Governance research

An open ended research into crypto and DAO governance

Why care about governance? Is DAO governance problematic? Yes and yes.

DAO governance suffers from low participation rate among voters, low information rate and an uneven distribution of voting power. The three are naturally connected, but the result is that the few can control the many and that the average protocol doesn’t actually have a decentralized governance structure that promises the credible neutrality and trustlessness that is web3’s promise.

Web3 is a technological innovation, but it’s an innovation that improves the social aspect of human coordination and trust. Trustlessness, credible neutrality and enhanced coordination rely on new forms of governance. This is why governance in web3 matters and why we should care about solving four problems that existing governance models have:

1. Increasing the number of participants in governance - aka voter participation. Increasing voter participation is a part of reducing value capture by a small group, increases trust and brings the benefit of better decision making by aggregating information from a diverse set of participants.
2. Ensuring that the right people are participating: It’s not only a question of participation but a matter of getting the right people to participate. The right people means honest participants who can offer value. Not bots or dishonest actors.
3. Making sure we’re voting on the right things: Decisions are only as important as what is being decided. Governance needs to find ways to structure the problems needing to be solved to ensure that problems offered and the participating community are deciding on things that matter.
4. Eliciting smart voting: Getting the right people to vote on issues that matter isn’t enough. Voters also have to be incentivized to make the best possible decision they can. Good governance dissuades random and malicious voting and rewards effortful voters.

While web3 and crypto are relatively new, these issues and problems aren’t. In fact they are related to established fields of research around democracy, corporate governance and mechanisms that crowdsource information for decision making, specifically peer prediction and information aggregation mechanisms. Some of these fields of research are hundreds of years old, others have only been established firmly for the past decade. Yet all provide fertile land for tokenomics designers and protocol builders to explore and learn.

Below are articles in my reading list on governance related topics. Thoughts and comments are welcome.

Management incentives & corporate governance articles

DAO governance is extremely similar to corporate management and suffers from many of the same issues: incentive management (the agency problem), delegation, shareholder voting, committees and more.

• The state of corporate governance research
• Governance Extractable Value
• The balance of power in closely held corporations
• One share, one vote
• Collusion and control in MakerDAO

To read:

Democracy, liquid democracy, coalition voting

Democratic voting coalitions and issues have been researched for years. There’s much governance can learn from existing research into democracy: incentivizing voting and informed voting, peer prediction models, coalition forming in governance and power plays.

• Flexible Representative Democracy: An Introduction with Binary Issues
• Weighted representative democracy
• Polls, Coalition Signals, and Strategic Voting: An Experimental Investigation of Perceptions and Effects
• Manipulation of voting schemes: A general result
• Motivating voting
• Extrinsic Rewards, Intrinsic Motivation and Voting
• Social Incentives and voter turnout: evidence from the Swiss mail ballot system
• Voter turnout and abstention pricing: quasi-experimental evidence on the effects of a marginal increase in the monetary enforcement of compulsory voting
• How Effective Are Monetary Incentives to Vote? Evidence from a Nationwide Policy
• If You Mobilize Them, They Will Become Informed: Experimental Evidence that Information Acquisition Is Endogenous to Costs and Incentives to Participate

Peer Prediction, Prediction and decision markets

Prediction of decisions and outcomes revolves around using a crowdsource mechanism to predict the future outcome of some ground truth. Similarly, peer prediction mechanisms deal in eliciting information about an existing ground truth from a set of peers. Decision markets revolve around informing right future decisions that are incentive compatible. They take prediction markets one step further.

This area of research is about getting people to vote and decide on the right outcomes.

Peer Prediction

• Measuring Performance Of Peer Prediction Mechanisms Using Replicator Dynamics
• Incentives for Effort in Crowdsourcing Using the Peer Truth Serum
• Crowdsourced Judgement Elicitation with Endogenous Proficiency
• A Bayesian Truth Serum for Subjective Data
• Testing a Hybrid mechanism for peer prediction
• Trick or Treat: Putting Peer prediction to the test (2014)

Prediction markets

• Elicitation of personal probabilities and expectations
• Shall we vote on values and beliefs?
• Interpreting prediction market prices as probabilities
• Information aggregation and manipulation in an experimental market
• Combinatorial information market design
• Logarithmic Market scoring rules
• Bluffing and strategic reticence in prediction markets
• Self resolving prediction markets for unverifiable outcomes
• Prediction markets on crowdsourcing platforms: potential gains for corporate governance
• Gaming prediction markets: equilibrium strategies with a market maker
• Prediction markets: Fundamentals, key design elements and applications
• Optimizing Bayesian information revelation strategy in prediction markets
• Foul play in information markets

Decision markets

• Decision Markets, Hanson (1999)
• Decision Rules and Decision Markets (2010)
• Decision markets with good incentives (2011)
• Eliciting Information from participants with Competing Incentives and Dependent Beliefs (2023)
• The application of prediction markets to project prioritization in the not-for-profit sector
• Prediction markets as decision support systems