Internship Topic: Agent-based Modeling of Fairness in Blockchains

Context

Bitcoin, introduced by Satoshi Nakamoto, is the core of decentralized cryptocurrency systems. Participants following this protocol can create together a distributed, economical, social and technical system where anyone can join and leave. The security and sustainability of blockchains, however, are not trivial and require increased participation, since each participant validates the diffused data, and keeps a replica of the entire blockchain. Participants consider worthwhile to join and stay in the system over time only if they find it fair. Miner participants find the system fair if they are able to create blocks as they expected, and user participants find the system fair if they manage to cancel their transactions and/or their transactions are confirmed as they expected. Considering miners, several formal studies have been conducted so far, concluding that Bitcoin-like blockchains are not promoting their participation.

In 2017, we have shown for the first time, by mathematical analyses, that Bitcoin-like blockchains are unfair for user participants and we proposed strategies for users and miners (modeled as agents) to improve their situations. In the same study, we have also discussed that for the time being it was not possible to explicitly cancel a transaction. To this end, recently LICIA have analysed mathematically the situation where it is possible to cancel a transaction by rolling back its state whether it is confirmed in a block or not under certain conditions. This way, the utility of users can be maximized and, consequently, their willingness to leave the system decreases.

Objective

The objective of this internship is to develop an agent-based model for analyzing fairness in blockchains and to study the implications of the developed model through simulations using MAX (an agent-based simulator developed by CEA LIST using java). To this end, parameter sweeps will be performed in order to build a quantitative model of fairness. The developed model will also be used for analyzing schemes like Lightning network8 and so on.