Nicholas Bishop 'Machine Learning and ABMs...'

For a full list of my publications I recommend checking out my Scholar profile.

You can also find my (probably outdated) CV here.

Below is a list of current working papers, as well as recent published work I am interested in extending/discussing.

Recent Work


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


Check out my blog post explaining the key ideas behind this paper!

Abstract: Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system’s constituent agents. Their high-fidelity nature enables hyper-local policy evaluation and testing of what-if scenarios, but is associated with large computational costs that inhibits their widespread use. Surrogate models can address these computational limitations, but they must behave consistently with the agent-based model under policy interventions of interest. In this paper, we capitalise on recent developments on causal abstractions to develop a framework for learning interventionally consistent surrogate models for agent-based simulators. Our proposed approach facilitates rapid experimentation with policy interventions in complex systems, while inducing surrogates to behave consistently with high probability with respect to the agent-based simulator across interventions of interest. We demonstrate with empirical studies that observationally trained surrogates can misjudge the effect of interventions and misguide policymakers towards suboptimal policies, while surrogates trained for interventional consistency with our proposed method closely mimic the behaviour of an agent-based model under interventions of interest.


Authors: Joel Dyer, Arnau Quera-Bofarull, Nicholas bishop, J. Doyne Farmer, Anisoara Calinescu, Michael Wooldridge


Check out my blog post explaining the key ideas behind this paper!

Abstract: 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.


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


Blog post coming soon…

Abstract: 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.