Fisher behaviour remains a key source of uncertainty in fisheries management. Failing to account for the behavioural response of fishers can lead to unexpected or unintended consequences of management; our understanding of fisher behaviour, as well as our ability to translate this understanding into predictive management models, is underdeveloped. This thesis aimed to develop an individual-based model (IBM) that could be used by fishers and managers to evaluate the impacts of management scenarios in the Isle of Man scallop fishery. Questionnaire interview data and a conjoint analysis were used to understand fishing behaviour and to generate realistic parameters to input to an IBM of fishing activity. Vessel monitoring system (VMS) and logbook data were also analysed to inform the model development, and to provide the data against which the model could be validated. There is increasing interest in using automatic identification system (AIS) as an alternative to VMS when investigating fishing activity, so a comparison of AIS and VMS data was presented, highlighting substantial gaps in the coverage of AIS data. By using simple foraging decision rules, parameterised by questionnaire data, it was possible to build an IBM that could reproduce patterns seen in the Isle of Man scallop fishery with reasonable similarity. Comparing multiple submodels of fishing behaviour provided insights into predicting fishing activity, and identified the most structurally realistic models. It illustrated the importance of incorporating random behaviour in a model design, potentially to account for social aspects of fishing decisions that are more difficult to quantify. It also demonstrated that predicting responses to management by modelling fishers as optimal foragers that act in an economically rational manner may overestimate the capacity of the fleet to compensate for restrictions such as closed areas, and underestimate the fishing footprint. Fishery systems may be too complex to distil to a single simple and ‘accurate’ model, but having a suite of models that together give a reasonable representation of the fishery could allow the range of likely impacts of management to be better considered. This thesis demonstrates the value of individual-based modelling for both understanding fisher behaviour and predicting the outcomes of management. It has also provided strong evidence to support the use of questionnaire interview data in modelling fishing activity. Comprehensively documenting the stages of model development provided a transparent model validation which would enable managers to make informed decisions about how to apply such a model. Using an IBM to predict the response of fishers to management could facilitate more informed compromises between management objectives, and reduce uncertainty in fisheries management.