Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-29T14:43:39.738Z Has data issue: false hasContentIssue false
  • English
  • Français

Asking the right question: Risk and expectation in multiagent contracting

Published online by Cambridge University Press:  12 February 2004

ALEXANDER BABANOV ,
JOHN COLLINS  and
MARIA GINI
ALEXANDER BABANOV
Affiliation:
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
JOHN COLLINS
Affiliation:
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
MARIA GINI
Affiliation:
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper we are interested in the decision problem faced by an agent when requesting bids for collections of tasks with complex time constraints and interdependencies. In particular, we study the problem of specifying an appropriate schedule for the tasks in the request for bids. We expect bids to require resource commitments, so we expect different settings of time windows to solicit different bids and different costs. The agent is interested in soliciting “desirable” bids, where desirable means bids that can be feasibly combined in a low-cost combination that covers the entire collection of tasks. Since the request for bids has to be issued before the agent can see any bids, in this decision process there is a probability of loss as well as a probability of gain. This requires the decision process to deal with the risk posture of the person or organization on whose behalf the agent is acting. We describe a model based on Expected Utility Theory and show how an agent can attempt to maximize its profits while managing its financial risk exposure. We illustrate the operation and properties of the model and discuss what assumptions are required for its successful integration in multiagent contracting applications.

Keywords

Automated AuctionsExpected UtilityMultiagent ContractingOptimizationRisk Estimation
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 3 , August 2003 , pp. 173 - 186
Copyright
© 2003 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Axelrod, R. (1997). The Complexity of Cooperation. Princeton, NJ: Princeton University Press.
Axelrod, R.M. (1984). The Evolution of Cooperation. New York: Basic Books.
Babanov, A., Collins, J., & Gini, M. (2002). Risk and expectations in a-priori time allocation in multi-agent contracting. Proc. First Int. Conf. Autonomous Agents and Multi-Agent Systems, vol. 1, pp. 53–60, Bologna, Italy.
Babanov, A., Ketter, W., & Gini, M. (2002). An evolutionary framework for large-scale experimentation in multi-agent systems. In Toward an Application Science: MAS Problem Spaces and Their Implications to Achieving Globally Coherent Behavior. Bologna, Italy.
Collins, J. (2002). Solving combinatorial auctions with temporal constraints in economic agents. PhD Thesis. University of Minnesota.
Collins, J., Bilot, C., Gini, M., & Mobasher, B. (2001). Decision processes in agent-based automated contracting. IEEE Internet Computing 5(2), 61–72.CrossRefGoogle Scholar
Collins, J., Ketter, W., & Gini, M. (2002). A multi-agent negotiation testbed for contracting tasks with temporal and precedence constraints. International Journal of Electronic Commerce 7(1), 35–57.CrossRefGoogle Scholar
Dias, M.B. & Stentz, A. (2000). A free market architecture for distributed control of a multirobot system. Sixth Int. Conf. Intelligent Autonomous Systems, pp. 115–122, Venice, Italy.
Dutta, P.S., Sen, S., & Mukherjee, R. (2001). Scheduling to be competitive in supply chains. IJCAI workshop on E-Business and the Intelligent Web.
Forrest, S. (1993). Genetic algorithms: Principles of natural selection applied to computation. Science 261, 872–878.CrossRefGoogle Scholar
Fujishima, Y., Leyton–Brown, K., & Shoham, Y. (1999). Taming the computational complexity of combinatorial auctions: Optimal and approximate approaches. Proc. 16th Int. Joint Conf. Artificial Intelligence, pp. 548–553.
Glass, A. & Grosz, B.J. (2000). Socially conscious decision-making. Proc. Fourth Int. Conf. Autonomous Agents, pp. 217–224.CrossRef
Grosof, B.N., Labrou, Y., & Chan, H.Y. (1999). A declarative approach to business rules in contracts: Courteous logic programs in XML. Proc. ACM Conf. Electronic Commerce (EC'99), pp. 68–77. New York: ACM.CrossRef
Guttman, R.H., Moukas, A.G., & Maes, P. (1998). Agent-mediated electronic commerce: A survey. Knowledge Engineering Review 13(2), 143–152.CrossRefGoogle Scholar
