This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bagnall A.J. (2004) A Multi-Agent Model of the UK Market in Electricity Generation. In: Bull (eds) Applications of Learning Classifier Systems, Studies in Fuzziness and Soft Computing 15.
Barr J and Saraceno F (2002) A computational theory of the firm. Journal of Economic Behavior and Organization 49:345–361
Bruderer E, Singh J.V (1996) Organizational Evolution, Learning, and Selection: A Genetic Algorithm-Based Model. Journal of Economic Behavior and Organization 39:1216–1231
Butz MV and Wilson SW (2001) An algorithmic description of XCS. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems. Lecture Notes in Artificial Intelligence 2321:253–272 Springer Verlag
Chen Y, Su XM, Rao JH and Xiong H, (2000) Agent-based microsimulation of economy from a complexity perspective. In: Gan R (eds) The ITBM2000 Conference-Information Technlology for Business Management. Beijing, China
Durand R, Guessoum Z (2001) Competence systemics and survival, simulation and emperical analysis. Competence 2000 Conference. Helsinki, Finland
Guessoum Z, Briot JP (1999) From active objects to autonomous agents. IEEE Concurrency 7:68–76
Holland JH (1991) Artificially adaptive agents in economic theory. American Economic Review 81: 265–370
Pajares J, Hermandez-Iglesias C, Lopez-Paredes L (2004) Modeling learning and RD in innovative environments: a cognitive multi-agent approach. Journal of Artificial Societies and Social Simulation 7
Péres-Uribe A, Hirsbrunner B, (2000) The risk of Exploration in multi-agent learning systems: a case study. Proc. Agents-00 Joint workshop on learning agents, Barcelona, June 2000.
Penrose ET (1959) The theory of the growth of the firm. Basil Blackwell
Rivero SLDM, Storb BH, Wazlawick RS (1999) Economic Theory, Anticipatory Systems and artificial Adaptative Agents. BEJE (Brazilian electronic Journal of Economics) 2
Schulenburg S, Ross P (2000) An Adaptive Agent Based Economic Model. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning Classifier Systems: From Foundations to Applications. Springer Verlag. Lecture Notes in Artificial Intelligence 1813: 265–284.
Sutton RT, Barto AG (1998) Reinforcement Learning: An Introduction. IT Press, Cambridge, MA
Tesauro G, Kephart JO (2002) Pricing in Agent Economies Using Multi-Agent Q-Learning. Autonomous Agents and Multi-Agent Systems 5: 289–304
Wilson SW, (1994) ZCS: A Zeroth Level Classifier System. Evolutionary computation 2: 1–18
Wilson SW, (1995) Classifier fitness based on accuracy. Evolutionary computation 3: 149–175
Wilson SW, (2000) State of XCS classifier system research. LNAI 1813: 63–83
Yildizoglu M. (2001) Modeling Adaptive Learning: RD Strategies in the Model of Nelson and Winter. In: DRUID’s Nelson and Winter Conference. Aalborg, Danemark
Yildizoglu M, (2000) Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks. European Journal of Economics and Social Systems 15
Zadeh L, (2001) A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22: 73–84
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rejeb, L., Guessoum, Z. (2006). Firms Adaptation in Dynamic Economic Systems. In: Beckmann, M., et al. Artificial Economics. Lecture Notes in Economics and Mathematical Systems, vol 564. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28547-4_5
Download citation
DOI: https://doi.org/10.1007/3-540-28547-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28578-6
Online ISBN: 978-3-540-28547-2
eBook Packages: Business and EconomicsEconomics and Finance (R0)