Behavioral Mechanism Design

Abstract

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, 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.We first show that with expected utility agents, the optimal contract-for any increasing objective of the principalalways takes the form of an output-independent fixed payment to some optimally chosen number of agents. In contrast, when agents behave according to prospect theory preferences, we show that a winner-take-all contest can dominate the fixed-payment contract, for large enough total payments, under a certain condition on the preference functions; we show that this condition is satisfied for the parameters given by the literature on econometric estimation of the prospect theory model [Tversky and Kahneman 1992;Bruhin et al. 2010]. Since these estimates are based on fitting the prospect theory model to extensive experimental data, this result provides a strong affirmative answer to our question for 'real' population preferences: a principal might indeed choose a fundamentally different kind of mechanism-an output-contingent contest versus a 'safe output-independent scheme-and do better as a result, if he accounts for deviations from the standard economic models of decision-making that are typically used in theoretical design.

The power of crowds

“…Given this unusual distribution of effort, we need to improve our understanding of how to maximize recruitment and retention of participants, enhance skill development, and maximize the total effort that participants contribute to projects (13). It may be even more challenging to maximize the efficiency with which human inputs, information sharing, and machine-based processing work together.…”