Learning in Temporally Complex Problems: The Role of External Knowledge
研究了决策者在行动与结果分离的时间复杂问题中如何利用外部知识和内部经验知识，发现外部知识虽有益但有限时可能反而降低绩效。
We explore how decision makers utilize external and internal knowledge in problems characterized by temporal complexity where there is a separation between action and outcome. Many strategic and organizational decisions have longer-term consequences, yet behavioral models of learning have underexplored this class of problems. Consequently, we do not know much about the role of external knowledge in tackling temporally complex problems, let alone how it interacts with internal knowledge from experiential learning processes. Our computational analyses show that, although having a greater level of external knowledge is generally advantageous, its positive impact diminishes notably or can even become negative when external knowledge is limited. Surprisingly, decision makers operating with limited external knowledge can perform worse than those without any external guidance at all. In a temporally complex problem, knowing what to do at any given point is not sufficient as one still needs to undertake a long sequence of actions before reaching the goal. In other words, external knowledge does not guarantee that optimal actions will be chosen in subsequent decision-making situations. This dynamic may lead the decision makers to overvalue actions that serve as stepping stones to the available external knowledge and undervalue alternative actions that may provide more desirable paths toward the goal. Funding: This research was supported by the Yonsei University Research Fund of 2021 [2021-22-0098]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.16469 .