AIAI Seminar - 30 January 2023 - Talks by Yifei Xie and Jake Barrett

Speaker:            Yifei Xie

Title：   

Decentralized Bi-level Optimization Model for Multi-committee BFT algorithms

Abstract:

The latest blockchain systems require high performance especially under a large scale of network peers. To address performance and scalability issues, an effective solution is to partition peer network into multiple small committees and run the consensus process in parallel. While partitioning the peer set, it is essential to optimally decide how many committees should be divided and how to allocate peers to each committee. Setting up optimal consensus committees is crucial to yield the best system transaction throughput. Therefore, my current work focuses on applying decentralized optimization model to optimally partition peer network for the purpose of improving performance in the multi-committee parallel consensus system

Speaker:        Jake Barrett

Title:

Now We’re Talking: Better Deliberation Groups through Submodular Optimization”

Abstract:

Citizens’ assemblies are groups of randomly selected constituents who are tasked with providing recommendations on policy questions. Assembly members form their recommendations through a sequence of discussions in small groups (deliberation), in which group members exchange arguments and experiences. We seek to support this process through optimization, by studying how to assign participants to discussion groups over multiple sessions, in a way that maximizes interaction between participants and satisfies diversity constraints within each group. Since repeated meetings between a given pair of participants have diminishing marginal returns, we capture interaction through a submodular function, which is approximately optimized by a greedy algorithm making calls to an ILP solver. This framework supports different submodular objective functions, and we identify sensible options, but we also show it is not necessary to commit to a particular choice: Our main theoretical result is a (practically efficient) algorithm that simultaneously approximates every possible objective function of the form we are interested in. Experiments with data from real citizens’ assemblies demonstrate that our approach substantially outperforms the heuristic algorithm currently used by practitioners.