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Kash, I

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Kash, I

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2010 - 20136

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Author's Publications: search.filters.isAuthorOfPublication.8fbd63d6-0f25-4daf-a8b2-c806421946c2

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Now showing 1 - 6 of 6
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    Enabling Sharing in Auctions for Short-Term Spectrum Licenses
    (Cambridge University Press, 2013) Kash, I; Murty, Rohan; Parkes, David
    Wireless spectrum is a valuable and scarce resource that currently suffers from under-use because of the dominant paradigm of exclusive-use licensing. We propose the SATYA auction (Sanskrit for truth), which allows short-term leases to be auctioned and supports diverse bidder types, including those willing to share access and those who require exclusive-use access. Thus, unlike unlicensed spectrum such as Wi-Fi, which can be shared by any device, and exclusive-use licensed spectrum, where sharing is precluded, SATYA improves efficiency through supporting sharing alongside quality-of-service protections. The auction is designed to be scalable, and also strategy proof, so that simple bidding protocols are optimal. The primary challenge is to handle the externalities created by allocating shared-use alongside exclusive-use bidders. Using realistic Longley-Rice based propagation modeling and data from the FCC’s CDBS database, we conduct extensive simulations that demonstrate SATYA’s ability to handle heterogeneous bidders involving different transmit powers and spectrum needs.
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    Decision Markets with Good Incentives
    (Springer Verlag, 2011) Chen, Yiling; Kash, I; Ruberry, Michael Edward; Shnayder, Victor
    Decision markets both predict and decide the future. They allow experts to predict the effects of each of a set of possible actions, and after reviewing these predictions a decision maker selects an action to perform. When the future is independent of the market, strictly proper scoring rules myopically incentivize experts to predict consistent with their beliefs, but this is not generally true when a decision is to be made. When deciding, only predictions for the chosen action can be evaluated for their accuracy since the other predictions become counterfactuals. This limitation can make some actions more valuable than others for an expert, incentivizing the expert to mislead the decision maker. We construct and characterize decision markets that are – like prediction markets using strictly proper scoring rules – myopic incentive compatible. These markets require the decision maker always risk taking every available action, and reducing this risk increases the decision maker’s worst-case loss. We also show a correspondence between strictly proper decision markets and strictly proper sets of prediction markets, creating a formal connection between the incentives of prediction and decision markets.
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    Market Manipulation with Outside Incentives
    (American Association for Artificial Intelligence, 2011) Chen, Yiling; Gao, Xi; Goldstein, Rick David; Kash, I
    Much evidence has shown that prediction markets, when used in isolation, can effectively aggregate dispersed information about uncertain future events and produce remarkably accurate forecasts. However, if the market prediction will be used for decision making, a strategic participant with a vested interest in the decision outcome may want to manipulate the market prediction in order to influence the resulting decision. The presence of such incentives outside of the market would seem to damage information aggregation because of the potential distrust among market participants. While this is true under some conditions, we find that, if the existence of such incentives is certain and common knowledge, then in many cases, there exists a separating equilibrium for the market where information is fully aggregated. This equilibrium also maximizes social welfare for convex outside payoff functions. At this equilibrium, the participant with outside incentives makes a costly move to gain the trust of other participants. When the existence of outside incentives is uncertain, however, trust cannot be established between players if the outside incentive is sufficiently large and we lose the separability in equilibrium.
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    Enabling Spectrum Sharing in Secondary Market Auctions
    (Institute of Electrical and Electronics Engineers, 2013) Kash, I; Murty, Rohan; Parkes, David
    Wireless spectrum is a scare resource, but in practice much of it is under-used by current owners. To enable better use of this spectrum, we propose an auction approach to dynamically allocate the spectrum in a secondary market. Unlike previous auction approaches, we seek to take advantage of the ability to share spectrum among some bidders while respecting the needs of others for exclusive use. Thus, unlike unlicensed spectrum (e.g. Wi-Fi), which can be shared by any device, and exclusive-use licensed spectrum, where sharing is precluded, we enable efficient allocation by supporting sharing alongside quality-of-service protections. We present SATYA (Sanskrit for “truth”), a strategyproof and scalable spectrum auction algorithm whose primary contribution is in the allocation of a right to contend for spectrum to both sharers and exclusive-use bidders. Achieving strategyproofness in our setting requires appropriate handling of the externalities created by sharing. We demonstrate SATYA’s ability to handle heterogeneous agent types involving different transmit powers and spectrum needs through extensive simulations.
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    Software Economies
    (Association for Computing Machinery, 2010) Bacon, David F.; Bokelberg, Eric; Chen, Yiling; Kash, I; Parkes, David; Rao, Malvika; Sridharan, Manu
    Software construction has typically drawn on engineering metaphors like building bridges or cathedrals, which emphasize architecture, specification, central planning, and determinism. Approaches to correctness have drawn on metaphors from mathematics, like formal proofs. However, these approaches have failed to scale to modern software systems, and the problem keeps getting worse. We believe that the time has come to completely re-imagine the creation of complex software, drawing on systems in which behavior is decentralized, self-regulating, non-deterministic, and emergent---like economies. In this paper we describe our vision for, and prelimary work on, the creation of software economies for both open systems and internal corporate development, and our plans to deploy these ideas within one of the largest developer communities at IBM.
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    Impersonation Strategies in Auctions
    (Springer Science + Business Media, 2010) Kash, I; Parkes, David
    A common approach to analyzing repeated auctions, such as sponsored search auctions, is to treat them as complete information games, because it is assumed that, over time, players learn each other’s types. This overlooks the possibility that players may impersonate another type. Many standard auctions (including generalized second price auctions and core-selecting auctions), as well as the Kelly mechanism, have profitable impersonations. We define a notion of impersonationproofness for the auction mechanism coupled with a process by which players learn about each other’s type, and show an equivalence to a problem of dominant-strategy mechanism design.