# Artificial Utopia: Simulation and Intelligent Agents for a Democratised Future

arXiv: https://arxiv.org/abs/2503.07364 · HTML: https://mlsanity.com/paper/2503.07364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 128 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [Machine Culture](https://mlsanity.com/paper/2311.11388/) (2023)
- [Project Sid: Many-agent simulations toward AI civilization](https://mlsanity.com/paper/2411.00114/) (2024)
- [LLM-based Human Simulations Have Not Yet Been Reliable](https://mlsanity.com/paper/2501.08579/) (2025)
- [Strong and weak alignment of large language models with human values](https://mlsanity.com/paper/2408.04655/) (2024)
- [AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N](https://mlsanity.com/paper/2208.07004/) (2022)
- [Proof of Witness Presence: Blockchain Consensus for Augmented Democracy in Smart Cities](https://mlsanity.com/paper/1907.00498/) (2019)
- [Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship](https://mlsanity.com/paper/2406.18702/) (2024)
- [Deep reinforcement learning in World-Earth system models to discover sustainable management strategies](https://mlsanity.com/paper/1908.05567/) (2019)
- [Deterministic limit of temporal difference reinforcement learning for stochastic games](https://mlsanity.com/paper/1809.07225/) (2018)
- [Natural-Language Multi-Agent Simulations of Argumentative Opinion Dynamics](https://mlsanity.com/paper/2104.06737/) (2021)

## Abstract (arXiv)

Prevailing top-down systems in politics and economics struggle to keep pace with the pressing challenges of the 21st century, such as climate change, social inequality and conflict. Bottom-up democratisation and participatory approaches in politics and economics are increasingly seen as promising alternatives to confront and overcome these issues, often with utopian overtones, as proponents believe they may dramatically reshape political, social and ecological futures for the better and in contrast to contemporary authoritarian tendencies across various countries. Institutional specifics and the associated collective human behavior or culture remains little understood and debated, however. In this article, I propose a novel research agenda focusing on utopian democratisation efforts with formal and computational methods as well as with artificial intelligence - I call this agenda Artificial Utopia. Artificial Utopias provide safe testing grounds for new political ideas and economic policies in-silico with reduced risk of negative consequences as compared to testing ideas in real-world contexts. An increasing number of advanced simulation and intelligence methods, that aim at representing human cognition and collective decision-making in more realistic ways, could benefit this process. This includes agent-based modelling, reinforcement learning, large language models and more. I clarify what some of these simulation approaches can contribute to the study of Artificial Utopias with the help of two institutional examples: the citizen assembly and the democratic firm.