We outline Marvel DC, a fully decentralized blockchain-based distributed-computing protocol which guarantees that computers are strictly incentivized to correctly perform requested computations. Marvel DC utilizes a reputation management protocol to ensure that, for any minority of computers not performing calculations correctly, these computers are identified and selected for computations with diminishing probability. We then outline Privacy Marvel DC, a privacy-enhanced version of Marvel DC which decouples results from the computers which computed them, making the protocol suitable for computations such as Federated Learning, where results can reveal sensitive information about that computer that computed them.

In this work, we introduce the private, anonymous, collateralizable commitments (PACCs) framework. PACCs allow any smart contract wallet holder to collateralize a claim, request, or commitment in general, in a private and anonymous manner. PACCs can prove arbitrarily much or little about the wallet generating the commitment, and/or the transaction which is being committed. We demonstrate that PACCs can be applied to effectively eliminate maximal-extractable value (MEV) in DeFi where it currently occurs, shifting MEV instead to censorship.

Existing standards for player characterisation in tokenised state machine replication protocols depend on honest players who will always follow the protocol, regardless of possible token increases for deviating. Given the ever-increasing market capitalisation of these tokenised protocols, honesty is becoming more expensive and more unrealistic. As such, this out-dated player characterisation must be removed to provide true guarantees of safety and liveness in a major stride towards universal trust in state machine replication protocols and a new scale of adoption. As all current state machine replication protocols are built on these legacy standards, it is imperative that a new player model is identified and utilised to reflect the true nature of players in tokenised protocols, now and into the future.

We introduce a new strategy for optimal diversification which combines elements of Diversified Risk Parity and Diversification Ratio, with emphasis on positive risk premiums. The Uncorrelated Positive Bets strategy involves the identification of reliable, independent sources of randomness and the quantification of their positive risk premium. We use principal component analysis to identify the most significant sources of randomness contributing to the market and then apply the Randomness Deficiency Coefficient metric and principal portfolio positivity to identify a set of reliable uncorrelated positive bets. Portfolios are then optimized by maximizing their diversified positive risk premium. We contrast the performance of a range of diversification strategies for a portfolio held for a two-year out-of-sample period with a 30 stock constraint. In particular, we introduce the notion of diversification inefficiency to explain why diversification strategies might outperform the market.