Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes

References

• Bapna R., Goes P., Gupta A., Karuga G. Predicting bidders: Willingness to pay in online multi-unit ascending auctions: Analytical and empirical insights. INFORMS J. Comput. (2008) 20(3):345–355Link, Google Scholar
• Benisch M., Andrews J., Sadeh N. Pricing for customers with probabilistic valuations as a continuous knapsack problem. Proc. 8th Internat. Conf. Electronic Commerce (2006) Fredericton, NB:38–46Crossref, Google Scholar
• Benisch M., Greenwald A., Grypari I., Lederman R., Naroditskiy V., Tschantz M. Botticelli: A supply chain management agent designed to optimize under uncertainty. ACM Trans. Comp. Logic (2004) 4(3):29–37Google Scholar
• Berry P., Conley K., Gervasio M., Peintner B., Uribe T., Yorke-Smith N. Deploying a personalized time management agent. Proc. Fifth Internat. Conf. Autonomous Agents and Multi-Agent Systems (2006) Hakodate, Japan:1546–1571Crossref, Google Scholar
• Bichler M., Gupta A., Ketter W. Designing smart markets. Inform. Systems Res. (2010) 21(4):688–699Link, Google Scholar
• Box G., Jenkins G.Time Series Analysis: Forecasting and Control (1994) 3rd ed.(Prentice Hall, Englewood Cliffs, NJ) Google Scholar
• Brown R. G., Meyer R. F., D'Esopo D. A. The fundamental theorem of exponential smoothing. Oper. Res. (1961) 9(5):673–687Link, Google Scholar
• Cachon G., Netessine S., Simchi-Levi D., Wu S. D., Shen Z.-J. Game theory in supply chain analysis. Handbook of Quantitative Supply Chain Analysis Modeling in the eBusiness Era (2004) (Kluwer Academic Publishers, Norwell, MA) 13–66Crossref, Google Scholar
• Chatzidimitriou K. C., Symeonidis A. L. Data-mining-enhanced agents in dynamic supply-chain-management environments. IEEE Intelligent Systems (2009) 24(3):54–63Crossref, Google Scholar
• Chatzidimitriou K. C., Symeonidis A. L., Kontogounis I., Mitkas P. A. Agent Mertacor: A robust design for dealing with uncertainty and variation in SCM environments. Expert Systems with Appl. (2008) 35(3):591–603Crossref, Google Scholar
• Collins J., Ketter W., Gini M. A multi-agent negotiation testbed for contracting tasks with temporal and precedence constraints. Internat. J. Electronic Commerce (2002) 7(1):35–57Crossref, Google Scholar
• Collins J., Ketter W., Gini M. Flexible decision control in an autonomous trading agent. Electronic Commerce Res. Appl. (2009) 8(2):91–105Crossref, Google Scholar
• Collins J., Ketter W., Gini M. Flexible decision support in dynamic interorganizational networks. Eur. J. Inform. Systems (2010a) 19(3):436–448Crossref, Google Scholar
• Collins J., Ketter W., Sadeh N. Pushing the limits of rational agents: The trading agent competition for supply chain management. AI Magazine (2010b) 31(2):63–80Crossref, Google Scholar
• Collins J., Arunachalam R., Sadeh N., Ericsson J., Finne N., Janson S. The supply chain management game for the 2006 trading agent competition. (2005) . Tech. Rep. CMU-ISRI-05-132, Carnegie Mellon University, PittsburghGoogle Scholar
• Dempster A. P., Laird N. M., Rubin D. B. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. Series B (1977) 39(1):1–38Google Scholar
• Elmaghraby W., Keskinocak P. Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Sci. (2003) 49(10):1287–1309Link, Google Scholar
• Enders W.Applied Econometric Time Series (1995) (Wiley, Hoboken, NJ) Google Scholar
• Fan M., Stallaert J., Whinston A. B. Decentralized mechanism design for supply chain organizations using an auction market. Inform. Systems Res. (2003) 14(1):1–22Link, Google Scholar
• Fine S., Singer Y., Tishby N. The hierarchical hidden Markov model: Analysis and applications. Machine Learn. (1998) 32(1):41–62Crossref, Google Scholar
• Ghani R. Price prediction and insurance for online auctions. Internat. Conf. Knowledge Discovery in Data Mining (2005) Chicago:411–418Crossref, Google Scholar
