Innovative Research Award
Yanshan Liu
Qingdao University, China
| Yanshan Liu |
| Affiliation |
Qingdao University |
| Country |
China |
| Scopus ID |
51764034200 |
| Documents |
41 |
| Citations |
398 |
| h-index |
11 |
| Subject Area |
Game Theory |
| Event |
Math Scientist Awards |
| ORCID |
0009-0001-2491-3766 |
The Innovative Research Award recognizes notable scholarly contributions that advance theoretical understanding and practical applications within a specialized academic field. This recognition highlights the research achievements of Yanshan Liu of Qingdao University, whose work in game theory, opinion dynamics, optimal control, reinforcement learning, and networked systems has contributed to contemporary mathematical and computational research. Through peer-reviewed publications and interdisciplinary investigations, Liu has explored mechanisms governing opinion evolution, strategic interactions, and control methodologies in complex networks.[1]
Abstract
Yanshan Liu’s research portfolio centers on mathematical modeling and strategic decision-making within dynamic social and networked environments. His studies integrate game-theoretic frameworks, optimal control theory, and reinforcement learning techniques to analyze opinion dynamics and collective behavior. Published across recognized scientific journals and conference proceedings, his work contributes to both theoretical mathematics and computational applications while addressing challenges associated with complex adaptive systems.[2]
Keywords
Game Theory, Opinion Dynamics, Optimal Control, Reinforcement Learning, Complex Networks, Mathematical Modeling, Dynamic Systems, Social Networks, Decision Sciences, Network Optimization.
Introduction
Modern game theory increasingly intersects with network science, artificial intelligence, and social computation. Researchers seek analytical approaches capable of explaining how opinions evolve, how strategic decisions influence collective outcomes, and how interventions can optimize system performance. Liu’s research addresses these questions through rigorous mathematical analysis and computational experimentation, creating models that help explain dynamic interactions among agents in complex environments.[3]
Research Profile
According to available scholarly metrics, Liu has produced 41 indexed documents, accumulated 398 citations, and achieved an h-index of 11. His academic activities primarily focus on mathematical decision sciences and game-theoretic analysis. The combination of citation performance and publication consistency reflects sustained engagement with contemporary challenges in network dynamics and optimal control.[1]
Research Contributions
- Development of average-oriented opinion dynamics game models for analyzing control strategies in social systems.
- Investigation of inverse dynamic models involving stubborn agents within social networks.
- Application of discounted linear quadratic regulator methods to optimal opinion control on complex networks.
- Integration of reinforcement learning approaches with stochastic opinion dynamics.
- Formulation of multi-stage game-theoretic frameworks for dynamic decision-making processes.
Collectively, these contributions demonstrate a commitment to advancing analytical tools capable of supporting decision optimization and understanding collective behavior in interconnected environments.[4]
Publications
- Comparison of Control Strategies in an Average-Oriented Opinion Dynamics Game.
- Optimal Control of Opinion Dynamics on Complex Networks via Discounted LQR: Theory and Computation.
- Reinforcement Learning-Based Optimal Control for Stochastic Opinion Dynamics.
- Two-Stage Game Model of Opinion Dynamics.
Research Impact
The practical significance of Liu’s research extends to social network analysis, distributed decision-making, policy intervention strategies, and computational intelligence. By combining mathematical rigor with algorithmic approaches, his work provides methodologies that can support future studies in collective behavior, optimization, and adaptive control systems. The interdisciplinary nature of these contributions strengthens their relevance across multiple academic domains.[5]
Award Suitability
The Innovative Research Award recognizes scholarly originality, methodological advancement, and measurable academic contribution. Liu’s publication record, citation performance, and research focus on emerging intersections of game theory, control science, and artificial intelligence align with these criteria. His investigations contribute meaningful insights into opinion dynamics and network optimization while supporting the broader development of mathematical sciences.[6]
Conclusion
Yanshan Liu’s scholarly work reflects a focused effort to address contemporary challenges within game theory and dynamic network analysis. Through contributions spanning opinion dynamics, optimal control, and reinforcement learning, he has established a research profile characterized by methodological depth and interdisciplinary relevance. These achievements provide a strong foundation for recognition through the Innovative Research Award at the Math Scientist Awards.[1]
External Links
References
- Elsevier. (n.d.). Scopus author details: Yanshan Liu, Author ID 51764034200. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=51764034200
- European Journal of Control. (2026). Comparison of Control Strategies in an Average-Oriented Opinion Dynamics Game.
https://doi.org/10.1016/j.ejcon.2026.101551
- Mathematics. (2026). Optimal Control of Opinion Dynamics on Complex Networks via Discounted LQR: Theory and Computation.
https://doi.org/10.3390/math14101623
- Scientific Reports. (2026). Reinforcement Learning-Based Optimal Control for Stochastic Opinion Dynamics.
https://doi.org/10.1038/s41598-026-42646-1
- Springer. (2025). Two-Stage Game Model of Opinion Dynamics.
https://doi.org/10.1007/978-3-031-97077-1_14
- ORCID. (n.d.). Researcher Profile: Yanshan Liu.
https://orcid.org/0009-0001-2491-3766