Revisiting Rogers' Paradox in the context of human-AI interaction
Clinical Snapshot
PICO Framework
| P — Population | Simulated agents representing humans and AI systems in learning networks |
| I — Intervention | Various learning strategies including individual learning, social learning from humans, and social learning from AI systems |
| C — Comparator | Individual learning alone versus different combinations of social learning strategies |
| O — Outcomes | Population fitness, equilibrium of collective world models, and network dynamics in human-AI learning systems |
Bottom Line
This computational simulation study extends Rogers' Paradox to examine learning dynamics in human-AI networks, exploring how different learning strategies affect collective understanding in uncertain environments. While theoretically interesting for understanding human-AI interaction principles, the study provides limited direct clinical applicability. The research may inform future development of AI-assisted clinical decision-making systems by highlighting potential feedback loops and learning dynamics. However, the abstract lacks sufficient methodological detail and empirical results to assess validity or precision. Clinicians should view this as foundational theoretical work that may eventually inform AI system design rather than evidence for immediate practice change. The findings require empirical validation in real-world clinical settings before informing healthcare AI implementation strategies.
Key Findings
P Value: Not reported
Effect Size: Not reported in abstract
Primary Outcome: Impact of learning strategies on equilibrium of collective world models in human-AI networks
Nnt Or Sensitivity: Not applicable for theoretical simulation study
Confidence Interval: Not reported
Clinical Application
Theoretical framework may inform design of clinical decision support systems but requires empirical validation Minimal direct relevance to Australian clinical practice; may inform TGA considerations for AI-based medical devices and RACGP guidelines for AI integration in general practice Limited direct clinical applicability; primarily relevant for understanding theoretical aspects of human-AI learning systems
Abstract
People learn about the world in many ways: from conducting experiments to copying others' behaviour. The choices we make about how to learn can impact the collective understanding of a whole population, were others to learn from us. Alan Rogers developed simulations to study these phenomena-where agents could individually or socially learn amidst a dynamic, uncertain world-and uncovered a surprising result: the availability of cheap social learning yielded no benefit to population fitness over individual learning. Rogers' Paradox spawned decades of work to understand factors that favour social learning and better model human cultural development. But what happens when humans can learn from artificial intelligence (AI) systems that are themselves learning from us? We revisit Rogers' Paradox in the context of human-AI interaction and extend the simulations towards a simplified network of humans and AIs learning together about an uncertain world. We examine the impact of several learning strategies on the equilibrium of a society's 'collective world model' and assess levers available to stakeholders in human-AI interactions to change network dynamics. We then model negative feedback loops that may arise from humans learning socially from AI, and consider other open questions that could be explored in our simulation framework. This article is part of the theme issue 'World models in natural and artificial intelligence'.
References
- 1.Collins, K., Bhatt, U., & Sucholutsky, I. (2026). Revisiting Rogers' Paradox in the context of human-AI interaction. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 384(2276). https://doi.org/10.1098/rsta.2025.0149
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