Incentive design is more likely to elicit desired outcomes when it is derived based on accurate models of agent behavior. A substantial literature in behavioral economics, however, demonstrates that individuals systematically and consistently deviate from the standard economic model---expected utility theory---for decision-making under uncertainty, %a central component of which is at the core of the equilibrium analysis necessary to facilitate mechanism design. Can these behavioral biases---as modeled by prospect theory [Kahneman and Tversky 1979]---in agents' decision-making make a difference to the optimal design of incentives in these environments? In this paper, we explore this question in the context of markets for online labor and crowdsourcing where workers make strategic choices about whether to undertake a task, but do not strategize over quality conditional on participation. We ask what kind of incentive scheme---amongst a broad class of contracts, including those observed on major crowdsourcing platforms such as fixed prices or base payments with bonuses (as on MTurk or oDesk), or open-entry contests (as on platforms like Kaggle or Topcoder)---a principal might want to employ, and how the answer to this question depends on whether workers behave according to expected utility or prospect theory preferences.
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Easley et al. (2015) studied this question.