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).