Encyclopedia of Operations Research and Management Science

2013 Edition
| Editors: Saul I. Gass, Michael C. Fu

Computational Organization Theory

Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-1153-7_143


As inexpensive and massive amounts of computing power have rapidly become more widely available, the operational aspects of computational-based organizational research have become a reality. Today, the concepts of Computational Organization Theory (COT) can be easily implemented and practiced by an ever-increasingly larger group of researchers. Some foresee such computer-science related computational thinking (Wing 2006), as the future of all scholarly research, and COT is part of this broader trend.

COT involves the theorizing about, describing, understanding, and predicting the behavior of organizations and the process of organizing, using quantitative-based and structured approaches (computational, mathematical and logical models). This involves computational abstractions that are incorporated into organizational research and practice through COT tools, procedures, measures and knowledge.

The notion of an organization, as used here, spans the wide range of...

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


  1. Ashworth, M., & Carley, K. M. (2004). Toward unified organization theory: Perspectives on the state of computational modeling. Proceedings of the NAACSOS 2004 Conference, Pittsburgh, PA.Google Scholar
  2. Ashworth, M., & Carley, K. M. (2007). Can tools help unify organization theory? Perspectives on the state of computational modeling. Computational and Mathematical Organization Theory, 13(1), 89–111.CrossRefGoogle Scholar
  3. Baligh, H. H., Burton, R. M., & Obel, B. (1990). Devising expert systems in organization theory: The organizational consultant. In M. Masuch (Ed.), Organization, management, and expert systems. Berlin: Walter De Gruyer.Google Scholar
  4. Baum, J., & Oliver, C. (1991). Institutional linkages and organizational mortality. Administrative Science Quarterly, 36, 187–218.CrossRefGoogle Scholar
  5. Blau, P. M. (1970). A formal theory of differentiation in organizations. American Sociological Review, 35(2), 201–218.CrossRefGoogle Scholar
  6. Blumer, H. (1969). Symbolic interactionism: Perspective and method. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  7. Bond, A., & Gasser, L. (Eds.). (1988). Readings in distributed artificial intelligence. San Mateo, CA: Kaufmann.Google Scholar
  8. Burt, R. (1992). Structural holes: The social structure of competition. Boston: Harvard University Press.Google Scholar
  9. Burton, R. M., & Obel, B. (1996). Organization. In S. I. Gass & C. M. Harris (Eds.), Encyclopedia of operations research and management science. Norwood, MA: Kluwer Academic Publishers.Google Scholar
  10. Carley, K. M. (1991). A theory of group stability. American Sociological Review, 56(3), 331–354.CrossRefGoogle Scholar
  11. Carley, K. M. (1992). Organizational learning and personnel turnover. Organization Science, 3(1), 20–46.CrossRefGoogle Scholar
  12. Carley, K. M. (1995). Computational and mathematical organization theory: Perspective and directions. Computational and Mathematical Organization Theory, 1(1), 39–56.CrossRefGoogle Scholar
  13. Carley, K. M., Kjaer-Hansen, J., Prietula, M., & Newell, A. (1992). Plural-soar: A prolegomenon to artificial agents and organizational behavior. In M. Masuch & M. Warglien (Eds.), Distributed intelligence: Applications in human organizations (pp. 87–118). Amsterdam: Elsevier Science.Google Scholar
  14. Carley, K. M., & Newell, A. (1994). The nature of the social agent. Journal of Mathematical Sociology, 19(4), 221–262.CrossRefGoogle Scholar
  15. Carley, K. M., & Prietula, M. J. (Eds.). (1994). Computational organization theory. Hillsdale, IN: Lawrence Erlbaum Associates.Google Scholar
  16. Carley, K. M., & Svoboda, D. M. (1996). Modeling organizational adaptation as a simulated annealing process. Sociological Methods and Research, 25, 138–168.CrossRefGoogle Scholar
  17. Cohen, M. D. (1986). Artificial intelligence and the dynamic performance of organizational designs. In J. G. March & R. Weissinger-Baylon (Eds.), Ambiguity and command: Organizational perspectives on military decision making (pp. 53–70). Marshfield, MA: Pitman.Google Scholar
  18. Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly, 17, 1–25.CrossRefGoogle Scholar
  19. Cyert, R., & March, J. G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  20. Decker, K. (1996). TAEMS: A framework for environment centered analysis and design of coordination mechanisms. In G. M. P. O'Hare & N. R. Jennings (Eds.), Foundations of distributed artificial intelligence. New York: John Wiley.Google Scholar
  21. Durfee, E. H., & Montgomery, T. A. (1991). Coordination as distributed search in a hierarchical behavior space. IEEE Transactions on Systems, Man, and Cybernetics, 21, 1363–1378.CrossRefGoogle Scholar
  22. Galbraith, J. (1973). Designing complex organizations. Reading, MA: Addison-Wesley.Google Scholar
  23. Gasser, L., & Huhns, M. N. (Eds.). (1989). Distributed artificial intelligence (Vol. 2). New York: Morgan Kaufmann.Google Scholar
  24. Gasser, L., & Majchrzak, A. (1992). HITOP-A: Coordination, infrastructure, and enterprise integration. Proceedings of the First International Conference on Enterprise Integration (pp. 373–378). Hilton Head, SC: MIT Press.Google Scholar
  25. Gasser, L., & Majchrzak, A. (1994). ACTION integrates manufacturing strategy, design, and planning. In P. Kidd & W. Karwowski (Eds.), Ergonomics of hybrid automated systems IV (pp. 133–136). Amsterdam: IOS Press.Google Scholar
