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.
[1] J. W. Forrester, Industrial Dynamics. Productivity Press, 1961.
[2] H. M. Amman, D. A. Kendrick, and J. Rust, eds., Handbook of
Computational Economics. North Holland, 1st ed., June 1996.
[3] L. Tesfatsion and K. L. Judd, eds., Handbook of Computational Economics,
Volume 2: Agent-Based Computational Economics. North
Holland, 1 ed., July 2006.
[4] L. Tesfatsion, "Chapter 16 Agent-Based Computational Economics: A
Constructive Approach to Economic Theory," Handbook of Computational
Economics, vol. 2, pp. 831-880, 2006.
[5] H. Akkermans, "Emergent supply networks: System dynamics simulation
of adaptive supply agents," p. 63, 2001. cited By (since 1996)
8.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] E. Bonabeau, "Predicting the unpredictable," Harvard Business Review,
vol. 80, no. 3, p. 109, 2002. cited By (since 1996) 26.
[11] 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.
[12] 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.
[13] J. D. Sterman, Business Dynamics. Systems Thinking and Modeling for
a Complex World. McGraw-Hill Higher, 2000.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] L. Tesfatsion, "Agent-based computational economics: Growing
economies from the bottom up," 2011. http://www.econ.iastate.edu/
tesfatsi/ace.htm.
[20] J. M. Epstein, Generative Social Science: Studies in Agent-Based
Computational Modeling. Princeton Studies in Complexity, Princeton
University Press, Jan. 2007.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] R. S. Kaplan and D. P. Norton, The Balanced Scorecard: Translating
Strategy into Action. Harvard Business Press, 1 ed., Sept. 1996.
[27] 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.
[1] J. W. Forrester, Industrial Dynamics. Productivity Press, 1961.
[2] H. M. Amman, D. A. Kendrick, and J. Rust, eds., Handbook of
Computational Economics. North Holland, 1st ed., June 1996.
[3] L. Tesfatsion and K. L. Judd, eds., Handbook of Computational Economics,
Volume 2: Agent-Based Computational Economics. North
Holland, 1 ed., July 2006.
[4] L. Tesfatsion, "Chapter 16 Agent-Based Computational Economics: A
Constructive Approach to Economic Theory," Handbook of Computational
Economics, vol. 2, pp. 831-880, 2006.
[5] H. Akkermans, "Emergent supply networks: System dynamics simulation
of adaptive supply agents," p. 63, 2001. cited By (since 1996)
8.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] E. Bonabeau, "Predicting the unpredictable," Harvard Business Review,
vol. 80, no. 3, p. 109, 2002. cited By (since 1996) 26.
[11] 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.
[12] 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.
[13] J. D. Sterman, Business Dynamics. Systems Thinking and Modeling for
a Complex World. McGraw-Hill Higher, 2000.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] L. Tesfatsion, "Agent-based computational economics: Growing
economies from the bottom up," 2011. http://www.econ.iastate.edu/
tesfatsi/ace.htm.
[20] J. M. Epstein, Generative Social Science: Studies in Agent-Based
Computational Modeling. Princeton Studies in Complexity, Princeton
University Press, Jan. 2007.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] R. S. Kaplan and D. P. Norton, The Balanced Scorecard: Translating
Strategy into Action. Harvard Business Press, 1 ed., Sept. 1996.
[27] 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.
@article{"International Journal of Information, Control and Computer Sciences:59904", author = "Petr Cernohorsky and Jan Voracek", title = "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.", keywords = "Agent-based computational economics, hybrid modeling,
strategic marketing, system dynamics.", volume = "5", number = "12", pages = "1668-9", }