Skip to main content
SpringerLink
Log in

A market-based computational approach to collaborative organizational learning

  • Special Article
  • Published:
Journal of the Operational Research Society

Abstract

We draw upon the concepts of knowledge market, organizational tacit knowledge, credit assignment, and single-loop learning in proposing a market-based conceptual model for collaborative organizational learning. Our proposed model is characterized by the local competition among seller agents and the global collaboration among winner agents in forming a plan, through a chain of ‘upstream–downstream’ working relationship, for task accomplishment. This feature is achieved through three closely coupled processes: the expert selection process, the capital reallocation process, and the plan formation process. Our model is intended for multiple-step learning environment in which each task consists of a sequence of single-step learning tasks. Learning at the global level is the result of a sequence of nested single-loop learning at the local level.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

References

  • Davenport TH and Prusak L (1998). Working Knowledge. Harvard Business School Press: Cambridge, MA.

    Google Scholar 

  • Carley KM and Prietula MJ (1994). ACT theory: extending the model of bounded rationality. In: Carley KM and Prietula MJ (eds). Computational Organizational Theory. Lawrence Erlbaum Associates: Hillsdale, NJ.

    Google Scholar 

  • Williamson OE (1975). Markets and Hierarchies: Analysis and Anti-trust Implications. Free Press: New York, NY.

    Google Scholar 

  • Williamson OE (1985). The Economic Institutions of Capitalism. Free Press: New York, NY.

    Google Scholar 

  • Robey D (1991). Designing Organizations. Irwin: Homewood, IL.

    Google Scholar 

  • Scott WR (1992). Organizations: Rational, Natural, and Open Systems, 3rd edn. Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Malone TW, Fikes RE, Grant KR and Howard MT (1988). Enterprise: a market-like task scheduler for distributed computing environments. In: Huberman BA (ed). The Ecology of Computation. North-Holland: Amsterdam.

    Google Scholar 

  • Kurose JF and Simha R (1989). A microeconomic approach to optimal resource allocation in distributed computer systems. IEEE Trans Comput 38 (5): 705–717.

    Article  Google Scholar 

  • Waldspurger CA et al (1992). Spawn: a distributed computational economy. IEEE Trans Software Eng 18 (2): 103–117.

    Article  Google Scholar 

  • Day RH (1975). Adaptive processes and economic theory. In: Day RH and Groves T (eds). Adaptive Economic Models. Academic Press: New York, NY.

    Google Scholar 

  • Cohen PR and Feigenbaum EA (eds) (1982). The Handbook of Artificial Intelligence, Vol 3. William Kaufmann: Los Altos, CA.

    Google Scholar 

  • Bogenrieder I and Nooteboom B (2002). The emergence of learning communities: a conceptual analysis. Proceedings of the Third European Conference on Organizational Knowledge, Learning and Capabilities. Athens, Greece.

    Google Scholar 

  • Tsoukas H (1996). The firm as a distributed knowledge system: a constructionist approach. Strategic Mngt J 17: 11–25.

    Article  Google Scholar 

  • Rerup C (2002). Knowledge gaps, brokering and learned ignorance: the NOVO way of management. Proceedings of the Third European Conference on Organizational Knowledge, Learning and Capabilities. Athens, Greece.

    Google Scholar 

  • Simon HA (1983). Why should machine learn? In: Michalski RS, Carbonell JG and Mitchell TM (eds). Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann: Los Altos, CA.

    Google Scholar 

  • Argyris C and Schön DA (1996). Organizational Learning II. Addison-Wesley: Reading, MA.

    Google Scholar 

  • Boisot MH (1995). Information Space: A Framework for Learning in Organizations, Institutions and Culture. Routledge: London, UK.

    Google Scholar 

  • Swart J and Pye A (2002). Conceptualising organizational knowledge as collective tacit knowledge: a model of redescription. Proceedings of the Third European Conference on Organizational Knowledge, Learning and Capabilities. Athens, Greece.

    Google Scholar 

  • Polanyi M (1967). The Tacit Dimension. Routledge: London, UK.

    Google Scholar 

  • Winter SG (1994). Organizing for continuous improvement: evolutionary theory meets the quality revolution. In: Baum JAC and Singh J (eds). The Evolutionary Dynamics of Organizations. Oxford University Press: New York, NY.

