Abstract
Organizations are driven by social interactions such as decision processes, negotiations or operations. Those interactions are composed of multiple simultaneous, dynamically evolving processes with several different agents. However, researchers in the field of management science traditionally focus on aggregated characteristics and assume equilibria, thus correctly neglecting the level of individual agents. Thus, most research methods in management science are based on cross-sectional data as well as stable and predictable events.
Recently, modeling and simulation methods are becoming increasingly accepted among management scientists in order to better cope with complex problems and to better capture the underlying processes of social interactions.
In this paper, we present simulation as an appropriate research method to better handle complexity within this field. In particular, we present two distinct simulation methods: Agent-based modeling and system dynamics. We discuss the value and use of simulation models for supporting theory building and testing in management science. Further, we discuss the prerequisits, advantages and challenges of simulation methods.
Aside of general advantages that any simulation method offers, we also point to differences between the two simulation methods. In summary, we advocate a stronger use of simulation as additional research method in management science because it may improve the reliability and soundness of existing theories by focusing on the social interactions which are drivers of most business processes. At the same time, we emphasize the need for in-depth methodological knowledge and a thorough understanding of adequacy of the simulation method for the problem under investigation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Therefore, pure spreadsheet software does not represent computer software for simulation. Even though some of the software packages provide users with a programmable interface which enables simulation, the major use of spreadsheets lies in the support of calculations without taking into account feedbacks, delay or nonlinearities. Spreadsheet software consequently supports analytical solutions. For that reason, we stress out that only under highly specific circumstances research conducted with spreadsheet software can be called simulation research.
References
Axelrod, Robert M. 1984. The evolution of cooperation. New York: Basic Books.
Axelrod, Robert M. 1997a. Advancing the art of simulation in the social sciences. Complexity 3(2): 16–22.
Axelrod, Robert M. 1997b. The complexity of cooperation: Agent-based models of competition and collaboration, Princeton studies in complexity. Princeton: Princeton University Press.
Baum, Joel A.C., and Jitendra V. Singh. 1994. Organization-environment coevolution. In Evolutionary dynamics of organizations, ed. J.A.C. Baum and J.V. Singh, 379–402. New York: Oxford University Press.
Becker, Gary S. 1974. A theory of social interaction. Journal of Political Economy 82(6): 1063–1093.
Berends, Peter, and Georges Romme. 1999. Simulation as a research tool in management studies. European Management Journal 17(6): 576–583.
Bratley, Paul, Bennett L. Fox, and Linus E. Schrage. 1987. A guide to simulation. New York: Springer.
Cobham, Alan. 1954. Priority assignment in waiting line problems. Journal of the Operations Research Society of America 2(1): 70–76.
Cohen, Kalman J. 1960. Simulation of the firm. The American Economic Review 50(2): 534–540.
Cohen, Kalman J., and Richard M. Cyert. 1961. Computer models in dynamic economics. The Quarterly Journal of Economics 75(1): 112–127.
Davies, Jason P., Kathleen M. Eisenhardt, and Christopher B. Bingham. 2007. Developing theory through simulation methods. The Academy of Management Review 32(2): 480–499.
Forrester, Jay W. 1958. Industrial dynamics: A major breakthrough for decision makers. Harvard Business Review 36(4): 37–66.
Forrester, Jay W. 1961. Industrial dynamics. Cambridge, MA: MIT Press.
Frigerio, Bartolomeo. 1629. L’economo prudente. Roma: Appresso Lodovico Grignani.
Granovetter, Mark S. 1973. The strength of weak ties. American Journal of Sociology 78(6): 1360–1380.
Güth, Werner, Rolf Schmittberger, and Bernd Schwarze. 1982. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior and Organization 3(4): 367–388.
Harrison, J. Richard, Zhiang Lin, Glenn R. Carroll, and Kathleen M. Carley. 2007. Simulation modeling in organizational and management research. The Academy of Management Review 32(4): 1229–1245.
Hirsch, Fred. 1995. Social limits to growth. London: Routledge.
Jackson, James R. 1957. Simulation research on job shop production. Naval Research Logistics Quarterly 4(4): 287–295.
Janis, Irving L. 1972. Victims of groupthink: A psychological study of foreign-policy decisions and fiascoes. Oxford: Houghton Mifflin.
Kahneman, Daniel, and Amos Tversky. 1979. Prospect theory: An analysis of decision under risk. Econometrica 47(2): 263–292.
Ketokivi, Mikko, and Saku Mantere. 2010. Two strategies for inductive reasoning in organizational research. Academy of Management Review 35(2): 315–333.
Korsgaard, M. Audrey, David M. Schweiger, and Harry J. Sapienza. 1995. Building commitment, attachment, and trust in strategic decision-making teams: The role of procedural justice. The Academy of Management Journal 38(1): 60–84.
Kreidler, Anja, and Meike Tilebein. 2013. Diversity and innovativeness in new product development teams: Addressing dynamic aspects with simulation. 31st international conference of the System Dynamics Society, Cambridge, MA, 21 July 2013.
Law, Averill M., and W. David Kelton. 2000. Simulation modeling and analysis, McGraw-Hill series in industrial engineering and management science, 3rd ed. Boston: McGraw-Hill.
