TY - GEN
T1 - Agent-based model coupled with Bayesian estimation for evaluation of social acceptance of geothermal development
AU - Masuda, Shuntaro
AU - Bahr, Kyle
AU - Tsuchiya, Noriyoshi
N1 - Funding Information:
The authors are grateful to Dr. Hanae Saishu (National Institute of Advanced Industrial Science and Technology (AIST)) for cooperation in data collection and suggesting the topic discussed in this paper. I would like to thank Dr. Hiromi Kubota (Central Research Institute of Electric Power Industry (CRIEPI)) for providing the information about social acceptance of geothermal development in Japan. This work is financially supported by AIST.
PY - 2017
Y1 - 2017
N2 - In Japan, geothermal development has stagnated because of social risks, mainly the opposition of stakeholders. For mitigation of the risks, many studies have been conducted for analysis of social acceptance for development. Among them, Agent-Based Model (ABM) of opinion diffusion in a social network has been used to understand how acceptance develops dynamically. The objective of this study is to improve an existing model that reproduces realworld social network behavior through the introduction of a new parameter which reflects individual characteristics of stakeholders. We estimate the parameter by using a Bayesian network, introduce it into ABM and perform a proof-of-concept simulation of the model. This result gives confidence in the usefulness of this parameter as a practical tool for analysis of social acceptance in the future.
AB - In Japan, geothermal development has stagnated because of social risks, mainly the opposition of stakeholders. For mitigation of the risks, many studies have been conducted for analysis of social acceptance for development. Among them, Agent-Based Model (ABM) of opinion diffusion in a social network has been used to understand how acceptance develops dynamically. The objective of this study is to improve an existing model that reproduces realworld social network behavior through the introduction of a new parameter which reflects individual characteristics of stakeholders. We estimate the parameter by using a Bayesian network, introduce it into ABM and perform a proof-of-concept simulation of the model. This result gives confidence in the usefulness of this parameter as a practical tool for analysis of social acceptance in the future.
KW - Agent-Based Model
KW - Bayesian network
KW - Modeling
KW - Simulation
KW - Social license to operate
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M3 - Conference contribution
AN - SCOPUS:85041059740
T3 - Transactions - Geothermal Resources Council
SP - 2056
EP - 2067
BT - Geothermal Energy
PB - Geothermal Resources Council
T2 - Geothermal Resources Council 41st Annual Meeting - Geothermal Energy: Power To Do More, GRC 2017
Y2 - 1 October 2017 through 4 October 2017
ER -
