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Tylkin, Paul

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Tylkin

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Paul

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Tylkin, Paul

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Now showing 1 - 2 of 2
  • Publication

    Thwarting Vote Buying Through Decoy Ballots

    (International Joint Conferences on Artificial Intelligence Organization, 2017-08) Parkes, David; Tylkin, Paul; Xiao, Lirong

    There is increasing interest in promoting participatory democracy, in particular by allowing voting by mail or internet and through random-sample elections. A pernicious concern, though, is that of vote buying, which occurs when a bad actor seeks to buy ballots, paying someone to vote against their own intent. This becomes possible whenever a voter is able to sell evidence of which way she voted. We show how to thwart vote buying through decoy ballots, which are not counted but are indistinguishable from real ballots to a buyer. We show that an Election Authority can significantly reduce the power of vote buying through a small number of optimally distributed decoys, and model societal processes by which decoys could be distributed. We also introduce a generalization of our model to non-binary election outcomes.

  • Publication

    Multi-View Decision Processes: The Helper-AI Problem

    (Neural Information Processing Systems Foundation) Dimitrakakis, Christos; Parkes, David; Radanovic, Goran; Tylkin, Paul

    We consider a two-player sequential game in which agents have the same reward function but may disagree on the transition probabilities of an underlying Markovian model of the world. By committing to play a specific policy, the agent with the correct model can steer the behavior of the other agent, and seek to improve utility. We model this setting as a multi-view decision process, which we use to formally analyze the positive effect of steering policies. Furthermore, we develop an algorithm for computing the agents' achievable joint policy, and we experimentally show that it can lead to a large utility increase when the agents' models diverge.