TY - GEN
T1 - A Hierarchical Approach for the Stochastic Stability Analysis of Evolutionary Dynamics
AU - Jaleel, Hassan
AU - Shamma, Jeff S.
N1 - H. Jaleel is with the Intelligent Machines & Sociotechnical Systems (iMaSS) Lab, Department of Electrical Engineering, Syed Babar Ali School of Science & Engineering at Lahore University of Management Science (LUMS), Lahore, Pakistan. J.S. Shamma is with the Robotics, Intelligent Systems & Control (RISC) Lab, Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) at King Abdullah University of Science and Technology (KAUST), Thuwal 23955–6900, Saudi Arabia. Emails: [email protected], [email protected]. Research supported by funding from LUMS.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - We propose a hierarchical approach for the stochastic stability analysis of evolutionary dynamics. Each layer in the hierarchy represents a compromise between computational effort and the resolution of information about the long-run behavior of evolutionary dynamics. Previously, we proposed a graphical reformulation of Evolutionarily Sable Strategy (ESS) analysis through which we identified a set of strategies that cannot be ESS. Moreover, we also computed a set of strategies that was guaranteed to contain the set of stochastically stable strategies. The previous analysis was developed by considering transitions resulting from single mutations only. We extend the graphical approach to higher order analysis by incorporating mutations of higher order and show that we can refine our solution estimate by identifying smaller subsets of strategies that contain the set of stochastically stable strategies. However, this refinement comes at a cost of increase in computational budget.
AB - We propose a hierarchical approach for the stochastic stability analysis of evolutionary dynamics. Each layer in the hierarchy represents a compromise between computational effort and the resolution of information about the long-run behavior of evolutionary dynamics. Previously, we proposed a graphical reformulation of Evolutionarily Sable Strategy (ESS) analysis through which we identified a set of strategies that cannot be ESS. Moreover, we also computed a set of strategies that was guaranteed to contain the set of stochastically stable strategies. The previous analysis was developed by considering transitions resulting from single mutations only. We extend the graphical approach to higher order analysis by incorporating mutations of higher order and show that we can refine our solution estimate by identifying smaller subsets of strategies that contain the set of stochastically stable strategies. However, this refinement comes at a cost of increase in computational budget.
UR - https://www.scopus.com/pages/publications/85099878385
UR - https://www.scopus.com/pages/publications/85099878385#tab=citedBy
U2 - 10.1109/CDC42340.2020.9304218
DO - 10.1109/CDC42340.2020.9304218
M3 - Conference contribution
AN - SCOPUS:85099878385
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1818
EP - 1823
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 59th IEEE Conference on Decision and Control, CDC 2020
Y2 - 14 December 2020 through 18 December 2020
ER -