In the cooperative context, subjects are put into groups of two or more and can pay a cost to give a larger benefit to the other(s) in their group. A meta-analysis finds that promoting intuition increases giving for women but not for men 26. Here, some studies conclude that promoting intuition increases altruistic behavior 15, while others find no effect of promoting intuition 20. In the giving context, subjects are simply asked whether they would like to give some of their money to another person (or charity). In this manipulation literature, some have made a distinction between behavior in giving contexts (e.g., dictator games) and in cooperative contexts (e.g., public goods games). However, it has recently been argued that RT data cannot be used as evidence for intuitive/deliberative processes, since they are sensitive to the particular choice problems used by the researchers 7, 16, 25.Īn alternative approach which does not have this limitation is to experimentally manipulate RT (e.g., using time pressure) or impose cognitive load to try to establish people’s intuitive responses. To answer this question, some dual-process researchers have examined relative response times (RT) 10, 11, 13, 14, 17, 18, 19, 23 to establish people’s intuitions. One question is whether social decisions are the result of a single comparison process, or the result of two processes: one, a fast and intuitive process and the other, a slow and deliberative process 7, 10? The second question is whether people exhibit a selfish or pro-social bias? The latter question is usually posed under the presumption of dual processes: given that there is an intuitive process, does it favor selfishness or pro-sociality? Recently there have been efforts to understand the dynamics of social decision making, with both single-process 7, 8, 9 and dual-process 10, 11, 12 models. There is a large literature describing the various factors that influence other-regarding behavior, including distributional preferences 1, 2, 3, reciprocity 4, social distance 5, and guilt-aversion 6, but these are all static models that simply predict choice outcomes. A basic goal in decision science is to understand the cognitive processes that underlie these social decisions. Social decisions typically involve conflicts between selfishness and pro-sociality. Our findings help reconcile the conflicting results concerning the cognitive processes of social decision making and highlight the importance of modeling the dynamics of the choice process. Using mini-dictator games in which subjects make binary decisions about how to allocate money between themselves and another participant, we find that pro-social subjects become more pro-social under time pressure and less pro-social under time delay, while selfish subjects do the opposite. We argue that behavior attributed to intuition can instead be seen as a starting point bias of a sequential sampling model (SSM) process, analogous to a prior in a Bayesian framework. Here, we propose a way to reconcile these two opposing frameworks. The cognitive processes underlying such decisions are not well understood, with some arguing for a single comparison process, while others argue for dual processes (one intuitive and one deliberative). We prove that our policy is consistent, finding a globally optimal alternative when given enough measurements, and show through simulations that it performs competitively with or significantly better than other policies.Social decision making involves balancing conflicts between selfishness and pro-sociality. This approach greatly reduces the measurement effort required, but it requires some prior knowledge on the smoothness of the function in the form of an aggregation function and computational issues limit the number of alternatives that can be easily considered to the thousands. We propose a hierarchical aggregation technique that uses the common features shared by alternatives to learn about many alternatives from even a single measurement. This policy myopically optimizes the expected increment in the value of sampling information in each time period. We use a Bayesian probability model for the unknown reward of each alternative and follow a fully sequential sampling policy called the knowledge-gradient policy. Each alternative may be characterized by a multi-dimensional vector of categorical and numerical attributes and has independent normal rewards. We propose a sequential sampling policy for noisy discrete global optimization and ranking and selection, in which we aim to efficiently explore a finite set of alternatives before selecting an alternative as best when exploration stops. Hierarchical Knowledge Gradient for Sequential Sampling
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