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Organization, learning and cooperation


  • Jason Barr

    (Rutgers Department of Economics)

  • Francesco Saraceno

    (Observatoire français des conjonctures économiques)


This paper models the organization of the firm as a type of artificial neural network in a duopoly setting. The firm plays a repeated Prisoner’s Dilemma type game, and must also learn to map environmental signals to demand parameters and to its rival’s willingness to cooperate. We study the prospects for cooperation given the need for the firm to learn the environment and its rival’s output. We show how profit and cooperation rates are affected by the sizes of both firms, their willingness to cooperate, and by environmental complexity. In addition, we investigate equilibrium firm size and cooperation rates.

Suggested Citation

  • Jason Barr & Francesco Saraceno, 2009. "Organization, learning and cooperation," Sciences Po publications info:hdl:2441/9832, Sciences Po.
  • Handle: RePEc:spo:wpmain:info:hdl:2441/9832

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    References listed on IDEAS

    1. Joseph Henrich, 2001. "In Search of Homo Economicus: Behavioral Experiments in 15 Small-Scale Societies," American Economic Review, American Economic Association, vol. 91(2), pages 73-78, May.
    2. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    3. Green, Edward J & Porter, Robert H, 1984. "Noncooperative Collusion under Imperfect Price Information," Econometrica, Econometric Society, vol. 52(1), pages 87-100, January.
    4. Cho, In-Koo, 1994. "Bounded Rationality, Neural Network and Folk Theorem in Repeated Games with Discounting," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 4(6), pages 935-957, October.
    5. Ho, Teck-Hua, 1996. "Finite automata play repeated prisoner's dilemma with information processing costs," Journal of Economic Dynamics and Control, Elsevier, vol. 20(1-3), pages 173-207.
    6. Barr, Jason & Saraceno, Francesco, 2002. "A computational theory of the firm," Journal of Economic Behavior & Organization, Elsevier, vol. 49(3), pages 345-361, November.
    7. Chang, Myong-Hun & Harrington, Joseph Jr., 2006. "Agent-Based Models of Organizations," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 26, pages 1273-1337 Elsevier.
    8. DeCanio, Stephen J. & Watkins, William E., 1998. "Information processing and organizational structure," Journal of Economic Behavior & Organization, Elsevier, vol. 36(3), pages 275-294, August.
    9. Verboven, Frank, 1997. "Collusive behavior with heterogeneous firms," Journal of Economic Behavior & Organization, Elsevier, vol. 33(1), pages 121-136, May.
    10. Carley, Kathleen M., 1996. "A comparison of artificial and human organizations," Journal of Economic Behavior & Organization, Elsevier, vol. 31(2), pages 175-191, November.
    11. Rubinstein, Ariel, 1986. "Finite automata play the repeated prisoner's dilemma," Journal of Economic Theory, Elsevier, vol. 39(1), pages 83-96, June.
    12. Jean Tirole, 1988. "The Theory of Industrial Organization," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262200716, January.
    13. Drew Fudenberg & Jean Tirole, 1991. "Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061414, January.
    14. Cyert, Richard M & DeGroot, Morris H, 1973. "An Analysis of Cooperation and Learning in a Duopoly Context," American Economic Review, American Economic Association, vol. 63(1), pages 24-37, March.
    15. Barr, Jason & Saraceno, Francesco, 2005. "Cournot competition, organization and learning," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 277-295, January.
    16. Casson, Mark, 1991. "The Economics of Business Culture: Game Theory, Transaction Costs, and Economic Performance," OUP Catalogue, Oxford University Press, number 9780198283751.
    17. Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers Archive 5075, Iowa State University, Department of Economics.
    18. Miller, John H., 1996. "The coevolution of automata in the repeated Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 29(1), pages 87-112, January.
    19. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
    20. Radner, Roy, 1993. "The Organization of Decentralized Information Processing," Econometrica, Econometric Society, vol. 61(5), pages 1109-1146, September.
    21. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, January.
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    Cited by:

    1. Jason Barr & Francesco Saraceno, 2005. "Modeling the Firm as an Artificial Neural Network," Working Papers Rutgers University, Newark 2005-011, Department of Economics, Rutgers University, Newark.
    2. Francesco Saraceno & Jason Barr, 2008. "Cournot competition and endogenous firm size," Journal of Evolutionary Economics, Springer, vol. 18(5), pages 615-638, October.
    3. Eva Bolfikova & Daniela Hrehova & Jana Frenova, 2010. "Manager’s decision-making in organizations empirical analysis of bureaucratic vs. learning approach," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics, vol. 28(1), pages 135-163.

    More about this item


    Artificial neural networks; Prisoner’s Dilemma; Cooperation; Firm learning;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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