Menu
Home
People
Places
Arts
History
Plants & Animals
Science
Life & Culture
Technology
Reference.org
Computer simulation and organizational studies
open-in-new
<h2 id="basic-distinctionsdefinitions">Basic distinctions/definitions</h2> <p>Researchers studying organizations and firms using computer simulations utilize a variety of basic distinctions and definitions that are common in computational science </p> <ul><li>Agent-based vs Equation-based: <a href="/facts/Agent-based_model/C3C1vR8s">agent-based models</a> 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)</li> <li>Model: simplified versions of the real world that contain only essential elements of theoretical interest<a class="footnote-ref" id="fnref:4" href="#fn:4"><sup>4</sup></a></li> <li>Complexity of the model: the number of conceptual parts in the model and the connections between those parts<a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a></li> <li>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</li> <li>Optimizing vs. Descriptive: models with actors that either seek optimums (like the peaks in fitness landscapes) or do not</li></ul> <h2 id="methodological-approaches">Methodological approaches</h2> <p>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) </p> <ul><li>Agent-based models: computational models investigating the interaction of multiple agents (many of the following approaches can be 'agent-based' as well)</li> <li>Cellular automata: models exploring multiple actors in physical space whose behavior is based on rules</li> <li>Dynamic network models: any model representing actors and non-actor entities (tasks, resources, locations, beliefs, etc.) as connected through relational links as in <a href="/facts/Dynamic_network_analysis/vW3scuva">dynamic network analysis</a></li> <li>Genetic Algorithms: models of agents whose genetic information can evolve over time</li> <li>Equation-based (or non-linear modeling): models using (typically <a href="/facts/Non-linear/4aYItALC">non-linear</a>) equations that determine the future state of its systems</li> <li>Social Network models: any model representing actors as connected through stereotypical 'ties' as in <a href="/facts/Social_network_analysis/CsJIFjVw">social network analysis</a></li> <li>Stochastic Simulation: models that involve random variables or source of stochasticity</li> <li><a href="/facts/System_dynamics/EPI2wWsI">System dynamics</a>: equation-based approach using casual-loops and <a href="/facts/Stock_and_flow/zpgeVptu">stocks & flows</a> of resources</li> <li>NK modeling: actors modeled as N nodes linked through K connections that are (typically) trying to reach the peak of a fitness landscape</li></ul> <h2 id="early-research">Early research</h2> <p>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: </p> <ul><li>Cohen, March, & Olsen's (1972) <i>Garbage Can Model of Organizational Choice</i> modeled organizations as a set of solutions seeking problems in a rather anarchic 'garbage can'-esque organization.</li> <li>March's (1991) study of <i>Exploration and Exploitation in Organizational Learning</i> utilized John Holland's (1975) basic explore/exploit distinction to show the value of slow learners in organizations.</li> <li>Nelson & Winter's (1982) <i>Evolutionary theory of economic change</i> used a simulation to show that an evolutionary model could produce the same sort of GDP / productivity numbers as neo-classical rational choice theorizing.</li></ul> <h2 id="later-research">Later research</h2> <p>Later research using computational simulation flowered in the 1990s and beyond. Highlights include: </p> <ul><li>Carroll & Harrison's (1998) model of organizational demography and culture</li> <li>Davis, Eisenhardt & Bingham's (2009) model of organization structure in unpredictable environments</li> <li>Gavetti, & Levinthal's (2000) model of cognitive and experiential search</li> <li>Levinthal's (1997) NK model of adaptation on rugged fitness landscapes</li> <li>Rivkin's (2000) study of strategic imitation</li> <li>Rudolph & Repenning's (2002) model of disastrous tipping points</li> <li>Sastry's (1997) model of punctuated organizational change</li> <li>Zott's (2003) model of strategic evolution and dynamic capabilities</li></ul> <h2 id="further-reading">Further reading</h2> <ul><li>Adner, R.; Levinthal, D. (2001). <a href="https://web.archive.org/web/20090925112426/http://www.wharton.upenn.edu/faculty/levinthd.html">"Demand Heterogeneity and Technology Evolution: Implications for Product and Process Innovation"</a>. <i>Management Science</i>. 47 (5): 611–628. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1287%2Fmnsc.47.5.611.10482">10.1287/mnsc.47.5.611.10482</a>. Archived from <a href="http://www.wharton.upenn.edu/faculty/levinthd.html">the original</a> on 2009-09-25. Retrieved 2006-07-14.</li> <li>Bruderer, E.; Singh, J. S. (1996). "Organizational Evolution, Learning, and Selection: A Genetic-Algorithm-Based Model". <i>Academy of Management Journal</i>. 