Robust Neural Posterior Estimation and Statistical Model Criticism
Robust Neural Posterior Estimation and Statistical Model Criticism NeurIPS 2022 Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes is often restricted by low data quality, as well as practical limits to model fidelity. In order to circumvent these difficulties, […]
Calibrating Agent-based Models to Microdata with Graphic Neural Networks
Calibrating Agent-based Models to Microdata with Graphic Neural Networks ICML 2022 Artificial Intelligence for Agent-based Modelling (AI4ABM) Workshop 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 inference methods have emerged as powerful tools for performing this task when the […]
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning ICML 2022 Artificial Intelligence for Agent-based Modelling (AI4ABM) Workshop Multi-agent reinforcement learning experiments and open-source training environments are typically limited in scale, supporting tens or sometimes up to hundreds of interacting agents. In this paper we demonstrate the use of Vogue, a high performance agent based model […]
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022 Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible. Recent advances in machine learning have introduced novel […]
Black-box Bayesian inference for economic agent-based models
Black-box Bayesian inference for economic agent-based models Preprint 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 of empirically observed behaviours of complex systems, give them broad appeal, and the increasing availability of cheap computing power has made their […]
A dynamic microsimulation model for epidemics
A dynamic microsimulation model for epidemics Social Science & Medicine, Vol. 291 A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the […]
Generating agent-based models from scratch with generic programming
Generating agent-based models from scratch with generic programming ALIFE 2021: The Conference on Artificial Life Program synthesis (PS) and genetic programming (GP) allow non-trivial programs to be generated from example data. Agent-based models (ABMs) are a promising field of application as their complexity at a macro level arises from simple agent-level rules. Previous attempts at […]
Deep signature statistics for likelihood-free time-series models
Deep signature statistics for likelihood-free time-series models ICML 2021 (INNF Workshop) Simulation-based inference (SBI) has emerged as a family of methods for performing inference on complex simulation models with intractable likelihood functions. A common bottleneck in SBI is the construction of low-dimensional summary statistics of the data. In this respect, time-series data, often being high-dimensional, […]
Trusting a black box: explaining complex simulation outcomes using LIME
Trusting a black box: explaining complex simulation outcomes using LIME The field of Artificial Intelligence (AI) has recently been suffering an “interpretability crisis”: black-box techniques like deep learning produce impressively accurate predictions, but fail to offer any human intelligible explanation, making it hard to establish their safety and fitness-of-purpose in highly regulated or safety-critical domains. […]
Machine learning surrogates for highly realistic simulations
Machine learning surrogates for highly realistic simulations High-fidelity simulations of real world systems such as traffic, physical infrastructure and the civilian population are the next frontier in improving the depth and sophistication of military simulations. Such complex systems are difficult to model with complete accuracy, and often involve unobserved free parameters. To make these simulations […]