# Serious Games: Human-AI Interaction, Evolution, and Coevolution

arXiv: https://arxiv.org/abs/2505.16388 · HTML: https://mlsanity.com/paper/2505.16388/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 119 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 18 of 18)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Playing repeated games with Large Language Models](https://mlsanity.com/paper/2305.16867/) (2023)
- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [Holistic Evaluation of Language Models](https://mlsanity.com/paper/2211.09110/) (2022)
- [Open-ended Learning in Symmetric Zero-sum Games](https://mlsanity.com/paper/1901.08106/) (2019)
- [The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities](https://mlsanity.com/paper/1803.03453/) (2018)
- [When Will AI Exceed Human Performance? Evidence from AI Experts](https://mlsanity.com/paper/1705.08807/) (2017)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [Artificial neural networks for neuroscientists: A primer](https://mlsanity.com/paper/2006.01001/) (2020)
- [Is artificial consciousness achievable? Lessons from the human brain](https://mlsanity.com/paper/2405.04540/) (2024)
- [Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers](https://mlsanity.com/paper/2206.04132/) (2022)
- [How Different AI Chatbots Behave? Benchmarking Large Language Models in Behavioral Economics Games](https://mlsanity.com/paper/2412.12362/) (2024)
- [Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games](https://mlsanity.com/paper/2012.08382/) (2020)
- [Self-Reproduction and Evolution in Cellular Automata: 25 Years after Evoloops](https://mlsanity.com/paper/2402.03961/) (2024)
- [Properties of Winning Iterated Prisoner's Dilemma Strategies](https://mlsanity.com/paper/2001.05911/) (2020)
- [A Comparison Between Human and Generative AI Decision-Making Attributes in Complex Health Services](https://mlsanity.com/paper/2505.08360/) (2025)

## Abstract (arXiv)

The serious games between humans and AI have only just begun. Evolutionary Game Theory (EGT) models the competitive and cooperative strategies of biological entities. EGT could help predict the potential evolutionary equilibrium of humans and AI. The objective of this work was to examine EGT models relevant to human-AI interaction, evolution, and co-evolution. Of thirteen EGT models considered, three were examined: the Hawk-Dove Game, Iterated Prisoner's Dilemma, and the War of Attrition. This selection was based on the widespread acceptance and clear relevance of these models to potential human-AI evolutionary dynamics and co-evolutionary trajectories. The Hawk-Dove Game predicts balanced mixed-strategy equilibria based on the costs of conflict. Iterated Prisoner's Dilemma suggests that repeated interaction may lead to cognitive co-evolution. The War of Attrition suggests that competition for resources may result in strategic co-evolution, asymmetric equilibria, and conventions on sharing resources. Each model was examined from the perspective of human and AI decision-making, from psychological and biological perspectives, and from an AI viewpoint. AI is being shaped by human input and is evolving in response to it. So too, neuroplasticity allows the human brain to evolve in response to stimuli. If humans and AI converge in future, what might be the result of human neuroplasticity combined with an ever-evolving AI? There are profound ethical and cognitive implications. EGT may provide a suitable framework to understand and predict human-AI interaction, evolution, and co-evolution. However, future research should extend beyond EGT and explore additional frameworks, empirical validation methods, and interdisciplinary perspectives. In the spirit of further exploration, an illustrative computational simulation is provided.