Skip to main content
SpringerLink
Log in

Computational and mathematical organization theory: Perspective and directions

  • Published:
Computational & Mathematical Organization Theory Aims and scope Submit manuscript

Abstract

Computational and mathematical organization theory is an interdisciplinary scientific area whose research members focus on developing and testing organizational theory using formal models. The community shares a theoretical view of organizations as collections of processes and intelligent adaptive agents that are task oriented, socially situated, technologically bound, and continuously changing. Behavior within the organization is seen to affect and be affected by the organization's, position in the external environment. The community also shares a methodological orientation toward the use of formal models for developing and testing theory. These models are both computational (e.g., simulation, emulation, expert systems, computer-assisted numerical analysis) and mathematical (e.g., formal logic, matrix algebra, network analysis, discrete and continuous equations). Much of the research in this area falls into four areas: organizational design, organizational learning, organizations and information technology, and organizational evolution and change. Historically, much of the work in this area has been focused on the issue how should organizations be designed. The work in this subarea is cumulative and tied to other subfields within organization theory more generally. The second most developed area is organizational learning. This research, however, is more tied to the work in psychology, cognitive science, and artificial intelligence than to general organization theory. Currently there is increased activity in the subareas of organizations and information technology and organizational evolution and change. Advances in these areas may be made possible by combining network analysis techniques with an information processing approach to organizations. Formal approaches are particularly valuable to all of these areas given the complex adaptive nature of the organizational agents and the complex dynamic nature of the environment faced by these agents and the organizations.

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

Similar content being viewed by others

References

  • Alluisi, E.A. (1991), “The Development of Technology for Collective Training: SIMNET, a Case History”,Human Factors, 33(3), 343–362.

    Google Scholar 

  • Anderson, P.A. and G.W. Fischer (1986), “A Monte Carlo Model of a Garbage Can Decision Process”, in J.G. March and R. Weissinger-Baylon (Eds.)Ambiguity and Command: Organizational Perspectives on Military Decision Making, Marshfield, MA: Pitman.

    Google Scholar 

  • Arrow, K.J. and R. Radner (1979), “Allocation of Resources in Large Teams”.Econometrica, 47, 361–385.

    Google Scholar 

  • Arthur, W.B. (1991), “Designing Economic Agents That Act Like Human Agents: A Behavioral Approach to Bounded Rationality”,American Economic Review Papers and Proceedings, 81, 353–359.

    Google Scholar 

  • Baligh, H.H., R.M. Burton and B. Obel (1994), “Validating the Organizational Consultant on the Fly”, in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Baligh, H.H., R.M. Burton, and B. Obel (1987), “Design of Organizational Structures: An Expert System Method,” in J.L. Roos (Ed.),Economics and Artificial Intelligence, Oxford, UK: Pergamon.

    Google Scholar 

  • Baligh, H.H., R.M. Burton and B. Obel (1990), “Devising Expert Systems in Organization Theory: The Organizational Consultant,” in M. Mausch (Ed.)Organization, Management, and Expert Systems, Berlin: Walter De Gruyter.

    Google Scholar 

  • Beroggi, G.E. and W.A. Wallace (1994), “A Decision Logic for Operational Risk Management,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Bond, A. and L. Gasser (1988),Readings in Distributed Artificial Intelligence. San Mateo, CA: Kaufmann.

    Google Scholar 

  • Bonini, C.P. (1963),Simulation of Information and Decision Systems in the Firm, Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Burt, R. S. (1973), “The Differential Impact of Social Integration on Participation in the Diffusion of Innovations,”Social Science Research, 2, 125–144.

    Google Scholar 

  • Burt, R.S. (1980), “Innovation as a Structural Interest: Rethinking the Impact of Network Position Innovation Adoption,”Social Networks, 4, 337–355.

    Google Scholar 

  • Burt, R.S. (1992).Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Burton, R.M. and B. Obel (1980), “A Computer Simulation Test of the M-form Hypothesis,”Administrative Science Quarterly, 25, 457–66.

    Google Scholar 

  • Burton, R.M. and B. Obel (1984).Designing Efficient Organizations: Modeling and Experimentation. Amsterdam: Elsevier Science.