39 (5): 1322–1349. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.2307%2F257001">10.2307/257001</a>. <a href="/facts/JSTOR_(identifier)/YTeVmaJ7">JSTOR</a> <a href="https://www.jstor.org/stable/257001">257001</a>.</li> <li>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 <a href="http://www.casos.cs.cmu.edu/bios/carley/carley.html">[1]</a>.</li> <li>Carroll, G.; Harrison, J. R. (1998). <a href="https://web.archive.org/web/20060910175659/http://www0.gsb.columbia.edu/whoswho/bio.cfm?id=56497&nav=n">"Organizational Demography and Culture: Insights from a Formal Model and Simulation"</a>. <i>Administrative Science Quarterly</i>. 43 (3): 637–667. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.2307%2F2393678">10.2307/2393678</a>. <a href="/facts/JSTOR_(identifier)/YTeVmaJ7">JSTOR</a> <a href="https://www.jstor.org/stable/2393678">2393678</a>. Archived from <a href="http://www0.gsb.columbia.edu/whoswho/bio.cfm?id=56497&nav=n">the original</a> on 2006-09-10. Retrieved 2006-07-14.</li> <li>Cohen, M. D.; March, J.; Olsen, J. P. (1972). "A Garbage Can Model of Organizational Choice". <i>Administrative Science Quarterly</i>. 17 (1): 1–25. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.2307%2F2392088">10.2307/2392088</a>. <a href="/facts/JSTOR_(identifier)/YTeVmaJ7">JSTOR</a> <a href="https://www.jstor.org/stable/2392088">2392088</a>.</li> <li>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". <i>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</i>. 42 (6): 1425–1439. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1109%2FTSMCA.2012.2199304">10.1109/TSMCA.2012.2199304</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:7985332">7985332</a>.</li> <li>Davis, J.P.; Eisenhardt, K.M.; Bingham, C.B. (2007). <a href="http://web.mit.edu/~jasond/www/simulation.htm">"Developing Theory with Simulation Methods"</a>. <i>Academy of Management Review</i>. 32 (2): 480–499. <a href="/facts/CiteSeerX_(identifier)/SceDmd3c">CiteSeerX</a> <a href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.562.5016">10.1.1.562.5016</a>. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.5465%2FAMR.2007.24351453">10.5465/AMR.2007.24351453</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:18394514">18394514</a>.</li> <li>Davis, J.P.; Eisenhardt, K.M.; Bingham, C.B. (2009). <a href="http://web.mit.edu/~jasond/www/optimalstructure.htm">"Optimal Structure, Market Dynamism, and the Strategy of Simple Rules"</a>. <i>Administrative Science Quarterly</i>. 54 (3): 413–452. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.2189%2Fasqu.2009.54.3.413">10.2189/asqu.2009.54.3.413</a>. <a href="/facts/Hdl_(identifier)/rdebSxmC">hdl</a>:<a href="https://hdl.handle.net/1721.1%2F52690">1721.1/52690</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:17029244">17029244</a>.</li> <li>Forrester, J. 1961. Industrial Dynamics. Cambridge, Massachusetts: MIT Press.</li> <li>Gavetti, G.; Levinthal, D. (2000). <a href="https://web.archive.org/web/20090925112426/http://www.wharton.upenn.edu/faculty/levinthd.html">"Looking Forward and Looking Backward: Cognitive and Experiential Search"</a>. <i>Administrative Science Quarterly</i>. 45 (1): 113–137. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.2307%2F2666981">10.2307/2666981</a>. <a href="/facts/JSTOR_(identifier)/YTeVmaJ7">JSTOR</a> <a href="https://www.jstor.org/stable/2666981">2666981</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:6282715">6282715</a>. Archived from <a href="http://www.wharton.upenn.edu/faculty/levinthd.html">the original</a> on 2009-09-25. Retrieved 2006-07-14.</li> <li>Harrison, J. R.; Lin, Z.; Carroll, G. R.; Carley, K. M. (2007). "Simulation Modeling in Organizational and Management Research". <i>Academy of Management Review</i>. 32 (4): 1229–1245. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.5465%2Famr.2007.26586485">10.5465/amr.2007.26586485</a>.</li> <li>Holland, J. H. 1975. Adaptation in natural and artificial systems. Ann Arbor, MI: The University of Michigan Press.</li> <li>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". <i>Journal of Occupational and Organizational Psychology</i>. 85 (3): 487–502. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1111%2Fj.2044-8325.2012.02053.x">10.1111/j.2044-8325.2012.02053.x</a>.</li> <li>Kauffman, S. 1989. Adaptation on rugged fitness landscapes. In E. Stein (Ed.), Lectures in the Science of Complexity. Reading, Mass.: Addison–Wesley.</li> <li>Kauffman, S. 1993. The Origins of Order. New York, NY: Oxford University Press.</li> <li>Langton, C. G. 1984. Self-Reproduction in Cellular Automata. Physica, 10D: 134–144.</li> <li>Lant, T.; Mezias, S. (1990). "Managing Discontinuous Change: A Simulation Study of Organizational Learning and Entrepreneurship". <i>Strategic Management Journal</i>. 11: 147–179. <a href="/facts/JSTOR_(identifier)/YTeVmaJ7">JSTOR</a> <a href="https://www.jstor.org/stable/2486675">2486675</a>.