Dynamics of a Stratified Population of Optimum Seeking Agents on a Network -- Part I: Modeling and Convergence Analysis
Nirabhra Mandal, Pavankumar Tallapragada

TL;DR
This paper models the dynamics of a stratified population of agents seeking to maximize payoff on a network, analyzing different levels of coordination and proving convergence to Nash equilibria.
Contribution
It introduces a generalized framework for population dynamics on networks with stratification and diminishing returns, analyzing three choice revision policies.
Findings
Existence and uniqueness of solutions for all dynamics
Convergence of solutions to Nash equilibria
Comparison of selfish, nodal, and population-wide coordination effects
Abstract
In this work, we consider a population composed of a continuum of agents that seek to maximize a payoff function by moving on a network. The nodes in the network may represent physical locations or abstract choices. The population is stratified and hence agents opting for the same choice may not get the same payoff. In particular, we assume payoff functions that model diminishing returns, that is, agents in "newer" strata of a node receive a smaller payoff compared to "older" strata. In this first part of two-part work, we model the population dynamics under three choice revision policies, each having varying levels of coordination -- i. no coordination and the agents are selfish, ii. coordination among agents in each node and iii. coordination across the entire population. To model the case with selfish agents, we generalize the Smith dynamics to our setting, where we have a stratified…
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