Imagine a choice between two gambles: getting $100 with a probability of 20% or getting $50 with a probability of 80%. In 1979, Kahneman and Tversky published prospect theory (1), a mathematically specified descriptive theory of how people make risky choices such as these. They explained numerous documented violations of expected utility theory, the dominant theory at the time, by using nonlinear psychophysical functions for perceiving underlying probabilities and evaluating resulting payoffs. Prospect theory revolutionized the study of choice behavior, showing that researchers could build formal models of decision-making based on realistic psychological principles (2). But in the ensuing decades, as dozens of competing theories have been proposed (3), there has been theoretical fragmentation, redundancy, and stagnation. There is little consensus on the best decision theory or model. On page 1209 of this issue, Peterson et al. (4) demonstrate the power of a more recent approach: Instead of relying on the intuitions and (potentially limited) intellect of human researchers, the task of theory generation can be outsourced to powerful machine-learning algorithms.

想象一下两种赌博之间的选择：以 20% 的概率获得 100 美元或以 80% 的概率获得 50 美元。1979 年，Kahneman 和 Tversky 发表了前景理论 ( 1 )，这是一种数学上指定的描述理论，用于说明人们如何做出诸如此类的冒险选择。他们通过使用非线性心理物理学函数来感知潜在概率并评估由此产生的收益，解释了许多记录在案的违反预期效用理论（当时的主导理论）的行为。前景理论彻底改变了选择行为的研究，表明研究人员可以建立基于现实心理学原理的正式决策模型 ( 2 )。但在随后的几十年里，随着数十种相互竞争的理论被提出（3)，出现了理论上的碎片化、冗余和停滞。关于最佳决策理论或模型几乎没有共识。在本期第 1209 页，Peterson等人。( 4 ) 展示了一种更新方法的力量：理论生成的任务可以外包给强大的机器学习算法，而不是依赖人类研究人员的直觉和（可能有限的）智力。