# The Digital Ecosystem of Beliefs: does evolution favour AI over humans?

arXiv: https://arxiv.org/abs/2412.14500 · HTML: https://mlsanity.com/paper/2412.14500/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [AI Deception: A Survey of Examples, Risks, and Potential Solutions](https://mlsanity.com/paper/2308.14752/) (2023)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [Evaluating the Social Impact of Generative AI Systems in Systems and Society](https://mlsanity.com/paper/2306.05949/) (2023)
- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [Natural Selection Favors AIs over Humans](https://mlsanity.com/paper/2303.16200/) (2023)
- [The Simple Rules of Social Contagion](https://mlsanity.com/paper/1308.5015/) (2013)
- [Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning](https://mlsanity.com/paper/2406.00392/) (2024)
- [JaxLife: An Open-Ended Agentic Simulator](https://mlsanity.com/paper/2409.00853/) (2024)
- [Cognitive cascades: How to model (and potentially counter) the spread of fake news](https://mlsanity.com/paper/2107.02828/) (2021)
- [mpEAd: Multi-Population EA Diagrams](https://mlsanity.com/paper/1607.05213/) (2016)
- [Deep learning based parameter search for an agent based social network model](https://mlsanity.com/paper/2107.06507/) (2021)

## Built on by (most cited first, 1 of 1)

- [LLM Agents Make Collective Belief Dynamics Programmable: Challenges and Research Directions](https://mlsanity.com/paper/2605.19915/) (2026)

## Abstract (arXiv)

As AI systems are integrated into social networks, there are AI safety concerns that AI-generated content may dominate the web, e.g. in popularity or impact on beliefs. To understand such questions, this paper proposes the Digital Ecosystem of Beliefs (Digico), the first evolutionary framework for controlled experimentation with multi-population interactions in simulated social networks. Following a Universal Darwinism approach, the framework models a population of agents which change their messaging strategies due to evolutionary updates. They interact via messages, update their beliefs following a contagion model, and maintain their beliefs through cognitive Lamarckian inheritance. Initial experiments with Digico implement two types of agents, which are modelled to represent AIs vs humans based on higher rates of communication, higher rates of evolution, seeding fixed beliefs with propaganda aims, and higher influence on the recommendation algorithm. These experiments show that: a) when AIs have faster messaging, evolution, and more influence on the recommendation algorithm, they get 80% to 95% of the views; b) AIs designed for propaganda can typically convince 50% of humans to adopt extreme beliefs, and up to 85% when agents believe only a limited number of channels; c) a penalty for content that violates agents' beliefs reduces propaganda effectiveness up to 8%. We further discuss Digico as a tool for systematic experimentation across multi-agent configurations, the implications for legislation, personal use, and platform design, and the use of Digico for studying evolutionary principles.