Simone Ferrari-Toniolo, Leo Chi U Seak, Wolfram Schultz
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The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted <i>Preference Changes</i> better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright <i>Preference Reversals</i> may reflect the long experience of our monkeys, their more graded <i>Preference Changes</i> corresponded to those reported for humans. 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引用次数: 0
摘要
期望效用理论(EUT)提供了在风险选择中实现效用最大化的公理。独立公理(IA)是其最苛刻的公理:通过将两个选项与共同的赌博混合在一起,从而平等地改变两个选项时,两个选项之间的偏好不应该发生变化。几个月来,我们使用多种二元期权组合,在数千次随机选择中测试了违反独立公理的共同后果(CC)和共同比率(CR)。三只猴子在最初偏好的赌局和相应的改变赌局之间一直表现出很少的直接偏好逆转(8%),但却有很大的分级偏好变化(46%)。线性判别分析(LDA)表明,在CC(72%)和CR(88%)测试中,赌博概率预测了大部分偏好变化。阿凯克信息标准(Akaike Information Criterion)表明,累积前景理论(CPT)中的概率加权比使用期望值(EV)或 EUT 的模型更能解释选择。用 CPT 的效用和概率加权函数拟合后,在马沙克-马奇纳三角形中出现了非线性和非平行的偏好曲线 (IC),这表明使用期望值或 EUT 的模型不符合 IA 标准。事实上,CPT 模型比 EV 和 EUT 模型更能预测偏好变化。样本外测试中的偏好点更接近 CPT 估算的 IC,而不是 EV 和 EUT IC。最后,虽然只有少数几个明显的偏好逆转可能反映了我们的猴子的长期经验,但它们更多的分级偏好变化与报告的人类偏好变化是一致的。得益于猴子广泛的测试可能性,我们严格的公理测试为风险决策提供了重要信息,并为研究神经元决策机制提供了基础:在线版本包含补充材料,可在10.1007/s11166-022-09388-7上查阅。
Risky choice: Probability weighting explains independence axiom violations in monkeys.
Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The Independence Axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA over several months in thousands of stochastic choices using a large variety of binary option sets. Three monkeys showed consistently few outright Preference Reversals (8%) but substantial graded Preference Changes (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most Preference Changes in CC (72%) and CR (88%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted Preference Changes better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright Preference Reversals may reflect the long experience of our monkeys, their more graded Preference Changes corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serves as basis for investigating neuronal decision mechanisms.
Supplementary information: The online version contains supplementary material available at 10.1007/s11166-022-09388-7.
期刊介绍:
The Journal of Risk and Uncertainty (JRU) welcomes original empirical, experimental, and theoretical manuscripts dealing with the analysis of risk-bearing behavior and decision making under uncertainty. The topics covered in the journal include, but are not limited to, decision theory and the economics of uncertainty, experimental investigations of behavior under uncertainty, empirical studies of real world risk-taking behavior, behavioral models of choice under uncertainty, and risk and public policy. Review papers are welcome.
The JRU does not publish finance or behavioral finance research, game theory, note length work, or papers that treat Likert-type scales as having cardinal significance.
An important aim of the JRU is to encourage interdisciplinary communication and interaction between researchers in the area of risk and uncertainty. Authors are expected to provide introductory discussions which set forth the nature of their research and the interpretation and implications of their findings in a manner accessible to knowledgeable researchers in other disciplines.
Officially cited as: J Risk Uncertain