arXiv subjects

Goshi Aoki

Publications and source records attributed to Goshi Aoki.

3 recordsLinked to original sources

arXiv2026

Fiscal Dynamics in Japan under Demographic Pressure

Japan's population is shrinking, the share of working-age people is falling, and the number of elderly is growing fast. These trends squeeze public finances from both sides--fewer people paying taxes and more people drawing on pensions and healthcare. Policy discussions often focus on one fix at a time, such as raising taxes, reforming pensions, or boosting productivity. However, these levers interact with each other through feedback loops and time delays that are not yet well understood. This study builds and calibrates an integrated system dynamics model that connects demographics, labor supply, economic output, and public finance to explore two questions: (RQ1) What feedback structure links demographic change to fiscal outcomes, and how do different policy levers work through that structure? (RQ2) Which combinations of policies can stabilize key fiscal indicators within a meaningful timeframe? The model, grounded in official statistics, tracks historical trends reasonably well. Policy experiments show that productivity improvements and controlling per-person costs offer the most effective near-term relief, because they act quickly through revenue and spending channels. In contrast, raising fertility actually worsens the fiscal picture in the medium term, since it takes decades for newborns to grow up and join the workforce. A combined scenario pairing moderate productivity gains with moderate cost control nearly eliminates the deficit by 2050. These findings underscore the importance of timing when evaluating demographic policy. Stabilizing finances within a practical timeframe requires levers that improve the budget directly, rather than those that work through slow demographic channels. The model serves as a transparent testing ground for designing time-aware fiscal policy packages in aging, high-debt economies.

physics.soc-ph↗

arXiv2026

AI Agents as Policymakers in Simulated Epidemics

AI agents are increasingly deployed as quasi-autonomous systems for specialized tasks, yet their potential as computational models of decision-making remains underexplored. We develop a generative AI agent to study repetitive policy decisions during an epidemic, embedding the agent, prompted to act as a city mayor, within a simulated SEIR environment. Each week, the agent receives updated epidemiological information, evaluates the evolving situation, and sets business restriction levels. The agent is equipped with a dynamic memory that weights past events by recency and is evaluated in both single- and ensemble-agent settings across environments of varying complexity. Across scenarios, the agent exhibits human-like reactive behavior, tightening restrictions in response to rising cases and relaxing them as risk declines. Crucially, providing the agent with brief systems-level knowledge of epidemic dynamics, highlighting feedbacks between disease spread and behavioral responses, substantially improves decision quality and stability. The results illustrate how theory-informed prompting can shape emergent policy behavior in AI agents. These findings demonstrate that generative AI agents, when situated in structured environments and guided by minimal domain theory, can serve as powerful computational models for studying decision-making and policy design in complex social systems.

cs.MA↗

arXiv2024

Large Language Models in Politics and Democracy: A Comprehensive Survey

The advancement of generative AI, particularly large language models (LLMs), has a significant impact on politics and democracy, offering potential across various domains, including policymaking, political communication, analysis, and governance. This paper surveys the recent and potential applications of LLMs in politics, examining both their promises and the associated challenges. This paper examines the ways in which LLMs are being employed in legislative processes, political communication, and political analysis. Moreover, we investigate the potential of LLMs in diplomatic and national security contexts, economic and social modeling, and legal applications. While LLMs offer opportunities to enhance efficiency, inclusivity, and decision-making in political processes, they also present challenges related to bias, transparency, and accountability. The paper underscores the necessity for responsible development, ethical considerations, and governance frameworks to ensure that the integration of LLMs into politics aligns with democratic values and promotes a more just and equitable society.

cs.CL↗