TY - GEN
T1 - Tuning Rate of Strategy Revision in Population Games
AU - Park, Shinkyu
N1 - KAUST Repository Item: Exported on 2023-07-06
Acknowledgements: This work was supported by funding from King Abdullah University of Science and Technology (KAUST).
PY - 2023/7/3
Y1 - 2023/7/3
N2 - We investigate a multi-agent decision problem in population games where each agent in a population makes a decision on strategy selection and revision to engage in repeated games with others. The strategy revision is subject to time delays which represent the time it takes for an agent revising its strategy needs to spend before it can adopt a new strategy and return back to the game. We discuss the effect of the time delays on long-term behavior of the agents’ strategy revision. In particular, when the time delays are large, the strategy revision would exhibit oscillation and the agents spend substantial time in "transitioning" between different strategies, which prevents the agents from attaining the Nash equilibrium of the game. As a main contribution of the paper, we propose an algorithm that tunes the rate of the agents’ strategy revision and show such tuning approach ensures convergence to the Nash equilibrium. We validate our analytical results using simulations.
AB - We investigate a multi-agent decision problem in population games where each agent in a population makes a decision on strategy selection and revision to engage in repeated games with others. The strategy revision is subject to time delays which represent the time it takes for an agent revising its strategy needs to spend before it can adopt a new strategy and return back to the game. We discuss the effect of the time delays on long-term behavior of the agents’ strategy revision. In particular, when the time delays are large, the strategy revision would exhibit oscillation and the agents spend substantial time in "transitioning" between different strategies, which prevents the agents from attaining the Nash equilibrium of the game. As a main contribution of the paper, we propose an algorithm that tunes the rate of the agents’ strategy revision and show such tuning approach ensures convergence to the Nash equilibrium. We validate our analytical results using simulations.
UR - http://hdl.handle.net/10754/683047
UR - https://ieeexplore.ieee.org/document/10156113/
U2 - 10.23919/acc55779.2023.10156113
DO - 10.23919/acc55779.2023.10156113
M3 - Conference contribution
BT - 2023 American Control Conference (ACC)
PB - IEEE
ER -