Selected Manuscripts

Working Papers

[4] Why Do AI Models Tell Left-Wing Voters to Support the Communist Party? AI Voting Advice in Japan’s 2026 General Election

Miyazaki, Sho and Andrew B. Hall. Under Review.

Non-technical summaries with policy and business implications available in English and Japanese.

Abstract

AI chatbots with web search are increasingly used for voting guidance, yet we know relatively little about their recommendation behavior outside the US. We query five models from three companies with 36,300 synthetic voter profiles during Japan’s 2026 Lower House election, plus a follow-up with today’s four frontier models. Policy stances dominate recommendations, producing 50–98 percentage point swings compared to 0.5–7.0 pp for demographics. Strikingly, left-leaning stances cause all five models to converge on Japan’s Communist Party, despite other parties holding similar positions on the relevant issues. We trace this to the information environment: JCP operates a fully open website with a party newspaper that models freely access and frequently misclassify as independent journalism, while major Japanese news outlets block AI crawlers. Without policy input, models show no uniform left-wing bias. Incorporating X search shifts recommendations modestly leftward, contradicting US-centric expectations. These findings suggest that AI models need better source discrimination in non-US contexts, and that news organizations blocking AI access may inadvertently cede influence over AI-mediated voting advice to partisan sources that remain open.

[3] How Does a Public Tally Affect When Voters Vote? Evidence from Online Roll Call Voting

Miyazaki, Sho. Under Review.

Abstract

Do voters signal'' their decisions by voting early, or do theysnipe’’ by casting their votes at the last minute? In the transparent sequential roll call voting of the U.S. House of Representatives, members can strategically choose when to cast their votes, but the timing of those votes is hard to observe and analyze. This paper examines the effect of a public tally on vote timing in a setting that records it exactly: corporate shareholder voting at a blockchain-based startup (DAO) whose roll call votes transitioned from private one-stage voting to transparent sequential voting. I collected individual-level voting records, delegated voting power, and precise vote timing. Fixed-effect regressions show that introducing a public tally increases the likelihood of early voting (signaling) or last-minute voting (sniping), consistent with game-theoretic predictions. Interaction estimates are consistent with more pivotal voters, measured by the Shapley-Shubik index, using early votes as signals rather than waiting to snipe. These results suggest that a public tally may harm information aggregation, as signaling can intensify the influence of pivotal voters.

[2] Malapportionment as a Gerrymandering Strategy: Exploring Electoral Manipulation in Malaysia Using a Simulation Algorithm

Miyazaki, Sho and Yuko Kasuya. Under Review.

Abstract

Gerrymandering and malapportionment both undermine fair representation and involve electoral district boundaries. However, existing research has treated these phenomena separately, leading to disconnected bodies of literature. This paper demonstrates that malapportionment can be used as an instrument of gerrymandering. Conventional gerrymandering tactics of cracking and packing can be more sophisticated when the district boundary drawers in power can control the district populations. We illustrate this new yet simple gerrymandering strategy using the case of Malaysia, a long-lasting electoral authoritarian regime that endured from the early 1970s to 2018, focusing on ethnic representation. By comparing the enacted districting plan with counterfactual electoral districts simulated through Sequential Monte Carlo redistricting simulation, we identify significant ethnic gerrymandering against the minority Chinese Malaysians, in which cracking and packing are the strategies adopted by the majority Malay-dominated ruling coalition.

[1] What Happens When Anyone Can Be Your Representative? Studying the Use of Liquid Democracy for High-Stakes Decisions in Online Platforms

Hall, Andrew B. and Sho Miyazaki.

Non-technical summaries available at the a16zcrypto article and the Hoover Institution video.

Abstract

Since the 19th century, political reformers have proposed broadening civic and corporate governance by allowing voters to delegate to any other voter—sometimes known as liquid democracy. Today, systems like liquid democracy have become an important part of ongoing efforts to create democratic online platforms governed by users rather than elites. We provide a first empirical political science study of liquid democracy in a high-stakes, real-world setting, analyzing data from over 250,000 voters and 1,700 proposals across 18 crypto projects (“DAOs”) built on the Ethereum blockchain. We find that, on average, 17% of voting tokens are delegated, with substantial clumping on the most-popular delegates. Delegation is primarily bottom-up, with smaller token-holders more likely to delegate. More active voters receive more delegations, suggesting somewhat informed decision-making. Using a difference-in-differences design, we estimate that creating online hubs to coordinate delegation significantly increases delegation and overall voting rates. In sum, liquid democracy can foster bottom-up participation, particularly when paired with tools for coordination. On the other hand, real-world participation remains relatively low among both voters and delegates, posing an important challenge to liquid democracy not yet contemplated in existing theoretical literature.