Game of Coins

TL;DR

This paper models strategic mining in multi-cryptocurrency markets as a game, proves convergence of better-response learning to equilibrium, and proposes a reward scheme to steer the system towards desired outcomes, introducing the first multi-coin strategic attack and reward design for learning miners.

Contribution

It formalizes multi-cryptocurrency mining as a game, proves convergence of learning dynamics, and introduces a reward scheme to control system equilibria, pioneering multi-coin strategic attack analysis.

Findings

Better-response learning converges to equilibrium in multi-coin mining games.

A reward design scheme can shift system equilibria to desired states.

First analysis of multi-coin strategic attack in adaptive mining systems.

Abstract

We formalize the current practice of strategic mining in multi-cryptocurrency markets as a game, and prove that any better-response learning in such games converges to equilibrium. We then offer a reward design scheme that moves the system configuration from any initial equilibrium to a desired one for any better-response learning of the miners. Our work introduces the first multi-coin strategic attack for adaptive and learning miners, as well as the study of reward design in a multi-agent system of learning agents.