@techreport{oai:shiga-u.repo.nii.ac.jp:00014352,
 author = {Kyoi, Shinsuke and 京井, 尋佑 and Mori, Koichiro and 森, 宏一郎},
 issue = {No. E-17},
 month = {Sep},
 note = {Technical Report, Pro-environmental behavior does not seem to diffuse sufficiently in society. Is there a way to enhance the degree of people’s pro-environmental behavior? This study aims to develop a dynamic model of mutual learning in social networks to simulate the diffusion of pro-environmental behavior and to search promising policies for promoting it. In terms of policy interventions, this study considers two ways of promoting individual environmentally friendly behavior: enhancing pro-environmental behavior of 1%, 5%, and 10% of people in social networks, and changing the learning patterns of 1%, 5%, and 10% of people. The people targeted for intervention are determined by random selection, selection in escending order of degree centrality, and selection in descending order of eigenvector centrality. One of the interesting findings is that changing individual learning patterns is much more effective for enhancing the degree of pro-environmental behavior in social networks than trying to directly enhance its degree. In addition, selection of target people based on the degree centrality or eigenvector centrality is more influential in encouraging environmentally friendly behavior than random selection, particularly in the policy of changing learning patterns. There is no clear difference between selections based on degree centrality and eigenvector centrality. Multiplier effects are also measured: the ratio of the net increase in the number of people who enhance their degree of pro-environmental behavior at the end of a certain number of time steps beyond business as usual (BAU) to the number of people intervened. Multiplier effects are always positive when learning patterns are changed, which means that the results of  interventions are always better than BAU. If it is prohibitively costly to select target people based on centralities in a practical manner, random selection can be applied when the option to change learning patterns is chosen., Discussion Paper, Series E, No. E-17, pp. 1-38},
 title = {Computer simulation of environmental learning in social networks},
 year = {2022},
 yomi = {キョウイ, シンスケ and モリ, コウイチロウ}
}