Computer simulation and organizational studies

Computer simulation is a prominent method in organizational studies and strategic management.

• Agent-based vs Equation-based: agent-based models unfold according to the interactions of relatively simple actions, while equation-based models unfold numerically based on a variety of dynamic or steady-state equations (Note: some argue this is something of a false distinction since some agent based models use equations to direct the behavior of their agents)
• Model: simplified versions of the real world that contain only essential elements of theoretical interest^[4]
• Complexity of the model: the number of conceptual parts in the model and the connections between those parts^[5]
• Deterministic vs. Stochastic: deterministic models unfold exactly as specified by some pre-specified logic, while stochastic models depend on a variety of draws from probability distributions
• Optimizing vs. Descriptive: models with actors that either seek optimums (like the peaks in fitness landscapes) or do not

Methodological approaches

There are a variety of different methodological approaches in the area of computational simulation. These include but are not limited to the following. (Note: this list is not Mutually Exclusive nor Collectively Exhaustive, but tries to be fair to the dominant trends. For three different taxonomies see Carley 2001; Davis et al. 2007; Dooley 2002)

• Agent-based models: computational models investigating the interaction of multiple agents (many of the following approaches can be 'agent-based' as well)
• Cellular automata: models exploring multiple actors in physical space whose behavior is based on rules
• Dynamic network models: any model representing actors and non-actor entities (tasks, resources, locations, beliefs, etc.) as connected through relational links as in dynamic network analysis
• Genetic Algorithms: models of agents whose genetic information can evolve over time
• Equation-based (or non-linear modeling): models using (typically
  non-linear
  ) equations that determine the future state of its systems
• Social Network models: any model representing actors as connected through stereotypical 'ties' as in social network analysis
• Stochastic Simulation: models that involve random variables or source of stochasticity
• System dynamics: equation-based approach using casual-loops and stocks & flows of resources
• NK modeling: actors modeled as N nodes linked through K connections that are (typically) trying to reach the peak of a fitness landscape

Early research

Early research in strategy and organizations using computational simulation concerned itself with either the macro-behavior of systems or specific organizational mechanisms. Highlights of early research included:

• Cohen, March, & Olsen's (1972) Garbage Can Model of Organizational Choice modeled organizations as a set of solutions seeking problems in a rather anarchic 'garbage can'-esque organization.
• March's (1991) study of Exploration and Exploitation in Organizational Learning utilized John Holland's (1975) basic explore/exploit distinction to show the value of slow learners in organizations.
• Nelson & Winter's (1982) Evolutionary theory of economic change used a simulation to show that an evolutionary model could produce the same sort of GDP / productivity numbers as neo-classical rational choice theorizing.

Later research

Later research using computational simulation flowered in the 1990s and beyond. Highlights include:

• Carroll & Harrison's (1998) model of organizational demography and culture
• Davis, Eisenhardt & Bingham's (2009) model of organization structure in unpredictable environments
• Gavetti, & Levinthal's (2000) model of cognitive and experiential search
• Levinthal's (1997) NK model of adaptation on rugged fitness landscapes
• Rivkin's (2000) study of strategic imitation
• Rudolph & Repenning's (2002) model of disastrous tipping points
• Sastry's (1997) model of punctuated organizational change
• Zott's (2003) model of strategic evolution and dynamic capabilities

