CALL FOR PAPERS
Special Issue on Agent-Based Modelling in Business Economics: Advancing Methodological Diversity and Practical Knowledge with Agent-Based Simulation and Modelling Techniques
- Kai Fischbach, University of Bamberg
- Johannes Marx, University of Bamberg
- Tim Weitzel, University of Bamberg
Submission deadline: December 01, 2019
Agent-based modelling (ABM) is a simulation technique for investigating the dynamics of social, economic, and other systems based on the interdependent decisions of their constituent agents (Bonabeau 2002). The ABM approach facilitates studying social, economic, and political phenomena in these systems by analysing the iterated interactions of individuals that give rise to them (Klein, Marx, and Fischbach 2018).
As the computing power needed for implementing such models has become increasingly available and the associated costs have decreased, a growing and interdisciplinary community of researchers has begun to harness ABM to analyse emergent phenomena, such as customer flows, organisational behaviour, market dynamics, and information diffusion (Bonabeau 2002). Models run the gamut from abstract social words to complex and empirically rich descriptions of behaviour deeply rooted in theory and data (Bruch and Atwell 2013) and enable insights into the generative processes that underlie the emergence of social structure, even when the system dynamics that result from agents’ interactions are not tractable employing mathematical techniques (Klein, Marx, and Fischbach 2018; Smith and Conrey 2007). Different platforms and programming languages are readily available for conducting ABM research, even for inexperienced programmers (Bandini et al. 2009).
ABM was applied early on in the field of business economics, for instance to the analysis of the dynamics of financial and other markets (Klein, Marx, and Scheller 2018), market design, and market microstructure (LeBaron 2006). In management and organisation science, ABM has been utilised to study interactions both within and between organisations (Fioretti 2012). Popular applications come from the fields of organisational behaviour (e.g., organisational design and learning), operations and logistics (e.g., supply networks, transportation, and healthcare logistics), information systems (Weitzel et al. 2006), marketing, and address other topics of general interest, such as strategic management and decision-making, education in management, human resources, and research and development (Gómez-Cruz et al. 2017).
In addition, ABM has been applied to emergent socio-technical phenomena that are of interest for information systems researchers studying social information systems and, especially, social media. These studies have focused on the evolution of online communities (Johnson et al. 2014) as well as opinion dynamics and collective intelligence in social media (e.g., Havakhor et al. 2018; Hwang et al. 2011; Ross et al. 2019). Considering the potential of digitally enabled social dynamics to support information collection and communication not only in various business contexts (e.g., digital innovation, knowledge management, opinion mining, and influencer marketing), but also to enhance, for instance, crisis and disaster management, social movements, and digital volunteers, ABM can be expected to increase our understanding of the inherent dynamics and expected outcomes of such phenomena for both economic value and the greater good.
The upcoming special issue on ABM in the Journal of Business Economics is intended to foster the dialogue between the source disciplines of ABM and business economics and management science for mutual benefit. Beyond insights into emergent phenomena that have recently attracted the interest of researchers in business economics and related disciplines, contributions can also aim at enhancing the further development of ABM techniques, based, for instance, on empirical data and realistic use cases from business economics. Potential areas of interest include, amongst others:
- Applications of ABM for analysing emergent phenomena with origins in or related to the domains of business economics and management science, including, but not limited to, supply chain management, transportation and logistics, finance, organisational behaviour and learning, diffusion of innovation, marketing, information systems and innovation management.
- Applications of ABM for analysing digitally enabled social phenomena that can be harnessed for business and other applications, for instance, crowdsourcing, digital innovation, wisdom of crowds, collective intelligence, diffusion of information, and rumour management, including, but not limited to, the study of social information systems, knowledge management systems, and decision support systems.
- Studies on methods and techniques for and applications of business economics data sources to inform and validate ABM, employing, for instance, enterprise information systems, geo-information systems, and social information systems (e.g., social media).
Bandini, S., Manzoni, S., and Vizzari, G. (2009). Agent Based Modeling and Simulation: An Informatics Perspective. Journal of Artificial Societies and Social Simulation 12(4), 4.
Bonabeau, E. (2002). Agent-Based Modeling: Methods and Techniques for Simulating Human Systems. Proceedings of the National Academy of Sciences of the United States of America 99(suppl. 3), 7280-7287.
Bruch, E., and Atwell, J. (2013). Agent-Based Models in Empirical Social Research. Sociological Methods & Research 44(2), 186-221.
Fioretti, G. (2012). Agent-Based Simulation Models in Organization Science. Organizational Research Methods 16(2), 227-242.
Gómez-Cruz, N.A., Saa, I.L., and Ortega Hurtado, F.F. (2017). Agent-Based Simulation in Management and Organizational Studies: A Survey. European Journal of Management and Business Economics 26(3), 313-328.
Havakhor, T., Soror, A.A., and Sabherwal, R. (2018). Diffusion of Knowledge in Social Media Networks: Effects of Reputation Mechanisms and Distribution of Knowledge Roles. Information Systems Journal 28(1), 104-141.
Hwang, Y., Yuan, S., and Weng, J. (2011). A Study of the Impacts of Positive/Negative Feedback on Collective Wisdom: Case Study on Social Bookmarking Sites. Information Systems Frontiers 13(2), 265-279.
Johnson, S.L., Faraj, S., and Kudaravalli, S. (2014). Emergence of Power Laws in Online Communities: The Role of Social Mechanisms and Preferential Attachment. MIS Quarterly 38(3), 795-808.
Klein, D., Marx, J., and Fischbach, K. (2018). Agent-Based Modeling in Social Science, History, and Philosophy: An Introduction. Historical Social Research 43(1), 7-27.
Klein, D., Marx, J. and Scheller, S. (2018). Rationality in context. On inequality and the epistemic problems of maximizing expected utility. Synthese (2018). https://doi.org/10.1007/s11229-018-1773-0.
LeBaron, B. (2006). Agent-Based Computational Finance. In L. Tesfatsion and K.L. Judd (eds.). Handbook of Computational Economics, Volume 2 (pp. 1187-1233). Elsevier.
Ross, B., Pilz, L., Cabrera, B., Brachten, F., Neubaum, G., and Stieglitz, S. (2019). Are Social Bots a Real Threat? an Agent-Based Model of the Spiral of Silence to Analyse the Impact of Manipulative Actors in Social Networks. European Journal of Information Systems 25(1), 1-19.
Smith, E.R., and Conrey, F.R. (2007). Agent-Based Modeling: A New Approach for Theory Building in Social Psychology. Personality and Social Psychology Review 11(1), 87-104.
Weitzel, T., Beimborn, D., and König, W. (2006). A Unified Economic Model of Standard Diffusion: The Impact of Standardization Cost, Network Effects and Network Topology. Management Information Systems Quarterly 30 (SI), p. 489-514.
- Please see the journal web site for details:
- First call for papers: June, 2019
- Submission of papers: December 01, 2019
- Author notification: January 31, 2020
- Submission of first revision: May 01, 2020
- Author notification: July 01, 2020
- Submission of a second revision: October 15, 2020
- Author notification: December 15, 2020
- Submission of final revisions: January 15, 2021
- Submission of camera-ready papers: January 31, 2021