Category: Publications

Models and Policy Paper

Our paper ‘Challenges on the interaction of models and policy for pandemic control’ has been published in the journal Epidemics.

ABSTRACT

The COVID-19 pandemic has seen infectious disease modelling at the forefront of government decision-making. Models have been widely used throughout the pandemic to estimate pathogen spread and explore the potential impact of different intervention strategies. Infectious disease modellers and policymakers have worked effec-tively together, but there are many avenues for progress on this interface. In this paper, we identify and discuss seven broad challenges on the interaction of models and policy for pandemic control. We then conclude with suggestions and recommendations for the future.

CITATION

Hadley L., Challenor, P., Dent, C., Isham, V., Mollison, D., Robertson, D. A., Swallow, B., Webb, C. R. (2021) ‘Challenges on the interaction of models and policy for pandemic control’, Epidemics, 37,
https://doi.org/10.1016/j.epidem.2021.100499

FULL PAPER

Epidemics paperDownload

Simulation Modelling Community Response to COVID-19

Our paper in Journal of Simulation ‘How simulation modelling can help reduce the impact of COVID-19‘ setting out how simulation modelling can help in the fight against COVID-19 and subsequent epidemics and pandemics. Click here to access the paper.

ABSTRACT

Modelling has been used extensively by all national governments and the World Health Organisation in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community.

Agent-Based Strategizing: New Book Published at Cambridge University Press

My new book, Agent-Based Strategizing, has been published at Cambridge University Press. It is available to download for free until 31 July 2019 at the link below. The book is an overview of how agent-based modelling has been (and can be) used in strategic management.

https://www.cambridge.org/core/elements/agentbased-strategizing/4AD9D0D7416DE46AEB7F1A5478772ACF

Abstract: Strategic management is a system of continual disequilibrium, with firms in a continual struggle for competitive advantage and relative fitness. Models that are dynamic in nature are required if we are to really understand the complex notion of sustainable competitive advantage. New tools are required to tackle challenges of how firms should compete in environments characterized by both exogeneous shocks and intense endogenous competition. Agent-based modelling of firms’ strategies offers an alternative analytical approach, where individual firm or component parts of a firm are modelled, each with their own strategy. Where traditional models can assume homogeneity of actors, agent-based models simulate each firm individually. This allows experimentation of strategic moves, which is particularly important where reactions to strategic moves are non-trivial. This Element introduces agent-based models and their use within management, reviews the influential NK suite of models, and offers an agenda for the development of agent-based models in strategic management.

Spatial Transmission Models: A Taxonomy and Framework

Risk Analysis

This paper , published in the journal Risk Analysis, sets out a review of the different methods used for modelling the spread of an idea, disease, etc. over space.

ABSTRACT

Within risk analysis and more broadly, the decision behind the choice of which modelling technique to use to study the spread of disease, epidemics, fires, technology, rumors, or more generally spatial dynamics, is not well documented.

While individual models are well defined and the modeling techniques are well understood by practitioners, there is little deliberate choice made as to the type of model to be used, with modelers using techniques that are well accepted in the field, sometimes with little thought as to whether alternative modelling techniques could or should be used.

In this paper, we divide modelling techniques for spatial transmission into four main categories: population-level models, where a macro-level estimate of the infected population is required; cellular models, where the transmission takes place between connected domains, but is restricted to a fixed topology of neighboring cells; network models, where host-to-host transmission routes are modelled, either as planar spatial graphs or where short cuts can take place as in social networks; and finally agent-based models which model the local transmission between agents, either as host-to-host geographical contacts, or by modelling the movement of the disease vector, with dynamic movement of hosts and vectors possible, on a Euclidian space or a more complex space deformed by the existence of information about the topology of the landscape using GIS techniques. We summarize these techniques by introducing a taxonomy classifying these modeling approaches.

Finally, we present a framework for choosing the most appropriate spatial modelling method, highlighting the links between seemingly disparate methodologies, bearing in mind that the choice of technique rests with the subject expert.

Agent-Based Models for Simulating Human Behavior: IFORS Conference 2017

This presentation including joint work with Alberto Franco, was presented at the IFORS (International Federation of Operational Research Societies) conference in Quebec City, QC, Canada.  We present two different agent-based models for simulating human behavior.

We use the example of group decision making.

The first model uses a cognitive fitness landscape to model the quality of a decision, where participants compare their decision with their nearest neighbor.  The decision is based on an external comparison.

The second model uses an internal comparison of a decision with the next best alternative.  The model is based on the psychological concept of hidden profiles, where participants only make the best decision by sharing information with the group.

