Skip to main content
SpringerLink
Log in
Book cover

International Economics of Resource Efficiency pp 289–315Cite as

Multi-agent Modeling of Economic Innovation Dynamics and Its Implications for Analyzing Emission Impacts

  • Chapter
  • First Online:

  • 1794 Accesses

Abstract

Undoubtedly many of the observable impacts on ecological systems (e.g. depletion of minerals and species, emissions and waste) can be derived from economic activities. But the relationship between the two is far from being fully clarified in scientific analysis. Either the focus on this impact perspective and/or lack of economic knowledge often leads to a specific framing of economic analysis in this environmental context: Firstly, only economic aggregates (like gross domestic product) and their dynamics are considered; secondly, this analysis is normally carried out by using computable modeling frame works (like computable general equilibrium models). What is missing in such a framework is a realistic consideration of the microeconomic foundation for the driving forces of economic processes. To assume a representative optimizing agency is not sufficient in this context because neither behavioral constraints (in terms of information processing and knowledge acquisition) nor non-linear interaction effects between economic actors can be taken into account by making this assumption. A realistic view on the microeconomic background of observable economic aggregates is not only important for explaining the (aggregate) economic output itself, it is also essential for assessing the possibilities and constraints for political regulation.

This paper has been previously published in International Economics and Economic Policy Special Issue on “International Economics of Resources and Resource Policy”, Volume 7, Numbers 2–3/August 2010.

Role of the funding source: This article is based on research conducted within the research project “2nd order innovations? An actor oriented analysis of the genesis of knowledge and institutions in regional innovation systems”, which was funded by the VolkswagenStiftung, Germany.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    This problem is often circumvented by postulating targets (e.g. in terms of emissions) without showing by means of which transitions agents can meet these targets and how these transitions can be triggered. If this would be specified the uncertainty with regard to emission scenarios could be reduced (cf. IPCC 2007; Pielke et al. 2008).

  2. 2.

    Depending on data availability there are generally two different ways to calibrate the initial values of the state variables and the parameters of the model: either indirectly by postulating the reproduction of given data time series or directly by doing behavioral observations (cf. Beckenbach et al. 2009; Beckenbach and Daskalakis 2008; Windrum et al. 2007; Edmonds 2001).

  3. 3.

    Hence, due to methodological reasons we will neither deal with interacting emissions, nor with impacts related to the extraction side, nor with the effects all these impacts have on the state and dynamics of the various parts of the ecological system.

  4. 4.

    This could be either a single human being in its essential properties or a group of human beings having at least one common property being essential for their way to act.

  5. 5.

    To assume one or several simple relations (e.g. as aggregated equations or as production functions) is still the state of the art in modern growth theory (cf. e.g. Fine 2000). No attempt has been made to show that these are empirically and methodologically legitimate assumptions. Using evolutionary methodologies defines an alternative path for conceptualizing growth in general and especially environmental innovation (cf. Frenken and Faber 2009). In the sequel we will follow this path of economic thinking.

  6. 6.

    This essential feature of novelty creation is ignored in theories of “endogenous growth” (e.g. Romer 1990) where r&d is a continuous and riskless activity separated from other firm activities.

  7. 7.

    To ignore this (non-linear) interdependency of new and old activities (and of their outcomes respectively) is another failure of most contributions to “endogenous growth theory”: here every innovation is immediately patented and the new activity is simply an add-on for total production (cf. Romer 1990).

  8. 8.

    According to this caveat the novelty creating process is totally conjectural without anything to generalize. Due to the idiosyncratic nature of the processes as well as of the persons involved in innovations there is seen only a limited possibility for some after-the-fact analysis on an aggregated level (cf. e.g. Vromen 2001).

  9. 9.

    Most prominent in this respect are revisions of the expected utility theory (e.g. prospect theory; cf. Kahneman and Tversky 1979) and enhancements of game theory (cf. Gintis 2003).

  10. 10.

    These explanantia – in model terms: variables – are moderated by behavioral traits (e.g. risk attitude, curiosity) – in model terms: parameters.

  11. 11.

    In the present context only routine, innovation and imitation are taken into account.

  12. 12.

    The background for this assumption is the positive relation between the broadness of knowledge and the firm’s flexibility as regards to the demand side.

  13. 13.

    The subscript for the sector is skipped here.

  14. 14.

    In developed market economies this intermediary part of the sectoral production is on average about 2/3 of the total sectoral production.

  15. 15.

    How these coefficients of intermediary production can be conceptualized dynamically is an intricate question which is beyond the scope of this elaboration (cf. Pan 2006).

  16. 16.

    This activity level is measured in value terms because price fluctuations are not dealt with in the model.

  17. 17.

    This means that conventional products as well as older innovative products are substituted by newer innovative products.

  18. 18.

    The final demand for conventional products is treated “amorphously” in our model, i.e. we do not distinguish single conventional products; rather we treat this part of final demand as if there is only one conventional product in each sector.

