On Incentive Compatible Role-based Reward Distribution in Algorand

TL;DR

This paper analyzes the incentive structure of Algorand's blockchain protocol, demonstrating that the current reward system may fail to promote cooperation, and proposes a new incentive-compatible reward sharing scheme to ensure network stability.

Contribution

It introduces a formal game-theoretic model of Algorand's reward system and proposes a novel incentive-compatible reward sharing approach to promote cooperation.

Findings

Current reward sharing is not a Nash equilibrium, risking network failure.

The proposed reward scheme guarantees cooperation among selfish users.

Simulation results confirm the effectiveness and efficiency of the new reward approach.

Abstract

Algorand is a recent, open-source public or permissionless blockchain system that employs a novel proof-of-stake byzantine consensus protocol to efficiently scale the distributed transaction agreement problem to billions of users. In addition to being more democratic and energy-efficient, compared to popular protocols such as Bitcoin, Algorand also touts a much high transaction throughput. This paper is the first attempt in the literature to study and address this problem. By carefully modeling the participation costs and rewards received within a strategic interaction scenario, we first empirically show that even a small number of nodes defecting to participate in the protocol tasks due to insufficiency of the available incentives can result in the Algorand network failing to compute and add new blocks of transactions. We further show that this effect can be formalized by means of a mathematical model of interaction in Algorand given its participation costs and the current (or planned) reward distribution/sharing approach envisioned by the Algorand Foundation. Specifically, on analyzing this game model we observed that mutual cooperation under the currently proposed reward sharing approach is not a Nash equilibrium. This is a significant result which could threaten the success of an otherwise robust distributed consensus mechanism. We propose a novel reward sharing approach for Algorand and formally show that it is incentive-compatible, i.e., it can guarantee cooperation within a group of selfish Algorand users. Extensive numerical and Algorand simulation results further confirm our analytical findings. Moreover, these results show that for a given distribution of stakes in the network, our reward sharing approach can guarantee cooperation with a significantly smaller reward per round.