Hierarchical Maximum Likelihood Parameter Estimation for Cumulative Prospect Theory: Improving the Reliability of Individual Risk Parameter Estimates
提出层次统计方法，利用群体信息打破个体风险偏好参数拟合中的统计平局，避免过拟合，显著提高累积前景理论中个体风险参数估计的可靠性。
An individual’s tolerance of risk can be quantified by using decision models with tuned parameters that maximally fit a set of risky choices the individual has made. A goal of this model fitting procedure is to identify parameters that correspond to stable underlying risk preferences. These preferences can be modeled as an individual difference, indicating a particular decision maker’s tastes and willingness to accept risk. Using hierarchical statistical methods, we show significant improvements in the reliability of individual risk preference parameter estimates over other common methods for cumulative prospect theory. This hierarchical procedure uses population-level information (in addition to an individual’s choices) to break “ties” (or near ties) in the fit quality for sets of possible risk preference parameters. By breaking these statistical ties in a sensible way, researchers can avoid overfitting choice data and thus more resiliently measure individual differences in people’s risk preferences. This paper was accepted by Yuval Rottenstreich, judgment and decision making.