October 2009-September 2014
Level of Access
The purpose of this project is to gain a better understanding of the way competition between individual fishermen lead to the emergence of private incentives and informal social arrangements that are (or are not) consistent with conservation of the resource. These informal arrangements and incentives are important because they help us understand the extent to which private interests might strengthen or weaken on-going resource management and, consequently, the sustainability of coupled human and natural systems. The broad hypothesis driving the study is that the informal social structure that emerges from competitive interactions among fishermen reflects the particular circumstances of the natural system. In some cases, successful competition requires secretive non-cooperative behavior; in others, cooperation tends to yield better competitive results. These different outcomes have different, and not always obvious, impacts on the feasibility and effectiveness of resource management.
We think of the relevant human social process as one in which individuals compete with one another through time-consuming and costly acquisition of valuable knowledge about a complex resource. To compete successfully, individuals must balance the immediate benefits that come from exploiting knowledge they currently hold with the costly need to explore for new knowledge; additionally, when seeking new knowledge, individuals must balance the costs and benefits of acquiring knowledge through cooperation or through autonomous search. In order to model this kind of competitive process, we employ a significantly modified version of a technique borrowed from computer science called a learning classifier system (LCS). LCS uses a genetic algorithm to mimic the way an agent (here a fisherman) uses his experience to continuously refine his knowledge and decisions about his natural and social environment. The importance of LCS is that it permits simulation of the co-evolving strategic interactions of self-interested fishermen who are only partially informed about the state of the resource they are exploiting and the fishermen with whom they compete.
The problem of understanding these kinds of competitive dynamics is evident in almost all coupled natural and human systems. We apply the approach to a comparative study of three Gulf of Maine fisheries which are characterized by significantly different temporal and spatial dynamics - sea urchins, lobster and cod. Each fishery will be modeled using a biophysical simulator of the natural system and a tightly integrated multi-agent learning classifier system that simulates the learning and interactions of fishermen. The design of each model will be based in part on extensive interviews with fishermen about their knowledge of the dynamics of the fisheries in which they work. We will use these models to explore past and prospective policy problems in each fishery.
Beyond the immediate applicability of these explorations, we expect this project will provide a foundation for the wider use of multi-agent learning models in other coupled systems. Project outcomes will be transmitted regularly to industry and managers.
Principal investigators include economists, biologists, anthropologists and computer scientists. All the PIs have years of experience in the fisheries of the Gulf of Maine and have well developed relationships with individual fishermen and managers. A masters level student in marine policy, a Ph.D. student in computer or marine science and a post-doctoral researcher in computer science will be employed on the project. In addition, the project will develop an undergraduate course in complex adaptive social-ecological systems and a graduate student/faculty workshop in the same area.
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Wilson, James A.; Acheson, James; Steneck, Robert; Chen, Yong; and Johnson, Teresa R., "CNH: Fine-Scale Dynamics of Human Adaptation in Coupled Natural and Social Systems: An Integrated Computational Approach Applied to Three Fisheries" (2014). University of Maine Office of Research Administration: Grant Reports. 439.