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Agent-based modeling with classifier...

Yan, Liying.

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Agent-based modeling with classifier system: A new modeling tool to investigate the dynamics of social/ecological systems with particular reference to the Maine Lobster Fishery.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Agent-based modeling with classifier system: A new modeling tool to investigate the dynamics of social/ecological systems with particular reference to the Maine Lobster Fishery./
Author:	Yan, Liying.
Description:	99 p.
Notes:	Source: Dissertation Abstracts International, Volume: 68-06, Section: B, page: 3917.
Contained By:	Dissertation Abstracts International68-06B.
Subject:	Agriculture, Fisheries and Aquaculture. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3267975
ISBN:	9780549069898

Agent-based modeling with classifier system: A new modeling tool to investigate the dynamics of social/ecological systems with particular reference to the Maine Lobster Fishery.
Yan, Liying.

Agent-based modeling with classifier system: A new modeling tool to investigate the dynamics of social/ecological systems with particular reference to the Maine Lobster Fishery. - 99 p.

Source: Dissertation Abstracts International, Volume: 68-06, Section: B, page: 3917.

Thesis (Ph.D.)--The University of Maine, 2007.

Over the last several decades scientific predictions of imminent over-fishing of the lobster fishery have been very inconsistent with the huge increases in landings and population numbers. To acquire a better understanding of the spatial/temporal dynamics of the fishery and explain this discrepancy, a new modeling approach, an agent-based model with John Holland's learning classifier system, is introduced to model the lobster fishery. Instead of modeling the system from prior knowledge of the major factors affecting the population of lobsters and the population of fishers, an agent-based modeling approach models the system from bottom-up, i.e., it simulates the interactive decisions (where to place traps and whether to cut others' traps) made by large numbers of individual fishers. Learning classifier systems allow 'fishers' to learn from their experiences and adapt their fishing strategies to the changing ecological and social environment. The aggregate patterns of the fishery 'emerge' as the product of thousands of individual decisions and give us a better understanding of the competitive dynamics that lay the foundation for the growth of self-governance in the fishery. The spatial, temporal and behavioral patterns produced by the model nicely match the patterns found in observations of 988,000 trap placements made by 44 fishers in the Gulf of Maine.

ISBN: 9780549069898Subjects--Topical Terms:

1020913
Agriculture, Fisheries and Aquaculture.

Agent-based modeling with classifier system: A new modeling tool to investigate the dynamics of social/ecological systems with particular reference to the Maine Lobster Fishery.
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100 1 $a Yan, Liying. $3 1924521
245 1 0 $a Agent-based modeling with classifier system: A new modeling tool to investigate the dynamics of social/ecological systems with particular reference to the Maine Lobster Fishery.
300 $a 99 p.
500 $a Source: Dissertation Abstracts International, Volume: 68-06, Section: B, page: 3917.
500 $a Adviser: James A. Wilson.
502 $a Thesis (Ph.D.)--The University of Maine, 2007.
520 $a Over the last several decades scientific predictions of imminent over-fishing of the lobster fishery have been very inconsistent with the huge increases in landings and population numbers. To acquire a better understanding of the spatial/temporal dynamics of the fishery and explain this discrepancy, a new modeling approach, an agent-based model with John Holland's learning classifier system, is introduced to model the lobster fishery. Instead of modeling the system from prior knowledge of the major factors affecting the population of lobsters and the population of fishers, an agent-based modeling approach models the system from bottom-up, i.e., it simulates the interactive decisions (where to place traps and whether to cut others' traps) made by large numbers of individual fishers. Learning classifier systems allow 'fishers' to learn from their experiences and adapt their fishing strategies to the changing ecological and social environment. The aggregate patterns of the fishery 'emerge' as the product of thousands of individual decisions and give us a better understanding of the competitive dynamics that lay the foundation for the growth of self-governance in the fishery. The spatial, temporal and behavioral patterns produced by the model nicely match the patterns found in observations of 988,000 trap placements made by 44 fishers in the Gulf of Maine.
590 $a School code: 0113.
650 4 $a Agriculture, Fisheries and Aquaculture. $3 1020913
650 4 $a Computer Science. $3 626642
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710 2 $a The University of Maine. $3 1029373
773 0 $t Dissertation Abstracts International $g 68-06B.
790 1 0 $a Wilson, James A., $e advisor
790 $a 0113
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3267975