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The research statements below are a representative sampling of real-world problems that are top of mind for Ripple and the XRP ledger team, as well as for the broader community working in blockchain, cryptocurrency and global digital payments. This list of topics is not intended to be prescriptive, but can be used as a resource for universities as they develop their own research strategies within the framework of the University Blockchain Research Initiative and beyond. This list is a working document that can be modified based on university partner feedback and the introduction of new topics, by Ripple and by university faculty and students.

Consensus / XRPL – Proposed by David Schwartz

  • What is the ideal number of validators for a network that uses a federated byzantine agreement algorithm? How reliable do validators have to be for the benefit of requiring more failures to lose forward to be exceeded by the benefit of increased decentralization? How is the latency of the consensus process affected by the number of validators that must agree? How are the computational and bandwidth costs affected? What are all the trade-offs involved and how can we make them so that people can make an intelligent decision whether adding an additional validator, even if not believed to be ideal, increases decentralization more than it increases the risk of losing forward progress should it fail?
  • Can a consensus algorithm agree to temporarily exclude non-functional nodes from the consensus process to improve liveness and partition tolerance without losing liveness? Specifically, how much would the XRP Ledger benefit from a proposal like this one: https://www.xrpchat.com/topic/33072-suggestion-robustness-improvements/ — and how many validator failures could be tolerated without increasing the risk of an unintentional fork or similar failure? Clearly, greater than 50% of all nodes, operational or not, must be required at all times. Or is that so clear? Is 51% enough if we make practical assumptions about what an attacker can do? (As almost every real-world public blockchain does.)
  • Consensus algorithms face a fundamental tradeoff. More validators improves decentralization but fewer validators reduces computational effort and can reduce latency. Some “best of both worlds” proposals have been made such as this one: https://www.xrpchat.com/topic/33073-suggestion-two-layer-consensus/ These approaches suggest an “inner layer” with a small number of validators that just ensures the ledger makes forward progress. An “outer layer” with more participants governs the participants in the inner layer. The idea is to keep the inner layer small and if it fails to work as desired, have the outer layer change its participants. However, if the inner layer halts, the outer layer has to work out where to resume from. And some protection from Byzantine accountability violations are needed (when an inner layer validator sends multiple conflicting validation). How can the interface between the two layers be designed to resolve these issues? Is this path worth pursuing?

Data Science – Proposed by Brad Chase

  • How do you ensure robustness of secure multiparty computation and/or federated learning? That is, in practice, how do you ensure counterparties are providing the “right” data into the system, when by design other participants cannot verify other’s data? For example, if one participant has a data integration problem and starts sending in data of poor quality, are there practical protocol level changes to limit the impact? Relatedly, are there good ways to ensure auditability and traceability for joint decisions? For example, suppose you used SMPC for a joint credit model; how can you explain the credit rating to parties involved?
  • Cross-blockchain entity resolution
  • Blockchain utility measures

Value Creation – Proposed by Sean Ivester

  • An Analysis of how it is that assets come to derive value and apply that to defining new real world use cases for XRP

Institutional Markets – Proposed by Aditya Tukhaira / Markus Infanger

  • Apply Clay Christensen’s model to what’s happening in financial services and make a very good argument that Financial Services (payments, exchanges, lending, borrowing, etc.) are being disrupted by blockchain and digital assets. And while it is not there yet to compete with the Citigroups of the world, it certainly is good enough for the unbanked and underbanked. Over time, this technology will get better and move upstream.
  • Structural Arbitrage opportunities in crypto: where are arb opportunities (net trading fees) in top coins, to what degree, time windows & patterns
  • Adding Crypto as Portfolio Optimization: diversification impact, optimal tangency portfolio in pure crypto- as well as global asset class portfolios
  • Key components and drivers of crypto liquidity: Role of onchain activity / economic transactions on off chain (& vice-versa), key drivers of liquidity growth in crypto markets, what volume is real vs fake?

DeFi Markets – Proposed by Aurelie Dhellemmes

Analysis on the core building blocks required for a Decentralized Finance ecosystem:

  • What are those elements, how do they interact with each other and what is the competitive landscape?
  • Analysis of the reward incentive models of liquidity pools and AMMs across the Dex Ecosystem. What are the differences, similarities, and pros and cons?
  • How could XRP play a bigger role in DeFi? What tech developments, integrations, key projects/chains to focus on?

Business Development – Proposed by Matt Castriconne

  • Industry overview of global eCommerce verticals, specifically focusing on retail and wholesale marketplaces. Potential market sizing, growth rates, geographic areas of note. High-level overview of economics, payments technologies used and challenges to eCommerce growth model. How do eCommerce companies fund their growth? Equity, Debt, Margin, Supply Chain Finance, or other? How are eCommerce companies thinking about blockchain and digital assets in general? How are eCommerce companies allying themselves with banks, fintechs, financial institutions? (Example is the Shopify, Stripe Capital, Goldman Sachs SMB Lending Program).