Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning

A model to mimic the search behaviour of fishermen is built with two neural networks to cope with two separate decision-making processes in fishing activities. One neural network deals with decisions to stay or move to new fishing grounds and the other is constructed for the purpose of finding prey within the fishing areas. Some similarities with the behaviour of real fishermen are found: concentrated local search once a prey has been located to increase the probability of remaining near a prey patch and the straightforward movement to other fishing grounds. The artificial fisherman prefers areas near the port when conditions in different fishing grounds are similar or when there is high uncertainty in its world. In the latter case a reluctance to navigate to other areas is observed. The artificial fisherman selects areas with higher concentration of prey, even if they are far from the port of departure, unless a high uncertainty is related to the fishing ground. Connected areas are preferred and followed in orderly fashion if a higher catch is expected. The observed behaviour of the artificial fisherman in uncertain scenarios can be described as a risk-averse attitude. The approach seems appropriate for an individual-based modelling of fishery systems, focusing on the learning and adaptive characteristics of fishermen and on interactions that take place at a fine scale.