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Modelling the “Captain’s Nose”: Exploring the Shift Towards Autonomous Fishing with Social Simulation

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Advances in Social Simulation

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

With the rapid advances in the development of autonomous vehicles, the fishing industry might be faced in the near future with a shift to (partially or fully) autonomous fishing vessels or operations. Large-scale utilization of autonomous fishing practices will lead to a reorganization of society in many respects, with critical challenges related to conservation, economy, governance, and ethics. This study focuses on some relevant topics to reflect on with social simulation approaches. These topics will have to be revisited when exploring the possible challenges and opportunities of moving from traditional to autonomous fishing activities.

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References

  1. Acheson, J.M., Reidman, R.: Technical innovation in the New England fin-fishing industry: an examination of the Downs and Mohr hypothesis. Am. Ethnol. 9(3), 538–558 (1982)

    Article  Google Scholar 

  2. Akanle, O.M., Zhang, D.Z.: Agent-based model for optimising supply-chain configurations. Int. J. Prod. Econ. 115(2), 444–460 (2008)

    Article  Google Scholar 

  3. Bahnes, N., Kechar, B., Haffaf, H.: Cooperation between intelligent autonomous vehicles to enhance container terminal operations. J. Innov. Digit. Ecosyst. 3(1), 22–29 (2016). https://doi.org/10.1016/j.jides.2016.05.002

    Article  Google Scholar 

  4. Barreteau, O., Bousquet, F., Attonaty, J.M.: Role-playing games for opening the black box of multi-agent systems: method and lessons of its application to Senegal River Valley irrigated systems. J. Artif. Soc. Soc. Simul. 4(2), 5 (2001)

    Google Scholar 

  5. Batalden, B.M., Leikanger, P., Wide, P.: Towards autonomous maritime operations. In: 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 1–6. IEEE (2017). https://doi.org/10.1109/CIVEMSA.2017.7995339

  6. von Brandt, A., Others: Fish catching methods of the world. Fishing News Books, Farnham (1984)

    Google Scholar 

  7. Costello, C., Ovando, D., Clavelle, T., Strauss, C.K., Hilborn, R., Melnychuk, M.C., Branch, T.A., Gaines, S.D., Szuwalski, C.S., Cabral, R.B., Others: Global fishery prospects under contrasting management regimes. Proc. Natl. Acad. Sci. 113(18), 5125–5129 (2016)

    Article  Google Scholar 

  8. DeAngelis, D.L., Mooij, W.M.: Individual-based modeling of ecological and evolutionary processes. Annu. Rev. Ecol. Evol. Syst. 36, 147–168 (2005)

    Article  Google Scholar 

  9. Dignum, F., Prada, R., Hofstede, G.J.: From autistic to social agents. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, pp. 1161–1164. International Foundation for Autonomous Agents and Multiagent Systems (2014)

    Google Scholar 

  10. Duddu, P.: The ten most traded food and beverage commodities (2014). http://www.foodprocessing-technology.com/features/featurethe-10-most-traded-food-and-beverage-commodities-4181217/

  11. Edmonds, B., Meyer, R.: Simulating Social Complexity: A Handbook. Springer, Cham (2017)

    Book  MATH  Google Scholar 

  12. Edwards, D.B., Bean, T.A., Odell, D.L., Anderson, M.J.: A leader-follower algorithm for multiple AUV formations. In: 2004 IEEE/OES Autonomous Underwater Vehicles, pp. 40–46. IEEE (2004). https://doi.org/10.1109/AUV.2004.1431191

  13. FAO: FAO Fisheries Glossary (2017). http://www.fao.org/faoterm/viewentry/en/?entryId=98375

  14. Gál, Z., Lux, G.: ET2050 Territorial Scenarios and Visions for Europe. Technical report, Final Report 30 (2014)

    Google Scholar 

  15. Ghorbani, A., Dijkema, G., Schrauwen, N.: Structuring qualitative data for agent-based modelling. J. Artif. Soc. Soc. Simul. 18(1), 2 (2015)

    Article  Google Scholar 

  16. Grimm, V., Wyszomirski, T., Aikman, D., Uchmański, J.: Individual-based modelling and ecological theory: synthesis of a workshop. Ecol. Model. 115(2–3), 275–282 (1999)

    Article  Google Scholar 

  17. Ioannou, P.: Automated Highway Systems. Springer Science & Business Media, New York (2013)

    Google Scholar 

  18. Johnsen, J.P., Holm, P., Sinclair, P., Bavington, D.: The cyborgization of the fisheries. On attempts to make fisheries management possible. MAST. 7(2), 9–34 (2009)

    Google Scholar 

  19. Koliba, C., Zia, A.: Governance Informatics: Using Computer Simulation Models to Deepen Situational Awareness and Governance Design Considerations. MIT Press, Cambridge (2013)

    Google Scholar 

  20. Le Page, C., Perrotton, A.: KILT: a modelling approach based on participatory agent-based simulation of stylized socio-ecosystems to stimulate social learning with local stakeholders. In: International Conference on Autonomous Agents and Multiagent Systems, pp. 31–44. Springer (2017)

