In this note, we highlight a range of technical options and considerations in designing a contingent system for a central bank digital currency (CBDC) in Canada and explore how these options achieve stated public policy goals.
Oracles are constructs used in decentralized finance to price assets relative to each other. However, oracles contain defects that could lead to manipulation attacks. Such attacks exploit pricing models embedded within oracles to defraud creators and users. We automatically verify defects, which if mitigated, improves the security of digital currency.
We demonstrate the ability of reinforcement learning techniques to estimate the best-response functions of banks participating in high-value payments systems—a real-world strategic game of incomplete information.
