论文标题

克服诱惑:跨期选择的激励设计

Overcoming Temptation: Incentive Design For Intertemporal Choice

论文作者

Sukumar, Shruthi, Ward, Adrian F., Elliott-Williams, Camden, Hakimi, Shabnam, Mozer, Michael C.

论文摘要

个人经常面临可能导致他们从长期目标中误入歧途的诱惑。我们有兴趣制定干预措施,这些干预措施会导致个人做出良好的初始决定,然后随着时间的推移维护这些决定。在财务决策的领域中,一种特别成功的方法是奖励储蓄帐户:通过将存款将存款绑在定期彩票中,该储蓄账户被激励将存款捐赠给储蓄者。尽管这些彩票在激励全球的储蓄者方面非常有效,但它们是一种既适合的解决方案。我们调查定制奖金是否更有效。我们将延迟谨慎的任务形式化为马尔可夫决策问题,并将个人描述为受到时间折扣的理性代理,与努力相关的成本以及意志力的波动。我们的理论能够解释跨期选择中的关键行为发现。我们创建了一个在线延迟刻薄的游戏,在该游戏中,玩家通过选择一个等待的队列然后执行一系列动作以前进到前线来得分。从游戏中收集的数据适合该模型,然后使用实例化模型在激励空间上优化预测的玩家性能。我们证明,定制的激励结构可以改善个人目标指导的决策。

Individuals are often faced with temptations that can lead them astray from long-term goals. We're interested in developing interventions that steer individuals toward making good initial decisions and then maintaining those decisions over time. In the realm of financial decision making, a particularly successful approach is the prize-linked savings account: individuals are incentivized to make deposits by tying deposits to a periodic lottery that awards bonuses to the savers. Although these lotteries have been very effective in motivating savers across the globe, they are a one-size-fits-all solution. We investigate whether customized bonuses can be more effective. We formalize a delayed-gratification task as a Markov decision problem and characterize individuals as rational agents subject to temporal discounting, a cost associated with effort, and fluctuations in willpower. Our theory is able to explain key behavioral findings in intertemporal choice. We created an online delayed-gratification game in which the player scores points by selecting a queue to wait in and then performing a series of actions to advance to the front. Data collected from the game is fit to the model, and the instantiated model is then used to optimize predicted player performance over a space of incentives. We demonstrate that customized incentive structures can improve an individual's goal-directed decision making.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源