Two Funded PhD Studentships in Agent-Based Modelling at Loughborough University School of Business and Economics

I am looking for high quality, numerate, candidates to fill these exciting PhD studentships with me as a (co-) supervisor at Loughborough’s School of Business and Economics.  Please note that this post has been updated with new links (in blue, below).

The first is modelling dynamic responses to dynamic threats; the second is using analytics in traditional industries.  Please see the links below for further details and how to apply.  Note that for further information, you will need to click on the blue links below.

Modelling Dynamic Responses to Dynamic Threats (with Professor Gilberto Montibeller)

One of the most challenging issues for policy makers dealing with bio-security threats is their dynamic nature: diseases may spread quickly and deadly among vulnerable populations and pandemics may cause many casualties.

Finding the appropriate response to threats is a major challenge.  Whilst models exist for understanding of the dynamics of the threats themselves, responses can be largely ad-hoc or ‘firefighting’.  The aim of this research is to produce robust responses for dynamic threats.

The research will build up as follows, from low to high complexity: static responses to static threats; static responses to dynamic threats; dynamic responses to static threats; and dynamic responses to dynamic threats.

We will use a variety of methods to define the best response: cellular automata, network analysis, spatial modelling, agent-based modelling, and the generation of dynamic fitness landscapes.

This PhD studentship is most suitable for candidates with a background in a quantitative discipline such as management science, operations research, engineering, physics and other natural sciences.

Business Analytics for Public Services and Regulated Industries: New Techniques for Analytics-Driven Decision Making in Traditional Industries (with Dr Maria Neiswand and Professor David Saal)

The rise of business analytics has given rise to enormous opportunities within the private sector, but these benefits have yet to be fully realized in public services and regulated industries such as energy, water, and transportation networks. Conversely, governments are mandating collection of data by installing smart metering devices. This gives rise to the need for innovative ways of thinking in industries that are still largely based on traditional economic thinking involving conventional assumptions on optimization and behaviour.

As an example, the energy sector is characterised by strongly defined market structures with incumbents and an ultimate need for energy network security, which not only prevents the quick adoption of technical changes but also translates into regulatory outcomes, such as price caps.

This exciting PhD opportunity will integrate theoretical and empirical approaches and spans two strengths of Loughborough’s School of Business and Economics: microeconomics and particularly rigorous analysis of the determinants of productivity and performance (including cost modelling) and management science (including simulation and network analysis).

We are therefore seeking a student with a quantitative background (whether in economics, management science, engineering, physics or other natural sciences). A willingness to learn new techniques such as, cost modelling, performance measurement, agent-based modelling and network analysis is desired.

 

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.

Eight Mile and the Emergence of Segregation

Eight Mile, epitomized by Eminem in the film of the same name, is a street in Detroit that marks the boundary between the majority white northern suburbs and the majority black neighborhoods closer to the inner city.

But what causes this segregation in the first place?

Hypothesis 1: The Central Planner

Zoning Map, 1930s, showing HOLC zoning, source: http://www.urbanoasis.org/projects/holc-fha/digital-holc-maps/

In Detroit’s case, as with many cities across the USA, it was, in part, due to the zoning of the city by the federal Home Owners’ Loan Corporation, which zoned the city into areas of risk, meaning that banks were indirectly encouraged to develop outer suburbs while not offering mortgages to inner city properties.  This lead to wealthier, generally white, residents moving to the suburbs.

Indeed, physical barriers, such at the Detroit Wall, also known as the Eight Mile Wall, were built to separate majority black and majority white neighborhoods.

Detroit Today

The legacy of these zones live on today, as seen in the map below from the 2010 US Census.  The dividing line between the green (black) areas and the blue (white) areas is Eight Mile Road.

DotMap http://demographics.virginia.edu/DotMap/ based on 2010 US Census

 

 

So, segregation exists, and is caused by a central actor. But is there an alternative explanation?

Alternative Hypothesis: Emergence

In 1971, Thomas Schelling set out to model the phenomenon, not by assuming a central planner, but by modelling the interactions of individuals.

Thomas Schelling’s model was this.  Assume individuals are placed in a grid, similar to being located on a chess board.  Allow individuals who are in a local minority to move.  In the example below, the blue circle is in a minority (with 5 out of its 6 neighbors being a different color), and according to the rules of the model, is unhappy.  It could decide to move to the vacant square to the north-west, but it would still be in a local minority (with 4 out of 6 neighbors being a different color) and would remain unhappy.  So instead, it chooses the space to the south west where 3 out of its 6 neighbors are of the same color, and not being in a minority, it settles there.

Agent Movement © Duncan Robertson after Thomas Schelling (1971)

Schelling, perhaps without knowing it, introduced agent-based modelling, where, instead of modelling the system as a whole, the modelling of individual agents enables us to see the emergence of macro-level properties, in this case segregation, via the modelling of micro-level (local) interactions.

We can see the effect of micro-level interactions causing macro-level segregation in the model below (developed by Duncan Robertson after Wilensky after Schelling). Each individual, or agent, decides whether they are unhappy or happy; if they are unhappy, they search until they find a vacant location where they will become happy.  This continues until all individuals attain happiness.

Three Class Segregation Model © Duncan Robertson after Wilensky after Schelling

So, perhaps segregation is not imposed, but is down to us.  Or maybe, in reality, it’s a little bit of both.

Please do get in touch if you would like to discuss building or working with agent-based models.

 

 

Simulating (Human) Behavior Session at IFORS Conference in Quebec

I am organizing a session at IFORS on Simulating (Human) Behavior as part of a Behavioural Operational Research stream. It would be good if other agent-based models could be presented.

To submit a paper, please do so here: http://ifors2017.ca/submit-abstract and use invitation code dfa90f58

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.

Building the Multiplex: An Agent-Based Model of Formal and Informal Network Relations

EURO Conference 2016 PoznanThis presentation from the EURO 2016 conference in Poznan, Poland, and from the GDN conference in Bellingham, WA, USA, joint work with Leroy White of Warwick Business School, shows how combining formal and informal organizational networks enables decisions to flow more freely around organizations, but at a cost, leading to an optimal size of informal organizational networks.  If organizations can control these, this leads to implications for optimal information flows in companies.

Agent-Based Modeling Toolkits: NetLogo, RePast, and Swarm

AMLEAgent-Based Modeling Toolkits: NetLogo, RePast, and Swarm (2005, Academy of Mangement Learning and Education 4(4), 525–527) sets out a comparison of three widely used agent-based modeling toolkits: RePast, NetLogo, and Swarm.  It shows the differences between the toolkits, setting out the advantages, disadvantages, and limitations of each software toolkit.

Agent-Based Models to Manage the Complex

MTCAgent-Based Models to Manage the Complex is a book chapter in Managing Organizational Complexity: Philosophy, Theory and Application: Volume 1 (ISCE Book Series – Managing the Complex) is an introduction to the use of agent-based models in management.  It demonstrates the use of models in Repast, an agent-based modeling toolkit, and links this to complexity science concepts of emergent systems.