Oyetayo Oyebisi

One problem I think is well suited to Agent-Based Modelling is understanding how individual decision-making and social interactions collectively produce population-level outcomes.In many of the problems I work around, the outcome is not determined by a single factor. Individuals differ…

Hello everyone,My name is Oyetayo Oyebisi, and I am a Statistician and researcher with a strong interest in statistical modelling, Bayesian machine learning, trustworthy AI, uncertainty quantification, and predictive modelling. I have an MSc in Statistics and a background spanning…

I would not choose an Agent-Based Model simply because the system is complex. I would ask whether the micro-level decision processes and interactions are themselves essential to explaining the macro-level outcome.For me, ABM becomes justified when three conditions are present:Heterogeneity…

I think the key issue is not whether LLMs or RL can make agents more adaptive, but whether that additional adaptivity can be made auditable, reproducible, and empirically defensible.In my experience, I would separate the problem into three layers: agent-level…

I think the key issue is not whether LLMs or RL can make agents more adaptive, but whether that additional adaptivity can be made auditable, reproducible, and empirically defensible.

In my experience, I would separate the problem into three layers: agent-level decision logic, emergent macro-behaviour, and validation.

1. LLM/RL integration: I see the strongest use case for LLMs or RL where fixed rules are clearly inadequate, for example, when agents must condition decisions on heterogeneous information, history, context, or interacting objectives. But I would be cautious about allowing an LLM to directly determine the entire agent policy. A better architecture is often to constrain the adaptive component within a statistically or theoretically specified decision framework, so we can still identify what information drives behaviour and what assumptions are being imposed.

2. Explaining emergent behaviour: I would not try to explain a macro-pattern simply by saying, \"the neural network learned it.\" That is not sufficient for policy use. I would use a layered explanation: identify the micro-level behavioural changes, quantify which agent characteristics and interactions contributed to them, trace how those changes propagated through the simulation, and then test whether the macro-pattern is robust to alternative model specifications, seeds, and parameter settings. In other words, explain the mechanism that generated the emergence, not merely the prediction produced by the model.

3. Where the black-box line is: For me, the boundary is reached when the model cannot answer three questions: What assumptions govern agent behaviour? What evidence supports those behavioural rules? And does the emergent result remain stable under reasonable perturbations of those assumptions? An adaptive model does not necessarily have to be fully interpretable internally, but its behavioural consequences need to be interrogable and its conclusions need to be validated.

I would therefore advocate for trustworthy ABM rather than simply more sophisticated ABM: combine adaptive agents with uncertainty quantification, sensitivity/robustness analysis, behavioural validation against empirical data, and interpretable diagnostics. LLMs and RL can provide richer behavioural representations, but they should not become a substitute for causal reasoning, validation, or transparency.

The question I would ask before deploying such a model for policy is not \"Is the agent intelligent enough?\" but \"Can we establish why this simulation produces this result, how uncertain that result is, and whether it survives plausible alternative assumptions?\" That distinction becomes important when the simulation is being used to support real-world decisions.