Abstract
This chapter introduces the book Agent-Based Simulation of Organizational Behavior presenting the idea of agent-based modeling as a “new frontier” for organizational research. After providing some indications of the challenge of bringing together cross-disciplinary and specialization tensions, the chapter suggests that autonomy, sociality, and cross-validation make this technique particularly suited to analyze organizational behavior research. An overview of the book follows with a short summary of the four parts of the book and each and every chapter. This introduction concludes with a map of what this new research frontier is about, covering both methodological and theoretical grounds.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Not all samples in organizational behavior have these characteristics; in fact, especially in organizational team research, observations are not independent and this violation led to the adoption of particular techniques called “multilevel regression analysis.”
References
Axelrod, R. (1997). The dissemination of culture: A model with local convergence and global polarization. Journal of Conflict Resolution, 41(2), 203–226.
Banerjee, A. V. (1992). A simple model of herd behavior. Quarterly Journal of Economics, 3, 797–817.
Bardone, E. (2015). Intervening via chance seeking. In D. Secchi & M. Neumann (Eds.), Agent-based simulation of organizational behavior: New frontiers of social science research. New York: Springer.
Coen, C. (2009). Simple but not simpler. Introduction CMOT special issue—simple or realistic. Computational and Mathematical Organization Theory, 15, 1–4.
Cohen, M. D., March, J. G., & Olsen, H. P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly, 17, 1–25.
Conte, R., Andrighetto, G., & Campenni, M. (2014). Minding norms. Mechanisms and dynamics of social order in agent societies. Oxford: Oxford University Press.
Dowling, D. (1999). Experimenting on theories. Science in Context, 12(2), 261–273.
Edmonds, B. (2015). Introduction to JASSS special issue—Using qualitative evidence to inform the specification of agent-based models. Journal of Artificial Societies and Social Simulation, 18(1), 18.
Epstein, J., & Axtell, R. (1996). Growing artificial societies. Social Science from the bottom-up. Washington DC: Brookings Institution Press.
Fiol, C. M., & O’Connor, E. J. (2003). Waking up! Mindfulness in the face of bandwagon. Academy of Management Review, 28(1), 54–70.
Fioretti, G. (2013). Agent-based simulation models in organization science. Organizational Research Methods, 16(2), 227–242.
Fioretti, G. (2015). Emergent organizations. In D. Secchi & M. Neumann (Eds.), Agent-based simulation of organizational behavior: New frontiers of social science research. New York: Springer.
Foss, N. J. (2003). Bounded rationality in the economics of organizations: ‘Much cited and little used’. Journal of Economic Psychology, 24, 245–264.
Granovetter, M. (1985). Economic action and social structure – The problem of embeddedness. American Journal of Sociology, 91(3), 481–510.
Heath, C., & Sitkin, S. B. (2001). Big-B versus Big-O: What is organizational about organizational behavior? Journal of Organizational Behavior, 22(1), 43–58.
Heckbert, S. (2013). MayaSim: An agent-based model of the ancient Maya social-ecological system. Journal of Artificial Societies and Social Simulation, 16(4), 11.
Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence: Models, analysis and simulation. Journal of Artificial Societies and Social Simulation, 5(3), 2.
Janssen, M. A., & Ostrom, E. (2006). Empirically based, agent-based models. Ecology and Society, 11(2), 37.
Knudsen, T., & Srikanth, K. (2014). Coordinated exploration: Organizing joint search by multiple specialists to overcome mutual confusion and joint myopia. Administrative Science Quarterly, 59, 409–441.
Lorscheid, I. (2012). Opening the ‘black box’ of simulations: Increased transparency and effective communication through the systematic design of experiments. Computational and Mathematical Organization Theory, 18(1), 22–62.
Meyer, R. E., Höllerer, A. M., Jancsary, D., & van Leeuwen, T. (2013). The visual dimension in organizing, organization, and organization research: Core ideas, current developments, and promising avenues. Academy of Management Annals, 7(1), 489–555.
Miller, K. D. (2015). Agent-based modeling and organization studies: A critical realist perspective. Organization Studies, 36(2), 175–196.
Miller, K. D., & Lin, S.-J. (2010). Different truths in different worlds. Organization Science, 21(1), 97–114.
Moss, S., & Edmonds, B. (2005). Sociology and simulation: Statistical and qualitative cross-validation.American Journal of Sociology, 110(4), 1095–1131.
Nelson, R., & Winter, S. (1982). An evolutionary theory of economic change (1st ed.). Cambridge, MA: Belknap Press.
Neumann, M. (2015). Grounded simulation. Journal of Artificial Societies and Social Simulation, 18(1), 9.
Neumann, M., & Cowley, S. (2015). Modelling social agency using diachronic cognition: Learning from the Mafia. In D. Secchi & M. Neumann (Eds.), Agent-based simulation of organizational behavior: New frontiers of social science research. New York: Springer.
Pentland, B. T., & Rueter, H. H. (1994). Organizational routines as grammars of action. Administrative Science Quarterly, 39, 484–510.
Powell, W. W., & DiMaggio, P. J. (Eds.). (1991). The new institutionalism in organizational analysis. Chicago, IL: Chicago University Press.
Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143–186.
Scott, R. (2001). Institutions and organizations. Thousand Oaks, CA: Sage.
Secchi, D. (2015). A case for agent-based model in organizational behavior and team research. Team Performance Management, 21(1/2), 37–50.
Secchi, D., Gullekson, N. (2012). The social and cognitive forces behind bandwagon processes: Models of organizational bandwagon. In European Academy of Management Annual Conference. Rotterdam, Netherlands.
Simon, H. A. (1993). Altruism and economics. American Economic Review, 83(2), 156–161.
Squazzoni, F., Jager, W., & Edmonds, B. (2014). Social simulation in the social sciences: A brief overview. Social Science Computer Review, 32(3), 279–294.
Thomsen, S. E. (2015). How docility impacts team efficiency. An agent-based modeling approach. In D. Secchi & M. Neumann (Eds.), Agent-based simulation of organizational behavior: New frontiers of social science research. New York: Springer.
Walsh, J. P. (1995). Managerial and organizational cognition: Notes from a trip down memory lane. Organization Science, 6(3), 280–321.
Woolridge, M. (2009). An introduction to multi-agent systems (2nd ed.). New York: Wiley.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Neumann, M., Secchi, D. (2016). Exploring the New Frontier: Computational Studies of Organizational Behavior. In: Secchi, D., Neumann, M. (eds) Agent-Based Simulation of Organizational Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-18153-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-18153-0_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18152-3
Online ISBN: 978-3-319-18153-0
eBook Packages: Business and ManagementBusiness and Management (R0)