Efficient Social Choice via NLP and Sampling
Lior Ashkenazy Nimrod Talmon
Attention-Aware Social Choice tackles the fundamental conflict faced by some
agent communities between their desire to include all members in the decision
making processes and the limited time and attention that are at the disposal of
the community members. Here, we investigate a combination of two techniques for
attention-aware social choice, namely Natural Language Processing (NLP) and
Sampling. Essentially, we propose a system in which each governance proposal to
change the status quo is first sent to a trained NLP model that estimates the
probability that the proposal would pass if all community members directly vote
on it; then, based on such an estimation, a population sample of a certain size
is being selected and the proposal is decided upon by taking the sample
majority. We develop several concrete algorithms following the scheme described
above and evaluate them using various data, including such from several
Decentralized Autonomous Organizations (DAOs).