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A complex adaptive systems investigation of the social-ecological dynamics of three fisheries

  • P. S. Hayes
  • , J. Wilson
  • , C. B. Congdon
  • , L. Yan
  • , J. Hill
  • , J. Acheson
  • , Y. Chen
  • , C. Cleaver
  • , A. Hayden
  • , T. Johnson
  • , M. Kersula
  • , G. Morehead
  • , R. Steneck
  • University of Maine
  • University of Southern Maine

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we describe a complex adaptive systems model of interactions between coupled human and natural system. We use learning classifier systems to create adaptive agents in a simulation of the Maine lobster fishery to explore the relationships among ecological, economic, and social characteristics. Our hypothesis is that the cost of information and learning drives agents' decisions to compete or co-operate and, consequently, the emergence of long-term relationships. Initial results provide tentative support for the hypothesis and the ability of this model to provide insight into the dynamics of individual interactions and the social relationships that emerge from those interactions.

Original languageEnglish
Title of host publicationComplex Adaptive Systems
Subtitle of host publicationEnergy, Information, and Intelligence - Papers from the AAAI Fall Symposium, Technical Report
Pages80-86
Number of pages7
StatePublished - 2011
Event2011 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 4 2011Nov 6 2011

Publication series

NameAAAI Fall Symposium - Technical Report
VolumeFS-11-03

Conference

Conference2011 AAAI Fall Symposium
Country/TerritoryUnited States
CityArlington, VA
Period11/4/1111/6/11

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