    Google Scholar 

  • Cammarata, S., D. McArthur and R. Steeb (1983), “Strategies of Cooperation in Distributed Problem Solving.”Proceedings of the Eighth International Conference on Artificial Intelligence.

  • Carley, K. (1986), “Efficiency in a Garbage Can: Implications for Crisis Management,” in J.G. March and R. Weissinger-Baylon (Eds.)Ambiguity and Command: Organizational Perspectives on Military Decision Making, Marshfield, MA: Pitman.

    Google Scholar 

  • Carley, K. (1990), “Coordinating for Success: Trading Information Redundancy for Task Simplicity.”Proceedings of the 23rd Annual Hawaii International Conference on System Sciences.

  • Carley, K. (1991), “Designing Organizational Structures to Cope with Communication Breakdowns: A Simulation Model,”Industrial, Crisis Quarterly, 5, 19–57.

    Google Scholar 

  • Carley, K. (1991), “A Theory of Group Stability,”American Sociological Review, 56(3), 331–354.

    Google Scholar 

  • Carley, K. (1992), “Organizational Learning and Personnel Turnover,”Organization Science, 3(1), 20–46.

    Google Scholar 

  • Carley, K. (forthcoming), “Communication Technologies and Their Effect on Cultural Homogeneity, Consensus, and the Diffusion of New Ideas,”Sociological Perspectives.

  • Carley, K., J. Kjaer-Hansen, M. Prietula, and A. Newell (1992), “Plural-Soar: A Prolegomenon to Artificial Agents and Organizational Behavior.” in Masuch M. and Warglien M. (Eds.),Artificial Intelligence in Organization and Management Theory. Amsterdam, The Netherlands: Elsevier Science Publishers.

    Google Scholar 

  • Carley, K. and Z. Lin Z. (1995), “Organizational Designs Suited to High Performance Under Stress,”IEEE—Systems, Man and Cybernetics, 25(1).

  • Carley, K. and M. Prietula (1992). “Toward a Cognitively Motivated Theory of Organizations.”Proceedings of the 1992 Coordination Theory and Collaboration Technology Workshops: Symposium Conducted for the National Science Foundation.

  • Carley, K. and M. Prietula (1994), “ACTS Theory: Extending the Model of Bounded Rationality,” in K. Carley and M. Prietula (Eds.),Computational Organization Theory. Hillsdale, NJ: Lawrence Erlbaum Assoc.

    Google Scholar 

  • Cohen, G.P. (1992), “The Virtual Design Team: An Information Processing Model of Coordination in Project Design Teams,” Ph.D. Dissertation, Stanford University, Department of Civil Engineering.

  • Cohen, M.D. (1986), “Artificial Intelligence and the Dynamic Performance of Organizational Designs,” on J.G. March and R. Weissinger-Baylon, (Eds.),Ambiguity and Command: Organizational Perspectives on Military Decision Making. Marshfield, MA: Pitman.

    Google Scholar 

  • Cohen, M.D., J.B. March J.B. and J.P. Olsen (1972), “A Garbage Can Model of Organizational Choice,”Administrative Science Quarterly, 17(1), 1–25.

    Google Scholar 

  • Cohen, K.J. and R.M. Cyert (1965), “Simulation of Organizational Behavior,” in J.G. March (Ed.),Handbook of Organizations, Chicago, IL: Rand McNally.

    Google Scholar 

  • Corkill, D. (1979). “Hierarchical Planning in a Distributed Environment.”Proceedings of the Sixth International Joint Conference on Artificial Intelligence. Tokyo, Japan.

  • Crecine, J.P. (1969).A Computer Simulation of Municipal Budgeting, Chicago, IL.: Rand McNally.

    Google Scholar 

  • Crowston, K. (1994). “Evolving Novel Organizational Forms,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Cyert, R.M. and J.G. March (1963).A Behavioral Theory of the Firm. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Davis, R. and R.G. Smith (1983), “Negotiation as a Metaphor for Distributed Problem Solving,”Artificial Intelligence, 20, 63–109.

