An Agent-Based Framework for Policy Simulation
Modeling Heterogeneous Behaviors With Modified Sigmoid Function and Evolutionary Training
Bibliographic Data
| ID | 22107935 |
|---|---|
| Authors | Songmin Yu (0000-0001-6062-4382, Fraunhofer Institute for Systems and Innovation Research, corresponding author) |
| Year | 2023 |
| Volume | 10 |
| Issue | 4 |
| Pages | 1901-1913 |
| Publication date | 2023-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3196737 |
| OpenAlex | W4291805100 |
| Language | EN |
| References cited | 63 |
This article proposes an agent-based policy simulation framework that can be applied to the cases satisfying: 1) the agents try to maximize some intertemporal preference and 2) the impacts of different factors on agents’ behavioral tendency are monotonic. By combining the simulation and optimization methods, this framework balances the flexibility and validity of agent-based models (ABMs): the sigmoid function is modified and used to model agents’ decision-making rules, and the evolutionary training method is used to calibrate agents’ behavioral parameters. Based on an example for the emission trading scheme, the application of the framework is presented and evaluated in detail
Agent-based model · Artificial neural network · Economics · Mathematical optimization · Microeconomics · Monotonic function · Multi-agent system · Preference · Sigmoid function · Complex Systems and Time Series Analysis · Computer Science · Innovation Diffusion and Forecasting · Mathematics · Opinion Dynamics and Social Influence · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |