Predicting people's bidding behavior in negotiation

This paper presents a statistical learning approach to predicting people's bidding behavior in negotiation. Our study consists multiple 2-player negotiation scenarios where bids of multi-valued goods can be accepted or rejected. The bidding task is formalized as a selection process in which a proposer player chooses a single bid to offer to a responder player from a set of candidate proposals. Each candidate is associated with features that affect whether not it is the chosen bid. These features represent social factors that affect people's play. We present and compare several algorithms for predicting the chosen bid and for learning a model from data. Data collection and evaluation of these algorithms is performed on both human and synthetic data sets. Results on both data sets show that an algorithm that reasons about dependencies between the features of candidate proposals is significantly more successful than an algorithm which assumes that candidates are independent. In the synthetic data set, this algorithm achieved near optimal performance. We also study the problem of inferring the features of a proposal given the fact that it was the chosen bid. A baseline importance sampling algorithm is first presented, and then compared with several approximations that attain much better performance.

[1]  Radford M. Neal.  Connectionist Learning of Belief Networks , 1992, Artif. Intell..

[2]  Andrew Y. Ng,et al.  Pharmacokinetics of a novel formulation of ivermectin after administration to goats , 2000, ICML.

[3]  Rajarshi Das,et al.  Agent-Human Interactions in the Continuous Double Auction , 2001, IJCAI.

[4]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[5]  Lise Getoor,et al.  Learning Probabilistic Relational Models , 1999, IJCAI.

[6]  Colin Camerer.  Behavioral Game Theory: Experiments in Strategic Interaction , 2003 .

[7]  L. Thompson,et al.  Social Utility and Decision Making in Interpersonal Contexts , 1989 .

[8]  Ya'akov Gal,et al.  Learning Social Preferences in Games , 2004, AAAI.

[9]  Daphne Koller,et al.  Utilities as Random Variables: Density Estimation and Structure Discovery , 2000, UAI.

[10]  M. Bazerman.  Judgment in Managerial Decision Making , 1990 .

[11]  Michael P. Wellman,et al.  Price Prediction Strategies for Market-Based Scheduling , 2004, ICAPS.

[12]  Colin Camerer,et al.  Foundations of Human Sociality - Economic Experiments and Ethnographic: Evidence From Fifteen Small-Scale Societies , 2004 .

[13]  Daphne Koller,et al.  Learning an Agent's Utility Function by Observing Behavior , 2001, ICML.

[14]  Peter Stone,et al.  Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation , 2002, ICML.

[15]  Sarit Kraus,et al.  The influence of social dependencies on decision-making: initial investigations with a new game , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[16]  Michael P. Wellman,et al.  The Michigan Internet AuctionBot: a configurable auction server for human and software agents , 1998, AGENTS '98.

[17]  Peter Stone,et al.  Bidding for customer orders in TAC SCM , 2004, AAMAS'04.

[18]  J.P. Cavano.  Computers as partners: a technology forecast for decision-making in the 21st century , 1999, Proceedings. Twenty-Third Annual International Computer Software and Applications Conference (Cat. No.99CB37032).

[19]  Richard D. Lawrence.  A Machine-Learning Approach to Optimal Bid Pricing , 2003 .