Evolutionary Climate-Change Modelling: A Multi-Agent Climate-Economic Model
- 219 Downloads
In climate-economic modelling, agent-based models are still an exception. Although numerous authors have discussed the usefulness of the approach, only a few models exist. The paper proposes an update to a multi-agent climate-economic model, namely the “battle of perspectives” (Janssen, 1996; Janssen and de Vries 1998). The approach of the paper is twofold. First, the reimplementation of the model follows the “model to model” concept. Supporters of the approach argue that replication is a useful way to check a model’s accuracy and robustness. Second, updating a model with current data and new scientific evidence is a robustness check in itself. The long-term validity and usefulness of a model depends on the variability of the data on which it is based, as well as on the model’s sensitivity to data changes. By offering this update, the paper contributes to the development of agent-based models in climate-economics. Acknowledging evolutionary processes in climate-policy represents a useful complement to intertemporal cost-benefit analyses, the latter of which derive optimal protection paths but are not able to explain why people do not follow them. Since the replication and update succeeded, the paper recommends using the model as a basis for further analysis.
KeywordsAgent-based modelling Evolutionary economics Climate change Climate-economic modelling Bounded rationality Learning
Compliance with ethical standards
Conflict of interest
The author declares that she has no conflict of interest.
No research involving human or animal participants has been conducted for the purposes of this paper.
- Arifovic, J. (1991). Learning by genetic algorithms in economic environments. Dissertation, University of Chicago.Google Scholar
- Arifovic, J., & Ledyard, J. (2002). Computer testbeds and Mechanism Design. In: Computing in Economics and Finance 2002 262, Society for Computational Economics.Google Scholar
- Beckenbach, F., & Briegel, R. (2009). Multi-agent modelling of economic innovation dynamics and its implication for analyzing emissions impact. Working Paper, University of Kassel.Google Scholar
- Bharwani, S., Bithell, M., Downing, T. E., New, M., Washington, R., & Ziervogel, G. (2005). Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 360(1463), 2183.CrossRefGoogle Scholar
- Boulanger, P. M. (2010). Three strategies for sustainable consumption. Sapiens, 3, 1–10.Google Scholar
- Dean, J. S., Gumerman, G. J., Epstein, J. M., Axtell, R. L., Swedlund, A. C., Parker, M. T., et al. (1999). Understanding Anasazi culture change through agent-based modeling. In T. A. Kohler & G. J. Gumerman (Eds.), Dynamics in human and primate societies: Agent-based modeling of social and spatial processes (pp. 179–205). Oxford: University Press.Google Scholar
- Douglas, M., & Wildawski, A. (1982). Risk and culture: An essay on the selection of technological and environmental dangers. Berkeley: University of California Press.Google Scholar
- Edmonds, B., & Hales, D. (2003). Replication, replication and replication: Some hard lessons from model alignment. Journal of Artificial Societies and Social Simulation, 6(4). http://jasss.soc.surrey.ac.uk/6/4/11.html.
- Filatova, T. (2009). Land markets from the bottom up. Micro-macro links in economics and implications for coastal risk management. PhD Thesis. University of Twente.Google Scholar
- Finus, M., & Pintassilgo, P. (2009). The role of uncertainty and learning for the success of international climate agreements. Stirling Economics discussion paper no. 2009-16.Google Scholar
- Geisendorf, S. (2009). The influence of innovation and imitation on economic performance. Economic Issues, 14, 65–94.Google Scholar
- Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading: Addison-Wesley.Google Scholar
- Holland, J. H., & Miller, J. H. (1991). Artificial adaptive agents in economic theory. The American Economic Review, 81, 365–370.Google Scholar
- IPCC. (2014). Climate change 2014: synthesis report. In Core Writing Team: R. K. Pachauri & L. A. Meyer (Eds.), Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change (IPCC) Geneva.Google Scholar
- Janssen, M. A. (1996). Meeting targets: Tools to support integrated assessment modelling of global change. PhD Thesis, University of Maastricht. ISBN 90-9009908-5Google Scholar
- LeBaron, B. (2006). Agent-based computational finance. In L. Tesfastsion & L. J. Kenneth (Eds.), Handbook of Computational Economics 2. Amsterdam: Elsevier.Google Scholar
- Lutz, W., Sanderson, W., & Scherbov, S. (2008). IIASA’s 2007 probabilistic world population projections. IIASA world population program online data base of results 2008. http://www.iiasa.ac.at/Research/POP/proj07/index.html?sb=5. Accessed March 15, 2015.
- Manne, A. S., Mendelsohn, R., & Richels, R. G. (1994). MERGE: A model for evaluating regional and global effects of GHG reduction policies. In N. Nakicenovic, W. D. Nordhaus, R. Richels & F. L. Toth (Eds.), Integrative assessment of mitigation, impacts, and adaptation to climate change (pp. 143–172). CP-94-0, IIASA, Laxenburg.Google Scholar
- Mitchell, M. (1997). An introduction to genetic algorithms (Vol. 3). Cambridge: MIT Press.Google Scholar
- Nordhaus, W. D. (1994). Managing the global commons: The economics of climate change. Cambridge: MIT Press.Google Scholar
- Nordhaus, W. D. (2008). A question of balance weighing the options on global warming policies. New Haven: Yale University Press.Google Scholar
- Nordhaus, W. D. & Sztorc, P. (2013). DICE 2013R: Introduction and user’s manual. http://www.econ.yale.edu/~nordhaus/homepage/DICE-science.htm. Accessed February 27, 2015.
- Oltedal, S., Moen, B.-E., Klempe, H., & Rundmo, T. (2004). Explaining risk perception. An evaluation of cultural theory. c Rotunde no. 85, Norwegian University of Science and Technology, Department of Psychology. Trondheim: RotundeGoogle Scholar
- Rachlinski, J. J. (2000). The psychology of global climate change. University of Illinois Law Review, 1, 299–319.Google Scholar
- Rouchier, J., Cioffi-Revilla, C., Polhill, J. G., & Takadama, K. (2008). Progress in model-to-model analysis. Journal of Artificial Societies and Social Simulation, (11), 28.Google Scholar
- Schelling, T. C. (2007, July). Climate change: The uncertainties, the certainties, and what they imply about action. The Economists’ Voice, 1–5.Google Scholar
- Statista. (2015). http://www.statista.com/statistics/276629/global-co2-emissions/. Accessed February 27, 2015.
- Wang, P., Gerst, M. D., & Borsuk, M. E. (2013). Exploring energy and economic futures using agent-based modeling and scenario discovery. In H. Qudrat-Ullah (Ed.), Energy policy modeling in the 21st century. Berlin: Springer.Google Scholar
- Wikipedia. (2015). http://en.wikipedia.org/wiki/Gross_world_product#cite_note-2012CIA-2. Accessed February 24, 2015.
- Wildawski, A., & Sweditorlow, B. (Eds.). (2005). Cultural analysis: Politics, public law and administration. Piscataway: Transaction Publishers.Google Scholar
- World Bank. (2015). http://data.worldbank.org, Accessed 15 September 2015.