Skip to main content

Simulation as Research Method: Modeling Social Interactions in Management Science

  • Chapter
Collective Agency and Cooperation in Natural and Artificial Systems

Part of the book series: Philosophical Studies Series ((PSSP,volume 122))

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.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Persky (1995, p. 222) argues that the Latin term homo oeconomicus stems from Vilfredo Pareto (1906). However, he also argues that Schumpeter (1954, p. 156) highlights Frigerio’s (1629) book on the economo prudente as an ancestor of the economic man.

  2. 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.

    Google Scholar 

  • Axelrod, Robert M. 1997a. Advancing the art of simulation in the social sciences. Complexity 3(2): 16–22.

    Article  Google Scholar 

  • Axelrod, Robert M. 1997b. The complexity of cooperation: Agent-based models of competition and collaboration, Princeton studies in complexity. Princeton: Princeton University Press.

    Google Scholar 

  • 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.

    Google Scholar 

  • Becker, Gary S. 1974. A theory of social interaction. Journal of Political Economy 82(6): 1063–1093.

    Article  Google Scholar 

  • Berends, Peter, and Georges Romme. 1999. Simulation as a research tool in management studies. European Management Journal 17(6): 576–583.

    Article  Google Scholar 

  • Bratley, Paul, Bennett L. Fox, and Linus E. Schrage. 1987. A guide to simulation. New York: Springer.

    Book  Google Scholar 

  • Cobham, Alan. 1954. Priority assignment in waiting line problems. Journal of the Operations Research Society of America 2(1): 70–76.

    Article  Google Scholar 

  • Cohen, Kalman J. 1960. Simulation of the firm. The American Economic Review 50(2): 534–540.

    Google Scholar 

  • Cohen, Kalman J., and Richard M. Cyert. 1961. Computer models in dynamic economics. The Quarterly Journal of Economics 75(1): 112–127.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Forrester, Jay W. 1958. Industrial dynamics: A major breakthrough for decision makers. Harvard Business Review 36(4): 37–66.

    Google Scholar 

  • Forrester, Jay W. 1961. Industrial dynamics. Cambridge, MA: MIT Press.

    Google Scholar 

  • Frigerio, Bartolomeo. 1629. L’economo prudente. Roma: Appresso Lodovico Grignani.

    Google Scholar 

  • Granovetter, Mark S. 1973. The strength of weak ties. American Journal of Sociology 78(6): 1360–1380.

    Article  Google Scholar 

  • Güth, Werner, Rolf Schmittberger, and Bernd Schwarze. 1982. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior and Organization 3(4): 367–388.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hirsch, Fred. 1995. Social limits to growth. London: Routledge.

    Google Scholar 

  • Jackson, James R. 1957. Simulation research on job shop production. Naval Research Logistics Quarterly 4(4): 287–295.

    Article  Google Scholar 

  • Janis, Irving L. 1972. Victims of groupthink: A psychological study of foreign-policy decisions and fiascoes. Oxford: Houghton Mifflin.

    Google Scholar 

  • Kahneman, Daniel, and Amos Tversky. 1979. Prospect theory: An analysis of decision under risk. Econometrica 47(2): 263–292.

    Article  Google Scholar 

  • Ketokivi, Mikko, and Saku Mantere. 2010. Two strategies for inductive reasoning in organizational research. Academy of Management Review 35(2): 315–333.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Lewin, Arie Y., Chris P. Long, and Timothy N. Carroll. 1999. The coevolution of new organizational forms. Organization Science 10(5): 535–550.

    Article  Google Scholar 

  • Lomi, Alessandro, and Erik R. Larsen. 2001. Dynamics of organizations. Computational modeling and organizational theories. Menlo Park: American Association for Artificial Intelligence.

    Google Scholar 

  • 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.

    Google Scholar 

  • Macy, Michael W., and Robert Willer. 2002. From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology 28: 143–166.

    Article  Google Scholar 

  • Maffei, Richard B. 1958. Simulation, sensitivity, and management decision rules. The Journal of Business 31(3): 177–186.

    Article  Google Scholar 

  • Malcolm, Donald G. 1960. Bibliography on the use of simulation in management analysis. Operations Research 8(2): 169–177.

    Article  Google Scholar 

  • Manski, Charles F. 2000. Economic analysis of social interactions. Journal of Economic Perspectives 14(3): 115–136.

    Article  Google Scholar 

  • McKelvey, Bill. 1997. Quasi-natural organization science. Organization Science 8(4): 352–380.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Mill, John S. 1848. Principles of political economy with some of their applications to social philosophy, 1909 edn.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Newell, Allen, and Herbert A. Simon. 1961. Computer simulation of human thinking. Science 134(3495): 2011–2017.

    Article  Google Scholar 

  • Pareto, Vilfredo. 1906. Manual of political economy. Oxford: 2014 Reprint. Montesano, Aldo, Alberto Zanni, Luigino Bruni, John S. Chipman, Michael McLure. Oxford University Press.

    Google Scholar 

  • Persky, Joseph. 1995. Retrospectives: The ethology of homo economicus. Journal of Economic Perspectives 9(2): 221–231.

    Article  Google Scholar 

  • Porter, Terry B. 2006. Coevolution as a research framework for organizations and the natural environment. Organization & Environment 19(4): 479–504.

    Article  Google Scholar 

  • Ricardo, David. 1821. The principles of political economy and taxation.

    Google Scholar 

  • Samuelson, Paul A. 1937. A note on measurement of utility. The Review of Economic Studies 1(2): 155–161.

    Article  Google Scholar 

  • Schelling, Thomas C. 1969. Models of segregation. The American Economic Review 59(2): 488–493.

    Google Scholar 

  • Schelling, Thomas C. 1971. Dynamics models of segregation. Journal of Mathematical Sociology 1(2): 143–186.

    Article  Google Scholar 

  • Schultz, Randall L. 1974. The use of simulation for decision making. Behavioral Science 19(5): 344–350.

    Article  Google Scholar 

  • Schumpeter, Joseph A. 1954. History of economic analysis. London: Allen and Unwin.

    Google Scholar 

  • Shubik, Martin. 1960. Simulation of the industry and the firm. The American Economic Review 50(5): 908–919.

    Google Scholar 

  • Simon, Herbert A. 1955. A behavioral model of rational choice. The Quarterly Journal of Economics 69(1): 99–118.

    Article  Google Scholar 

  • Simon, Herbert A. 1957. Administrative behavior: A study of decision-making processes in administrative organizations. New York: The Free Press.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Sterman, John D. 2000. Business dynamics: Systems thinking and modeling for a complex world. Boston: Irwin/McGraw-Hill.

    Google Scholar 

  • Thaler, Richard H. 1988. Anomalies: The ultimatum game. The Journal of Economic Perspectives 2(4): 195–206.

    Article  Google Scholar 

  • Tilebein, Meike, and Vera Stolarski. 2009. The contribution of diversity to successful R&D processes, Wien, 21 June 2009.

    Google Scholar 

  • Tversky, Amos. 1972. Elimination by aspects: A theory of choice. Psychological Review 79(4): 281–299.

    Article  Google Scholar 

  • Tversky, Amos, and Daniel Kahneman. 1981. The framing of decisions and the psychology of choice. Science 211(4481): 453–458.

    Article  Google Scholar 

  • Varian, Hal R. 2010. Intermediate microeconomics. A modern approach, 8th ed. New York: W.W. Norton & Co.

    Google Scholar 

  • von Neumann, John, and Oskar Morgenstern. 1944. Theory of games and economic behavior. Princeton: Princeton University Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roland Maximilian Happach .

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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

Publish with us

Policies and ethics