• Ghose A., Smith M. D., Telang R. Internet exchanges for used books: An empirical analysis of product cannibalization and welfare impact. Inform. Systems Res. (2006) 17(1):3–19Link, Google Scholar
• Gibbons J. D. Nonparametric statistical inference. Technometrics (1986) 28(3):239–250Google Scholar
• Gray J. A., Spencer D. E. Price prediction errors and real activity: A reassessment. Econom. Inquiry (1990) 28(4):658–681Crossref, Google Scholar
• Gupta A., Stahl D. O., Whinston A., Amman H., Rustem B., Whinston A. B. The Internet: A future tragedy of the commons? Computational Approaches to Economic Problems (1997) (Kluwer Academic Publishers, Dordrecht, The Netherlands) 347–361Crossref, Google Scholar
• He M., Rogers A., Luo X., Jennings N. R. Designing a successful trading agent for supply chain management. Proc. Fifth Internat. Conf. Autonomous Agents and Multi-Agent Systems (2006) Hakodate, Japan:1159–1166Crossref, Google Scholar
• Jordan P. R., Kiekintveld C., Wellman M. P. Empirical game-theoretic analysis of the TAC supply chain game. Proc. Sixth Internat. Conf. Autonomous Agents and Multi-Agent Systems (2007) Honolulu:1188–1195Crossref, Google Scholar
• Kambil A., van Heck E. Reengineering the Dutch flower auctions: A framework for analyzing exchange organizations. Inform. Systems Res. (1998) 9(1):1–19Link, Google Scholar
• Kaplan S., Sawhney M. E-hubs: The new B2B marketplaces. Harvard Bus. Rev. (2000) 78(3):97–103Google Scholar
• Karmarkar N. A new polynomial-time algorithm for linear programming. Proc. Sixteenth Annual ACM Sympos. Theory Comput. (1984) (ACM, New York) 302–311Crossref, Google Scholar
• Ketter W. Identification and prediction of economic regimes to guide decision making in multi-agent marketplaces. (2007) . Ph.D. thesis, University of Minnesota, Twin-CitiesGoogle Scholar
• Ketter W., Collins J., Reddy P., Flath C. The power trading agent competition. (2011) . Tech. Rep. ERS-2011-011-LIS, RSM Erasmus University, Rotterdam, The NetherlandsGoogle Scholar
• Ketter W., Collins J., Gini M., Gupta A., Schrater P., van Heck E., et al. Strategic sales management guided by economic regimes. Edited Volume of the 2nd Smart Business Network Initiative Discovery Event (2006) (Springer Verlag, Berlin) 113–125Google Scholar
• Ketter W., Collins J., Gini M., Gupta A., Schrater P. Detecting and forecasting economic regimes in multi-agent automated exchanges. Decision Support Systems (2009) 47(4):307–318Crossref, Google Scholar
• Ketter W., Kryzhnyaya E., Damer S., McMillen C., Agovic A., Collins J., Gini M. MinneTAC sales strategies for supply chain TAC. Internat. Conf. Autonomous Agents and Multi-Agent Systems (2004) (ACM, New York) 1372–1373Google Scholar
• Khreich W., Granger E., Miri A., Sabourin R. On the memory complexity of the forward-backward algorithm. Pattern Recognition Lett. (2010) 31(2):91–99Crossref, Google Scholar
• Kiekintveld C., Miller J., Jordan P. R., Callender L. F., Wellman M. P. Forecasting market prices in a supply chain game. Electronic Commerce Res. Appl. (2009) 8(2):63–77Crossref, Google Scholar
• Kleindorfer P. R., Wu D. J. Integrating long-and short-term contracting via business-to-business exchanges for capital-intensive industries. Management Sci. (2003) 49(11):1597–1615Link, Google Scholar
• Kontogounis I., Chatzidimitriou K. C., Symeonidis A. L., Mitkas P. A. A robust agent design for dynamic SCM environments. Proc. 4th Hellenic Joint Conf. Artificial Intelligence (SETN) (2006) Heraklion, Greece:127–136Crossref, Google Scholar
• Kullback S.Information Theory and Statistics (1959) (Dover Publications, New York) Google Scholar
• Kullback S., Leibler R. A. On information and sufficiency. Ann. Math. Statist. (1951) 22(1):79–86Crossref, Google Scholar
