Learning with limited memory: Bayesianism vs heuristics
研究了在认知能力受限时，贝叶斯分析是否仍为最优，以及何时需要采用简化策略（如忽略信息或环境差异）来获得满意结果。
Bayesian analysis is considered the optimal way of processing information. However, it often leads to problems for decision-makers with constrained cognitive capacity. Modeling such constrained capacity by finite automata, we answer two questions in the context of Wald's (1947) sequential analysis, namely in what environments is optimal Bayesian analysis possible even with constraints; also, when it is not possible what simplifications in the analysis enable us to obtain a satisfactory outcome. We identify two features of the simplified analysis: information stickiness (ignoring information) and rule stickiness (ignoring small differences in the environment).