Success Biased Imitation Increases the Probability of Effectively Dealing with Ecological Disturbances

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

Ecological disturbances (i.e. pests, invasive species, floods, fires etc.) are a fundamental challenge in managing connected social-ecological systems. Even if treatment for such disturbances is available, often managers do not act quickly enough or not at all. In this paper we build an agent based model that examines: a) under what circumstances are managers locked into non-action that favors ecological disturbances? b) what learning strategies are most effective in avoiding management lock-in? The model we develop relates adoption of treatment strategies to eradicate ecological disturbances with the type of learning preferred by individuals (success bias, conformist and individual). We further model treatment strategy adoption as a function of treatment cost, ability of the ecological system to recover once treated and the disturbance effect on the social system. Our model shows the importance of success-bias imitation and system size in affecting the odds of eradicating ecological disturbances on connected landscapes.

Original languageAmerican English
Title of host publicationProceedings of the 2016 Winter Simulation Conference
Subtitle of host publicationSimulating Complex Service Systems, WSC 2016
EditorsTheresa M. Roeder, Peter I. Frazier, Robert Szechtman, Enlu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1702-1712
Number of pages11
ISBN (Electronic)9781509044863
DOIs
StatePublished - 1 Jan 2016
Event2016 Winter Simulation Conference, WSC 2016 - Arlington, United States
Duration: 11 Dec 201614 Dec 2016

Publication series

Name0891-7736

Conference

Conference2016 Winter Simulation Conference, WSC 2016
Country/TerritoryUnited States
CityArlington
Period11/12/1614/12/16

Keywords

  • biological system modeling
  • micromechanical devices
  • silicon
  • smoothing methods
  • switches
  • uncertainty

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