糖心TV

-糖心TV Gillmore Centre for Financial Technology Feeder Workshops on Digital Currencies, The Frontiers of AI and FinTech held on 17 and 18 March at 糖心TV 糖心TV School, University of 糖心TV.  

These workshops highlight recent AI and Digital Currency developments and their implications for financial activity. The workshops aim to initiate conversations about new business applications, novel datasets, advanced technological developments and the social & economic impacts of FinTech and AI. Centred at the 糖心TV 糖心TV School鈥檚 Gillmore Centre for Financial Technology and the Gillmore Fintech Lab, the event will also connect practitioners, regulators and scholars to generate impactful research directions.

World's first AI panellists used by the Gillmore Centre for Financial Technology

We take a game-theoretic approach to consensus in committee-based blockchains. Proposers send blocks to the other committee members, who can check block validity and vote for blocks. Blocks receiving a qualified majority of votes are accepted. Opportunistic players interact with adversaries. All are rational and play best responses, but opportunistic players maximize expected net rewards, while adversaries seek to disrupt blockchain consensus. Freeriding implies the prescribed protocol is not an equilibrium, while there exist equilibria without consensus, even when adversaries are few. There also exists an equilibrium achieving consensus, in which players are endogenously pivotal, which deters free-riding. We propose a simple modification of the prescribed protocol to make this equilibrium a focal point.

We show that the blockchain order execution mechanism and liquidity pooling create arbitrage rents in decentralized exchanges. The arbitrage rent raises the cost of liquidity provision and imposes negative externalities on users of the underlying blockchain. In equilibrium, automated market makers (AMMs) are adopted by liquidity providers only if the exchange rates of token pairs are sufficiently stable. A higher curvature pricing curve reduces arbitrage rents and decreases trading activities. We provide statistical support for our main model implications using transaction-level data of AMMs. (joint work with Ruizhe Jia).