• Lawrence R. A machine learning approach to optimal bid pricing. 8th INFORMS Comput. Soc. Conf. Optim. Comput. Network Era (2003) Phoenix, AZ:1–22Crossref, Google Scholar
• Mark B., Perrault R. C. Calo: Cognitive assistant that learns and organizes. (2006) . Accessed on February 27, 2012, http://www.ai.sri.com/project/CALOGoogle Scholar
• Massey C., Wu G. Detecting regime shifts: The causes of under- and overestimation. Management Sci. (2005) 51(6):932–947Link, Google Scholar
• Muth J. F. Rational expectations and the theory of price movements. Econometrica (1961) 29(3):315–335Crossref, Google Scholar
• Nagali V., Hwang J., Gaskins D. S. M., Pridgen M., Thurston T., Mackenroth P., Branvold D., Scholler P., Shoemaker G. Procurement risk management (PRM) at Hewlett-Packard company. Interfaces (2008) 38(1):51–60Link, Google Scholar
• Nogales F. J., Contreras J., Conejo A. J., Espinola R. Forecasting next-day electricity prices by time series models. IEEE Trans. Power Systems (2002) 17(2):342–348Crossref, Google Scholar
• Osborn D. R., Sensier M. The prediction of business cycle phases: Financial variables and international linkages. National Inst. Econ. Rev. (2002) 182(1):96–105Crossref, Google Scholar
• Pardoe D., Stone P. Bidding for customer orders in TAC SCM: A learning approach. Workshop on Trading Agent Design and Analysis at AAMAS (2004) (New York)52–58Google Scholar
• Pardoe D., Stone P. Tactex-05: A champion supply chain management agent. Proc. Twenty-First National Conf. Artificial Intelligence (2006) (AAAI, Boston) 1389–1394Google Scholar
• Pardoe D., Stone P., Adomavicius G., Gupta A. An autonomous agent for supply chain management. Handbooks in Information Systems Series: Business Computing (2007) (Elsevier, Bingley, UK) 141–172Google Scholar
• Pauwels K., Hanssens D. Windows of change in mature markets. Eur. Marketing Acad. Conf. (2002) Braga, Portugal:1–6Google Scholar
• Podobnik V., Petric A., Jezic G. An agent-based solution for dynamic supply chain management. J. Universal Comput. Sci. (2008) 14(7):1080–1104Google Scholar
• Sandholm T. Expressive commerce and its application to sourcing: How we conducted $35 billion of generalized combinatorial auctions. AI Magazine (2007) 28(3):45–58Google Scholar
• Shannon C. E. A mathematical theory of communication. Bell System Tech. J. (1948) 27(3):379–423623656Crossref, Google Scholar
• Shmueli G. To explain or to predict? Statist. Sci. (2010) 25(3):289–310Crossref, Google Scholar
• Sodomka E., Collins J., Gini M. Efficient statistical methods for evaluating trading agent performance. Proc. Twenty-Second National Conf. Artificial Intelligence (2007) Vancouver, Canada:770–775Google Scholar
• Swaminathan J. M., Tayur S. R. Models for supply chains in e-business. Management Sci. (2003) 49(10):1387–1406Link, Google Scholar
• Swaminathan J. M., Smith S. F., Sadeh N. M. Modeling supply chain dynamics: A multiagent approach. Decision Sci. (1998) 29(3):607–632Crossref, Google Scholar
• Titterington D., Smith A., Makov U.Statistical Analysis of Finite Mixture Distributions (1985) (Wiley, New York) Google Scholar
• Wang S., Jank W., Shmueli G. Explaining and forecasting online auction prices and their dynamics using functional data analysis. J. Bus. Econom. Statist. (2008) 26(2):144–160Crossref, Google Scholar
• Zhang X., Cheung W. K. Learning global models based on distributed data abstractions. Nineteenth Internat. Joint Conf. Artificial Intelligence (2005) Edinburgh, Scottland:1645–1646Google Scholar
• Zhang D., Zhao K., Liang C.-M., Huq G. B., Huang T.-H. Strategic trading agents via market modeling. SIGecom Exchanges (2004) 4(3):46–55Crossref, Google Scholar
• Zhao W., Zheng Y.-S. Optimal dynamic pricing for perishable assets with nonhomogeneous demand. Management Sci. (2000) 46(3):375–388Link, Google Scholar