By 2003 27% of all the fisheries had collapsed (Costello et al 2008). The trend of collapsing fisheries has since then accelerated ( Worm et al 2006). Policymakers have responded to this crisis by implementing a variety of policies: season closures, gear restrictions, trip length regulations, discard bans, marketable quotas and the like. Unfortunately existing modelling techniques have not been able to predict the effect of each policy and the ability of fishers to adapt and defeat them forcing policy to proceed by blind trial and error. Moreover existing models are silent when the solution need to emerge from the appropriators rather being set top down by government.
I'd like to show how Agent-Based Models, with their focus on adaptation and bounded rationality, are the right tool for the job. The work I present forms the basis of a planned "fisheries simulator" that policy-makers can use to search for the best policy ensemble they need.