文章基本信息

标题：The Nature of Belief-Directed Exploratory Choice in Human Decision-Making
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作者：Knox, W. Bradley ; Otto, A. Ross ; Stone, Peter 等
期刊名称：Frontiers in Psychology
电子版ISSN：1664-1078
出版年度：2012
卷号：2
页码：1-12
DOI：10.3389/fpsyg.2011.00398
出版社：Frontiers Media
摘要：In non-stationary environments, there is a conflict between exploiting currently favored options and gaining information by exploring lesser-known options that in the past have proven less rewarding. Optimal decision making in such tasks requires considering future states of the environment (i.e., planning) and properly updating beliefs about the state of environment after observing outcomes associated with choices. Optimal belief-updating is reflective in that beliefs can change without directly observing environmental change. For example, after ten seconds elapse, one might correctly believe that a traffic light last observed to be red is now more likely to be green. To understand human decision-making when rewards associated with choice options change over time, we develop a variant of the classic “bandit” task that is both rich enough to encompass relevant phenomena and sufficiently tractable to allow for ideal actor analysis of sequential choice behavior. We evaluate whether people update beliefs about the state of environment in a reflexive (i.e., only in response to observed changes in reward structure) or reflective manner. In contrast to purely "random" accounts of exploratory behavior, model-based analyses of the subjects’ choices and latencies indicate that people are reflective belief-updaters. However, unlike the Ideal Actor model, our analyses indicate that people's choice behavior does not reflect consideration of future environmental states. Thus, although people update beliefs in a reflective manner consistent with the ideal actor, they do not engage in optimal long-term planning, but instead myopically choose the option on every trial that is believed to have the highest immediate payoff.
关键词：Decision Making; exploitation; exploration; ideal actor; ideal observer; planning; POMDP; reinforcement learning