Contests for Experimentation

Halac, Marina; Kartik, Navin; Liu, Qingmin

doi:10.1086/693040

Cited by 109 publications

(60 citation statements)

References 48 publications

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“…Our article is also related to the literature on innovation contests with exponential‐bandit experimentation (see Halac et al., , and references therein). In these models, it is uncertain whether the innovation is feasible.…”

Section: Relation To the Literaturementioning

confidence: 99%

Inducing Variety: A Theory of Innovation Contests

Letina

Schmutzler

2019

Int Economic Review

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This article analyzes the design of innovation contests when the quality of an innovation depends on the research approach, but the best approach is unknown. Inducing a variety of research approaches generates an option value. We show that suitable contests can induce such variety. The buyer‐optimal contest is a bonus tournament, where suppliers can choose only between a low bid and a high bid. This contest implements the socially optimal variety for a suitable parameter range. Finally, we compare the optimal contest to scoring auctions and fixed‐prize tournaments.

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Section: Relation To the Literaturementioning

confidence: 99%

Inducing Variety: A Theory of Innovation Contests

Letina

Schmutzler

2019

Int Economic Review

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“…To our knowledge, there are only a few recent game-theoretic papers on this topic. Bimpikis et al (2014) and Halac et al (2016) both study "innovation races" and consider technical uncertainties in innovation races -namely whether it is feasible to solve the problem at all. They illustrate that feedback on the one hand exposes a discouraging performance gap, but on the other hand updates contestants' perceptions of the feasibility of solving the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

The Role of Feedback in Dynamic Crowdsourcing Contests: A Structural Empirical Analysis

2016

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In this paper, we empirically examine the impact of performance feedback on the outcome of crowdsourcing contests. We develop a dynamic structural model to capture the economic processes that drive contest participants' behavior, and estimate the model using a rich data set collected from a major online crowdsourcing design platform. The model captures key features of the crowdsourcing context, including a large participant pool, entries by new participants throughout the contest, exploitation (revision of previous submissions) and exploration (radically novel submissions) behaviors by contest incumbents, and the participants' strategic choice among these entry, exploration, and exploitation decisions in a dynamic game. We find that the cost associated with exploratory actions is higher than the cost associated with exploitative actions. High-performers prefer the exploitative strategy, while low-performers tend to make fewer follow-up submissions and prefer the exploratory strategy. Using counter-factual simulations, we compare the outcome of crowdsourcing contests under alternative feedback disclosure policies and award levels. Our simulation results suggest that the full feedback policy (providing feedback throughout the contest) may not be optimal.The late feedback policy (providing feedback only in the second half of the contest) leads to a better overall contest outcome.

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“…Specifically, she recommends the movie to a fraction of agents in each period based on her information at that point in time. 13 The designer discounts the welfare in period t = 2 by a factor δ ∈ (0, 1).…”

Section: Illustrative Examplementioning

confidence: 99%

Optimal Design for Social Learning

Che

Hörner²

2015

SSRN Journal

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This paper studies the design of a recommender system for organizing social learning on a product. To improve incentives for early experimentation, the optimal design trades off fully transparent social learning by over-recommending a product (or "spamming") to a fraction of agents in the early phase of the product cycle. Under the optimal scheme, the designer spams very little about a product right after its release but gradually increases the frequency of spamming and stops it altogether when the product is deemed sufficiently unworthy of recommendation. The optimal recommender system involves randomly triggered spamming when recommendations are public-as is often the case for product ratings-and an information "blackout" followed by a burst of spamming when agents can choose when to check in for a recommendation. Fully transparent recommendations may become optimal if a (socially-benevolent) designer does not observe the agents' costs of experimentation.

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Contests for Experimentation

Cited by 109 publications

References 48 publications

Inducing Variety: A Theory of Innovation Contests

Inducing Variety: A Theory of Innovation Contests

The Role of Feedback in Dynamic Crowdsourcing Contests: A Structural Empirical Analysis

Optimal Design for Social Learning

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