A Mean Field Game Analysis of Consensus Protocol Design

TL;DR

This paper develops a mathematical mean field game framework to analyze and optimize consensus protocol design in decentralized blockchains, linking game theory, PDEs, and blockchain security.

Contribution

It introduces a novel mean field game approach to model and analyze blockchain consensus protocols, including numerical methods and application to Bitcoin.

Findings

Mean field equilibrium characterizes blockchain security.

Numerical methods compute steady-state and dynamic equilibria.

Application demonstrated on Bitcoin mechanism design.

Abstract

A decentralized blockchain is a distributed ledger that is often used as a platform for exchanging goods and services. This ledger is maintained by a network of nodes that obeys a set of rules, called a consensus protocol, which helps to resolve inconsistencies among local copies of a blockchain. In this paper, we build a mathematical framework for the consensus protocol designer, specifying (a) the measurement of a resource which nodes strategically invest in and compete for to win the right to build new blocks in the blockchain; and (b) a payoff function for such efforts. Thus, the equilibrium of an associated stochastic differential game can be implemented by selecting nodes in proportion to this specified resource and penalizing dishonest nodes by its loss. This associated, induced game can be further analyzed using mean field games. The problem can be broken down into two coupled PDEs, where an individual node's optimal control path is solved using a Hamilton-Jacobi-Bellman equation, and where the evolution of states distribution is characterized by a Fokker-Planck equation. We develop numerical methods to compute the mean field equilibrium for both steady states at the infinite time horizon and evolutionary dynamics. As an example, we show how the mean field equilibrium can be applied to the Bitcoin blockchain mechanism design. We demonstrate that a blockchain can be viewed as a mechanism that operates in a decentralized setup and propagates properties of the mean field equilibrium over time, such as the underlying security of the blockchain.