Using the Dual Concept of Evolutionary Game and Reinforcement Learning in Support of Decision-Making Process of Community Regeneration—Case Study in Shanghai
Abstract
:1. Introduction
2. Literature Review
2.1. The Impact of Digital Technology on the Improvement of Urban Management Refinement
2.2. Decision-Making Methods and Community Regeneration
2.3. The Conceptual Framework of the Decision-Making Process of Community Regeneration Proposed by Evolutionary Game and Reinforcement Learning
3. Method
3.1. Model-Related Hypotheses, Parameters, and Payoffs
- Hypothesis 1: The government, enterprises, and residents are all finite rational, and will repeatedly adjust their strategies according to their benefits, and finally choose the optimal strategy to maximize their interests.
- Hypothesis 2: The government’s strategy choice is (positive governance, loose governance). Positive governance refers to the government’s full interference in the promotion of work, including subsidies, publicity, work promotion, etc. Additionally, through rewards and punishments, the government promotes social organizations and residents to actively participate in community renewal. Loose governance means that the government does not interfere too much in the process of work.
- Hypothesis 3: The strategy choice of enterprises is (active participation, negative participation). Active participation means that social organizations provide quality services to residents and can enjoy government subsidies; negative participation means that social organizations maintain the status quo and have low participation.
- Hypothesis 4: The strategy choice of residents is (active participation, negative participation). Active participation means that residents are in favor of community renewal and participate in it to improve their quality of life, to express their ideas and join in the activities, while negative participation means that residents are indifferent to the process, and the benefits they obtain depend only on the strategic choices of the government and enterprises.
3.2. Model Framework and Solution
4. Evolutionary Game Analysis
4.1. Model Parameter Substitution
4.2. Stakeholders’ Demands in Each Segment
4.3. Stakeholder Characteristics and Cooperation Methods
5. Discussion
5.1. Digital Transformation Requires Balancing the Interests of Multiple Parties
5.2. Digital Transformation Requires Adequate Participatory Coordination Mechanisms
5.3. Digital Transformation Requires Intelligent Governance and Decision Aids
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
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Parameter | Meaning | Remarks |
---|---|---|
S | Subsidies given to businesses and residents when the government is aggressive in governance | |
Costs paid by the government when it adopts a proactive strategy | ||
M | Penalties for negative participation of enterprises when the government is active in governance | |
The percentage of cost saved by the government and residents’ income increased when community planners are involved in the project | ||
The proportion of cost savings for the government and enterprises when the government and enterprises cooperate | ||
Direct and indirect benefits such as economic and prestige gained by government departments when community governance is progressed | ||
Loss of prestige due to poor living experience of residents when the government is passive | ||
Costs to businesses when they actively participate | ||
The cost of active participation by the government and passive participation by enterprises | ||
The basic benefits of public services provided by market players | ||
Additional economic benefits for each market player due to increased reputation in the industry | ||
Indirect benefits to residents in terms of physical, mental, and quality of life improvement | ||
Compensation costs for residents who suffer as a result of the negative participation strategy of market players | ||
The benefit loss of residents when the community renewal effect is poor |
Strategy Selection | Earnings | ||||
---|---|---|---|---|---|
Government | Companies | Residents | Government | Companies | Residents |
C | C | C | |||
C | C | D | |||
C | D | C | |||
C | D | D | |||
D | C | C | |||
D | C | D | |||
D | D | C | |||
D | D | D |
Equilibrium Point | Eigenvalues of the Jacobian Matrix | Symbol of the Real Part | Stability Conclusion | ||
---|---|---|---|---|---|
ESS | |||||
Instability point | |||||
Instability point | |||||
Instability point | |||||
ESS | |||||
Instability point | |||||
Instability point | |||||
Instability point | |||||
0 | 0 | Instability point | |||
0 | Uncertain point | ||||
Uncertain point |
Parameter | Initial Value | Remarks |
---|---|---|
20 | ||
30 | ||
15 | ||
0.2 | ||
0.15 | ||
55 | ||
20 | ||
30 | ||
10 | ||
35 | ||
20 | ||
70 | ||
25 | ||
30 |
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Zhou, Y.; Lei, H.; Zhang, X.; Wang, S.; Xu, Y.; Li, C.; Zhang, J. Using the Dual Concept of Evolutionary Game and Reinforcement Learning in Support of Decision-Making Process of Community Regeneration—Case Study in Shanghai. Buildings 2023, 13, 175. https://doi.org/10.3390/buildings13010175
Zhou Y, Lei H, Zhang X, Wang S, Xu Y, Li C, Zhang J. Using the Dual Concept of Evolutionary Game and Reinforcement Learning in Support of Decision-Making Process of Community Regeneration—Case Study in Shanghai. Buildings. 2023; 13(1):175. https://doi.org/10.3390/buildings13010175
Chicago/Turabian StyleZhou, Youmei, Hao Lei, Xiyu Zhang, Shan Wang, Yingying Xu, Chao Li, and Jie Zhang. 2023. "Using the Dual Concept of Evolutionary Game and Reinforcement Learning in Support of Decision-Making Process of Community Regeneration—Case Study in Shanghai" Buildings 13, no. 1: 175. https://doi.org/10.3390/buildings13010175