    Google Scholar 

  • Choo CW (1998). The Knowing Organization. Oxford University Press: New York, NY.

    Google Scholar 

  • Badaracco JL (1991). The Knowledge Link: How Firms Compete Through Strategic Alliance. Harvard Business School Press: Boston, MA.

    Google Scholar 

  • Brown JS and Duguid P (1991). Organizational learning and communities-of-practice: toward a unified view of working, learning, and innovation. Organization Sci 2 (1): 40–57.

    Article  Google Scholar 

  • Orr JE (1990). Sharing knowledge, celebrating identity: community of memory in a service culture. In: Middleton D and Edwards D (eds) Collective Remembering. Sage: UK.

    Google Scholar 

  • Wenger E (1991). Communities of practice: where learning happens. Benchmark, 82–84.

  • March JG (1981). Decisions in organizations and theories of choice. In: Van de Ven AH and Joyce WF (eds). Perspectives on Organization Design and Behavior. John Wiley: New York, NY.

    Google Scholar 

  • Winter SG (1987). Knowledge and competence as strategic assets. In: Teece DJ (ed). The Competitive Challenge: Strategies for Industrial Innovation and Renewal. Ballinger: Cambridge, UK.

    Google Scholar 

  • March JG and Olsen JP (1976). Ambiguity and Choice in Organizations. Universitetsforlaget: Bergen, Norway.

    Google Scholar 

  • Liu Y and Yao X (1998). A cooperative ensemble learning system. Proceedings of the IEEE International Joint Conference on Neural Networks. Anchorage AK, pp 2202–2207.

    Google Scholar 

  • Tan M (1993). Multi-agent reinforcement learning: independent vs. cooperative agents. Proceedings of the Tenth International Conference on Machine Learning. Amherst, MA.

    Google Scholar 

  • Chaston I (1999). Role of business networks in assisting knowledge management and competence acquisition: an investigation within UK manufacturing firm. Available from〈http://www.vitalcgi.co.uk/~isbauk/papers/messages/16.html〉.

  • Hargadon A and Sutton RI (1997). Technology brokering and innovation in a product development firm. Admin Sci Quart 42: 716–749.

    Article  Google Scholar 

  • Weick KE and Roberts KH (1993). Collective mind in organizations: heedful interrelating on flight decks. Admin Science Quart 38: 357–381.

    Article  Google Scholar 

  • Ahmadabadi MN and Asadpour M (2002). Expertness based cooperative Q-learning. IEEE Trans Systems Man Cybernet 32 (1): 66–76.

    Article  Google Scholar 

  • Deng PS, Holsapple CW and Whinston AB (1990). A skill refinement learning model for rule-based expert systems. IEEE Expert, 5 (2): 15–27.

    Article  Google Scholar 

  • Holland JH and Reitman JS (1978). Cognitive systems based on adaptive algorithms. In: Waterman DA and Hayes-Roth F (eds). Pattern Directed Inference Systems. Academic Press: New York, NY.

    Google Scholar 

  • Minsky ML (1961). Steps towards artificial intelligence. Proc Inst Radio Eng 49: 8–30.

    Google Scholar 

  • Deng PS (1996). Using temporal difference for the design of adaptive agent systems for multi-armed bandit problems, Report for the Research, Scholarship and Creative Activity Grants, California State University, Stanislaus.

    Google Scholar 

  • Stone P (2000). Layered Learning in Multi-agent Systems. MIT Press: Boston, MA.

    Google Scholar 

  • Stone P and Veloso M (2000). Layered learning. Proceedings of the 11th European Conference on Machine Learning. Barcelona, Spain, pp 369–381.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. California State University, Stanislaus, Turlock, CA, USA

    P-S Deng & E G Tsacle

Authors
  1. P-S Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E G Tsacle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P-S Deng.

Additional information

This manuscript is a revised version of the article that appeared in the Proceedings of the Third European Conference on Organizational Knowledge, Learning and Capabilities, April 5–6, 2002, Athens, Greece.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Deng, PS., Tsacle, E. A market-based computational approach to collaborative organizational learning. J Oper Res Soc 54, 924–935 (2003). https://doi.org/10.1057/palgrave.jors.2601604

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/palgrave.jors.2601604

Keywords

Search

Navigation