Hunsberger, L. & Grosz, B.J. (2000). A combinatorial auction for collaborative planning. Proc. 4th Int. Conf. Multi-Agent Systems, pp. 151–158. Boston: IEEE Computer Society Press.CrossRef
Jullien, B. & Salanié, B. (2000). Estimating preferences under risk: The case of racetrack bettors. The Journal of Political Economy 108(3), 503–530.CrossRefGoogle Scholar
Kjenstad, D. (1998). Coordinated supply chain scheduling. PhD Thesis. Norwegian University of Science and Technology.
Leyton–Brown, K., Pearson, M., & Shoham, Y. (2000). Towards a universal test suite for combinatorial auction algorithms. Proc. ACM Conf. Electronic Commerce (EC'00), pp. 66–76, Minneapolis, MN.CrossRef
Machina, M.J. (1987). Choice under uncertainty: Problems solved and unsolved. The Journal of Economic Perspectives 1(1), 121–154.CrossRefGoogle Scholar
Machina, M.J. (1989). Dynamic consistency and non-expected utility models of choice under uncertainty. The Journal of Economic Literature 27(4), 1622–1668.Google Scholar
Mas–Colell, A., Whinston, M.D., & Green, J.R. (1995). Microeconomic Theory. New York: Oxford University Press.
McAfee, R. & McMillan, P.J. (1987). Auctions and bidding. Journal of Economic Literature 25, 699–738.Google Scholar
Nelson, R.R. (1995). Recent evolutionary theorizing about economic change. Journal of Economic Literature 33(1), 48–90.Google Scholar
Nisan, N. (1999). Bidding and allocation in combinatorial auctions. Proc. ACM Conf. Electronic Commerce (EC'00), pp. 1–12, Minneapolis, MN. New York: ACM.
Parkes, D.C. & Ungar, L.H. (2001). An auction-based method for decentralized train scheduling. Proc. Fifth Int. Conf. Autonomous Agents, pp. 43–50, Montreal. New York: ACM.
Pratt, J.W. (1964). Risk aversion in the small and in the large. Econometrica 32, 122–136.CrossRefGoogle Scholar
Reeves, C.R. (1993). Modern Heuristic Techniques for Combinatorial Problems. New York: Wiley.
Reeves, D.M., Wellman, M.P., & Grosof, B.N. (2001). Automated negotiation from declarative contract descriptions. Proc. Fifth Int. Conf. Autonomous Agents, pp. 51–58. Montreal. New York: ACM.CrossRef
Rode, D. (1997). Market efficiency, decision processes, and evolutionary games. Carnegie Mellon University.
Rothkopf, M.H., Pekeč, A., & Harstad, R.M. (1998). Computationally manageable combinatorial auctions. Management Science 44(8), 1131–1147.CrossRefGoogle Scholar
Sadeh, N.M., Hildum, D.W., Kjenstad, D., & Tseng, A. (1999). MASCOT: An agent-based architecture for coordinated mixed-initiative supply chain planning and scheduling. Workshop on Agent-Based Decision Support in Managing the Internet-Enabled Supply-Chain, Agents '99, pp. 133–138.
Sandholm, T. (1999). An algorithm for winner determination in combinatorial auctions. Proc. 16th Int. Joint Conf. Artificial Intelligence, pp. 524–547.
Sandholm, T. (2000). Approaches to winner determination in combinatorial auctions. Decision Support Systems 28(1–2), 165–176.CrossRefGoogle Scholar
Sandholm, T., Suri, S., Gilpin, A., & Levine, D. (2001). CABOB: A fast optimal algorithm for combinatorial auctions. Proc. 17th Int. Joint Conf. Artificial Intelligence, Seattle, WA, pp. 1102–1108.
Sandholm, T.W. (1996). Negotiation Among Self-Interested Computationally Limited Agents. PhD Thesis. University of Massachusetts, Amherst.
Shen, W. & Norrie, D.H. (1999). Agent-based systems for intelligent manufacturing: A state-of-the-art survey. Knowledge and Information Systems 1(2), 129–156.CrossRefGoogle Scholar
Smith, V.K. & Desvousges, W.H. (1987). An empirical analysis of the economic value of risk changes. The Journal of Political Economy 95(1), 89–114.CrossRefGoogle Scholar
Sycara, K., Decker, K., & Williamson, M. (1997). Middle-agents for the Internet. Proc. 15th Joint Conf. Artificial Intelligence, pp. 578–583.
Tesfatsion, L. (2001). Agent-Based Computational Economics: Growing Economies from the Bottom Up. ISU Economics Working Paper No. 1. Ames, IA: Iowa State University.
Walsh, W.E., Wellman, M., & Ygge, F. (2000). Combinatorial auctions for supply chain formation. Proc. ACM Conf. Electronic Commerce (EC'00), pp. 260–269.CrossRef
Wellman, M.P., Walsh, W.E., Wurman, P.R., & MacKie-Mason, J.K. (2001). Auction protocols for decentralized scheduling. Games and Economic Behavior 35, 271–303.CrossRefGoogle Scholar
Wurman, P.R., Wellman, M.P., & Walsh, W.E. (1998). The Michigan Internet AuctionBot: A configurable auction server for human and software agents. Second Int. Conf. Autonomous Agents, pp. 301–308.CrossRef