A Geometric Method to Construct Minimal Peer Prediction Mechanisms

Frongillo, Rafael M.; Witkowski, Jens

doi:10.1609/aaai.v30i1.10050

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Note that more complex designs are also allowed by our framework, such as the one proposed in (Faltings, Li, and Jurca 2014). For more information on the properties of different minimal peer-predictions and their relationships, we refer the reader to (Frongillo and Witkowski 2016).…”

Section: Peer-prediction Constraints (Ppc)mentioning

confidence: 99%

Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints

Radanovic

Singla

Krause

et al. 2018

AAAI

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We study a problem of optimal information gathering from multiple data providers that need to be incentivized to provide accurate information. This problem arises in many real world applications that rely on crowdsourced data sets, but where the process of obtaining data is costly. A notable example of such a scenario is crowd sensing. To this end, we formulate the problem of optimal information gathering as maximization of a submodular function under a budget constraint, where the budget represents the total expected payment to data providers. Contrary to the existing approaches, we base our payments on incentives for accuracy and truthfulness, in particular, peer prediction methods that score each of the selected data providers against its best peer, while ensuring that the minimum expected payment is above a given threshold. We first show that the problem at hand is hard to approximate within a constant factor that is not dependent on the properties of the payment function. However, for given topological and analytical properties of the instance, we construct two greedy algorithms, respectively called PPCGreedy and PPCGreedyIter, and establish theoretical bounds on their performance w.r.t. the optimal solution. Finally, we evaluate our methods using a realistic crowd sensing testbed.

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Section: Peer-prediction Constraints (Ppc)mentioning

confidence: 99%

Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints

Radanovic

Singla

Krause

et al. 2018

AAAI

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“…In other words, we remove an undesirable skew in agents' expected payoffs that might occur due to the presence of non-strategic reports. 3 Without additional restrictions on agents' beliefs, it is possible to show that the PTS mechanism is uniquely truthful (Frongillo and Witkowski 2016). Condition ( 2) is called the self-predicting condition, and it is crucial for ensuring the truthfulness of PTS.…”

Section: Our Approachmentioning

confidence: 99%

“…In a basic elicitation setting, agents share a common belief system regarding the parameters of the setting (Miller, Resnick, and Zeckhauser 2005;Prelec 2004). While the mechanisms typically allow some deviations from this assumption (Frongillo and Witkowski 2016;Radanovic and Faltings 2014), these deviations can be quite constrained, especially when private information has a complex structure. 2 We also mention mechanisms that operate in more specialized settings that allow agents to have more heterogeneous beliefs.…”

Section: Introductionmentioning

confidence: 99%

Partial Truthfulness in Minimal Peer Prediction Mechanisms With Limited Knowledge

Radanovic

Faltings

2018

AAAI

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We study minimal single-task peer prediction mechanisms that have limited knowledge about agents' beliefs. Without knowing what agents' beliefs are or eliciting additional information, it is not possible to design a truthful mechanism in a Bayesian-Nash sense. We go beyond truthfulness and explore equilibrium strategy profiles that are only partially truthful. Using the results from the multi-armed bandit literature, we give a characterization of how inefficient these equilibria are comparing to truthful reporting. We measure the inefficiency of such strategies by counting the number of dishonest reports that any minimal knowledge-bounded mechanism must have. We show that the order of this number is θ(log n), where n is the number of agents, and we provide a peer prediction mechanism that achieves this bound in expectation.

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“…As a matter of fact, Harsanyi's original paper [38], as well as classic textbooks in economics (e.g., [40,57]) introduce Bayesian games directly in the context of subjective beliefs. 4 Similar notions of subjective priors and `subjective equilibria"" have also been studied rather extensively for general Bayesian games in economics [3,4,28,37,42,43,68] and computer science [27,72].…”

mentioning

confidence: 99%

On the Complexity of Equilibrium Computation in First-Price Auctions

et al. 2023

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We consider the problem of computing a (pure) Bayes--Nash equilibrium in the firstprice auction with continuous value distributions and discrete bidding space. We prove that when bidders have independent subjective prior beliefs about the value distributions of the other bidders, computing an \varepsi -equilibrium of the auction is PPAD-complete, and computing an exact equilibrium is FIXP-complete. We also provide an efficient algorithm for solving a special case of the problem for a fixed number of bidders and available bids.

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A Geometric Method to Construct Minimal Peer Prediction Mechanisms

Cited by 7 publications

References 16 publications

Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints

Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints

Partial Truthfulness in Minimal Peer Prediction Mechanisms With Limited Knowledge

On the Complexity of Equilibrium Computation in First-Price Auctions

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