http://www.hpl.hp.com/research/scl/papers/reputationExpt/reputation-expts-and-Prosper.pdf How people use reputation information. reputation incentive design experimental economics We evaluated how people use reputation in a laboratory market and in Prosper, an online microfinance business. We found people use information on past behavior to ostracize previous poor performance in both cases. The laboratory market did not show significant price discrimination, but people used their ability to not fulfill contracts to punish poor performers. Price discrimination was significantly correlated with reputation in Prosper. Thus we find people apply multiple strategies to deal with reputation. Kay-Yut Chen, Scott Golder, Tad Hogg and Cecilia Zenteno 2008-08-19 12:00:00 http://arxiv.org/abs/0707.4195 How people perform in an auction using simulated quantum information processing. quantum information incentive design experimental economics We describe human-subject laboratory experiments on probabilistic auctions based on previously proposed auction protocols involving the simulated manipulation and communication of quantum states. These auctions are probabilistic in determining which bidder wins, or having no winner, rather than always having the highest bidder win. Comparing two quantum protocols in the context of first-price sealed bid auctions, we find the one predicted to be superior by game theory also performs better experimentally. We also compare with a conventional first price auction, which gives higher performance. Thus to provide benefits, the quantum protocol requires more complex economic scenarios such as maintaining privacy of bids over a series of related auctions or involving allocative externalities. Kay-Yut Chen and Tad Hogg 2008-08-19 12:00:00 http://arxiv.org/abs/0704.0800 A privacy-preserving auction using quantum information processing. quantum information incentive design game theory economics We present a quantum auction protocol using superpositions to represent bids and distributed search to identify the winner(s). Measuring the final quantum state gives the auction outcome while simultaneously destroying the superposition. Thus non-winning bids are never revealed. Participants can use entanglement to arrange for correlations among their bids, with the assurance that this entanglement is not observable by others. The protocol is useful for information hiding applications, such as partnership bidding with allocative externality or concerns about revealing bidding preferences. The protocol applies to a variety of auction types, e.g., first or second price, and to auctions involving either a single item or arbitrary bundles of items (i.e., combinatorial auctions). We analyze the game-theoretical behavior of the quantum protocol for the simple case of a sealed-bid quantum, and show how a suitably designed adiabatic sear h reduces the possibilities for bidders to game the auction. This design illustrates how incentive rather that computational constraints affect quantum algorithm choices. Tad Hogg, Pavithra Harsha and Kay-Yut Chen 2008-08-19 12:00:00 http://www.hpl.hp.com/personal/Thomas_Sandholm/sandholm2008a.pdf Tradeoffs between using spot and reservation markets. tycoon incentive design resource allocation markets We propose, implement and evaluate three admission models for computational Grids. The models take the expected demand into account and offer a specific performance guarantee. The main issue addressed is how users and providers should make the tradeoff between a best effort (low guarantee) spot market and an admission controlled (high guarantee) reservation market. Using a realistically modeled high performance computing workload and utility models of user preferences, we run experiments highlighting the conditions under which different markets and admission models are efficient. The experimental results show that providers can make large efficiency gains if the admission model is chosen dynamically based on the current load, likewise we show that users have an opportunity to optimize their job performance by carefully picking the right market based on the state of t e system, and the characteristics of the application to be run. Finally, we provide simple functional expressions that can guide both users and providers when making decisions about guarantee levels to request or offer. Thomas Sandholm, Kevin Lai, and Scott Clearwater 2008-06-06 12:00:00 http://www.hpl.hp.com/research/idl/papers/privacy/index.html An alternative to trusted third parties. incentive design (click link to view abstract) Bernardo A. Huberman and Tad Hogg 2007-01-01 00:00:00 http://www.hpl.hp.com/research/idl/abstracts/ECommerce/privacy.html How do you keep privacy while using reputations to select recommendations? incentive design (click link to view abstract) Bernardo A. Huberman, Matt Franklin and Tad Hogg 2007-01-01 00:00:00