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Agent Based Economics

  • Gerald B. Sheblé
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (PEPS)

Abstract

This chapter describes the application of artificial life techniques (ALIFE) to the study of auction markets for electric power optimization. Artificial life techniques include: artificial neural networks (ANN), genetic algorithms (GA) and genetic programming (GP). All ALIFE techniques are based on biological models of evolution and of neurological functions.

Keywords

Price Discovery Future Contract Bidding Strategy Auction Mechanism Double Auction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Anwar, and G. B. Sheblé, “Application Of Optimal Power Flow To Interchange Brokerage Transaction”, Electric Power System Research Journal, vol. 30, no. 1, pp. 83–90, 1995.CrossRefGoogle Scholar
  2. 2.
    Arthur, W.B., “On Learning and Adaptation in the Economy,” The Santa Fe Institute, Report SFI 92–07–038.Google Scholar
  3. 3.
    Arthur (1993), “On Designing Economic Agents that Behave Like Human Agents,” Journal of Evolutionary Economics 3, pp. 1–22.CrossRefGoogle Scholar
  4. 4.
    Ashlock, “GP-automata for Dividing the Dollar,” Mathematics Department,Iowa State University, Ames, IA 1995.Google Scholar
  5. 5.
    Ashlock, M. D. Smucker, E. A. Stanley, and L. Tesfatsion (1996), “Preferential Partner Selection in an Evolutionary Study of Prisoner’s Dilemma,” BioSystems 37, pp. 99–125.CrossRefGoogle Scholar
  6. 6.
    Axelrod (1984), The Evolution of Cooperation, Basic Books, New York.Google Scholar
  7. 7.
    Axelrod (1987), “The Evolution of Strategies in the Iterated Prisoner’s Dilemma,” in L. Davis, Ed., Genetic Algorithms and Simulated Annealing, Morgan Kaufmann, Los Altos, CA.Google Scholar
  8. 8.
    Barkovich, and D. Hawk, “Charting a new course in California”, IEEE Spectrum, Vol. 33, July 1996, p26.CrossRefGoogle Scholar
  9. 9.
    Bard, “Short-Term Scheduling of Thermal-Electric Generators Using LaGrangian Relaxation,” Operations Research, 36, 756–766, 1988.zbMATHCrossRefGoogle Scholar
  10. 10.
    Baughman and W. W. Lee, “A Monte Carlo Model for Calculating Spot Market Prices of Electricity,” 91 WM 179–2 PWRS.Google Scholar
  11. 11.
    Bellman, Dynamic Programming, Princeton University Press, Princeton, New Jersey, 1957.zbMATHGoogle Scholar
  12. 12.
    Belew, and L. B. Booker, (Editors), Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1991.Google Scholar
  13. 13.
    Bertsekas and P. Tseng, “Relaxation Methods for Minimum Cost Ordinary and Generalized Network Flow Problems,” Operations Research, 36, 93–114, 1988.MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Binger and E. Hoffman. Microeconomics with Calculus, HarperCollins. 1988.Google Scholar
  15. 15.
    Brock and S. N. Durlauf (1995), “Discrete Choice with Social Interactions,” Working Paper No. 95–10–084, Santa Fe Institute, Santa Fe, NM.Google Scholar
  16. 16.
    Bulfin, R. L. and Unger, V. E., “Computational Experience with an Algorithm for the Lock-Box Problem,” Proceedings, Association f o r Computing Machinery, 16–19, 1973.Google Scholar
  17. 17.
    Bullard and J. Duffy (1994), “A Model of Learning and Emulation with Artificial Adaptive Agents,” Working Paper, Department of Economics, University of Pittsburgh.Google Scholar
  18. 18.
    Chao, “Peak Load Pricing And Capacity Planning With Demand And Supply Uncertainty”, Bell Journal of Economics, Vol. 14, No. 1, 1983, pp. 179–190.CrossRefGoogle Scholar
  19. 19.
    Caramanis, N. Roukos, F. C. Schweppe, “WRATES: A Tool for Evaluating the Marginal Cost of Wheeling,” paper presented at the 1988 IEEE PES Summer Power Meeting, New York, New York.Google Scholar
  20. 20.
    Cooper, and D. Steinberg, Methods and Applications of Linear Programming, W. B. Saunders Company, Philadelphia, Pennsylvania, 1974.Google Scholar
  21. 21.
    Cowan, and J. H. Miller, Economic Life on a Lattice: Some Game Theoretic Results,The Santa Fe Institute, Report SFI 90–010.Google Scholar
  22. 22.
    Davis, (Editor), Genetic Algorithms and Simulated Annealing,Pitman,London, 1987.Google Scholar
  23. 23.
    Davis, (Editor), Handbook of Genetic Algorithms, Van Nnostrand Reinhold, New York, NY, 1991.Google Scholar
  24. 24.