  19. 19.

    In the debate on decarbonisation the reduction of emissions is only dealt with in terms of technological potentials. How these potentials are transformed into technologies of market agents by means of competition and environmental instruments remains rather vague in this analysis. But even given these instruments the overall effect on emissions depends on the assumption about the growth of GDP which is treated as a separated issue (cf. Green et al. 2007).

  20. 20.

    In the initial phase (until about t = 10) the model is swinging in: The first innovative products have to be developed (which is time-consuming) before they can be put on the market, and their diffusion starts only slowly. Therefore the nearly constant level of final demand and emissions in this transient phase rather constitutes an artefact of the model.

  21. 21.

    In the given case the rebound effect has a direct component resulting from the increasing attractiveness of innovative products for their appliers and an indirect component resulting from intersectoral interdependencies. These two components are normally not taken into account because the focus is on cost reduction in a given sector.

  22. 22.

    yts is increased from 0.02 to 0.5 for t > 60 [cf. section III(4)].

  23. 23.

    For a specification of this perspective of political regulation (cf. Kemp and Zundel 2007; Beckenbach 2007).

  24. 24.

    The reason for that is that the increase of innovation costs is reducing the relative force toward innovation on the agent level (cf. above).

References

  • Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211

    Article  Google Scholar 

  • Arthur WB, Durlauf SN, Lane DA (eds) (1997) The economy as an evolving complex system II. Addison-Wesley, Reading, MA

    Google Scholar 

  • 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

    Google Scholar 

  • Beckenbach F (2007) Moderating instead of steering? In: Lehmann-Waffenschmidt M (ed) Innovations towards sustainability. Physica, Heidelberg

    Google Scholar 

  • Beckenbach F, Daskalakis M (2008) Behavioral foundations of innovation surveys. Int J Foresight Innov Policy 4(3–4):181–199

    Article  Google Scholar 

  • Beckenbach F, Daskalakis M, Briegel R (2007a) Behavioural foundation and agent-based simulation of regional innovation dynamics. Papers on agent-based economics, No 3, University of Kassel

    Google Scholar 

  • 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

    Google Scholar 

  • Camerer CF et al (2005) Neuroeconomics: how neuroscience can inform economics. J Econ Lit XLIII(1):9–64

    Article  Google Scholar 

  • 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. North Holland, Amsterdam

    Google Scholar 

  • Cyert RM, March JG (1992) A behavioral theory of the firm. Blackwell, Oxford

    Google Scholar 

  • 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

    Google Scholar 

  • Edmonds B (2001) Towards a descriptive model of agent strategy search. Comput Econ 18:113–135

    Article  Google Scholar 

  • Fagiolo G, Dosi G (2003) Exploitation, exploration and innovation in a model of endogenous growth with locally interacting agents. Struct Change Econ Dyn 14:237–273

    Article  Google Scholar 

  • Farmer J (2001) Toward agent-based models for investment. Benchmarks and Attribution Analysis, Association for Investment and Management Research, Mimeo

    Google Scholar 

  • Fine B (2000) Endogenous growth theory: a critical assessment. Camb J Econ 24:245–265

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Gintis H (2000) Beyond homo economicus: evidence from experimental economics. Ecol Econ 35:311–322

    Article  Google Scholar 

  • Gintis H (2003) Towards a unity of the human behavioral sciences. Santa Fe Institute, Santa Fe (working paper)

    Google Scholar 

  • Green C, Baski S, Dilmaghani M (2007) Challenges to a climate stabilizing energy future. Energy Policy 35:616–626

    Article  Google Scholar 

  • Holland JH (1996) Hidden order: how adaptation builds complexity. Addison-Wesley, Reading

    Google Scholar 

  • Holland JH (1998) Emergence: from chaos to order. Addison-Wesley, Reading

    Google Scholar 

  • Intergovernmenatal Panel on Climate Change (IPCC) (2007) Climate change 2007. Cambridge University Press, Cambridge

    Google Scholar 

  • Janssen M (1998) Use of complex adaptive systems for modeling global change. Ecosystems 1:457–463

    Article  Google Scholar 

  • Janssen MA (ed) (2002) Complexity and ecosystem management: the theory and practice of multi-agent systems. Cheltenham, Edward Elgar

    Google Scholar 

  • Janssen M (2004) Agent-based models. In: Proops J, Safonov P (eds) Modelling in ecological economics. Cheltenham, Edward Elgar, pp 155–172

    Google Scholar 

  • Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:265–291

    Article  Google Scholar 

  • Kemp R, Zundel S (2007) Environmental innovation policy – is steering innovation processes possible? In: Lehmann-Waffenschmidt M (ed) Innovations towards sustainability. Physica, Heidelberg

    Google Scholar 

  • Kirman A, Vriend NJ (2001) Evolving market structure: an ACE model of price dispersion and loyality. J Econ Dyn Control 25:459–502

