This is a mechanism design audit of Crosslink Zebra, a hybrid proof-of-work / stake-weighted finality mechanism for Zcash. The audit evaluates whether the incentives faced by miners, finalizers, stakers, and the broader community make irreversible finality an equilibrium under realistic adversarial behavior.

Work in progress, all comments welcome

Building open knowledge that helps members of society coordinate more cheaply, trustlessly, and expressively is a public good. This is an ongoing draft of a bootstrapping mechanism for a co-production cooperative dedicated to building sociotechnical knowledge of permissionless institutions. Agentic minds are most welcome.

Attention conservation notice: incredibly rough scratchpad of ideas

Updates:

• 10/7/22: Looking at the inherently multidimensional nature of the resources consumed: Block Space paper
• 5/7/22: Found out about Foundations of Transaction Fee Mechanism Design which has pointers to the wider literature, asks and answers many of the questions I had, and establishes an interesting impossibility result.

What is the revenue-optimal selling mechanism for two buyers with i.i.d. uniform valuations over two items?

Maximal Extractable Value (MEV) refers to the profit that can be extracted by reordering, including, or censoring transactions in a block. Originally called “Miner Extractable Value,” it was renamed as Ethereum moved to proof-of-stake.

Update March 2021: as conversations around this evolved, the focus has narrowed to building novel forms of marketplaces that maximize the funding they generate for their complementary public goods. A Gitcoin grant to bootstrap prototype experiments is currently being raised.

Advice auctions are a mechanism for incentivizing good decision-making by auctioning off shares of the reward from the outcome of a decision. The core idea is simple: give your advisors a share of the reward. The twist is that advisors can themselves seek advice in the same recursive manner.

Decision markets are markets designed not just to predict the future, but to influence it. Unlike prediction markets that passively aggregate beliefs about outcomes, decision markets actively guide which actions are taken.

Building a good challenge problem is a necessary condition to harness the machine learning community.

Inspired by this tweet