Choice without Beliefs

没有信仰的选择

• 批准号: 9514908
• 负责人: David Easley
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-03-15 至 1999-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9514908&HistoricalAwards=false
• 关键词: Choice; without; Beliefs

9514908 Easley This project studies the following important question: What does an individual do when confronted with a new potentially complex, stochastic decision problem? According to standard expected utility theory, before making any choices the individual determines all possible stochastic processes that could be driving the states of the world and accesses a prior over these processes. As appealing as this theory is in relatively simple settings in which no further learning need occur, it is difficult to accept in complex settings. This project develops an alternative evolutionary foundation for static expected utility maximization. Within this alternative framework individuals do not access beliefs over all possible stochastic processes. They do not even attempt to determine the set of possible processes. Instead, they act and learn from their experience. Here an individual begins with a set of candidate decision rules, starts with weights on these rules, and selects an initial rule using these weights as probabilities. She/he then acts according to the recommendation made by the selected rule. After observing the state and the payoff that is achieved, the individual updates the weights on rules using an evolutionary dynamic. He/she selects the rule to use at the next date based on these updated weights, and so on. Based on the work completed so far, it is known that with three natural axioms on the dynamic: symmetry, monotonicity, and independence, the individual eventually chooses a rule that is a subjective expected utility maximizer with correct expectations. One part of the project is to determine if these axioms are minimal for this conclusion. This project extends the process oriented model of choice to richer settings and an analysis of interaction between multiple evolutionary individuals.

小行星9514908伊斯利 这个项目研究了以下重要问题：当一个人面对一个新的潜在复杂的随机决策问题时，他会做什么？ 根据标准的期望效用理论，在做出任何选择之前，个体确定所有可能驱动世界状态的随机过程，并获得这些过程的先验。 尽管这一理论在相对简单的环境中很有吸引力，不需要进一步学习，但在复杂的环境中很难接受。 这个项目为静态期望效用最大化开发了一个替代的进化基础。 在这个替代框架内，个体并不访问所有可能的随机过程的信念。 他们甚至不试图确定可能的过程集。 相反，他们采取行动并从他们的经验中学习。 在这里，个人从一组候选决策规则开始，从这些规则的权重开始，并使用这些权重作为概率选择初始规则。 然后，她/他根据所选规则提出的建议采取行动。 在观察到状态和获得的收益之后，个体使用进化动态更新规则的权重。 他/她根据这些更新的权重选择下一个日期使用的规则，等等。基于到目前为止完成的工作，我们知道，有三个自然公理的动态：对称性，单调性和独立性，个人最终选择的规则是一个主观的期望效用最大化与正确的期望。 项目的一部分是确定这些公理是否是这个结论的最小公理。 该项目将过程导向的选择模型扩展到更丰富的环境，并分析多个进化个体之间的相互作用。