Commenced in January 2007
Paper Count: 31100
Computational Modeling in Strategic Marketing
Abstract:Well-developed strategic marketing planning is the essential prerequisite for establishment of the right and unique competitive advantage. Typical market, however, is a heterogeneous and decentralized structure with natural involvement of individual or group subjectivity and irrationality. These features cannot be fully expressed with one-shot rigorous formal models based on, e.g. mathematics, statistics or empirical formulas. We present an innovative solution, extending the domain of agent based computational economics towards the concept of hybrid modeling in service provider and consumer market such as telecommunications. The behavior of the market is described by two classes of agents - consumer and service provider agents - whose internal dynamics are fundamentally different. Customers are rather free multi-state structures, adjusting behavior and preferences quickly in accordance with time and changing environment. Producers, on the contrary, are traditionally structured companies with comparable internal processes and specific managerial policies. Their business momentum is higher and immediate reaction possibilities limited. This limitation underlines importance of proper strategic planning as the main process advising managers in time whether to continue with more or less the same business or whether to consider the need for future structural changes that would ensure retention of existing customers or acquisition of new ones.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076040Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1352
 J. W. Forrester, Industrial Dynamics. Productivity Press, 1961.
 H. M. Amman, D. A. Kendrick, and J. Rust, eds., Handbook of Computational Economics. North Holland, 1st ed., June 1996.
 L. Tesfatsion and K. L. Judd, eds., Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. North Holland, 1 ed., July 2006.
 L. Tesfatsion, "Chapter 16 Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, vol. 2, pp. 831-880, 2006.
 H. Akkermans, "Emergent supply networks: System dynamics simulation of adaptive supply agents," p. 63, 2001. cited By (since 1996) 8.
 L. L┬¿attil┬¿a, P. Hilletofth, and B. Lin, "Hybrid simulation models - when, why, how?," Expert Systems with Applications, vol. 37, no. 12, pp. 7969- 7975, 2010. cited By (since 1996) 1.
 A. Borshchev, Y. Karpov, and V. Kharitonov, "Distributed simulation of hybrid systems with AnyLogic and HLA," Future Generation Computer Systems, vol. 18, pp. 829-839, May 2002.
 S. Kortelainen and L. L┬¿attil┬¿a, "Modeling strategic technology management with a hybrid model," in Proceedings of the 27th International Conference of the System Dynamics Society, (New Mexico, USA), 2009.
 Y. L. Doz and M. Kosonen, "Embedding strategic agility: A leadership agenda for accelerating business model renewal," Long Range Planning, vol. 43, no. 2-3, pp. 370-382, 2010. cited By (since 1996) 6.
 E. Bonabeau, "Predicting the unpredictable," Harvard Business Review, vol. 80, no. 3, p. 109, 2002. cited By (since 1996) 26.
 P. Twomey and R. Cadman, "Agent-based modelling of customer behaviour in the telecoms and media markets," Info, vol. 4, no. 1, pp. 56- 63, 2002. cited By (since 1996) 7.
 C. Pettey et al., "Gartner Identifies the Top 10 Strategic Technologies for 2011, Next Generation Analytics," in Gartner Symposium / ITxpo, (Orlando), Gartner, Inc., October 17-21 2010. http://www.gartner.com/ it/page.jsp?id=1454221.
 J. D. Sterman, Business Dynamics. Systems Thinking and Modeling for a Complex World. McGraw-Hill Higher, 2000.
 A. Borshchev and A. Filippov, "From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools - Multimethod Simulation Software Tool AnyLogic," in The 22nd International Conference of the System Dynamics Society, (Oxford, England), July 25 - 29 2004.
 H. Rahmandad and J. Sterman, "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," MANAGEMENT SCIENCE, vol. 54, pp. 998-1014, May 2008.
 E. Mazhari, J. Zhao, N. Celik, S. Lee, Y.-J. Son, and L. Head, "Hybrid simulation and optimization-based design and operation of integrated photovoltaic generation, storage units, and grid," Simulation Modelling Practice and Theory, vol. 19, no. 1, pp. 463-481, 2011.
 V. S. Koritarov, "Real-world market representation with agents," IEEE Power and Energy Magazine, vol. 2, no. 4, pp. 39-46, 2004. cited By (since 1996) 30.
 G. Conzelmann, G. Boyd, V. Koritarov, and T. Veselka, "Multi-agent power market simulation using emcas," vol. 3, pp. 2829-2834, 2005. cited By (since 1996) 7.
 L. Tesfatsion, "Agent-based computational economics: Growing economies from the bottom up," 2011. http://www.econ.iastate.edu/ tesfatsi/ace.htm.
 J. M. Epstein, Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton Studies in Complexity, Princeton University Press, Jan. 2007.
 M. J. North, C. M. Macal, J. S. Aubin, P. Thimmapuram, M. Bragen, J. Hahn, J. Karr, N. Brigham, M. E. Lacy, and D. Hampton, "Multiscale agent-based consumer market modeling," Complexity, vol. 15, no. 5, pp. 37-47, 2010.
 L. B. Said, T. Bouron, and A. Drogoul, "Agent-based interaction analysis of consumer behavior," in AAMAS -02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems, (New York, NY, USA), pp. 184-190, ACM Press, 2002.
 S. B. Schwaiger, A., "Simmarket: Multiagent-based customer simulation and decision support for category management," vol. 2831, pp. 74-84, 2003. cited By (since 1996) 4.
 I. Nikolic, L. A. Bollinger, and C. B. Davis, "Agent based modeling of large-scale socio-technical metal networks," pp. 779-788, 2010. cited By (since 1996) 0.
 J. April, F. Glover, J. P. Kelly, and M. Laguna, "Simulation-based optimization: practical introduction to simulation optimization," in WSC -03: Proceedings of the 35th conference on Winter simulation, pp. 71- 78, Winter Simulation Conference, 2003.
 R. S. Kaplan and D. P. Norton, The Balanced Scorecard: Translating Strategy into Action. Harvard Business Press, 1 ed., Sept. 1996.
 M. Calisti and D. Greenwood, "Goal-Oriented Autonomic Process Modeling and Execution for Next Generation Networks," in Modelling Autonomic Communications Environments (S. van der Meer, M. Burgess, and S. Denazis, eds.), vol. 5276 of Lecture Notes in Computer Science, ch. 4, pp. 38-49, Berlin, Heidelberg: Springer Berlin / Heidelberg, 2008.