Agent-Based Modelling of Socio-Ecosystems: A Methodology for the Analysis of Adaptation to Climate Change

The integrated—environmental, economic and social—analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. Past research on artificial intelligence and social simulation has developed a promising methodology. Literature on agent-based modelling (ABM) shows it’s potential to couple social and environmental models and incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few studies that concretely apply this methodology to the study of climate change related issues. The analysis in this paper supports the idea that today ABM is a consolidated interdisciplinary approach for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system’s components. approaches that employ equilibrium models. In particular, such a top down analysis of the human-environment system doesn’t consider the emergence of social behavioural patterns. This eventually leads to a flawed policy making process which relies on unrealistic assumptions (Moss et al., 2001). Yet, the ultimate source of anthropogenic climate change is the agency of human individuals grouped in social networks and their interaction. At the same time, the responses to climate change, in terms of DOI: 10.4018/jats.2010100103 18 International Journal of Agent Technologies and Systems, 2(4), 17-38, October-December 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. mitigation of greenhouse gases emissions and in terms of adaptation to climatic variability and slow changes in mean conditions, have to be found in humans behaviour. In our global system where human activities prevail and endlessly modify the environment, climate change is providing the chance to concretely understand how the environment responds, suggesting a change in human behaviour, both at a local and global level. Climate change can no longer be addressed separately from a broader context of systemic sustainability and adaptation strategies. The endogenous feedbacks between socioeconomic and biophysical processes and the co-evolution of the human-environment system are precisely those kind of dynamics included in the notion of social-ecological systems, or socio-ecosystems (SESs). SESs are complex and adaptive systems (CASs) where social (human) and ecological (biophysical) agents are interacting at multiple temporal and spatial scales (Rammel et al., 2007). CASs are dynamic networks of many agents acting in parallel, constantly acting and reacting to the behaviour of other agents. The control of a CAS tend to be highly dispersed and decentralized. If there is to be any coherent behaviour in the system, it has to arise from competition and cooperation among the agents themselves. The overall behaviour of the system is the result of a large number of decisions made every moment by many individual agents (Waldrop, 1992). CASs display an ever changing dynamic equilibrium, which fluctuates between chaotic and ordered states. On the edge of chaos, these systems are very sensitive to any perturbation from the individual components (Holland, 1992). CASs are inherently unpredictable as a whole: “their futures are not determined and their global behaviours emerge from their local interactions in complex, historically contingent and unpredictable ways” (Bradbury, 2002). Since the study of CASs is an attempt to better understand systems which are difficult to grasp analytically, often the best available way to investigate them is through computer simulations (Gilbert & Troitzsch, 1999). Introducing Agent-Based Thinking Past research on computer science (e.g. Wooldridge & Jennings, 1995; Ferber, 1999; Huhns & Stephens, 1999; Weiss, 1999) has shown how CASs can be represented by means of multi-agent systems (MASs). MASs is a concept derived from distributed artificial intelligence (DAI), which firstly used it in order to reproduce the knowledge and reasoning of several heterogeneous agents that need to coordinate to jointly solve planning problems. Typically MASs refer to software agents and are implemented in computer simulations. Pure MASs, as conceived in DAI, are not fully relevant for modelling SESs, which are real systems based on the law of physics and on human social interactions. However, including the fundamental contribution of past research on artificial life (AL) (e.g. Reynolds, 1987, Holland, 1992, Langton, 1992), individual-based modelling (IBM) (e.g. Huston, et al. 1988; Grimm, 1999), and social simulations (e.g. Schelling 1978; Axelrod & Hamilton, 1981; Epstein & Axtell, 1996), we are provided with a very promising framework for the innovative modelling of combined SESs and policy-making in the context of sustainable development (Boulanger & Bréchet, 2005). Further Expansion of ABM To date, ABM has been used to reformulate some main issues of social and natural science (Bousquet & Le Page, 2004). In fact, there exists a consistent body of work on ABM in sociology and social processes (e.g. Conte et al., 2001; Macy & Willer, 2002; Gilbert & Troitzsch, 1999), economics and finance (e.g. LeBaron, 2000; Tesfatsion, 2002) and in a set of environmental issues including land use and cover change (e.g. Parker et al., 2003; Veldkamp & Verburg, 2004; Matthews et al., 2007) ecology and natural resource management (e.g. Lansing & Kremer, 1993; Bousquet & Le Page, 2004), agriculture (e.g. Balmann, 1997; Berger, 2001), urban planning (e.g. Torrens & O Sullivan, 2001; Batty, 2005), and archaeology (e.g. 20 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/agent-based-modelling-socioecosystems/47414?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Computer Science, Security, and Information Technology. 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