Sankarshan Damle supervised by Dr. Sujit Gujar received his doctorate in Computer Science and Engineering (CSE). Here’s a summary of his research work on Designing Game-theoretically Sound, Fair, and Private Multi-agent Systems:
Multi-agent systems (MAS) are distributed systems composed of multiple autonomous agents interacting to achieve a common goal. The usefulness of MAS lies in its ability to tackle complex and dynamic problems that a single agent cannot solve, resulting in better problem-solving abilities, enhanced reliability, and improved scalability. This thesis explores the challenges facing MAS, particularly related to their game-theoretic soundness, fairness, security, and privacy guarantees. A game-theoretically sound MAS is a system in which the interactions between agents can be modeled as a game and analyzed using game-theoretic concepts. This leads to a more stable and efficient system, as agents are incentivized to make decisions that align with the system’s overall goals. Additionally, the game-theoretic analysis provides a mathematical framework for understanding and predicting the behavior of agents in MAS. In this thesis, we focus on civic crowdfunding, a popular method for raising funds through voluntary contributions for public projects (e.g., public parks or libraries). Our work enriches the existing civic crowdfunding literature to design more inclusive mechanisms (richer agent and system models), provide fairer rewards, and are efficient when deployed over the blockchain as a smart contract. Furthermore, fairness is also an important aspect of MAS as it ensures that the actions and outcomes of agents are equitable and just. Additionally, fairness is crucial in ensuring MAS’s long-term stability and sustainability. This thesis looks at fair incentives in Transaction Fee Mechanisms (TFM). These are mechanisms that blockchains employ to include transactions in a block from the set of outstanding transactions. We argue that existing TFMs’ incentives are misaligned with respect to a cryptocurrency’s greater market penetration. Instead, we propose TFMs, which provide fairer rewards to the transaction creators and minimize the surplus collected through rebates to the creators. Last, security and privacy are crucial aspects of MAS, as the autonomy and decentralization of agents in MAS can lead to the exposure of sensitive information. Moreover, the increasing use of MAS in various fields, such as healthcare, finance, and the military, has heightened the need for privacy. In this focus, we specifically focus on privacy guarantees for popular MAS such as (i) auctions, (ii) voting, and (iii) distributed constraint optimization (DCOPs). We propose privacy-preserving applications that preserve agents’ sensitive information while also proving the verifiability of the computation.
June 2024