    Google Scholar 

  • DeGroot, M.H. (1970).Optimal Statistical Decisions. New York, NY: McGraw-Hill.

    Google Scholar 

  • DeGroot, M.H. (1974), “Reaching a Consensus,”Journal of American Statistical Association 69, 118–121.

    Google Scholar 

  • Decker, K.S. and V.R. Lesser (1992), “Generalizing the Partial Global Planning Algorithm,”International Journal of Intelligent and Cooperative Information Systems, 1(2), 319–346.

    Google Scholar 

  • Decker, K.S. and V.R. Lesser (1993), “Analyzing a Quantitative Coordination Relationship,”Group Decision and Negotiation, 2(3), 195–217.

    Google Scholar 

  • DiMaggio, P.J. and W.W. Powell (1983), “The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields,”American Sociological Review, 147–160.

  • DiMaggio, P.J. (1986), “Structural Analysis of Organizational Fields: A Blockmodel Approach,”Research in Organizational Behavior, 8, 335–370.

    Google Scholar 

  • Doreian, P. (1990). “Mapping Networks Through Time,” in: J. Weesie and H. Flap (Eds.),Social Networks Through Time, 245–264, ISOR/University of Utrecht, Belgium.

    Google Scholar 

  • Durfee E.H. (1988).Coordination of Distributed Problem Solvers. Boston, MA: Kluwer Academic Publishers.

    Google Scholar 

  • Durfee, E. H. and T.A. Montgomery (1991), “Coordination as Distributed Search in a Hierarchical Behavior Space,”IEEE Transactions on Systems, Man, and Cybernetics, 21(6), 1363–1378.

    Google Scholar 

  • Dutton, J.M. and W.H. Starbuck (1971).Computer Simulation of Human Behavior. New York: Wiley.

    Google Scholar 

  • Freeman, L.C. (1984), “Impact of Computer-based Communication on the Social Structure of an Emerging Scientific Specialty,”Social Networks, 6, 201–221.

    Google Scholar 

  • Galbraith, J.R. (1973).Designing Complex Organizations, Addison-Wesley Publishing Company.

  • Galbraith, J.R. (1977).Organization Design, Addison-Wesley Publishing Company.

  • Gasser, L, I. Hulthage, B. Leverich, J. Lieb, and A. Majchrzak (1993), “Organizations as Complex, Dynamic Design Problems,” in M. Filgueiras and L. Damas (Eds.),Progress in Artificial Intelligence in Lecture Notes in Artificial Intelligence 727, Springer Verlag.

  • Gasser, L. and A. Majchrzak (1994), “ACTION Integrates Manufacturing Strategy, Design, and Planning,” in P. Kidd and W. Karwowski (Eds.)Ergonomics of Hybrid Automated Systems IV, IOS Press, Netherlands.

    Google Scholar 

  • Gasser, L. and A. Majchrzak (1992) “HITOP-A: Coordination, Infrastructure, and Enterprise Integration,”Proceedings of the First International Conference on Enterprise Integration, Hilton Head, South Carolina: MIT Press.

    Google Scholar 

  • Gasser, L. and I. Toru (1991). “A Dynamic Organizational Architecture for Adaptive Problem Solving,”Proceedings of the Ninth National Conference On Artificial Intelligence, Anaheim.

  • Gasser L. and M.N. Huhns (Eds.) (1989).Distributed Artificial Intelligence. Vol. 2, Morgan Kaufmann.

  • Gibson, F.P. and D.C. Plaut (1995), “A Connectionist Formulation of Learning in Dynamic Decision-Making Tasks,” inProceedings of the 17th Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Glance, N.S. and B.A. Huberman (1993), “The Outbreak of Cooperation,”Journal of Mathematical Sociology, 17(4), 281–302.

    Google Scholar 

  • Glance, N.S. and B.A. Huberman (1994a), “Dynamics of Social Dilemmas,”Scientific American.

  • Glance, N.S. and B.A. Huberman (1994b), “Social Dilemmas and Fluid Organizations,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Gloves, T. and J. Ledyard J. (1977), “Optimal Allocations of Public Goods: A Solution to the Free-Rider Problem,”Econometrica, 45, 738–809.

