Modeling human behavior selection under environmental subsidy policy by multi-agent simulation

Tomoko Imoto, Shin'Ya Nakano, Tomoyuki Higuchi

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

Abstract

In recent years, the importance of environment is widely recognized and various kinds of environmental policies have been implemented. However, when one establishes a policy, there are few cases that the contents and the period of the policy are evaluated by using prediction method for its effect. In this study, we aim to predict the effects of a policy in micro-level by using agent simulations and estimate the optimal period of the policy implementation by totally evaluating its effect. For sugarcane farmer of an area in Japan, we performed 200 thousand patterns of simulations in order to optimize the subsidy of green manure and its implementation period. In particular, when the values of the parameters in the simulation model are not clearly defined, we are able to show various futures caused by the policy implementation of interest based on large-scale simulations with a huge variety of the parameter values.

Original languageEnglish
Title of host publicationAdvanced Agent Technology - AAMAS 2011 Workshops, AMPLE, AOSE, ARMS, DOCM3AS, ITMAS, Revised Selected Papers
Pages350-358
Number of pages9
DOIs
Publication statusPublished - 2012
EventInternational Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2011 - Taipei, Taiwan, Province of China
Duration: 2011 May 22011 May 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7068 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2011
Country/TerritoryTaiwan, Province of China
CityTaipei
Period11/5/211/5/6

Keywords

  • Agent Simulation
  • Environmental Policy
  • Merge Action
  • Parameter Variety

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