个人历史影响参考点:代码力的案例研究

Takeshi Kurashima, Tomoharu Iwata, T. Tominaga, Shuhei Yamamoto, Hiroyuki Toda, K. Takemura
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引用次数: 0

摘要

人类根据自身的内在价值函数做出决策,其形状会围绕一个点发生扭曲和偏差,行为经济学研究界将其称为参考点。人们会加强在他们的参照点范围内的活动,并避免一旦越过参照点就会导致损失的行为。然而,过去的经验对参考点周围的决策的影响还没有得到很好的研究。通过分析从竞争性编程网站收集的一系列用户级决策,我们发现历史对用户在参考点周围的决策有明显的影响。过去的经验可以加强(有时也会削弱)参照点周围的决策偏差。在达到参照点后,对过去困难的经历会加强对损失厌恶的倾向。当一个人第一次越过一个参考点时,认知决策偏差是显著的。然而,重复这种交叉会逐渐削弱效果。我们还展示了我们的见解在预测用户行为任务中的价值。结合我们的见解的预测模型可以用来激励人们保持更活跃。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personal History Affects Reference Points: A Case Study of Codeforces
Humans make decisions based on their internal value function, and its shape is known to be distorted and biased around a point, which the research community of behavior economics refers to as the reference point. People intensify activities that come to lie within the reach of their reference point, and abstain from acts that would incur losses once they've crossed the point. However, the impact of past experiences on decision making around the reference point has not been well studied. By analyzing a long series of user-level decisions gathered from a competitive programming website, we find that history has a clear impact on user's decision making around the reference point. Past experiences can strengthen, and sometimes weaken, the decision bias around the reference point. Experiences of past difficulties can strengthen the tendency towards loss aversion after achieving the reference point. When a person crosses a reference point for the first time, the cognitive decision bias is significant. However, repeating this crossing gradually weakens the effect. We also show the value of our insights in the task of predicting user behavior. Prediction models incorporating our insights may be used for motivating people to remain more active.
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