Incorporating AI Methods in Micro-dynamic Analysis to Support Group-Specific Policy-Making

被引：0

作者：

Chang, Shuang ^{[1
]}

Asai, Tatsuya ^{[1
]}

Koyanagi, Yusuke ^{[1
]}

Uemura, Kento ^{[1
]}

Maruhashi, Koji ^{[1
]}

Ohori, Kotaro ^{[1
]}

机构：

[1] Fujitsu Res, 4-1-1 Kamikodanaka,Nakahara Ku, Kawasaki, Kanagawa 2118588, Japan

来源：

PRIMA 2022: PRINCIPLES AND PRACTICE OF MULTI-AGENT SYSTEMS | 2023年 / 13753卷

关键词：

Analysis method; Agent-based model; Long-term care services; AGENT-BASED SIMULATION; CARE;

D O I：

10.1007/978-3-031-21203-1_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An agent-based modelling approach is a powerful means of understanding social phenomena by modelling individual behaviours and interactions. However, the advancements in modelling pose challenges in the model analysis process for understanding the complex effects of input factors, especially when it comes to offering concrete policies for improving system outcomes. In this work, we propose a revised micro-dynamic analysis method that adopts advanced artificial intelligence methods to enhance the model interpretation and to facilitate group-specific policy-making. It strengthens the explanation power of the conventional micro-dynamic analysis by eliminating ambiguity in the result interpretation and enabling a causal interpretation of a target phenomenon across subgroups. We applied our method to understand an agent-based model that evaluates the effects of a long-term care scheme on access to care. Our findings showed that the method can suggest policies for improving the equity of access more efficiently than the conventional scenario analysis.

引用

页码：122 / 138

页数：17