Lewin, Arie Y., Chris P. Long, and Timothy N. Carroll. 1999. The coevolution of new organizational forms. Organization Science 10(5): 535–550.
Lomi, Alessandro, and Erik R. Larsen. 2001. Dynamics of organizations. Computational modeling and organizational theories. Menlo Park: American Association for Artificial Intelligence.
Luce, R. Duncan, Josiah Macy Jr., Lee S. Christie, and D. Harvie Hay. 1953. Information flow in task-oriented groups. Research laboratory of electronics, Massachusetts Institute of Technology technical report No. 264.
Macy, Michael W., and Robert Willer. 2002. From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology 28: 143–166.
Maffei, Richard B. 1958. Simulation, sensitivity, and management decision rules. The Journal of Business 31(3): 177–186.
Malcolm, Donald G. 1960. Bibliography on the use of simulation in management analysis. Operations Research 8(2): 169–177.
Manski, Charles F. 2000. Economic analysis of social interactions. Journal of Economic Perspectives 14(3): 115–136.
McKelvey, Bill. 1997. Quasi-natural organization science. Organization Science 8(4): 352–380.
Meadows, Dennis H., Donella L. Meadows, Jorgen Randers, and William W. Behrens III. 1972. The limits to growth: A report for the club of Rome’s project on the predicament of mankind, Potomac associates books. New York: Universe Books.
Mill, John S. 1848. Principles of political economy with some of their applications to social philosophy, 1909 edn.
Morehouse, N. Frank, Robert H. Strotz, and S.J. Horwitz. 1950. An electro-analog method for investigating problems in economic dynamics: Inventory oscillations. Econometrica 18(4): 313–328.
Newell, Allen, and Herbert A. Simon. 1961. Computer simulation of human thinking. Science 134(3495): 2011–2017.
Pareto, Vilfredo. 1906. Manual of political economy. Oxford: 2014 Reprint. Montesano, Aldo, Alberto Zanni, Luigino Bruni, John S. Chipman, Michael McLure. Oxford University Press.
Persky, Joseph. 1995. Retrospectives: The ethology of homo economicus. Journal of Economic Perspectives 9(2): 221–231.
Porter, Terry B. 2006. Coevolution as a research framework for organizations and the natural environment. Organization & Environment 19(4): 479–504.
Ricardo, David. 1821. The principles of political economy and taxation.
Samuelson, Paul A. 1937. A note on measurement of utility. The Review of Economic Studies 1(2): 155–161.
Schelling, Thomas C. 1969. Models of segregation. The American Economic Review 59(2): 488–493.
Schelling, Thomas C. 1971. Dynamics models of segregation. Journal of Mathematical Sociology 1(2): 143–186.
Schultz, Randall L. 1974. The use of simulation for decision making. Behavioral Science 19(5): 344–350.
Schumpeter, Joseph A. 1954. History of economic analysis. London: Allen and Unwin.
Shubik, Martin. 1960. Simulation of the industry and the firm. The American Economic Review 50(5): 908–919.
Simon, Herbert A. 1955. A behavioral model of rational choice. The Quarterly Journal of Economics 69(1): 99–118.
Simon, Herbert A. 1957. Administrative behavior: A study of decision-making processes in administrative organizations. New York: The Free Press.
Simon, Herbert A. 1972. Theories of bounded rationality. In Decision and organization, ed. C.B. McGuire and R. Radner, 161–176. Amsterdam: North-Holland Publishing Company.
Simon, Henrik, Sven Meyer, and Meike Tilebein. 2008. Bounded rationality in management research: Computational approaches to model the coevolution of organizations and their environments. International Federation of Scholarly Associations of Management (IFSAM) 9th world congress, Shanghai, 26 July 2008.
Sterman, John D. 2000. Business dynamics: Systems thinking and modeling for a complex world. Boston: Irwin/McGraw-Hill.
Thaler, Richard H. 1988. Anomalies: The ultimatum game. The Journal of Economic Perspectives 2(4): 195–206.
Tilebein, Meike, and Vera Stolarski. 2009. The contribution of diversity to successful R&D processes, Wien, 21 June 2009.
Tversky, Amos. 1972. Elimination by aspects: A theory of choice. Psychological Review 79(4): 281–299.
Tversky, Amos, and Daniel Kahneman. 1981. The framing of decisions and the psychology of choice. Science 211(4481): 453–458.
Varian, Hal R. 2010. Intermediate microeconomics. A modern approach, 8th ed. New York: W.W. Norton & Co.
von Neumann, John, and Oskar Morgenstern. 1944. Theory of games and economic behavior. Princeton: Princeton University Press.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Happach, R.M., Tilebein, M. (2015). Simulation as Research Method: Modeling Social Interactions in Management Science. In: Misselhorn, C. (eds) Collective Agency and Cooperation in Natural and Artificial Systems. Philosophical Studies Series, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-319-15515-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-15515-9_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15514-2
Online ISBN: 978-3-319-15515-9
eBook Packages: Humanities, Social Sciences and LawPhilosophy and Religion (R0)