Center for Neural Science, New York University, New York, NY 10003.
Proc Natl Acad Sci U S A. 2013 Sep 24;110(39):15788-93. doi: 10.1073/pnas.1308718110. Epub 2013 Sep 9.
Experimental economic techniques have been widely used to evaluate human risk attitudes, but how these measured attitudes relate to overall individual wealth levels is unclear. Previous noneconomic work has addressed this uncertainty in animals by asking the following: (i) Do our close evolutionary relatives share both our risk attitudes and our degree of economic rationality? And (ii) how does the amount of food or water one holds (a nonpecuniary form of "wealth") alter risk attitudes in these choosers? Unfortunately, existing noneconomic studies have provided conflicting insights from an economic point of view. We therefore used standard techniques from human experimental economics to measure monkey risk attitudes for water rewards as a function of blood osmolality (an objective measure of how much water the subjects possess). Early in training, monkeys behaved randomly, consistently violating first-order stochastic dominance and monotonicity. After training, they behaved like human choosers--technically consistent in their choices and weakly risk averse (i.e., risk averse or risk neutral on average)--suggesting that well-trained monkeys can serve as a model for human choice behavior. As with attitudes about money in humans, these risk attitudes were strongly wealth dependent; as the animals became "poorer," risk aversion increased, a finding incompatible with some models of wealth and risk in human decision making.
实验经济学技术已被广泛用于评估人类风险态度,但这些测量的态度与个人整体财富水平的关系尚不清楚。以前的非经济学工作通过以下问题解决了动物中的这种不确定性:(i) 我们的近亲是否同时具有与我们相同的风险态度和经济理性程度?以及 (ii) 一个人持有的食物或水的数量(一种非金钱形式的“财富”)如何改变这些选择者的风险态度?不幸的是,现有的非经济学研究从经济学角度提供了相互矛盾的见解。因此,我们使用人类实验经济学的标准技术来衡量猴子对水奖励的风险态度,作为血液渗透压的函数(受试者所拥有的水量的客观衡量标准)。在训练早期,猴子的行为是随机的,始终违反一阶随机优势和单调性。经过训练,它们的行为与人类选择者相似——在选择上具有技术一致性,并且具有较弱的风险厌恶(即平均而言,风险厌恶或风险中性)——这表明经过良好训练的猴子可以作为人类选择行为的模型。与人类对金钱的态度一样,这些风险态度强烈依赖于财富;随着动物变得“更贫穷”,风险厌恶程度增加,这一发现与人类决策中的一些财富和风险模型不一致。