  26. Gilbert, N., & Doran, J. (Eds.). (1994). Simulating societies: The computer simulation of social phenomena. London: UCL Press.Google Scholar
  27. Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. The American Journal of Sociology, 91, 481–510.CrossRefGoogle Scholar
  28. Hannan, M. T., & Freeman, J. (1977). The population ecology of organizations. The American Journal of Sociology, 82, 929–964.CrossRefGoogle Scholar
  29. Hannan, M. T., & Freeman, J. (1989). Organizational ecology. Cambridge, MA: Harvard University Press.Google Scholar
  30. Kang, M., Waisel, L. B., & Wallace, W. A. (1998). Team-soar: A model for team decision making. In M. Prietula, K. Carley, & L. Glasser (Eds.), Simulating organizations: Computational models of institutions and groups (pp. 23–45). Menlo Park, CA: AAAI Press/The MIT Press.Google Scholar
  31. Kaufer, D. S., & Carley, K. M. (1993). Communication at a distance: The effect of print on socio-cultural organization and change. Hillsdale, IN: Lawrence Erlbaum Associates.Google Scholar
  32. Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9, 109–134.CrossRefGoogle Scholar
  33. Lee, J-S., & Carley, K. M. (2004). OrgAhead: A computational model of organizational learning and decision making [Version 2.1.5] (Technical Report CMU-ISRI-04-117), Carnegie Mellon University, School of Computer Science, Institute for Software Research International.Google Scholar
  34. Lesser, D. D., & Corkill, D. D. (1988). Functionally accurate, cooperative distributed systems. In A. H. Bond & L. Gasser (Eds.), Readings in distributed artificial intelligence. San Mateo, CA: Morgan Kaufmann.Google Scholar
  35. Levinthal, D., & March, J. G. (1981). A model of adaptive organizational search. Journal of Economic Behavior and Organization, 2, 307–333.CrossRefGoogle Scholar
  36. Levitt, R. E., Cohen, G. P., Kunz, J. C., Nass, C. I., Christiansen, T., & Jin, Y. (1994). The Virtual Design Team: Simulating how organization structure and information processing tools affect team performance. In K. M. Carley & M. J. Prietula (Eds.), Computational organization theory (pp. 1–18). Hillsdale, IN: Erlbaum.Google Scholar
  37. Majchrzak, A., & Gasser, L. (1991). On using artificial intelligence to integrate the design of organizational and process change in US manufacturing. Artificial Intelligence and Society, 5, 321–338.Google Scholar
  38. Majchrzak, A., & Gasser, L. (1992). HITOP-A: A tool to facilitate interdisciplinary manufacturing systems design. International Journal of Human Factors in Manufacturing, 2(3), 255–276.CrossRefGoogle Scholar
  39. Malone, T. W. (1986). Modeling coordination in organizations and markets. Management Science, 33, 1317–1332.CrossRefGoogle Scholar
  40. March, J., & Simon, H. (1958). Organizations. New York: John Wiley.Google Scholar
  41. Nersessian, N. J. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R. N. Giere (Ed.), Cognitive models of science (Vol. 15). Minneapolis, MN: Minnesota Press.Google Scholar
  42. Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource dependence perspective. New York: Harper and Row.Google Scholar
  43. Powell, W. W., & DiMaggio, P. J. (1991). The new institutionalism in organizational analysis. Chicago: University of Chicago Press.Google Scholar
  44. Prietula, M. J., Carley, K. M., & Gasser, L. (Eds.). (1998). Simulating organizations: Computational models of institutions and groups. Menlo Park, CA: AAAI Press/The MIT Press.Google Scholar
  45. Salancik, G. R., & Pfeffer, J. (1978). A social information professing approach to job attitudes and task design. Administrative Science Quarterly, 23, 224–253.CrossRefGoogle Scholar
  46. Salanick, G. R., & Leblebici, H. (1998). Variety and form in organizing transactions: A generative grammar of organization. Research in the Sociology of Organizations, 6, 1–31.Google Scholar
  47. Simon, H. A. (1947). Administrative behavior. New York: Free Press.Google Scholar
  48. Stryker, S. (1980). Symbolic interactionism: A social structure version. Menlo Park, CA: Benjamin/Cummings Publishing.Google Scholar
  49. Stuart, T. E., & Podolny, J. M. (1996). Local search and the evolution of technological capabilities. Strategic Management Journal, 17, 21–38.CrossRefGoogle Scholar
  50. Thompson, J. D. (1967). Organizations in action. New York: McGraw-Hill.Google Scholar
  51. Waisel, L., Wallace, W. A., & Willemain, T. (1998). Using diagrammatic reasoning in mathematical modeling: The sketches of expert modelers. Proceedings of the AAAI 1997 Fall Symposium on Reasoning with Diagrammatic Representations II. Menlo Park, CA: AAAI Press.Google Scholar
  52. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.CrossRefGoogle Scholar
  53. Wasserman, S., & Galaskiewicz, J. (Eds.). (1994). Advances in social network analysis: Research in the social and behavioral sciences. Thousand Oaks, CA: Sage.Google Scholar
  54. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.CrossRefGoogle Scholar
  55. Zhiang, L., & Carley, K. (1995). DYCORP: A computational framework for examining organizational performance under dynamic conditions. Journal of Mathematical Sociology, 20(2–3), 193–218.Google Scholar
  56. Zweben, M., & Fox, M. S. (Eds.). (1994). Intelligent scheduling. San Mateo, CA: Morgan Kaufmann.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.HSBC Business SchoolPeking UniversityShenzhenChina
  2. 2.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  3. 3.Rensselaer Polytechnic InstituteTroyUSA