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Learning and incentives in user-generated content: multi-armed bandits with endogenous arms
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Social computing and user-generated content: a game-theoretic approach
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Many websites rank user-generated content (UGC) using viewer votes, displaying higher quality contributions more prominently and suppressing lower quality ones. Such an allocation of attention constitutes a mechanism, which can influence the quality of content elicited from attention-motivated contributors. In this paper, we analyze equilibrium behavior in the widely used rank-order mechanism, where contributions are allocated positions on the page in decreasing order of their ratings, and the proportional mechanism which distributes attention in proportion to the number of positive ratings, in a game-theoretic model where agents are motivated by attention and the cost of making a contribution is increasing in its quality. The rank-order mechanism always possesses a symmetric mixed strategy equilibrium in which all agents decide whether to contribute with the same probability, and randomly draw a quality from a common distribution conditional on participating. We investigate equilibrium behavior in the limit of diverging attention, and show that the lowest quality that can arise in a mixed strategy equilibrium of the rank-order mechanism becomes optimal as the amount of available attention diverges. We then compare equilibrium qualities in the proportional and the rank-order mechanism and show that the probability an agent chooses a higher quality in the rank-order mechanism than in the proportional mechanism goes to one as the amount of available attention diverges. Thus the rank-order mechanism almost always incentivizes higher quality contributions in equilibrium than the proportional mechanism.