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

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

Stefano Balbi (Ca’ Foscari University of Venice, Italy) and Carlo Giupponi (Ca’ Foscari University of Venice, Italy)
Copyright: © 2010 |Pages: 22
DOI: 10.4018/jats.2010100103
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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.
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Global Change and Complex Systems

There is an increasing awareness that global change dynamics and the related socio-economic implications involve a degree of complexity which is not captured by traditional economic 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 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 socio-economic 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 MAS refers to software agents and is 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).

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