Research

We do research (DBLP, GS) on digital democracy, bringing ideas from computational social choice, artificial intelligence, game theory, and combinatorial optimization to develop the foundations and design tools that enable groups and communities to collaboratively achieve their goals in a fair way, in the digital realm with an emphasis on the blockchain and the DAO ecosystem. 

Concretely, in our pursuit of digital democracy we observe the need for devising tools that enable a community to collaboratively decide on various topics; and to do so while taking into account that community members have limited attention to participate in corresponding mechanisms. 

Specifically, we advance within these main routes:

• We consider aggregating preferences to select committees (multiwinner elections) as well as doing participatory budgeting; this research is partially funded by the ISF. 

• We generalize some of these ideas to a unified approach is of interest, and we are especially interested in aggregation text documents -- we are still developing these approaches.

• We are interested in the application of vote delegation and liquid democracy as it improves decision making by taking-in expertise in a time-efficient way; this research is partially funded by the EU.

• We concentrate on applications that relate to blockchain and DAOs; e.g., retrofunding.

Currently, we work on these subjects:

• Text aggregation using an iterative voting process.

• Text aggregation using AI-mediation.

• Producing fair time-based allocations with applications in water distribution and energy, also with several resource sources.

• Making adjusted winner behave in an indivisible way.

• Doing perpetual voting with external fairness constraints (partially funded by BGU-NJIT).

• Developing algorithms for retrofunding (partially funded by Optimism).