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Karma games for proportional resource allocations in population with variable clusters
Karma games belong to the class of Dynamic Population Games (DPG). They are formulated as repeated auction-like games for a population of self-interested agents and ensure fair and efficient resource allocation in such a population.
Motivated by its application for priority distribution among Connected and Automated Vehicles (CAVs), we are interested in designing a karma game for proportional resource allocations in populations with variable clusters.
The research question is described with an example of CAV traffic. Assume CAVs are assigned into clusters based on safety criteria and jointly take actions to avoid collisions. Every time a new collision is detected, a new cluster is formed, lasting until the threat is solved. The number of CAVs within a cluster and the cluster duration are variable. CAVs compete to win priority values inside clusters. How can we design a karma game to distribute priority fairly and efficiently among all the CAVs?
The applications of such a game are not limited to CAVs; they can be further extended for other applications of proportional resource allocations, such as shared servers.
Keywords: Karma games, Dynamic population games, Proportional resource allocations,
Consider a population of self-interested agents, where every agent has a level of karma and urgency (see figure). At every instance, some agents get assigned to a cluster to compete over a partial resource acquisition. The agents bid karma units according to their private states (such as urgency and karma value) to win a portion of the resource. The cluster dimension (number of agents within a cluster) and cluster duration (the time period that a cluster lasts) are random variables, and information on probability distribution and correlation functions are available. The outcomes of the resource allocation will last for the whole cluster duration. Your task is to design a karma mechanism to address fair and efficient proportional resource allocations for the described population.
Consider a population of self-interested agents, where every agent has a level of karma and urgency (see figure). At every instance, some agents get assigned to a cluster to compete over a partial resource acquisition. The agents bid karma units according to their private states (such as urgency and karma value) to win a portion of the resource. The cluster dimension (number of agents within a cluster) and cluster duration (the time period that a cluster lasts) are random variables, and information on probability distribution and correlation functions are available. The outcomes of the resource allocation will last for the whole cluster duration. Your task is to design a karma mechanism to address fair and efficient proportional resource allocations for the described population.
● Conduct a literature review on dynamic population games, karma games, and game-based approaches for proportional resource allocation.
● Develop a karma mechanism for proportional resource allocation in a population of self-interested agents assigned in random clusters with variable dimensions and durations.
● Investigate the existence of and convergence to the Stationary Nash Equilibrium (SNE).
● Design simulation studies to investigate the performance of the designed karma mechanism.
● Compare the karma mechanism to other policies.
● Conduct a literature review on dynamic population games, karma games, and game-based approaches for proportional resource allocation.
● Develop a karma mechanism for proportional resource allocation in a population of self-interested agents assigned in random clusters with variable dimensions and durations.
● Investigate the existence of and convergence to the Stationary Nash Equilibrium (SNE).
● Design simulation studies to investigate the performance of the designed karma mechanism.
● Compare the karma mechanism to other policies.
Kimia Chavoshi (kimiac@ethz.ch), Ezzat Elokda (elokdae@ethz.ch)
Kimia Chavoshi (kimiac@ethz.ch), Ezzat Elokda (elokdae@ethz.ch)
Karma for clusters.pdf
