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Authors: Fumi Okura 1 ; I. Wayan Budiasa 2 and Tasuku Kato 3

Affiliations: 1 United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, Tokyo, Japan ; 2 Faculty of Agriculture, Udayana University, Bali, Indonesia ; 3 Institute of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan

Keyword(s): Agent-Based Model, Water Management, Irrigation and Drainage.

Abstract: Due to climate change and population growth, the agriculture sector has been faced with two challenges; securing water and food and transferring into sustainable resource management. To systematize resource management which currently mainly relies on farmers’ experience, digital technologies have been developed. Considering current tighter resource availability, it is desirable to examine resource management behavior of beneficiaries using scarce resources to analyze resilience and adaptability of institutions. In this study, we analyzed factors of water use behavior of Water Users Associations (WUAs) to solve water allocation problem with Agent-Based Model (ABM). The simulation results show that factors of water use behavior were water resources and the existence of different water use laws, and downstream WUAs developed adaptation methods. To enhance sustainable resource management, ABM can be applied to analyze factors and their rules and/or laws to understand what enhances resili ence and adaptability of institutions. (More)

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Paper citation in several formats:
Okura, F.; Budiasa, I. and Kato, T. (2020). Agent-Based Model Application for Resource Management Analysis. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 242-249. DOI: 10.5220/0009093302420249

@conference{icaart20,
author={Fumi Okura. and I. Wayan Budiasa. and Tasuku Kato.},
title={Agent-Based Model Application for Resource Management Analysis},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2020},
pages={242-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009093302420249},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Agent-Based Model Application for Resource Management Analysis
SN - 978-989-758-395-7
IS - 2184-433X
AU - Okura, F.
AU - Budiasa, I.
AU - Kato, T.
PY - 2020
SP - 242
EP - 249
DO - 10.5220/0009093302420249
PB - SciTePress