    Google Scholar 

  • Granovetter, M.S. (1973), “The Strength of Weak Ties.”American Journal of Sociology, 68, 1360–1380.

    Google Scholar 

  • Granovetter, M.S. (1974).Getting a Job: A Study of Contacts and Careers. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Grefenstette, J.J. (1991), “Strategy Acquisition with Genetic Algorithms,” in L. Davis (Ed.)Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.

    Google Scholar 

  • Gode, D.K. and S. Sunder (1993), “Allocative Efficiency of Markets with Zero Intelligence Traders: Market as a Partial Substitute for Individual Rationality,”Journal of Political Economy, 101(1), 119–137.

    Google Scholar 

  • Gode, D.K. and S. Sunder (1994), “Human and Artificially Intelligent Traders in Computer Double Auctions,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Hannan, M.T. and J. Freeman (1977), “The Population Ecology of Organizations”American Journal of Sociology, 82, 929–64.

    Google Scholar 

  • Humman, N.P. and T.J. Fararo (1995), “Actors and networks as objects,”Social Networks, 17, 1–26.

    Google Scholar 

  • Hage, J. (1965), “An Axiomatic Theory of Organizations,”Administrative Science Quarterly, 10, 289–320.

    Google Scholar 

  • Hanneman, R. (1988).Computer-Assisted Theory Building: Modeling Dynamic Social Systems, Beverly Hills, CA: Sage.

    Google Scholar 

  • Hanneman, R., R. Collins, G. Mordit (1992). “Long-Term Dynamics of State Legitimacy and Imperialist Capitalism: A Simulation of Neo-Weberian Theory,” Unpublished Paper, Riverside, CA.

  • Harrison, J.R. and G.R. Carrol (1991), “Keeping the Faith: A Model of Cultural Transmission in Formal Organizations,”Administrative Science Quarterly, 36, 552–582.

    Google Scholar 

  • Holland, J.H. and J. Miller (1991), “Artificial Adaptive Agents in Economic Theory,”American Economic Review, Papers and Proceedings 81, 365–70.

    Google Scholar 

  • Huang, Z. and M. Masuch (1993), “ALX3, A Multi-Agent ALX Logic,” CCSOM Working Paper 93-102.

  • Huber, G. (1990), “A Theory of The Effects of Advanced Information Technologies on Organizational Design, Intelligence and Decision Making,”Academy of Management Review, 15(1), 47–71.

    Google Scholar 

  • Jin, Y. and R.E. Levitt (1994), “i-AGENTS: Modeling Organization Problem Solving in Multiagent Teams,”International Journal of Intelligent Systems in Accounting, Finance and Management.

  • Kaufer, D. and K. Carley (1993).Communication at a Distance: The Effect of Print on Socio-Cultural Organization and Change. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Krackhardt, D. and R.N. Stern (1988), “Informal Networks and Organizational Crises: An Experimental, Simulation,”Social Psychology Quarterly, 51(2), 123–140.

    Google Scholar 

  • Krackhardt, D. (1994), “Graph Theoretical Dimensions of Informal Organizations,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Kumar, A., P.S. Ow, and M.J. Prietula (1993), “Organizational Simulation and Information Systems Design: An Operations Level Example,”Management Science, 39(2), 218–239.

    Google Scholar 

  • Lant, T.L. (1994), “Computer Simulations of Organizations as Experimental Learning Systems: Implications for Organization Theory,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Lant, T.L. and S.J. Mezias (1990), “Managing Discontinuous Change: A Simulation Study of Organizational Learning and Entrepreneurship,”Strategic Management Journal, 11, 147–179.

    Google Scholar 

  • Lant, T.L. and S.J. Mezias (1992), “An Organizational Learning Model of Convergence and Reorientation,”Organization Science, 3(1), 47–71.

    Google Scholar 

  • Lawrence, P.R. and J.W. Lorsch, (1969),Developing Organizations: Diagnosis and Action, Addison-Wesley Publishing Company, Inc., Reading, Massachusetts.