    David and Y. Z. Li, “Effect of Inter-temporal Factors on the Real Time Pricing of Electricity,” 92 WM 117–2 PWRS.Google Scholar
  25. 25.
    Doty and P. L. McEntire, “An Analysis of Electric Power Brokerage Systems,” IEEE Transactions on PAS, vol. PAS-101, no. 2, February 1982, pp. 389–396.Google Scholar
  26. 26.
    De Vany (1996), “The Emergence and Evolution of Self-Organized Coalitions,” pp. 25–50 in M. Gilli (ed.), Computational Economic Systems: Models, Methods, and Econometrics, Kluwer Scientific Publications, New York.Google Scholar
  27. 27.
    Durlauf (1996), “Neighborhood Feedbacks, Endogenous Stratification, and Income Inequality,” in W. A. Barnett, G. Gandolfo, and C. Hillinger (eds.), Disequilibrium Dynamics: Theory and Applications, Cambridge University Press, Cambridge.Google Scholar
  28. 28.
    Epstein and R. Axtell (1996), Growing Artificial Societies: Social Science from the Bottom Up, MIT Press/Brookings, Cambridge, Mass.Google Scholar
  29. 29.
    Fand, D. A. Richards, and G. B. Sheblé, “The Implementation Of An Energy Brokerage System Using Linear Programming”, IEEE Trans. on Power Systems, Vol. 7, No. 1, pp. 90–96, 1991.Google Scholar
  30. 30.
    Fand, and G. B. Sheblé, “Optimal Power Flow Emulation Of Interchange Brokerage System Using Linear Programming”, IEEE Trans. on Power Systems,Vol. 7, No. 2, pp. 497–504, 1992.Google Scholar
  31. 31.
    Finlay, Optimal Bidding Strategies in Competitive Electric Power Pools,Masters thesis, University of Illinois, Urbana-Champaign, IL, 1995. 32.Gedra and P. P. Varaiya, “Markets and Pricing for InterruptibleGoogle Scholar
  32. 33.
    Gilchrist, Statistical Forecasting. New York, NY: John Wiley & Sons, 1976.Google Scholar
  33. 34.
    Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, Inc., Reading, Massachusetts, 1989.Google Scholar
  34. 35.
    Suran Goonatilake, John A. Campbell, and Nesar Ahmad. “Genetic-Fuzzy Systems for Financial Decision Making,” Lecture Notes in Computer Science, vol. 1011, pp. 202–223. Springer-Verlag: New York. 1995.Google Scholar
  35. 36.
    Grefensteete, (Editor), Proceedings of the First International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Inc., Hillsdale, New Jersey, 1985.Google Scholar
  36. 37.
    Grefensteete, (Editor), Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Inc., Hillsdale, NJ, 1987.Google Scholar
  37. 38.
    Jean-Michel Guldmann, “A Marginal-cost Pricing Model for Gas Distribution Utilities,” pp. 851–863, Operations Research, Vol. 34, No. 6, November-December, 1986.Google Scholar
  38. 39.
    Hakin, Neural Networks: A Comprehensive Foundation, Englewood Cliffs, NJ: Macmillan Publishing Company, 1994.Google Scholar
  39. 40.
    Holland, J.H.,Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA, 1975.Google Scholar
  40. 41.
    R A Howard, “Decision Analysis: Practice And Promise” Management Science, 1988: 679–675.Google Scholar
  41. 42.
    IEEE Power System Reliability Subcommittee, “Reliability Test System”, presented at IEEE PES Winter Meeting, 96 WM 183–7 PWRS, 1996.Google Scholar
  42. 43.
    Ionnides, “Evolution of Trading Structures,” Department of Economics, Tufts University, Medford, MA 02155, 1995.Google Scholar
  43. 44.
    Ionnides (1996), “Evolution of Trading Structures,” Working Paper 9604–020, Santa Fe, NM.Google Scholar
  44. 45.
    Jensen and J. W. Barnes, Network Flow Programming, John Wiley & Sons, New York, New York, 1980.Google Scholar
  45. 46.
    Ba t Kosko. Neural Networks and Fizzy Systems: A Dynamical Systems AçproxhtoMachineIntelligence Englewood Cliffs, NJ: Prent ice Hall,1992. 47.Koza, John, Genetic Programming, MIT Press, Cambridge,Massachusetts, 1992.Google Scholar
  46. 48.
    Kumar, K. H. Ng, and G. B. Sheblé, “AGC Simulator For Price Based Operation Part I: A Model”, accepted and to be presented at IEEE PES Summer Meeting, 96 SM 588–4 PWRS, 1996.Google Scholar
  47. 49.
