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Many of the world’s fisheries are “data-limited” where the information does not allow precise determination of fish stock status and limits the development of appropriate management responses. Two approaches are proposed for use in data-limited stock management strategy evaluations to guide the evaluations and to understand the sources of uncertainty: rejection sampling methods and the incorporation of more complex socio-economic dynamics into management evaluations using agent-based models. In rejection sampling (or rejection filtering) a model is simulated many times with a wide range of priors on parameters and outcomes are compared multiple filtering criteria. Those simulations that pass all the filters form an ensemble of feasible models. The ensemble can be used to look for robust management strategies, robust to both model uncertainties. Agent-based models of fishery economics can be implemented within the rejection framework, integrating the biological and economic understanding of the fishery. A simple artificial example of a difference equation bio-economic model is given to demonstrate the approach. Then rejection sampling is applied to an agent-based model for the hairtail (Trichiurus japonicas) fishery, where an operating model is constructed with rejection/agent-based methods and compared to known data and analyses of the fishery. The usefulness of information and rejection filters are illuminated and efficacy examined. The methods can be helpful for strategic guidance where multiple states of nature are possible as a part of management strategy evaluation.

Avoiding the ‘tragedy of the commons’ remains a challenge in many natural resource systems, and open-access fisheries are well-studied in this context. Here, an agent-based model is used to investigate how variation in fisher goals change what policies best solve the tragedy. When fishers’ goals are easily satisfied, commons problems are avoided without management interventions, but the imposition of quota limits triggers the tragedy. Thus, commons problems are not necessarily inevitable and sophisticated governance institutions or regulations are not always required to manage them; the same policy may prevent the tragedy or trigger it, depending on the fisher's goals. Given that it is difficult to ascertain them, by using a simulation model we can find patterns that help us identify fishers' goals and incorporate these patterns within our management procedure. This can assist adaptive management to better incorporate behaviour into policy evaluation.

To effectively manage complex human-environment fisheries systems, it is necessary to understand the psychology of fisher agents. While bio-economic models typically provide simple, abstract approaches for human behaviour (e.g. fully informed profit maximisers), fisher agents are of course neither simple nor perfect. Imperfections of learning, memory, and information availability, combined with the diversity of value preferences within populations, can lead to substantial deviations and unanticipated effects of interventions. This paper presents a computational model of fisher agents’ decision-making that draws on theoretical and empirical psychological insights to enrich this critical component. The model includes mechanisms for information integration (learning), social comparisons, and thresholds for economic satisfaction. In offering this enriched account, the model captures how fishers may adapt behaviourally given changes in policy, economic conditions, or social pressures. Furthermore, the model can be parameterised to capture the effects of different socio-cultural contexts can be simulated. The model of fisher agents has been implemented as part of POSEIDON (an agent-based fisheries management model), showing that fishers imbued with the model learn and adapt when responding dynamically to changing conditions. The model is thus demonstrated in a fisheries environment, but we discuss how its architecture could be implemented for simulation in other human-environment systems, such as designing policies to combat the human-environment problems.