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High fishing effort remains in many of the world’s fisheries despite a variety of policies that have been implemented to reduce it. These policies have predominantly focused on models of cause and effect that ignore the possibility that the intended outcomes are altered by social behavior and bounded rationality of autonomous agents. This paper presents a spatially explicit agent-based model for the Philippine tuna purse seine fishery that specifically includes social factors and bounded rationality in the decision making of agents. The model has been informed by interviews, and is verified and validated against data. Sensitivity analysis is used to determine the effects of social factors and bounded rationality on macro-level outcomes (fishing effort, fish stock and industry profit). Three social factors are identified to have considerable effect on these outcomes. These factors are a culturally and personally motivated resistance to exit the business, a social norm regarding the spatial distribution of vessels, and the use of imperfect information by potential entrants in their investment decisions. Existing fishery policies do not explicitly consider these social factors. The results suggest that both research and the management of fishing effort could be improved by viewing fisheries as Complex Adaptive Systems, in which social factors and bounded rationality have a considerable effect on the decision making of fishers (fishermen and fishing companies) and on the macro-level outcomes. [ABSTRACT FROM AUTHOR]

• Socio-ecological system (SES) has underemphasised the social aspect of these systems. • SESs show kinds of complexity which ecosystems considered alone lack; specifically, agents able to perceive and attempt to change global system features. • The choice of modelling approach is not determined solely by the system modelled; multiple models may be required, so modellers should think in terms of modelling projects which may also encompass multiple SESs. • Agent-based modelling is an essential component of models which aim to advance understanding of SESs, due to agent-based models expressivity. • However, this very expressivity raises problems of its own. Formal ontologies can help to deal with these problems in several ways, notably by improving model transparency. Socio-Ecological Systems (SESs) are the systems in which our everyday lives are embedded, so understanding them is important. The complex properties of such systems make modelling an indispensable tool for their description and analysis. Human actors play a pivotal role in SESs, but their interactions with each other and their environment are often underrepresented in SES modelling. We argue that more attention should be given to social aspects in models of SESs, but this entails additional kinds of complexity. Modelling choices need to be as transparent as possible, and to be based on analysis of the purposes and limitations of modelling. We recommend thinking in terms of modelling projects rather than single models. Such a project may involve multiple models adopting different modelling methods. We argue that agent-based models (ABMs) are an essential tool in an SES modelling project, but their expressivity, which is their major advantage, also produces problems with model transparency and validation. We propose the use of formal ontologies to make the structure and meaning of models as explicit as possible, facilitating model design, implementation, assessment, comparison and extension. [ABSTRACT FROM AUTHOR]