用优先权启发式进行估值

IF 2.2 4区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Mathematical Psychology Pub Date : 2024-09-16 DOI:10.1016/j.jmp.2024.102883
Konstantinos V. Katsikopoulos
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引用次数: 0

摘要

优先权启发式是一种在赌局之间进行选择的词典半排序法。它有一些优点,比如在样本外预测人们的多数选择比前景理论等基准更准确,而且已经公理化,在逻辑上意味着严重违反预期效用理论。启发式也有不足之处,比如未能考虑个体差异和错综复杂的选择模式,在某些环境下的预测准确度不如各种模型组合和神经网络。本说明重点讨论启发式的一个重要缺陷,即它无法产生赌注的估值。我指出,对于优先权启发式来说,赌博的确定性等价物是已知的,并建议利用这一事实来扩大启发式的范围。事实上,通过简单的辅助假设和计算,我证明了优先权启发式可以解释圣彼得堡悖论和股票溢价之谜,而且可以说比标准方法更简洁、更合理。
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Making valuations with the priority heuristic

The priority heuristic is a lexicographic semi-order for choosing between gambles. It has merits such as predicting, out-of-sample, people's majority choice more accurately than benchmarks such as prospect theory, having been axiomatized, and logically implying major violations of expected utility theory. The heuristic has shortcomings too, such as failing to account for individual differences and intricate choice patterns, and predicting less accurately than various model ensembles and neural networks in some environments. This note focuses on an important purported shortcoming of the heuristic, that it cannot produce valuations of gambles. I point out that the certainty equivalent of a gamble for the priority heuristic is known and suggest that this fact can be used to enhance the scope of the heuristic. Indeed, by making simple auxiliary assumptions and calculations, I demonstrate that the priority heuristic can explain the Saint Petersburg paradox and the equity premium puzzle, and to do so arguably more parsimoniously and plausibly than standard approaches.

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来源期刊
Journal of Mathematical Psychology
Journal of Mathematical Psychology 医学-数学跨学科应用
CiteScore
3.70
自引率
11.10%
发文量
37
审稿时长
20.2 weeks
期刊介绍: The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome. Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation. The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology. Research Areas include: • Models for sensation and perception, learning, memory and thinking • Fundamental measurement and scaling • Decision making • Neural modeling and networks • Psychophysics and signal detection • Neuropsychological theories • Psycholinguistics • Motivational dynamics • Animal behavior • Psychometric theory
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