Evolutionary Efficiency and Happiness
将幸福建模为衡量个体相对成功的工具，通过随时间更新的参照点来优化行为选择，并类比最优激励问题，用代理理论解释幸福感的波动与均值回归。
We model happiness as a measurement tool used to rank alternative actions. Evolution favors a happiness function that measures the individual’s success in relative terms. The optimal function is based on a time-varying reference point—or performance benchmark—that is updated over time in a statistically optimal way in order to match the individual’s potential. Habits and peer comparisons arise as special cases of such an updating process. This updating also results in a volatile level of happiness that continuously reverts to its long-term mean. Throughout, we draw a parallel with a problem of optimal incentives, which allows us to apply statistical insights from agency theory to the study of happiness.