Price Michael Holton, Jones James Holland
Santa Fe Institute, Santa Fe, California, USA.
Department of Earth System Science, Stanford University, Stanford, California, USA.
Evol Hum Sci. 2020 Jun 1;2:e28. doi: 10.1017/ehs.2020.28. eCollection 2020.
The standard theory of rationality posits that agents order preferences according to average utilities associated with different choices. Expected utility theory has repeatedly failed as a predictive theory, as reflected in a growing literature in behavioural economics. Evolutionary theorists have suggested that seemingly irrational behaviours in contemporary contexts may have once served important functions, but existing work linking fitness and choice has not adequately addressed the challenges of constructing an evolutionary theory of decision making. In particular, fitness itself is not a reasonable metric for decision making since its timescale exceeds the lifespan of the decision-maker. Consequently, organisms use proximate systems that work on appropriate timescales and are amenable to feedback and learning. We develop an evolutionary principal-agent model in which individuals utilize a set of proximal choice variables to account for the non-linear dependence of these variables on consumption. While this is insufficient to maximize fitness in the presence of environmental stochasticity, maximum fitness can be achieved by adopting pessimistic probability weightings compatible with the rank-dependent expected utility family of choice models. In particular, pessimistic probability weighting emerges naturally in an evolutionary framework because of extreme intolerance to zeros in multiplicative growth processes.
标准理性理论假定，主体根据与不同选择相关的平均效用对偏好进行排序。预期效用理论作为一种预测理论屡屡失败，行为经济学中不断增多的文献对此有所反映。进化理论家认为，当代背景下看似不理性的行为可能曾经发挥过重要作用，但将适应性与选择联系起来的现有研究尚未充分应对构建进化决策理论的挑战。特别是，适应性本身并非决策的合理指标，因为其时间尺度超过了决策者的寿命。因此，生物体使用在适当时间尺度上起作用且易于反馈和学习的近端系统。我们构建了一个进化委托 - 代理模型，其中个体利用一组近端选择变量来解释这些变量对消费的非线性依赖。虽然在存在环境随机性的情况下这不足以使适应性最大化，但通过采用与等级依赖预期效用选择模型族兼容的悲观概率加权，可以实现最大适应性。特别是，由于在乘法增长过程中对零极端不容忍，悲观概率加权在进化框架中自然出现。