Review of ‘Agent-Based Modeling and Simulation’, OR Essentials

Book review to be published in The Journal of Artificial Societies and Social Simulation (JASSS)

Taylor, Simon J. E. (Ed.) (2014) Agent-Based Modeling and Simulation, OR Society and Palgrave Macmillan: Basingstoke

 

Agent-Based Modeling and Simulation is the first in the Operational Research Society’s OR Essentials series.   OR Essentials brings together multidisicpinary research from the management, decision, and computer sciences.  This edition within the series is edited by Simon Taylor, who is co-founder of the Journal of Simulation, also published by the OR Society.

The edited book is divided into 14 chapters, and the bulk of its contents convers the application of agent based modelling (ABM) to specific problem domains.  The book adds to this by introducing agent-based modelling as a technique, and setting it in context with other simulation approaches.  A very helpful chapter by Macal and North of Argonne National Laboratory offers a tutorial on what agent-based modelling is, focusing on the autonomy and interconnectedness of agents, and showing how agent-based models should be built.  The book ends with thoughtful chapters on a testing framework for ABM (Gürcan,  Dikenelli, Bernon), and a comparison with discrete-event simulation by Brailsford, elegantly closing the package opened by Taylor’s introduction comparing ABM with system dynamics and discrete event simulation within the context of modelling and simulation more generally.

The academic rigour of the book is confirmed by each article being reprinted from published articles from the Journal of Simulation.  The book brings together several excellent examples of agent-based modelling, together with a very clear understanding of how ABM fits in with more traditional simulation techniques such as DES (Discrete Event Simulation) and SD (System Dynamics) –  both Talyor and Brailsford show how and when ABM should be used.  Macal and North offer a very useful tutorial for understanding the building blocks of an ABM simulation, while Heath and Hill show ABM’s evolution from cellular automata and complexity science through complex adaptive systems.

Domain specific chapters cover applications in the management of hospital-acquired infection (Meng, Davies, Hardy, and Hawkey); product diffusion of a novel biomass fuel (Günther, Stummer, Wakolbinger, Wildpaner); urban evacuation (Chen, Zhan); people management (Siebers, Aickelin, Celia, Clegg); pharmaceutical supply chains (Jetly, Rossetti, Handfield); workflow scheduling (Merdan, Moser, Sunindyo, Biffl, Vrba); credit risk (Jonsson); and historical infantry tactics (Rubio-Campillo, Cela, Cardona).

Agent-Based Modeling and Simulation offers a very useful collection of applications of ABM, and showcases how ABM can be successfully incorporated in to mainstream, published research.  The contributions to the book are diverse, and from internationally regarded scholars.  It is also useful to see the diverse ways that agent-based modelling research is presented, from some papers that show code, some that show running models, and some that do not show the model or code but instead describe results.

The glue that binds the book is methodological.  Seeing how ABM has been used in diverse application areas is important, given the trans-disciplinary nature of the approach.  It is an excellent introduction into agent-based modelling within a wide range of business and operations applications, and should be read by scholars and practitioners alike.

Agent-Based Models of A Banking Network

Agent-Based Models of a Banking Network as an Example of a Turbulent Environment: The Deliberate vs Emergent Strategy Debate Revisited is a paper that I wrote that uses an agent-based model to simulate the strategic positioning decisions of firms within a competitive environment.  It links firm strategy (particularly banking strategy) to the competitive environment: where should firms locate to gain most customers given that all firms are trying to achieve the same objective, that of maximizing market share / revenue / profits?  It is an interesting question, and one that is very suited to modelling the dynamics of an industry using agent-based models.

The Complexity of the Corporation

HSMIn my paper, The Complexity of the Corporation, I introduce complexity science applied to management, discussing complex adaptive systems, emergence, co-evolution, and power laws.

“We discuss the notion of complexity as applied to firms and corporations. We introduce the background to complex adaptive systems, and discuss whether this presents an appropriate model or metaphor to be used within management science. We consider whether a corporation should be thought of as a complex system, and conclude that a firm within an industry can be defined as a complex system within a complex system.Whether we can say that the use of complexity research will fundamentally improve firm performance will depend on the effect on success derived from its application.”

When does Brokerage Matter? Citation Impact of Research Teams in an Emerging Academic Field

StrategicOrganizationThis paper with François Collet of ESADE and Daniela Lup of the LSE analyzes the emergence of the strategic management filed showing the benefits of network brokerage are stronger during the early phase of development and diminish over time.

Through exposure to heterogeneous sources of knowledge, actors who broker between unconnected contacts are more likely to generate valuable output. We contribute to the theory of social capital of brokerage by considering the impact of field maturity. Using longitudinal data from the field of strategic management we find that the benefits of network brokerage are stronger during the early stages of field development and diminish as the field matures. The results of our study call for further research on the interplay between network structures and processes of field emergence.