Giulia Fanti, PhD on Blockchain Incentive Mechanisms | Chainlink Research Reports

SquirRL: Automating Attack Discovery on Blockchain Incentive Mechanisms with Deep Reinforcement Learning by Giulia Fanti, Ph.D. from Carnegie Mellon University
arxiv.org/pdf/1912.01798.pdf
Dr. Giulia Fanti is an assistant professor of Electrical and Computer Engineering at Carnegie Mellon University. Her research focus is on the security and privacy implications of data transparency and sharing, including the algorithmic and theoretical foundations of distributed systems, machine learning, and privacy-enhancing technologies.
www.andrew.cmu.edu/user/gfanti/
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0:00 Introduction
0:37 (Guilia Fanti Intro)
1:03 Blockchain Incentive Mechanisms
1:51 Handling Incentive Attacks
4:58 Incentives In Bitcoin
6:06 Selfish Mining
7:29 Markov Decision Process (MDP)
8:10 Value/Policy Iteration
9:16 Deep Reinforcement Learning
11:01 SquirRL
12:22 Bitcoin Selfish Mining
14:40 Selfish Mining w Multiple Players
17:08 Casper the Friendly Finality Gadget
19:40 Explaining Nash Equilibrium
21:05 Incentive Mechanism Analysis
22:47 Permissioned Blockchains
26:48 Machine Learning In Crypto
29:50 Other Consensus Models
30:33 How Scaling Affects Model