Research on Multi-Agent Interaction and Policy Optimization in the Elevator Insurance Market Based on Evolutionary Game Theory

DOI

10.2991/978-94-6463-552-2_21How to use a DOI?

Keywords

Elevator insurance; Evolutionary game theory; Policy optimization; Public safety; Multi-agent interaction

Abstract

This paper uses evolutionary game theory to study the interaction behavior and policy optimization among the government, insurance companies, and elevator usage units in the elevator insurance market. By constructing a tripartite evolutionary game model, the benefits of each agent under different strategy choices are analyzed, and the stability of the model is verified through numerical simulation. The study finds that government intervention(such as subsidies and penalties) has a significant impact on the publicity enthusiasm of insurance companies and the willingness of elevator usage units to purchase insurance. At different stages, the strategy choices of the three parties exhibit dynamic evolutionary characteristics, eventually achieving a stable equilibrium in the insurance market. The research results indicate that reasonable policy design can promote the development of the elevator insurance market and enhance the level of public safety. The research findings not only enrich the theoretical framework of the elevator insurance market but also provide a scientific basis for policy formulation, helping to promote the healthy development of the elevator insurance market and improve public safety.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bo Zhang
AU  - Jun Huang
AU  - Hao Zou
AU  - Tingjie Zhang
PY  - 2024
DA  - 2024/10/27
TI  - Research on Multi-Agent Interaction and Policy Optimization in the Elevator Insurance Market Based on Evolutionary Game Theory
BT  - Proceedings of the 4th International Conference on Management Science and Software Engineering (ICMSSE 2024)
PB  - Atlantis Press
SP  - 215
EP  - 231
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-552-2_21
DO  - 10.2991/978-94-6463-552-2_21
ID  - Zhang2024
ER  -