一类bandit问题中的近似最优性与风险/收益权衡
Approximate optimality and the risk/reward tradeoff in a class of bandit problems
AI总结：
本文研究收益分布已知的序贯决策问题，利用渐近性和新的中心极限定理，推导出风险态度决定决策者应专注单一行动还是跨期多样化的条件。
摘要：
本文研究一个序贯决策问题，其中收益分布已知且收益的风险性至关重要。等价地，它研究从一组重复的独立彩票中进行的序贯选择。假设决策者追求对大视野近似最优的策略。通过利用渐近性提供的易处理性，推导出了一些条件，刻画了何时始终专注于一种行动或彩票是渐近最优的，以及何时最优性需要跨期多样化。关键在于风险态度的恒定或可变性。主要技术工具是一个新的中心极限定理。
英文摘要：
This paper studies a sequential decision problem where payoff distributions are known and where the riskiness of payoffs matters. Equivalently, it studies sequential choice from a repeated set of independent lotteries. The decision-maker is assumed to pursue strategies that are approximately optimal for large horizons. By exploiting the tractability afforded by asymptotics, conditions are derived characterizing when specialization in one action or lottery throughout is asymptotically optimal and when optimality requires intertemporal diversification. The key is the constancy or variability of risk attitude. The main technical tool is a new central limit theorem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