    Google Scholar 

  21. Levin, S., Xepapadeas, T., Crépin, A.S., Norberg, J., De Zeeuw, A., Folke, C., Hughes, T., Arrow, K., Barrett, S., Daily, G., Others: Social-ecological systems as complex adaptive systems: modeling and policy implications. Environ. Dev. Econ. 18(2), 111–132 (2013)

    Article  Google Scholar 

  22. Mazuki, R., Man, N., Omar, S.Z., Bolong, J., D’Silva, J.L., Azril, H., Shaffril, M.: Technology adoption among fishermen in Malaysia. J. Am. Sci. 8(12), 1–4 (2012)

    Google Scholar 

  23. Mccarthy, J.G., Sabbadini, T., Sachs, S.R.: Multi-agent model of technological shifts. Work 24, 112–127 (2008)

    Google Scholar 

  24. Nooteboom, B.: Agent-based simulation of trust. In: Lyon, F., et al. (eds.) Handbook of Research Methods on Trust, pp. 65–74. Edward Elgar Publishing, Cheltenham (2015)

    Chapter  Google Scholar 

  25. Pálsson, G., Durrenberger, P.: To dream of fish: the causes of Icelandic skippers’ fishing success. J. Anthropol. Res. 38(2), 227–242 (1982)

    Article  Google Scholar 

  26. Pieters, K.: The Near Future of Unmanned Vessels. A Complexity-Informed Perspective (2017). https://www.researchgate.net/publication/316364255_The_Near_Future_of_Unmanned_Vessels_A_Complexity-Informed_Perspective

  27. Poedjono, B., Pai, S., Maus, S., Manoj, C., Paynter, R., Others: Using autonomous marine vehicles to enable accurate wellbore placement in the Arctic. In: OTC Arctic Technology Conference. Offshore Technology Conference (2015)

    Google Scholar 

  28. Price, R.R., Hall, S.G.: Design, development, and testing of an autonomous boat to reduce predatory birds on aquaculture ponds. Biol. Eng. Trans. 5(2), 61–70 (2012). https://doi.org/10.13031/2013.41399

    Article  Google Scholar 

  29. Raser, J.R.: Simulation and Society: An Exploration of Scientific Gaming. Allyn and Bacon, Boston (1969)

    Google Scholar 

  30. Robins, C.M., Wang, Y.G., Die, D.: The impact of global positioning systems and plotters on fishing power in the northern prawn fishery, Australia. Can. J. Fish. Aquat. Sci. 55(7), 1645–1651 (1998)

    Article  Google Scholar 

  31. Salamon, A., Housten, D., Drewes, P.: Increasing situational awareness through the use of UXV teams while reducing operator workload. J. Field Rob. 25(9), 598–614 (2008)

    Article  Google Scholar 

  32. Squazzoni, F.: Agent-Based Computational Sociology. Wiley, Hoboken (2012)

    Book  Google Scholar 

  33. Squires, D., Vestergaard, N.: Technical change in fisheries. Mar. Policy 42, 286–292 (2013)

    Article  Google Scholar 

  34. Stummer, C., Kiesling, E., Günther, M., Vetschera, R.: Innovation diffusion of repeat purchase products in a competitive market: an agent-based simulation approach. Eur. J. Oper. Res. 245(1), 157–167 (2015)

    Article  Google Scholar 

  35. Ullrich, G.: The history of automated guided vehicle systems. In: Hollier, R.H. (ed.) Automated Guided Vehicle Systems, pp. 1–14. Springer, Berlin/Heidelberg (2015). https://doi.org/10.1007/978-3-662-44814-4_1

    Google Scholar 

  36. Vanhée, L., Dignum, F., Ferber, J.: Towards simulating the impact of national culture on organizations. In: MABS2013: 14th International Workshop on Multi-Agent-Based Simulation, Saint Paul, p. 12 (2013)

    Google Scholar 

  37. Vanhée, L., Borit, M., Santos, J.: Autonomous fishing vessels roving the seas: what multiagent systems have got to do with it. In: Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, pp. 1193–1197. International Foundation for Autonomous Agents and Multiagent Systems (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. UiT The Arctic University of Norway (University of Tromsø), Tromsø, Norway

    Jorge Santos

  2. Norwegian College of Fishery Science, UiT The Arctic University of Norway, Tromsø, Norway

    Melania Borit

  3. GREYC University of Caen, Caen, France

    Loïs Vanhée

Authors
  1. Jorge Santos
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  2. Melania Borit
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  3. Loïs Vanhée
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Corresponding author

Correspondence to Melania Borit .

Editor information

Editors and Affiliations

  1. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden

    Harko Verhagen

  2. Norwegian College of Fishery Science, UiT The Arctic University of Norway, Tromsø, Norway

    Melania Borit

  3. Linnaeus University Centre for Data Intensive Sciences and Applications, Växjö, Sweden

    Giangiacomo Bravo

  4. Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden

    Nanda Wijermans

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Santos, J., Borit, M., Vanhée, L. (2020). Modelling the “Captain’s Nose”: Exploring the Shift Towards Autonomous Fishing with Social Simulation. In: Verhagen, H., Borit, M., Bravo, G., Wijermans, N. (eds) Advances in Social Simulation. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-34127-5_39

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