of gratitude, inducing reactance. We hypothesize that such reactance and its impact on giving will vary by donor loyalty. Loyal donors are more likely to experience reactance to additional asks, muting the feeling of reciprocity aroused by the expression of gratitude to suppress giving. In contrast, non-loyal donors are less likely to experience reactance, and therefore more likely to channel the feeling of reciprocity toward giving. We test our hypothesis using a large-scale natural field experiment involving nearly 180,000 past donors to a leading charity in India. We find evidence in support of our hypothesis. We therefore recommend that additional asks only be made to nonloyal donors. Such differentially targeted ask messages based on past donation behavior, using data readily available to charities, can increase overall donation amounts by 12.8-17.5%. Our findings highlight that purely cross-sectional experiments that do not account for past donor/customer history may offer incomplete insight and lead to erroneous managerial implications.
Information dissemination and aggregation are key economic functions of financial markets. How intelligent do traders have to be for the complex task of aggregating diverse information (i.e., approximate the predictions of the rational expectations equilibrium) in a competitive double auction market? An apparent ex-ante answer is: intelligent enough to perform the bootstrap operation necessary for the task—to somehow arrive at prices that are needed to generate those very prices. Constructing a path to such equilibrium through rational behavior has remained beyond what we know of human cognitive abilities. Yet, laboratory experiments report that profit motivated human traders are able to aggregate information in some, but not all, market environments (Plott and Sunder 1988, Forsythe and Lundholm 1990). Algorithmic agents have the potential to yield insights into how simple individual behavior may perform this complex market function as an emergent phenomenon. We report on a computational experiment with markets populated by algorithmic traders who follow cognitively simple heuristics humans are known to use. These markets, too, converge to rational expectations equilibria in environments in which human markets converge, albeit slowly and noisily. The results suggest that high level of individual intelligence or rationality is not necessary for efficient outcomes to emerge at the market level; the structure of the market itself is a source of rationality observed in the outcomes.
Improving school quality with limited resources is a key issue of policy. It has been suggested that instructing teachers to follow specific practices together with tight monitoring of their activities may help improve outcomes in under-performing schools that usually serve poor populations. This paper uses an RCT to estimate the effectiveness of guided instruction methods as implemented in under-performing schools in Chile. The intervention improved performance substantially and by equal amounts for boys and girls. However, the effect is mainly accounted for by children from relatively higher income backgrounds and not for the most deprived. Based on the CLASS instrument we document that quality of teacher-student interactions is positively correlated with the performance of low income students; however, the intervention did not affect these interactions. Guided instruction can improve outcomes, but it is a challenge to reach the most deprived children.
We propose a multiple-prior model of preferences under ambiguity that provides a unified lens through which to understand different formalizations of ambiguity aversion, as well as context-dependent negative and positive ambiguity attitudes documented in experiments. This model, Boolean expected utility (BEU), represents the belief the decision-maker uses to evaluate any uncertain prospect as the outcome of a game between two conflicting forces, Pessimism and Optimism. We prove, first, that BEU provides a novel representation of the class of invariant biseparable preferences (Ghirardato, Maccheroni, and Marinacci, 2004). Second, BEU accommodates rich patterns of ambiguity attitudes, which we characterize in terms of the relative power allocated to each force in the game.