Learning and incentives in user-generated content

Ghosh, Aritra; Hummel, Patrick

doi:10.1145/2422436.2422465

Cited by 30 publications

(25 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key difference between this existing literature and our work is that prior work has largely focused on models and analysis for rewarding contributions, whereas we focus on the problem of rewarding contributors for their overall contributions to a site, rather than incentivizing desirable behavior in a single contribution decision. That is, the research thus far models and prescribes reward allocation mechanisms for a single "unit" on a site-how to allocate points among the set of answers contributed to a single question [Chen et al 2009;Ghosh and McAfee 2012;Ghosh and Hummel 2012], or attention among the set of reviews for one product on Amazon or a particular restaurant on Yelp McAfee 2011, 2012;Ghosh and Hummel 2013]; or how to distribute prize money among the contestants in a single crowdsourcing contest [Chawla et al 2012;Archak and Sundarajan 2009;Ghosh and McAfee 2012]. While some of these models (such as in Ghosh and McAfee [2011] and Ghosh and Hummel [2012]) could arguably be extended to the contributor-reward problem, the analysis there regards mechanisms that are meaningful in the context of single contributions rather than overall contributor rewards.…”

Section: Related Workmentioning

confidence: 99%

Incentives, Gamification, and Game Theory: An Economic Approach to Badge Design

EasleyDavid

GhoshArpita

2016

ACM Trans. Econ. Comput.

Self Cite

View full text Add to dashboard Cite

Gamification is growing increasingly prevalent as a means to incentivize user engagement of social media sites that rely on user contributions. Badges, or equivalent rewards, such as top-contributor lists that are used to recognize a user's contributions on a site, clearly appear to be valued by users who actively pursue and compete for them. However, different sites use different badge designs, varying how, and for what, badges are awarded. Some sites, such as StackOverflow, award badges for meeting fixed levels of contribution. Other sites, such as Amazon and Y! Answers, reward users for being among some top set of contributors on the site, corresponding to a competitive standard of performance. Given that users value badges, and that contributing to a site requires effort, how badges are designed will affect the incentives-therefore the participation and effort-elicited from strategic users on a site.We take a game-theoretic approach to badge design, analyzing the incentives created by widely used badge designs in a model in which winning a badge is valued, effort is costly, and potential contributors to the site endogenously decide whether or not to participate, and how much total effort to put into their contributions to the site. We analyze equilibrium existence, as well as equilibrium participation and effort, in an absolute standards mechanism M α in which badges are awarded for meeting some absolute level of (observed) effort, and a relative standards mechanism M ρ corresponding to competitive standards, as in a top-ρ contributor badge. We find that equilibria always exist in both mechanisms, even when the value from winning a badge depends endogenously on the number of other winners. However, M α has zero-participation equilibria for standards that are too high, whereas all equilibria in M ρ elicit nonzero participation for all possible ρ, provided that ρ is specified as a fixed number rather than as a fraction of actual contributors (note that the two are not equivalent in a setting with endogenous participation). Finally, we ask whether or not a site should explicitly announce the number of users winning a badge. The answer to this question is determined by the curvature of the value of winning the badge as a function of the number of other winners.

show abstract

Section: Related Workmentioning

confidence: 99%

Incentives, Gamification, and Game Theory: An Economic Approach to Badge Design

EasleyDavid

GhoshArpita

2016

ACM Trans. Econ. Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A discussion of industrial applications of MAB can be found in (Agarwal et al, 2017). e three-way tradeo between exploration, exploitation and incentives has been studied in several se ings other than ours: incentivizing exploration in a recommendation system (e.g., Che and Hörner, 2018;Frazier et al, 2014;Kremer et al, 2014;Mansour et al, 2020;Bimpikis et al, 2018;Bahar et al, 2016;Immorlica et al, 2020), dynamic auctions (e.g., Athey and Segal, 2013;Bergemann and Välimäki, 2010;Kakade et al, 2013), pay-per-click ad auctions with unknown click probabilities (e.g., Babaio et al, 2014;Devanur and Kakade, 2009;Babaio et al, 2015), coordinating search and matching by self-interested agents (Kleinberg et al, 2016), as well as human computation (e.g., Ho et al, 2016;Ghosh and Hummel, 2013;Singla and Krause, 2013). Bolton and Harris (1999); Keller et al (2005); Gummadi et al (2012) studied models with self-interested agents jointly performing exploration, with no principal to coordinate them.…”

Section: Related Workmentioning

confidence: 99%

The Perils of Exploration under Competition

Aridor

Liu

Slivkins

et al. 2019

Proceedings of the 2019 ACM Conference on Economics and Computation

View full text Add to dashboard Cite

We empirically study the interplay between exploration and competition. Systems that learn from interactions with users often engage in exploration: making potentially suboptimal decisions in order to acquire new information for future decisions. However, when multiple systems are competing for the same market of users, exploration may hurt a system's reputation in the near term, with adverse competitive effects. In particular, a system may enter a "death spiral", when the short-term reputation cost decreases the number of users for the system to learn from, which degrades the system's performance relative to competition and further decreases the market share.We ask whether better exploration algorithms are incentivized under competition. We run extensive numerical experiments in a stylized duopoly model in which two firms deploy multi-armed bandit algorithms and compete for myopic users. We find that duopoly and monopoly tend to favor a primitive "greedy algorithm" that does not explore and leads to low consumer welfare, whereas a temporary monopoly (a duopoly with an early entrant) may incentivize better bandit algorithms and lead to higher consumer welfare. Our findings shed light on the first-mover advantage in the digital economy by exploring the role that data can play as a barrier to entry in online markets.

show abstract

“…In some cases, workers may be motivated by task-specific factors. For example, if a task involves creating content to be posted on the web, workers may be motivated by the possibility of receiving attention [25].…”

Section: Incentives and Other Human Factorsmentioning

confidence: 99%

Online decision making in crowdsourcing markets

Slivkins

Vaughan

2014

SIGecom Exch.

View full text Add to dashboard Cite

Over the past decade, crowdsourcing has emerged as a cheap and efficient method of obtaining solutions to simple tasks that are difficult for computers to solve but possible for humans. The popularity and promise of crowdsourcing markets has led to both empirical and theoretical research on the design of algorithms to optimize various aspects of these markets, such as the pricing and assignment of tasks. Much of the existing theoretical work on crowdsourcing markets has focused on problems that fall into the broad category of online decision making; task requesters or the crowdsourcing platform itself make repeated decisions about prices to set, workers to filter out, problems to assign to specific workers, or other things. Often these decisions are complex, requiring algorithms that learn about the distribution of available tasks or workers over time and take into account the strategic (or sometimes irrational) behavior of workers.As human computation grows into its own field, the time is ripe to address these challenges in a principled way. However, it appears very difficult to capture all pertinent aspects of crowdsourcing markets in a single coherent model. In this paper, we reflect on the modeling issues that inhibit theoretical research on online decision making for crowdsourcing, and identify some steps forward. This paper grew out of the authors' own frustration with these issues, and we hope it will encourage the community to attempt to understand, debate, and ultimately address them. * Microsoft Research.

show abstract

Learning and incentives in user-generated content

Cited by 30 publications

References 23 publications

Incentives, Gamification, and Game Theory: An Economic Approach to Badge Design

Incentives, Gamification, and Game Theory: An Economic Approach to Badge Design

The Perils of Exploration under Competition

Online decision making in crowdsourcing markets

Contact Info

Product

Resources

About