    Google Scholar 

  • Lawrence P.R. and J. Lorsch. (1967).Organization and Environment: Managing Differentiation and Integration. Boston, MA: Harvard University.

    Google Scholar 

  • Levinthal, D. and J.G. March (1981), “A Model of Adaptive Organizational Search,”Journal of Economic Behavior and Organization 2, 307–333.

    Google Scholar 

  • Levitt, R.E., G.P. Cohen, J.C. Kunz, C.I. Nass, T. Christiansen and Y. Jin (1994), “The ‘Virtual Design’ Team: Simulating How Organization Structure and Information Processing Tools affect Team Performance,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Lin, Z. (1994), “A Theoretical Evaluation Of Measures Of Organizational Design: Interrelationship And Performance Predictability,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Mackenzie, K.D. (1978).Organizational Structures, AHM Publishing Corporation, Arlington Heights, Illinois.

    Google Scholar 

  • Macy, M.W. (1990), “Learning Theory and the Logic of Critical Mass,”American Sociological Review, 55, 809–826.

    Google Scholar 

  • Macy, M.W. (1991), “Learning to Cooperate: Stochastic and Tacit Collusion in Social Exchange,”American Journal of Sociology, 97(3), 808–43.

    Google Scholar 

  • Majchrzak, A. and L. Finley (1995), “A Practical Theory and Tool for Specifying, Sociotechnical Requirements to Achieve Organizational Effectiveness,” in: J.J. Benders, J. De Haan, and D. Bennett (Eds.)Symbiotic Approaches: Work and Technology, London: Taylor and Francis.

    Google Scholar 

  • Majchrzak, A. and L. Gasser (1992a), “Towards a Conceptual Framework for Specifying Manufacturing Workgroups Congruent with Technological Change,”International Journal of Computer-Integrated Manufacturing, 5(2), 118–131.

    Google Scholar 

  • Majchrzak, A. and L. Gasser (1992b), “HITOP-A: A Tool to Facilitate Interdisciplinary Manufacturing Systems Design,”International Journal of Human Factors in Manufacturing, 2(3), 255–276.

    Google Scholar 

  • Majchrzak, A. and L. Gasser (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 

  • Malone, T.W. (1987), “Modeling Coordination in Organizations and Markets,”Management Science, 33(10), 1317–32.

    Google Scholar 

  • Marschak, J. (1955), “Elements for a Theory of Teams,”Management Science, 1, 127–137.

    Google Scholar 

  • March, J. and R. Weissinger-Baylon (Eds.). (1986)Ambiguity and Command: Organizational Perspectives on Military Decision Making. Boston, MA: Pitman.

    Google Scholar 

  • March, J. and H. Simon (1958).Organizations. New York, NY: Wiley.

    Google Scholar 

  • Masuch, M. and Z. Huang (1994), “A Logical Deconstruction of Organizational Action: Formalizing J. D. Thompson's Organizations in Action, in a Multi-Agent Action Logic,” CCSOM Working Paper 94-120.

  • Masuch, M. (1990).Organization, Management and Expert Systems: Models of Automated Reasoning Berlin, New York: Walter de Gruyter.

    Google Scholar 

  • Masuch, M. and P. LaPotin (1989), “Beyond Garbage Cans: An AI Model, of Organizational Choice,”Administrative Science Quarterly, 34, 38–67.

    Google Scholar 

  • Mezias, S.J. and M.A. Glynn (forthcoming), “Using Computer Simulation to Understand the Management of Technology: Applications for Theory Development,”Technology Studies.

  • Mintzberg, H., (1983).Structures in Five: Designing Effective Organizations, Prentice Hall Inc.

  • Padgett, J.F. (1980), “Managing Garbage Can Hierarchies,”Administrative Science Quarterly, 25(4), 583–604.

    Google Scholar 

  • Panning, W.H., (1986), “Information Pooling and Group Decisions in Non-experimental Settings,” in F.M. Jablin, L.L. Putnam, K.H. Roberts and L.W. Porter (Eds.),Handbook of Organizational Communication: An Interdisciplinary Perspective, Sage, Beverly Hills, CA.