    Kumar, K. H. Ng, and G. B. Sheblé, “AGC Simulator For Price Based Operation Part II: Case Study Results”, accepted and to be presented at IEEE PES summer meeting, 96 SM 373–1 PWRS, 1996.Google Scholar
  48. 50.
    Kumar, G. Sheblé, “Auction Game in Electric Power Market Place, ”Proceedings of the 58th American Power Conference, 1996, pp. 356364.Google Scholar
  49. 51.
    Kumar, G. Sheblé, “Framework for Energy Brokerage System with Reserve Margin and Transmission Losses,” 1996 IEEE/PES Winter Meeting, 96WM 190–9 PWRS. New York: IEEEGoogle Scholar
  50. 52.
    Kumar, G. Sheblé, “Auction Market Simulator For Price Based Operation,” presented at the 1997 IEEE PES Summer Power Meeting, Berlin, Germany, in press, 1997.Google Scholar
  51. 53.
    Lane, Artificial Worlds and Economics, The Santa Fe Institute, Report SF192–09–048.Google Scholar
  52. 54.
    LeBaron, “Experiments in Evolutionary Finance,” University of Wisconsin - Madison, Madison, Wisconsin, 1995.Google Scholar
  53. 55.
    Lî and A. K. David, “Optimal Multi-Area Wheeling,” 93 WM 174–3 PWRS.Google Scholar
  54. 56.
    Luenberger, Introduction to Linear and Non-Linear Programming, Addison-Wesley Publishing Company, Reading, Massachusetts, 1973.Google Scholar
  55. 57.
    Kevin A. McCabe, S. J. Rassenti, and Vernon L. Smith, “Auction Design for Composite Goods: the Natural Gas Industry,” Journal of Economic Behavior and Organization, 14 (1900) 127–149.CrossRefGoogle Scholar
  56. 58.
    Kevin A. McCabe, S. J. Rassenti, and Vernon L. Smith, “Experimental Research on Deregulated Markets for Natural Gas Pipeline and Electric Power Transmission Networks,” Research in Law and Economics, Vol. 13, pp.161–189, JAI Press, Inc.Google Scholar
  57. 59.
    Manne and J. S. Rogers, “A Structure for Modeling Bulk Power Transfers among Systems in a NERC Region,” sp 91–211.Google Scholar
  58. 60.
    Yusuf M. Mansur. Fuzzy Sets and Economics, Edward Eiger Publishing Limited: Vermont. 1995.Google Scholar
  59. 61.
    Milgrom, Auctions and Bidding: A Primer. Journal of Economic Perspectives,Vol. 3, No. 3. Summer 1989, pp. 3–22, 1989.Google Scholar
  60. 62.
    Jo Min, “Unbundling the quality attributes of electric power: models of alternative market structures,” uer-165, California Energy Studies Report, April 1986.Google Scholar
  61. 63.
    Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, New York, NY, 1992.zbMATHGoogle Scholar
  62. 64.
    Yusuf M. Mansur, Fuzzy Sets and Economics, Edward Eiger Publishing Limited: Vermont. 1995.Google Scholar
  63. 65.
    Nemhauser, Introduction to Dynamic Programming, John Wiley & Sons, Inc., New York, New York, 1966.Google Scholar
  64. 66.
    Papalexopoulos, S. Hao, T. Peng, “An implementation of a neural network-based load forecasting model for the ems,” Presented at the 1994 IEEE/PES Winter Meeting, 94 WM 209–7 PWRS. New York: IEEE, 1994.Google Scholar
  65. 67.
    Post, S. S. Coppinger, and G. B. Sheblé, “Application Of Auctions As A Pricing Mechanism For The Interchange Of Electric Power”, IEEE Trans. on Power Systems, Vol. 10, No. 3, pp. 1580–1584, 1995.CrossRefGoogle Scholar
  66. 68.
    Post, Electric Power Interchange Transaction Analysis And Selection, Master’s thesis, Iowa State University, Ames, IA, 1994.Google Scholar
  67. 69.
    Rassenti, V. L. Smith, and R. L. Bulfin, “A Combinatorial Auction Mechanism for Airport Time Slot Allocation,” The Bell Journal of Economics, 13, 402–417, 1982.CrossRefGoogle Scholar
  68. 70.
    Rassenti and R. L. Bulfin, “A Generalized 0–1 Programming Problem:Algorithm and Application,” presented to the Canadian Operations Research Society/TIMS/ORSA Meeting, Toronto, Canada, April 1981.Google Scholar
  69. 71.
    Charles W. Richter, Jr. and Gerald B. Sheblé. “Genetic Algorithm Evolution of Utility Bidding Strategies for the Competitive Marketplace,” Presented at the 1997 IEEE/PES Summer Meeting, in press, 1997.Google Scholar
  70. 72.
    Charles William Richter, Jr., Developing Bidding Strategies For Electric Utilities In A Competitive Environment,Master’s thesis, Iowa State University, Ames, IA. 1996.Google Scholar
  71. 73.
