NDLI: Behavioral Mechanism Design: Optimal Crowdsourcing Contracts and Prospect Theory

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Behavioral Mechanism Design: Optimal Crowdsourcing Contracts and Prospect Theory

Content Provider	ACM Digital Library
Author	Easley, David Ghosh, Arpita
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, %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. We first show that with expected utility agents, the optimal contract---for any increasing objective of the principal---always 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.
Starting Page	679
Ending Page	696
Page Count	18
File Format	PDF
ISBN	9781450334105
DOI	10.1145/2764468.2764513
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Behavioral Crowdsourcing Contests Mechanism design Game theory Contracts
Content Type	Text
Resource Type	Article

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