As part of our work for the Fishscape project, Drs. Webster and Roozmand developed a simplified agent based model of fisher search behavior that incorporates communication and cooperation. This is an important aspect of fishing effectiveness, particularly in such a large and dynamic region (area covered is about the size of the continental US), where good information can have a major impact on fishing success and related costs of production. Indeed, in many smaller-scale fisheries, groups of fishers who cooperate are careful to protect the information that they share regarding fishing locations. However, in interviews with fishers in the EPO, Dr. Webster noted that all respondents reported cheating behavior by themselves and others. This included falsely reporting that fish were not present in an area when schools were plentiful (omission) and sharing true information with fishers from outside of the individual’s “code group” (infidelity). Given the contrast with other well-known fisheries, like the lobster gangs of Maine, this finding lead us to develop the hypothesis that fishers may accept higher levels of certain types of cheating when the search process is more difficult and therefore information is more valuable. The Fishscape: Cooperation ABM will help us to test this hypothesis and better understand relationships between costs of information and cooperation/cheating.

FS.2 Fishscape: Cooperation, Basic Run

Figure FS.2 above shows the Fishscape:Cooperation model in action. The black point is the only port in this early model. Small boats represent fisher agents and the red points are fish meta-populations. The intensity of the red color indicates the size of the meta-population, which is diminished due to fishing pressure and natural mortality but increases with fish reproduction. We have tested multiple versions of this model, shifting the size of fishing grounds, location relative to port, and mobility of fish populations. The informational benefits of cheating are perceptible in the model but we still need to generate a comparable “lobster” fishery model to really dig into the different costs/benefits of cheating in each system. This will help us to understand how the structural context of a fishery alters fisher communication and cooperation, which in turn can have significant impacts on fishing effectiveness that are not captured in most stock assessment models (Squires and Vestergaard 2013).

References:

Squires, Dale, and Niels Vestergaard. 2013. “Technical Change and The Commons.” The Review of Economics and Statistics 95 (5): 1769–1787.