77 citations · 120 across the 16 of their papers we have counts for
16 papers
Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model
M Lopes Alves, Joel Dyer, Doyne Farmer +2
Agent-based modelling (ABM) is a widespread approach to simulate complex systems. Advancements in computational processing and storage have facilitated the adoption of ABMs across…
Bayesian Decision Making around Experts
Daniel Jarne Ornia, Joel Dyer, Nicholas Bishop +2
Complex learning agents are increasingly deployed alongside existing experts, such as human operators or previously trained agents. However, it remains unclear how should learners…
Sandbagging in a Simple Survival Bandit Problem
Joel Dyer, Daniel Jarne Ornia, Nicholas Bishop +2
Evaluating the safety of frontier AI systems is an increasingly important concern, helping to measure the capabilities of such models and identify risks before deployment. However,…
Using causal abstractions to accelerate decision-making in complex bandit problems
Joel Dyer, Nicholas Bishop, Anisoara Calinescu +2
Although real-world decision-making problems can often be encoded as causal multi-armed bandits (CMABs) at different levels of abstraction, a general methodology exploiting the inf…
Automatic Differentiation of Agent-Based Models
Arnau Quera-Bofarull, Nicholas Bishop, Joel Dyer +4
Agent-based models (ABMs) simulate complex systems by capturing the bottom-up interactions of individual agents comprising the system. Many complex systems of interest, such as epi…
Emergent Risk Awareness in Rational Agents under Resource Constraints
Daniel Jarne Ornia, Nicholas Bishop, Joel Dyer +4
Advanced reasoning models with agentic capabilities (AI agents) are deployed to interact with humans and to solve sequential decision-making problems under (approximate) utility fu…
Forecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model
Samuel Wiese, Jagoda Kaszowska-Mojsa, Joel Dyer +6
In the last few years, economic agent-based models have made the transition from qualitative models calibrated to match stylised facts to quantitative models for time series foreca…
Causally Abstracted Multi-armed Bandits
Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer +4
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…
Interventionally Consistent Surrogates for Agent-based Simulators
Joel Dyer, Nicholas Bishop, Yorgos Felekis +4
Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents. Their high-fidelit…
Some challenges of calibrating differentiable agent-based models
Arnau Quera-Bofarull, Joel Dyer, Anisoara Calinescu +1
Agent-based models (ABMs) are a promising approach to modelling and reasoning about complex systems, yet their application in practice is impeded by their complexity, discrete natu…
Bayesian calibration of differentiable agent-based models
Arnau Quera-Bofarull, Ayush Chopra, Anisoara Calinescu +2
Agent-based modelling (ABMing) is a powerful and intuitive approach to modelling complex systems; however, the intractability of ABMs' likelihood functions and the non-differentiab…
Calibrating Agent-based Models to Microdata with Graph Neural Networks
Joel Dyer, Patrick Cannon, J. Doyne Farmer +1
Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose. In recent years, simulation-based inferenc…
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Joel Dyer, Patrick Cannon, Sebastian M Schmon
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference…
Black-box Bayesian inference for economic agent-based models
Joel Dyer, Patrick Cannon, J. Doyne Farmer +1
Simulation models, in particular agent-based models, are gaining popularity in economics. The considerable flexibility they offer, as well as their capacity to reproduce a variety…
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 post…
Public risk perception and emotion on Twitter during the Covid-19 pandemic
Joel Dyer, Blas Kolic
Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play impo…