/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2014-02-17 22:29:012016-08-22 15:45:34Signal Structure and Strategic Information Acquisition: Deliberative Auctions with Interdependent Values
E Brinkman, MP Wellman, and SE Page International Conference on Autonomous Agents and Multiagent Systems, pages 229–236, May 2014. Abstract The ability to gather information can affect outcomes in auctions and other games of incomplete…
/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2013-05-03 16:06:112017-06-24 02:17:30Accounting for Price Dependencies in Simultaneous Sealed-Bid Auctions
BA Mayer, E Sodomka, A Greenwald, and MP Wellman 14th ACM Conference on Electronic Commerce, pages 679–696, June 2013. Abstract Current autonomous bidding strategies for complex auctions typically employ a two phased architecture: first,…
/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2012-08-03 17:56:162017-12-23 23:14:20Self-confirming price-prediction strategies for simultaneous one-shot auctions
MP Wellman, E Sodomka, and A Greenwald Games and Economic Behavior, 102:339–372, 2017. Abstract Bidding in simultaneous auctions is challenging because an agent's value for a good in one auction may depend on the outcome of other auctions;…
/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2012-04-17 14:54:252016-08-22 15:49:26Peer-to-peer tangible goods rental
JA Hill and MP Wellman AAMAS-12 Workshop on Agent-Mediated Electronic Commerce (AMEC) and Trading Agent Design and Analysis (TADA), June 2012. Abstract We present a game-theoretic model of online tangible private goods rental. Rental mechanisms…
/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2011-06-03 13:02:552016-08-22 16:04:21Weighted description logics preference formulas for multiattribute negotiation
A Ragone, T Di Noia, FM Donini, E Di Sciascio, and MP Wellman Third International Conference on Scalable Uncertainty Management, pages 193–205, September 2009. includes material from a paper presented at the AAMAS-09 Workshop on Declarative…
/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2011-06-02 19:35:402021-07-27 19:58:52The structure of signals: Causal interdependence models for games of incomplete information
MP Wellman, L Hong, and SE Page Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, July 2011. Abstract Traditional economic models typically treat private information, or signals, as generated from some underlying state.…
/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2010-05-24 00:01:272016-08-22 15:59:14Multiattribute auctions based on generalized additive independence
We develop multiattribute auctions that accommodate generalized additive independent (GAI) preferences. We propose an iterative auction mechanism that maintains prices on potentially overlapping GAI clusters of attributes, thus decreases elicitation and computational burden, and creates an open competition among suppliers over a multidimensional domain. Most significantly, the auction is guaranteed to achieve surplus which approximates optimal welfare up to a small additive factor, under reasonable equilibrium strategies of traders. The main departure of GAI auctions from previous literature is to accommodate non-additive trader preferences, hence allowing traders to condition their evaluation of specific attributes on the value of other attributes. At the same time, the GAI structure supports a compact representation of prices, enabling a tractable auction process. We perform a simulation study, demonstrating and quantifying the significant efficiency advantage of more expressive preference modeling. We draw random GAI-structured utility functions with various internal structures, generate additive functions that approximate the GAI utility, and compare the performance of the auctions using the two representations. We find that allowing traders to express existing dependencies among attributes improves the economic efficiency of multiattribute auctions.
/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2010-03-27 22:27:592016-08-22 16:07:12Bidding Strategies for Simultaneous Ascending Auctions
Simultaneous ascending auctions present agents with various strategic problems, depending on preference structure. As long as bids represent non-repudiable offers, submitting non-contingent bids to separate auctions entails an exposure problem: bidding to acquire a bundle risks the possibility of obtaining an undesired subset of the goods. With multiple goods (or units of a homogeneous good) bidders also need to account for their own effects on prices. Auction theory does not provide analytic solutions for optimal bidding strategies in the face of these problems. We present a new family of decision-theoretic bidding strategies that use probabilistic predictions of final prices: self-confirming distribution-prediction strategies. Bidding based on these is provably not optimal in general. But evidence using empirical game-theoretic methods we developed indicates the strategy is quite effective compared to other known methods when preferences exhibit complementarities. When preferences exhibit substitutability, simpler demand-reduction strategies address the own price effect problem more directly and perform better.
/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2010-03-27 22:19:062016-08-22 16:04:07Stronger CDA Strategies through Empirical Game-Theoretic Analysis and Reinforcement Learning
We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-theoretic analysis with reinforcement learning. We apply this methodology to the classic Continuous Double Auction game, conducting the most comprehensive CDA strategic study published to date. Empirical game analysis confirms prior findings about the relative performance of known strategies. Reinforcement learning derives new bidding strategies from the empirical equilibrium environment. Iterative application of this approach yields strategies stronger than any other published CDA bidding policy, culminating in a new Nash equilibrium supported exclusively by our learned strategies.
/wp-content/uploads/2016/07/SRG-black-background.png 0 0 wellman /wp-content/uploads/2016/07/SRG-black-background.png wellman2010-03-27 22:09:442021-06-24 12:02:44Information Feedback and Efficiency in Multiattribute Double Auctions
We investigate tradeoffs among expressiveness, operational cost, and economic efficiency for a class of multiattribute double-auction markets. To enable polynomial-time clearing and information feedback operations, we restrict the bidding language to a form of multiattribute OR-of-XOR expressions. We then consider implications of this restriction in environments where bidders' preferences lie within a strictly larger class, that of complement-free valuations. Using valuations derived from a supply chain scenario, we show that an iterative bidding protocol can overcome the limitations of this language restriction. We further introduce a metric characterizing the degree to which valuations violate the substitutes condition, theoretically known to guarantee efficiency, and present experimental evidence that the actual efficiency loss is proportional to this metric.