Agent-based modeling for decentralized autonomous organizations and decentralized finance

Qingsong Ruan, Guojun Wang, Yunbo Lu, Yilei Dong and Lin William Cong

Abstract

In this paper we introduce agent-based modeling for research on decentralized autonomous organizations and decentralized finance (DeFi). As an illustration, we employ agent-based modeling to investigate how initial token allocations, inflationary policies and staking incentives influence the concentration of governance tokens, a critical issue concerning decentralized networks. Elaborate agent-based simulations reveal that while fair initial token allocation does not resolve token concentration issues in the long run, both staking programs and inflationary token supply policies significantly mitigate governance token concentration, as measured by the Gini coefficient or normalized Shannon entropy, by up to 30% over time. Among inflationary policies, exponential inflation proves more effective at reducing token concentration than linear inflation. In addition, staking mechanisms with mediumterm lockup periods (60 time steps) yield greater reductions in token concentration compared with short-term (30 time steps) or long-term (90 time steps) lockups. The flexible framework can be extended to tackle other research and practical challenges in DeFi and inform governance designs in decentralized autonomous organizations.