    Richter and G. B. Sheblé, “Competitive Fuzzy Bidding Strategies For The Successful Generator,” presented and published in the proceedings of the North American Power Conference,Boston, 1997.Google Scholar
  72. 74.
    Charles W. Richter, Jr., Tim T. Mee eld, and Gerald B. Sheblé. “ (Inetic Algorithm De/ elcpmentof a Healthcare Expert System,” Proceedings of the 4th Amual Midwest Bectro- Technology Ccnference, pp. 35–38.Ames, IA. 1995.Google Scholar
  73. 75.
    Rust, R. Palmer, and J. Miller, “A Double Auction Market for Computerized Traders,” The Santa Fe Institute, Report SFI 89–001, 1989.Google Scholar
  74. 76.
    Schaffer, (Editor), Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1989.Google Scholar
  75. 77.
    Schweppe, M. C. Caramanis, R. D. Tabors, and R. E. Bohn, Spot Pricing of Electricity, Kluwer Academic Publishers, Boston, 1987.Google Scholar
  76. 78.
    Gerald B. Sheblé, “Solution of the Unit Commitment Problem by the Method of Unit Periods,” presented at the 1989 IEEE Power Engineering Society Summer Meeting and accepted for publication in the IEEE Transactions on Power Systems, 1989.Google Scholar
  77. 79.
    Sheblé, “Electric energy in a fully evolved marketplace,” Presented at the 1994 North American Power Symposium, Kansas State University, KS, 1994.Google Scholar
  78. 80.
    Sheblé and J. McCalley, “Discrete auction systems for power system management,” Presented at the 1994 National Science Foundation Workshop, Pullman, WA, 1994.Google Scholar
  79. 81.
    Sheblé, “Simulation Of Discrete Auction Systems For Power System Risk Management,” Frontiers of Power, Oklahoma, 1994.Google Scholar
  80. 82.
    Sheblé, M. Ilic, B. Wollenberg, and F. Wu, Lecture notes from: Engineering Strategies for Open Access Transmission Systems,A Two-Day Short Course presented Dec. 5 and 6, 1996 in San Francisco, CA.Google Scholar
  81. 83.
    Sheblé, “Priced Based Operation in an Auction Market Structure”, presented atthe 1996 IEEE/PES Winter Meeting, Baltimore, MD, 1996.Google Scholar
  82. 84.
    Gerald B. Sheblé, “EPRI Auction Market Simulator (EPRI-AMS),” published as EPRI Technical Report.Google Scholar
  83. 85.
    Simmons, Nonlinear Programming for Operations Research, Prentice-Hall, Inc., Englewood Cliffs, N. J., 1975.Google Scholar
  84. 86.
    Siddiqi and M. L. Baughman, “Reliability Differentiated Real-Time Pricing of Electricity,” 92 WM 115–6 PWRS.Google Scholar
  85. 87.
    Smucker, E. A. Stanley, and D. Ashlock (1994), “Analyzing Social Network Structures in the Iterated Prisoner’s Dilemma with Choice and Refusal,” Department of Computer Sciences Technical Report CS-TR94–1259, UW-Madison.Google Scholar
  86. 88.
    Stanley, D. Ashlock, and L. Tesfatsion (1994), “Iterated Prisoner’s Dilemma with Choice and Refusal of Partners, ” pp. 131–175 in C. Langton, ed., Artificial Life Ill, Proceedings, Volume 17, Santa Fe Institute Studies in the Sciences of Complexity, Addison-Wesley, Reading, MA.Google Scholar
  87. 89.
    Chin-Woo Tan, “Prices for Interruptible Electric Power Service,” ISU presentation, July, 1991.Google Scholar
  88. 90.
    Tesfatsion, “A Trade Network Game with Endogenous Partner Selection,” Economic Report Series, Department of Economics, Iowa State University, Ames, IA, 1995.Google Scholar
  89. 91.
    Thompson, and S. Thore, Computational Economics: Economic Modeling with Optimization, San Francisco, CA: Scientific Press, 1992.Google Scholar
  90. 92.
    Vojdani, C. Imparto, N. Saini, B. Wollenberg, and H. Happ,“Transmission Access Issues,” Presented at the 1995 IEEE/PESWinter Meeting, 95 WM 121–4 PWRS. New York: IEEE, 1994.Google Scholar
  91. 93.
    Wood, and B. F. Wollenberg, Power Generation, Operation, and Control, New York, NY: John Wiley & Sons, 1984.Google Scholar
  92. 94.
    A New Laboratory for Economists,“ Science and Technology Section of Business Week, pages 96–97, March 17, 1997.Google Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Gerald B. Sheblé
    • 1
  1. 1.Department of Electrical & Computer EngineeringIowa State University AmesUSA

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