Implementing the "Wisdom of the Crowd"

Kremer, Ilan; Mansour, Yishay; Perry, Motty

doi:10.1145/2482540.2482542

Cited by 78 publications

(136 citation statements)

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“…Such dynamic control of information is present in Gershkov and Szentes (2009), but that paper considers a very different environment, as there are direct payoff externalities (voting). Much more closely related to the present paper is a recent study by Kremer, Mansour and Perry (2014). They study the optimal mechanism for inducing agents to explore over multiple products.…”

Section: Introductionmentioning

confidence: 88%

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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Section: Introductionmentioning

confidence: 88%

Optimal Design for Social Learning

Che

Hörner²

2015

SSRN Journal

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“…The above policy maximizes social welfare even if we do not restrict the information flow, and the planner announces to the agents the actions' realizations. In the case that µ 1 > µ 2 > 0 > µ 3 , we can execute for the first two agents the above strategy, and essentially reduce the number of actions to two, for which the optimal policy was given by Kremer et al [10]. For this reason, we assume that 0 > µ 2 > µ 3 .…”

Section: Optimal Bic Algorithm For K Actionsmentioning

confidence: 99%

“…The different type of recommendation policy algorithm we introduce for this setting is a generalization of the partition policy, originally defined in Kremer et al [10].…”

Section: Continuous Distribution For the A Priori Best Action's Rewardmentioning

confidence: 99%

“…The social planner recommends to each agent an action, and if the recommendation is Bayesian incentive compatible (henceforth, BIC), the agent will follow the action. This model was presented in Kremer et al [10] and studied in [11][12][13]. The work of Kremer et al [10] presented an optimal algorithm for the social planner in the case of two actions with deterministic outcome.…”

mentioning

confidence: 99%

“…This model was presented in Kremer et al [10] and studied in [11][12][13]. The work of Kremer et al [10] presented an optimal algorithm for the social planner in the case of two actions with deterministic outcome. (Deterministic outcome implies that each time the action is performed we receive the same reward, and the uncertainty is what that value will be, which is governed by the prior distribution.…”

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confidence: 99%

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Optimal Algorithm for Bayesian Incentive-Compatible Exploration

Cohen

Mansour

2019

Proceedings of the 2019 ACM Conference on Economics and Computation

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IsraelWe consider a social planner faced with a stream of myopic selfish agents. The goal of the social planner is to maximize the social welfare, however, it is limited to using only information asymmetry (regarding previous outcomes) and cannot use any monetary incentives. The planner recommends actions to agents, but her recommendations need to be Bayesian Incentive Compatible to be followed by the agents.Our main result is an optimal algorithm for the planner, in the case that the actions realizations are deterministic and have limited support, making significant important progress on this open problem. Our optimal protocol has two interesting features. First, it always completes the exploration of a priori more beneficial actions before exploring a priori less beneficial actions. Second, the randomization in the protocol is correlated across agents and actions (and not independent at each decision time).(This can be due to regulatory constraints, business model, social norms, or any other reason.) The main advantage of the planner in our model is the information asymmetry, namely, the fact that the planner has much more information than the agents. As a motivating example for information asymmetry, consider a GPS driving application. The application (social planner) is recommending to the drivers (agents) the best route to drive (action), given the changing road delays, and observes the actual road delays when the route is driven. While the application can recommend driving routes, ultimately, the driver decides which route to actually drive. The application needs periodically to send drivers on exploratory routes, where it has uncertainty regarding the actual delay, in order to observe their delay. The driver is aware that the application has updated information regarding the current delays on various roads. For this reason, the driver would be willing to follow the recommendation even if she knows that there is a small probability that she is asked to explore. On the other extreme, if the driver would assume that with high probability a certain recommended route has a higher delay, she might drive an alternate route. This inherent balancing of exploration and exploitation while satisfying agents' incentives, is at the core of this work.The abstract model that we consider is the following. There is a finite set of actions, and for each action there is a prior distribution on its rewards. A social planner is faced with a sequence of myopic selfish agents, and each agent appears only once. The social planner would like to maximize the social welfare, the sum of the agents' utilities. The social planner recommends to each agent an action, and if the recommendation is Bayesian incentive compatible (henceforth, BIC), the agent will follow the action. This model was presented in Kremer et al. [10] and studied in [11][12][13]. The work of Kremer et al. [10] presented an optimal algorithm for the social planner in the case of two actions with deterministic outcome. (Deterministic outcome implies that each time the ...

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Incentivizing Exploration with Heterogeneous Value of Money

Han

Kempe

Qiang

2015

Web and Internet Economics

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Recently, Frazier et al. proposed a natural model for crowdsourced exploration of different a priori unknown options: a principal is interested in the long-term welfare of a population of agents who arrive one by one in a multi-armed bandit setting. However, each agent is myopic, so in order to incentivize him to explore options with better long-term prospects, the principal must offer the agent money. Frazier et al. showed that a simple class of policies called time-expanded are optimal in the worst case, and characterized their budget-reward tradeoff. The previous work assumed that all agents are equally and uniformly susceptible to financial incentives. In reality, agents may have different utility for money. We therefore extend the model of Frazier et al. to allow agents that have heterogeneous and non-linear utilities for money. The principal is informed of the agent's tradeoff via a signal that could be more or less informative. Our main result is to show that a convex program can be used to derive a signal-dependent time-expanded policy which achieves the best possible Lagrangian reward in the worst case. The worst-case guarantee is matched by so-called "Diamonds in the Rough" instances; the proof that the guarantees match is based on showing that two different convex programs have the same optimal solution for these specific instances. These results also extend to the budgeted case as in Frazier et al. We also show that the optimal policy is monotone with respect to information, i.e., the approximation ratio of the optimal policy improves as the signals become more informative.Comment: WINE 201

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Implementing the "Wisdom of the Crowd"

Cited by 78 publications

References 8 publications

Optimal Design for Social Learning

Optimal Design for Social Learning

Optimal Algorithm for Bayesian Incentive-Compatible Exploration

Incentivizing Exploration with Heterogeneous Value of Money

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