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Joel Dyer

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6 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Emergent Risk Awareness in Rational Agents under Resource Constraints

Daniel Jarne Ornia
Nicholas Bishop
Joel Dyer
Wei-Chen Lee
Anisoara Calinescu
Doyne Farmer
Michael Wooldridge

Advanced reasoning models with agentic capabilities (AI agents) are deployed to interact with humans and to solve sequential decision‑making problems under (often approximate) utility functions and internal models. When such problems have resource or failure constraints where action sequences may be forcibly terminated once resources are exhausted, agents face implicit trade‑offs that reshape their utility-driven (rational) behaviour. Additionally, since these agents are typically commissioned by a human principal to act on their behalf, asymmetries in constraint exposure can give rise to previously unanticipated misalignment between human objectives and agent incentives. We formalise this setting through a survival bandit framework, provide theoretical and empirical results that quantify the impact of survival‑driven preference shifts, identify conditions under which misalignment emerges and propose mechanisms to mitigate the emergence of risk-seeking or risk-averse behaviours. As a result, this work aims to increase understanding and interpretability of emergent behaviours of AI agents operating under such survival pressure, and offer guidelines for safely deploying such AI systems in critical resource‑limited environments.

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ICML Conference 2025 Conference Paper

Learning Likelihood-Free Reference Priors

Nicholas Bishop
Daniel Jarne Ornia
Joel Dyer
Anisoara Calinescu
Michael J. Wooldridge

Simulation modeling offers a flexible approach to constructing high-fidelity synthetic representations of complex real-world systems. However, the increased complexity of such models introduces additional complications, for example when carrying out statistical inference procedures. This has motivated a large and growing literature on likelihood-free or simulation-based inference methods, which approximate (e. g. , Bayesian) inference without assuming access to the simulator’s intractable likelihood function. A hitherto neglected problem in the simulation-based Bayesian inference literature is the challenge of constructing minimally informative reference priors for complex simulation models. Such priors maximise an expected Kullback-Leibler distance from the prior to the posterior, thereby influencing posterior inferences minimally and enabling an “objective” approach to Bayesian inference that does not necessitate the incorporation of strong subjective prior beliefs. In this paper, we propose and test a selection of likelihood-free methods for learning reference priors for simulation models, using variational approximations to these priors and a variety of mutual information estimators. Our experiments demonstrate that good approximations to reference priors for simulation models are in this way attainable, providing a first step towards the development of likelihood-free objective Bayesian inference procedures.

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UAI Conference 2024 Conference Paper

Approximate Bayesian Computation with Path Signatures

Joel Dyer
Patrick Cannon
Sebastian M. Schmon

Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable. Approximate Bayesian computation generates likelihood-free posterior samples by comparing simulated and observed data through some distance measure, but existing approaches are often poorly suited to time series simulators, for example due to an independent and identically distributed data assumption. In this paper, we propose to use path signatures in approximate Bayesian computation to handle the sequential nature of time series. We provide theoretical guarantees on the resultant posteriors and demonstrate competitive Bayesian parameter inference for simulators generating univariate, multivariate, and irregularly spaced sequences of non-iid data.

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UAI Conference 2024 Conference Paper

Causally Abstracted Multi-armed Bandits

Fabio Massimo Zennaro
Nicholas Bishop
Joel Dyer
Yorgos Felekis
Anisoara Calinescu
Michael J. Wooldridge
Theodoros Damoulas

Multi-armed bandits (MAB) and causal MABs (CMAB) are established frameworks for decision-making problems. The majority of prior work typically studies and solves individual MAB and CMAB in isolation for a given problem and associated data. However, decision-makers are often faced with multiple related problems and multi-scale observations where joint formulations are needed in order to efficiently exploit the problem structures and data dependencies. Transfer learning for CMABs addresses the situation where models are defined on identical variables, although causal connections may differ. In this work, we extend transfer learning to setups involving CMABs defined on potentially different variables, with varying degrees of granularity, and related via an abstraction map. Formally, we introduce the problem of causally abstracted MABs (CAMABs) by relying on the theory of causal abstraction in order to express a rigorous abstraction map. We propose algorithms to learn in a CAMAB, and study their regret. We illustrate the limitations and the strengths of our algorithms on a real-world scenario related to online advertising.

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NeurIPS Conference 2024 Conference Paper

Interventionally Consistent Surrogates for Complex Simulation Models

Joel Dyer
Nicholas Bishop
Yorgos Felekis
Fabio Massimo Zennaro
Anisoara Calinescu
Theodoros Damoulas
Michael Wooldridge

Large-scale simulation models of complex socio-technical systems provide decision-makers with high-fidelity testbeds in which policy interventions can be evaluated and what-if scenarios explored. Unfortunately, the high computational cost of such models inhibits their widespread use in policy-making settings. Surrogate models can address these computational limitations, but to do so they must behave consistently with the simulator under interventions of interest. In this paper, we build upon recent developments in causal abstractions to develop a framework for learning interventionally consistent surrogate models for large-scale, complex simulation models. We provide theoretical results showing that our proposed approach induces surrogates to behave consistently with high probability with respect to the simulator across interventions of interest, facilitating rapid experimentation with policy interventions in complex systems. We further demonstrate with empirical studies that conventionally trained surrogates can misjudge the effect of interventions and misguide decision-makers towards suboptimal interventions, while surrogates trained for interventional consistency with our method closely mimic the behaviour of the original simulator under interventions of interest.

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AAMAS Conference 2024 Conference Paper

Population Synthesis as Scenario Generation for Simulation-based Planning under Uncertainty

Joel Dyer
Arnau Quera-Bofarull
Nicholas Bishop
J. Doyne Farmer
Anisoara Calinescu
Michael Wooldridge

Agent-based models have the potential to become instrumental tools in real-world decision-making, equipping policy-makers with the ability to experiment with high-fidelity representations of complex systems. Such models often rely crucially on the generation of synthetic populations with which the model is simulated, and their behaviour can depend strongly on the population’s composition. Existing approaches to synthesising populations attempt to model distributions over agent-level attributes on the basis of data collected from a real-world population. Unfortunately, these approaches are of limited utility when data is incomplete or altogether absent – such as during novel, unprecedented circumstances – so that considerable uncertainty regarding the characteristics of the population being modelled remains, even after accounting for any such data. What is therefore needed in these cases are tools to simulate and plan for the possible future behaviours of the complex system that can be generated by populations that are consistent with this remaining uncertainty. To this end, we frame the problem of synthesising populations in agent-based models as a problem of scenario generation. The framework that we present is designed to generate synthetic populations that are on the one hand consistent with any persisting uncertainty, while on the other hand matching closely a target, user-specified scenario that the decision-maker would like to explore and plan for. We propose and compare two generic approaches to generating synthetic populations that produce target scenarios, and demonstrate through simulation studies that these approaches are able to automatically generate synthetic populations whose behaviours match the target scenario, thereby facilitating simulation-based planning under uncertainty.

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