So, it's a good time to be in the Agent Based Modelling (ABM) business. More and more attention is being given to the idea that while we have a certain level of understanding of the physical, chemical and biological processes around climate change, in order to change what's happening, we need to look at the social systems which are contributing.
Two inspirations for this post; firstly, a piece on Nature's [http://blogs.nature.com/climatefeedback/ Climate Feedback] blog, about the need for social science in climate change:
[http://blogs.nature.com/climatefeedback/2009/04/ihdp_should_90_of_climate_chan.html IHDP: should 90% of climate change research be social science?]. It started from the keynote of the International Human Dimensions Programme on Global Environmental Change (IHDP), quoting Hans Joachim Schellnhuber of Potsdam Institute for Climate Impacts Research
Secondly, a piece in [http://seedmagazine.com/ Seed Magazine]:
[http://seedmagazine.com/content/article/the_last_experiment/ The Last Experiment], looking largely at the work of [http://www.johnson.cornell.edu/faculty/profiles/Ho/ Benjamin Ho], but strongly referencing [http://environment.yale.edu/profile/leiserowitz/ Leiserowitz], [[wp:Richard Thaler]] (behavioural finance/economics, "Nudge") and [[wp:Daniel Kahneman]] ([[wp:Prospect Theory]]) among others.
The Seed piece is generally directed towards communication, education and modification of the public's behaviour around climate change, while the Nature one has more of an implication towards modelling of behaviour (at least the way I read it...)
In general, this is one of the strongest motivations for ABM - the understanding that people do not behave in an economically rational manner, and hence that to understand the effects of human behaviour, you need to have some kind of model of how their decisions are made. When you combine these social science and psychological theories with computer science and modelling, you end up with ABM.
This relates quite strongly to a lot of the work we're doing here at CECS: by modelling the decision process of (for example) farmers, we attempt to understand what factors are important to them, and hence how we can motivate them to behave differently. By then coupling these models to biophysical ones, we can try to find the most useful behavioural modification in terms of the greatest benefits at the lowest cost.