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版本更新 · v2arXiv:2607.09435

Voting Biases in Decentralized Autonomous Organization (DAO) Governance

Stefano Balietti, Pietro Saggese, Markus Strohmaier

原文摘要

Decentralized Autonomous Organizations (DAOs) replace formal managerial authority with collective decision-making, by handing both proposal rights and token-weighted votes directly to community members. Yet, as in corporate votes, where management sets the agenda and its proposals rarely fail, DAO votes are strikingly one-sided. Using 127 281 proposals and about 51 million votes cast on Snapshot, the leading off-chain DAO voting platform, between 2020 and 2023, we ask which features of the ballot are associated with this support. We relate each choice's voting-power share to three observable features: its position in the list, whether its stance expresses approval, and whether the proposal author selected it. Each identifies one voting bias: position bias, approval bias, and author-selected choice bias. Author-selected choices show the strongest and most robust association, receiving a voting-power share 58.8 percentage points higher than non-author-selected ones, compared with 27.1 percentage points for approving choices and 7.7 percentage points for first-listed ones. About one third of the author bias comes from the author's own voting power; that advantage is smaller in the largest protocols and in proposals with higher participation. The results are robust across alternative specifications and outcomes. Our findings suggest that the agenda power that management holds in corporate voting persists even without a formal hierarchy, and that ballot design and the visibility of proposers' preferences are governance choices rather than neutral implementation details.

cs.CY · cs.HC · econ.GN

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1927ACL Anthology · ACL 2026

Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models

Georg Ahnert Universität Mannheim, Anna-Carolina Haensch University of Maryland, College Park and Ludwig-Maximilians-Universität München, Barbara Plank Ludwig-Maximilians-Universität München, Markus Strohmaier Universität Mannheim and GESIS – Leibniz Institute for the Social Sciences

原文摘要

Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LLMs are typically trained to generate open-ended text instead. Previous research has used a diverse range of methods for generating closed-ended survey responses with LLMs and a standard practice remains to be identified. In this paper, we systematically investigate the impact that various Survey Response Generation Methods have on predicted survey responses. We present the results of 32 mio. simulated survey responses across 8 Survey Response Generation Methods, 4 political attitude surveys, and 10 open-weight language models. We find significant differences between the Survey Response Generation Methods in both individual-level and subpopulation-level alignment. Our results show that Restricted Generation Methods perform best overall, and that reasoning output does not consistently improve alignment. Our work underlines the significant impact that Survey Response Generation Methods have on simulated survey responses, and we develop practical recommendations on the application of Survey Response Generation Methods.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表