Simulation of Opinion and Voting Dynamics in Decentralized Governance

Level: MA/MAP
Responsible person: Dr Mark C. Ballandies
Keywords:  DAO, Blockchain, Agent-based modeling, social influence, opinion models

Decentralized autonomous organizations (DAOs) rely on collective decision-making under noisy information, social influence, and strategic incentives. This project builds an agent-based model (ABM) [1] of opinion formation on a social network and couples it to DAO voting mechanisms (token-weighted, quadratic, conviction, delegation). You will study how incentives and adversarial behavior (bribery, Sybils, collusion) shape polarization, turnout, and decision quality—and propose mechanism that improve robustness. 

In particular, DAOs often debate proposals off-chain (forums, calls, social media) and then decide on-chain. This project formalizes that two-stage process (i) a deliberation stage where opinions evolve and (ii) a decision/propagation stage. You will analyze how the deliberation parameters (confidence bounds, stubborn minorities) reshape tipping points in the subsequent decision phase, especially under token-weighted power distributions typical in DAOs. Moreover, the temporal dependence between the two stages can also be examined.  

Starting point will be a previously introduced toy model [2] from which you will examine the impact of deliberation on the decision making in DAOs. 

What you’ll need: Python; network science; agent-based/Monte-Carlo simulation  

References: 
[1] Helbing, D., 2012. Agent-based modeling. In Social self-organization: Agent-based simulations and experiments to study emergent social behavior (pp. 25-70). Berlin, Heidelberg: Springer Berlin Heidelberg.  
[2] Ballandies, M.C., Carpentras, D. and Pournaras, E., 2024. DAOs of collective intelligence? Unraveling the complexity of blockchain governance in decentralized autonomous organizations. arXiv preprint arXiv:2409.01823.