    Google Scholar 

  • Papageorgiou, C.P. (1992), “Cognitive Model of Decision Making: Chunking and the Radar Detection Task,”CMU-CS Bachelors Thesis, Pittsburgh, PA.

  • Patrick, S. (1974). “Complex Model Variations: The Affects of Selected Parameter Changes to Simulation, Outcomes,” Paper presented at the ORSA/TIMS Workshop on Mathematical and Computational Organization Theory.

  • Pete, A., K.R. Pattipati and D.L. Kleinman (1993), “Optimal Team and Individual Decision Rules in Uncertain Dichotomous Situations,”Public Choice, 75, 205–230.

    Google Scholar 

  • Pfeffer, J. (1978).Organizational Design, AHM Publishing Corporation, Arlington Heights, Illinois.

    Google Scholar 

  • Radner, R. (1987).Decentralization and Incentives, University of Minnesota Press.

  • Reuter, Peuter, Mitchell, Benett, and Grimes (1994). “The Training Impact Decisions System (TIDES): A Decision-Aiding System for Personnel Utilization and Training in U.S. Air Force Occupational Specialties,” Paper presented at the ORSA/TIMS Workshop on Mathematical and Computational Organization Theory.

  • Scott, J. (1991).Social Network Analysis: A Handbook, Newbury Park, CA: Sage.

    Google Scholar 

  • Scott, W.R. (1987).Organizations: Rational, Natural, and Open Systems, Prentice Hall, Inc., Englewood Cliffs, New Jersey.

    Google Scholar 

  • Shapley L. and B. Grofman (1984), “Optimizing Group Judgmental Accuracy in the Presence of Interdependencies,”Public Choice, 43, 329–343.

    Google Scholar 

  • Simon, H.A. (1973), “Applying Information Technology to Organizational Design,” Public Administrative Review, 33, 268–278.

    Google Scholar 

  • Snijders T. (1990), “Testing for Change in a Digraph at Two Time Points,”Social Networks, 12, 539–373.

    Google Scholar 

  • Staw, B.M., L.E. Sanderlands and J.E. Dutton (1981), “Threat-Rigidity Effects in Organization al Behavior: A Multilevel Analysis,”Administrative Science Quarterly, 26, 501–524.

    Google Scholar 

  • Thompson, J.D. (1962/67).Organizations in Action. McGraw-Hill.

  • Tsuchiya, S. (1993) “Artificial Intelligence and Organizational Learning: How Can AI Contribute to Organizational Learning?” Working Paper.

  • Verhagen, H. and M. Masuch (1994), “TASCCS: a Synthesis of Double-AISS, and Plural-SOAR,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Wasserman, S. (1980), “Analyzing Social Networks as a Stochastic Process,”Journal of the American Statistical Association, 75, 280–294.

    Google Scholar 

  • Weber, M. (1922), “Bureaucracy,” in H. Gerth and C.W. Mills (Eds.),Max Weber: Essays in Sociology. Oxford, England: Oxford University Press.

    Google Scholar 

  • Wasserman S. and K. Faust (1994).Social Network Analysis: Methods and Applications, Cambridge University Press, Cambridge.

    Google Scholar 

  • Woodward, J. (1965).Industrial Organization: Theory and Practice, Oxford University Press, London.

    Google Scholar 

  • Zhou, C., P.B. Luh, and D.L. Kleinman (1993). “Modeling Distributed Team Resource Allocation within a Geographical Environment,”Proceedings of 1993 American Control Conference, San Francisco, CA: June 1993; IEEE Service Center, Piscataway, NJ: Vol 3: 2735–2739.

    Google Scholar 

  • Zweben, M. and M.S. Fox (Eds.) (1994).Intelligent Scheduling, Morgan Kaufmann Pub.Co., San Mateo CA.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Social and Decision Sciences, Carnegie Mellon University, 15213, Pittsburgh, PA

    Kathleen M. Carley

Authors
  1. Kathleen M. Carley
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Carley, K.M. Computational and mathematical organization theory: Perspective and directions. Comput Math Organiz Theor 1, 39–56 (1995). https://doi.org/10.1007/BF01307827

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01307827

Keywords

Search

Navigation