References

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Further reading

• Adner, R.; Levinthal, D. (2001). "Demand Heterogeneity and Technology Evolution: Implications for Product and Process Innovation". Management Science. 47 (5): 611–628.
  doi:10.1287/mnsc.47.5.611.10482. Archived from the original
  on 2009-09-25. Retrieved 2006-07-14.
• Bruderer, E.; Singh, J. S. (1996). "Organizational Evolution, Learning, and Selection: A Genetic-Algorithm-Based Model". Academy of Management Journal. 39 (5): 1322–1349.
  JSTOR 257001
  .
• Carley, K. M. 2001. Computational Approaches to Sociological Theorizing. In J. Turner (Ed.), Handbook of Sociological Theory: 69–84. New York, NY: Kluwer Academic/Plenum Publishers [1].
• Carroll, G.; Harrison, J. R. (1998). "Organizational Demography and Culture: Insights from a Formal Model and Simulation". Administrative Science Quarterly. 43 (3): 637–667.
  JSTOR 2393678. Archived from the original
  on 2006-09-10. Retrieved 2006-07-14.
• Cohen, M. D.; March, J.; Olsen, J. P. (1972). "A Garbage Can Model of Organizational Choice". Administrative Science Quarterly. 17 (1): 1–25.
  JSTOR 2392088
  .
• Crowder, R. M.; Robinson, M. A.; Hughes, H. P. N.; Sim, Y. W. (2012). "The development of an agent-based modeling framework for simulating engineering team work". IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. 42 (6): 1425–1439.
  S2CID 7985332
  .
• Davis, J.P.; Eisenhardt, K.M.; Bingham, C.B. (2007). "Developing Theory with Simulation Methods". Academy of Management Review. 32 (2): 480–499.
  S2CID 18394514
  .
• Davis, J.P.; Eisenhardt, K.M.; Bingham, C.B. (2009). "Optimal Structure, Market Dynamism, and the Strategy of Simple Rules". Administrative Science Quarterly. 54 (3): 413–452.
  S2CID 17029244
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• Forrester, J. 1961. Industrial Dynamics. Cambridge, Massachusetts: MIT Press.
• Gavetti, G.; Levinthal, D. (2000). "Looking Forward and Looking Backward: Cognitive and Experiential Search". Administrative Science Quarterly. 45 (1): 113–137.
  S2CID 6282715. Archived from the original
  on 2009-09-25. Retrieved 2006-07-14.
• Harrison, J. R.; Lin, Z.; Carroll, G. R.; Carley, K. M. (2007). "Simulation Modeling in Organizational and Management Research". Academy of Management Review. 32 (4): 1229–1245.
  doi:10.5465/amr.2007.26586485
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• Holland, J. H. 1975. Adaptation in natural and artificial systems. Ann Arbor, MI: The University of Michigan Press.
• Hughes, H. P. N.; Clegg, C. W.; Robinson, M. A.; Crowder, R. M. (2012). "Agent-based modelling and simulation: The potential contribution to organizational psychology". Journal of Occupational and Organizational Psychology. 85 (3): 487–502.
  doi:10.1111/j.2044-8325.2012.02053.x
  .
• Kauffman, S. 1989. Adaptation on rugged fitness landscapes. In E. Stein (Ed.), Lectures in the Science of Complexity. Reading, Mass.: Addison–Wesley.
• Kauffman, S. 1993. The Origins of Order. New York, NY: Oxford University Press.
• Langton, C. G. 1984. Self-Reproduction in Cellular Automata. Physica, 10D: 134–144.
• Lant, T.; Mezias, S. (1990). "Managing Discontinuous Change: A Simulation Study of Organizational Learning and Entrepreneurship". Strategic Management Journal. 11: 147–179.
  JSTOR 2486675
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• Lave, C., & March, J. G. 1975. An Introduction to Models in the Social Sciences. New York, NY: Harper and Row.
• Law, A. M., & Kelton, D. W. 1991. Simulation Modeling and Analysis (2nd ed.). New York, NY: McGraw–Hill.
• Levinthal, D (1997). "Adaptation on Rugged Landscapes". Management Science. 43 (7): 934–950.
  doi:10.1287/mnsc.43.7.934
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• Lomi, A.; Larsen, E. (1996). "Interacting Locally and Evolving Globally: A Computational Approach to the Dynamics of Organizational Populations". Academy of Management Journal. 39 (5): 1287–1321.
  JSTOR 257000
  .
• March, J. G. (1991). "Exploration and Exploitation in Organizational Learning". Organization Science. 2 (1): 71–87.
  doi:10.1287/orsc.2.1.71
  .
• Nelson, R. R., & Winter, S. G. 1982. An Evolutionary Theory of Economic Change. Cambridge, Massachusetts: Belknap – Harvard University Press.
• Repenning, N (2002). "A Simulation-Based Approach to Understanding the Dynamics of Innovation Implementation". Organization Science. 13 (2): 109–127.
  hdl:1721.1/3803
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• Rivkin, J. (2000). "Imitation of Complex Strategies". Management Science. 46 (6): 824–844.
  doi:10.1287/mnsc.46.6.824.11940
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• Rivkin, J. (2001). "Reproducing Knowledge: Replication Without Imitation at Moderate Complexity". Organization Science. 12 (3): 274–293.
  doi:10.1287/orsc.12.3.274.10106
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• Rudolph, J.; Repenning, N. (2002). "Disaster Dynamics: Understanding the Role of Quantity in Organizational Collapse". Administrative Science Quarterly. 47 (1): 1–30.
  S2CID 745136.^{[permanent dead link}
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• Sastry, M. A. (1997). "Problems and paradoxes in a model of punctuated organizational change". Administrative Science Quarterly. 42 (2): 237–275.
  JSTOR 2393920
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• Schelling, T (1971). "Dynamic models of segregation". Journal of Mathematical Sociology. 1 (2): 143–186.
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• Simon, H. 1996 (1969; 1981) The Sciences of the Artificial (3rd Edition) MIT Press [2].
• Sterman, J. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. New York, NY: Irwin McGraw–Hill.
• Sterman, J.; Repenning, N.; Kofman, F. (1997). "Unanticipated Side Effects of Successful Quality Programs: Exploring a Paradox of Organizational Improvement". Management Science. 43 (4): 503–521.
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• Wolfram, S. 2002. A New Kind of Science. Champaign, IL: Wolfram Media.
• Zott, C (2003). "Dynamic Capabilities and the Emergence of Intra-industry Differential Firm Performance: Insights from a Simulation Study". Strategic Management Journal. 24 (2): 97–125.
  S2CID 15415778
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