@article{fdi:010035668,
  title = {{M}odeling performance and information exchange between fishing vessels with artificial neural networks},
  author = {{D}reyfus {L}eon, {M}. and {G}aertner, {D}aniel},
  editor = {},
  language = {{ENG}},
  abstract = {{A} fishery is simulated in which 20 artificial vessels learn to make decisions through an artificial neural network in order to search for schools of fish among the available fishing grounds. {T}hree scenarios with different degrees of variability including uncertainty in the searching process, are considered. {T}he simulation model accounts for the main features commonly observed in a purse seine tuna fishery in a time and a space scale. {V}essel strategies are chosen by the artificial neural network, on the basis of the following decision criteria: information concerning time searching in a specific area, previous performance in this area, knowledge of the quality of surrounding fishing grounds, presence of other vessels fishing actively and trip length. {A}n analysis of the effects of sharing information between vessels is done and this was compared to individual artificial fishing vessels. {I}n general, a group of fishing vessels show higher performance than individual vessels. {A} convex performance comparison curve for several group sizes is found in all scenarios considered. {T}he optimum group size differs according to the variability of the artificial world. {A}t bigger group sizes performance decreases, probably due to competition and depletion effects of some fishing grounds. (c) 2005 {E}lsevier {B}.{V}. {A}ll rights reserved.},
  keywords = {artificial neural networks ; individual based model ; yellowfin tuna ; fishery performance ; information sharing},
  booktitle = {},
  journal = {{E}cological {M}odelling},
  volume = {195},
  numero = {1-2 {S}pecial {I}ss.},
  pages = {30--36},
  ISSN = {0304-3800},
  year = {2006},
  DOI = {10.1016/j.ecolmodel.2005.11.006},
  URL = {https://www.documentation.ird.fr/hor/fdi:010035668},
}