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Waggoner, Bo Franklin

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Waggoner

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Bo Franklin

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Waggoner, Bo Franklin

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2014 - 20162

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Author's Publications: search.filters.isAuthorOfPublication.e04ac522-968c-4adb-bf16-a4d99573999d

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    Acquiring and Aggregating Information from Strategic Sources
    (2016-07-28) Waggoner, Bo Franklin; Chen, Y.; Parkes, D.; Kleinberg, R.; Conitzer, V.
    This thesis considers, from a theoretical perspective, the design of mechanisms to accomplish the objective described in the title. Two cases of this problem are considered: information as represented by data points in a machine-learning context, and information as represented by beliefs in a general prediction context. While there is significant literature on the acquisition or aggregation problems as considered separately, past approaches are often inapplicable or inefficient when considering both together. For the case of data, the thesis proposes an active procurement approach whereby data points are selectively purchased depending on their utility to the learning algorithm. A model is proposed and a purchasing scheme designed that interacts in black-box fashion with the user’s choice of learning algorithm. For a large class of problems, via a specific choice of learning algorithm, risk and regret bounds are proven as a function of budget and the “monetary difficulty” of the problem. For the case of beliefs, the thesis proposes a theory of substitutes and complements of pieces of information. In particular, this theory is used to analyze prediction markets, which are natural and popular mechanisms for simultaneously acquiring and aggregating beliefs. In addition, the thesis examines several additional problems involving information acquisition and aggregation in the fields of crowdsourcing and mechanism design with and without money.
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    Output Agreement Mechanisms and Common Knowledge
    (2014) Waggoner, Bo Franklin; Chen, Yiling
    The recent advent of human computation – employing nonexperts to solve problems – has inspired theoretical work in mechanism design for eliciting information when responses cannot be verified.We study a popular practical method, output agreement, from a theoretical perspective. In output agreement, two agents are given the same inputs and asked to produce some output; they are scored based on how closely their responses agree. Although simple, output agreement raises new conceptual questions. Primary is the fundamental importance of common knowledge: We show that, rather than being truthful, output agreement mechanisms elicit common knowledge from participants.We show that common knowledge is essentially the best that can be hoped for in any mechanism without verification unless there are restrictions on the information structure. This involves generalizing truthfulness to include responding to a query rather than simply reporting a private signal, along with a notion of common-knowledge equilibria. A final important issue raised by output agreement is focal equilibria and player computation of equilibria. We show that, for eliciting the mean of a random variable, a natural player inference process converges to the common-knowledge equilibrium; but this convergence may not occur for other types of queries. Portions of this work were presented at the 2013 Workshop on Social Computing and User-Generated Content, at the 14th ACM Conference on Electronic Commerce.