</li> <li>Lave, C., & March, J. G. 1975. An Introduction to Models in the Social Sciences. New York, NY: Harper and Row.</li> <li>Law, A. M., & Kelton, D. W. 1991. Simulation Modeling and Analysis (2nd ed.). New York, NY: McGraw–Hill.</li> <li>Levinthal, D (1997). "Adaptation on Rugged Landscapes". <i>Management Science</i>. 43 (7): 934–950. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1287%2Fmnsc.43.7.934">10.1287/mnsc.43.7.934</a>.</li> <li>Lomi, A.; Larsen, E. (1996). "Interacting Locally and Evolving Globally: A Computational Approach to the Dynamics of Organizational Populations". <i>Academy of Management Journal</i>. 39 (5): 1287–1321. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.2307%2F257000">10.2307/257000</a>. <a href="/facts/JSTOR_(identifier)/YTeVmaJ7">JSTOR</a> <a href="https://www.jstor.org/stable/257000">257000</a>.</li> <li>March, J. G. (1991). "Exploration and Exploitation in Organizational Learning". <i>Organization Science</i>. 2 (1): 71–87. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1287%2Forsc.2.1.71">10.1287/orsc.2.1.71</a>.</li> <li>Nelson, R. R., & Winter, S. G. 1982. An Evolutionary Theory of Economic Change. Cambridge, Massachusetts: Belknap – Harvard University Press.</li> <li>Repenning, N (2002). "A Simulation-Based Approach to Understanding the Dynamics of Innovation Implementation". <i>Organization Science</i>. 13 (2): 109–127. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1287%2Forsc.13.2.109.535">10.1287/orsc.13.2.109.535</a>. <a href="/facts/Hdl_(identifier)/rdebSxmC">hdl</a>:<a href="https://hdl.handle.net/1721.1%2F3803">1721.1/3803</a>.</li> <li>Rivkin, J. (2000). "Imitation of Complex Strategies". <i>Management Science</i>. 46 (6): 824–844. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1287%2Fmnsc.46.6.824.11940">10.1287/mnsc.46.6.824.11940</a>.</li> <li>Rivkin, J. (2001). "Reproducing Knowledge: Replication Without Imitation at Moderate Complexity". <i>Organization Science</i>. 12 (3): 274–293. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1287%2Forsc.12.3.274.10106">10.1287/orsc.12.3.274.10106</a>.</li> <li>Rudolph, J.; Repenning, N. (2002). <a href="http://sph.bumc.bu.edu/directory/displayDetails.aspx?INDEX=10457">"Disaster Dynamics: Understanding the Role of Quantity in Organizational Collapse"</a>. <i>Administrative Science Quarterly</i>. 47 (1): 1–30. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.2307%2F3094889">10.2307/3094889</a>. <a href="/facts/JSTOR_(identifier)/YTeVmaJ7">JSTOR</a> <a href="https://www.jstor.org/stable/3094889">3094889</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:745136">745136</a>.[<i>permanent dead link</i>]</li> <li>Sastry, M. A. (1997). "Problems and paradoxes in a model of punctuated organizational change". <i>Administrative Science Quarterly</i>. 42 (2): 237–275. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.2307%2F2393920">10.2307/2393920</a>. <a href="/facts/JSTOR_(identifier)/YTeVmaJ7">JSTOR</a> <a href="https://www.jstor.org/stable/2393920">2393920</a>.</li> <li>Schelling, T (1971). "Dynamic models of segregation". <i>Journal of Mathematical Sociology</i>. 1 (2): 143–186. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1080%2F0022250x.1971.9989794">10.1080/0022250x.1971.9989794</a>.</li> <li>Simon, H. 1996 (1969; 1981) The Sciences of the Artificial (3rd Edition) MIT Press <a href="https://www.amazon.com/dp/0262691914">[2]</a>.</li> <li>Sterman, J. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. New York, NY: Irwin McGraw–Hill.</li> <li>Sterman, J.; Repenning, N.; Kofman, F. (1997). "Unanticipated Side Effects of Successful Quality Programs: Exploring a Paradox of Organizational Improvement". <i>Management Science</i>. 43 (4): 503–521. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1287%2Fmnsc.43.4.503">10.1287/mnsc.43.4.503</a>. <a href="/facts/Hdl_(identifier)/rdebSxmC">hdl</a>:<a href="https://hdl.handle.net/1721.1%2F2506">1721.1/2506</a>.</li> <li>Wolfram, S. 2002. A New Kind of Science. Champaign, IL: Wolfram Media.</li> <li>Zott, C (2003). <a href="http://www.insead.edu/facultyresearch/faculty/profiles/czott">"Dynamic Capabilities and the Emergence of Intra-industry Differential Firm Performance: Insights from a Simulation Study"</a>. <i>Strategic Management Journal</i>. 24 (2): 97–125. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1002%2Fsmj.288">10.1002/smj.288</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:15415778">15415778</a>.</li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>Harrison, Lin, Carroll, & Carley, 2007 <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>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. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>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. doi:10.1109/TSMCA.2012.2199304. S2CID 7985332. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>Lave and March 1975 <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>Simon 1969 <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> </ol>