    Article  Google Scholar 

  • Klos TB, Nooteboom B (2001) Agent-based computational transaction cost economics. J Econ Dyn Control 25:503–526

    Article  Google Scholar 

  • Lee FS, Keen S (2004) The incoherent emperor: a heterodox critique of neoclassical microeconomic theory. Rev Soc Econ LXII(2):169–199

    Article  Google Scholar 

  • Leontief W (1991) The economy as a circular flow. Struct Change Econ Dyn 2(1):181–212

    Article  Google Scholar 

  • Luna F, Stefansson B (2000) Economic simulations in swarm: agent-based modelling and object oriented programming. Kluwer, Dordrecht

    Google Scholar 

  • March JG, Simon H (1993) Organizations. Blackwell, Cambridge

    Google Scholar 

  • McGuffie K, Henderson-Sellers A (1997) A climate modelling primer. Wiley, Chichester

    Google Scholar 

  • Nill J (2009) Ökologische Innovationspoltik: Eine evolutorisch-ökonomische Perspektive. Metropolis, Marburg

    Google Scholar 

  • Nordhaus WD (1992) An optimal transition path for controlling greenhouse gases. Science 258:1315–1319

    Article  Google Scholar 

  • Nordhaus WD, Bojer J (2000) Warming the world. Economic models of global warming. MIT Press, Cambrindge, MA

    Google Scholar 

  • Pan H (2006) Dynamic and endogenous change of input–output structure with specific layers of technology. Struct Change Econ Dyn 17:200–223

    Article  Google Scholar 

  • Pielke R et al (2008) Dangerous assumptions. Nature 452:531–532

    Article  Google Scholar 

  • Prietula MJ et al (eds) (1998) Simulating organisations: computational models for institutions and groups. MIT Press, Cambridge, MA

    Google Scholar 

  • Rayner S, Malone EL (eds) (1998) Human choice and climate change: the societal framework, vol 1. Battelle, Columbus, OH

    Google Scholar 

  • Rogers EM (1995) Diffusion of innovations. Free Press, New York

    Google Scholar 

  • Romer PM (1990) Endogenous technological change. J Polit Econ 98(5):71–102

    Article  Google Scholar 

  • Simon HA (2000) Bounded rationality in social science: today and tomorrow. Mind Soc 1:25–39

    Article  Google Scholar 

  • Sorrell S (2007) The rebound effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency. UK Energy Research Center, London

    Google Scholar 

  • Sun R (2001) Cognitive science meets multi-agent systems: a prolegomenon. Philos Psychol 14:5–28

    Article  Google Scholar 

  • Sun R (ed) (2006) Cognition and multi-agent interaction: from cognitive modelling to social simulation. Cambridge University Press, Cambridge

    Google Scholar 

  • Tesfatsion L (2002) Agent-based computational economics: growing economies from the bottom up. Artif Life 8(1):55–82

    Article  Google Scholar 

  • Tesfatsion L, Judd KL (2006) Handbook of computational economics, vol 2. North-Holland, Amsterdam

    Google Scholar 

  • Vromen JJ (2001) The human agent in evolutionary economics. In: Nightingale J, Laurent J (eds) Darwinism and evolutionary economics. Cheltenham, Edward Elgar, pp 184–208

    Google Scholar 

  • Walker B, Steffen W (1996) Global change and terrestrial ecosystems. Cambridge University Press, Cambridge

    Google Scholar 

  • Wilhite A (2001) Bilateral trade and ‘small world’ networks. Comput Econ 18:49–64

    Article  Google Scholar 

  • Windrum P et al (2007) Empirical calibration of agent-based models: alternatives and prospects. J Artif Soc Soc Simul 10(2):8

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Economics, Department of Ecological and Behavioral Economics, University of Kassel, Kassel, Germany

    Frank Beckenbach & Ramón Briegel

Authors
  1. Frank Beckenbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramón Briegel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. , Research Group 3, Wuppertal Institut, Döppersberg 19, Wuppertal, 42103, Germany

    Raimund Bleischwitz

  2. Europäisches Institut für Internationale, Wirtschaftsbeziehungen (EIIW) e.V., Bergische Universität Wuppertal, Rainer-Gruenter-Straße 21, Wuppertal, 42119, Germany

    Paul J.J. Welfens

  3. East-West Center, East-West Road 1601, Honolulu, 96848-1601, USA

    ZhongXiang Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Physica-Verlag HD

About this chapter

Cite this chapter

Beckenbach, F., Briegel, R. (2011). Multi-agent Modeling of Economic Innovation Dynamics and Its Implications for Analyzing Emission Impacts. In: Bleischwitz, R., Welfens, P., Zhang, Z. (eds) International Economics of Resource Efficiency. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2601-2_14

Download citation

Publish with us

Policies and ethics

Search

Navigation