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340 F. Beckenbach, R. Briegel agent-based analysis of the dynamics on the micro-level are essential features of such an approach. By using such a framework it is possible to bring more conceptual realism into economic models without losing the required property of computability. In this contribution it is suggested to specify bounded rational agents by picking up insights of modern behavioral research. The agent’s ability to act is given in terms of different modes of action the selection of which depends on behavioral and competitive conditions the agents themselves are generating. Novelty creation (i.e. innovation and imitation) is one mode of action being triggered endogenously in the model. Hence, innovation and imitation are explained endogenously. This is the basis for reconstructing the dynamics of economic aggregates without referring to representative agencies and optimizing activities. References Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211 Arthur B et al (ed) (1997) The Economy as an Evolving Complex System II. Addison-Wesley Publishing Company, Reading Balbi S, Giupponi C (2009). Reviewing agent-based modelling of socio-economic systems: a methodology for the analysis of climate change and sustainability. Working Paper University of Venice Beckenbach F (2007) Moderating instead of steering? In: Lehmann-Waffenschmidt M (ed) Innovations towards sustainability. Physica, Heidelberg Beckenbach F, Daskalakis M (2008) Behavioral Foundations of Innovation Surveys. International Journal of Foresight and Innovation Policy 4(3–4):181–199 Beckenbach F, Daskalakis M, Briegel R (2007) Behavioural foundation and agent-based simulation of regional innovation dynamics. Papers on agent-based economics, No 3, University of Kassel Beckenbach F, Daskalakis M, Briegel R (2009) Evolution and dynamics of networks in ‘Regional Innovation Systems’ (RIS). In: Pyka A et al (eds) Innovation networks. Springer, Berlin Camerer CF et al (2005) Neuroeconomics: how neuroscience can inform economics. J Econ Lit XLIII (1):9–64 Chung MH, Harrington JE (2006) Agent-based models of organizations. In: Judd KL, Tesfatsion L (eds) Handbook of computational economics II: agent-based computational economics. Amsterdam, North- Holland, pp 1273–1337 Cyert RM, March JG (1992) A behavioral theory of the firm. Blackwell, Oxford Dawid H (2006) Agent-based models of innovation and technological change. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics, vol 2. Amsterdam, North-Holland, pp 1235–1272 Edmonds B (2001) Towards a descriptive model of agent strategy search. Comput Econ 18:113–135 Fagiolo G, Dosi G (2003) Exploitation, exploration and innovation in a model of endogenous growth with locally interacting agents. Struct Chang Econ Dyn 14:237–273 Farmer J (2001) Toward agent-based models for investment. Mimeo Fine B (2000) Endogenous growth theory: a critical assessment. Camb J Econ 24:245–265 Frenken K, Faber A (2009) Introduction: evolutionary methodologies for analyzing environmental innovations and the implications for environmental policy. Technol Forecast Soc Change 76:449–452 Gintis H (2000) Beyond Homo Economicus: evidence from experimental economics. Ecol Econ 35:311– 322 Gintis H (2003). Towards a unity of the human behavioral sciences. Santa Fe Institute/working paper. Santa Fe Green C, Baski S, Dilmaghani M (2007) Challenges to a climate stabilizing energy future. Energy Policy 35:616–626 Holland JH (1996) Hidden order: how adaptation builds complexity. Addison-Wesley Publishing Company, Reading Holland JH (1998) Emergence: from chaos to order. Addison-Wesley Publishing Company, Reading
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