Philip Garnett

Philip Garnett, BSc MSc PhD FRSA


Former affiliations

Accepting PhD Students

PhD projects

Complex systems, systems theory, and network analysis, and modelling/simulation of organisations.
Information processing, data analytics.

Personal profile

Research interests

Main Research Interests: Complexity Theory, Network Analysis, Machine Learning, Data Mining, and Big Data

  • The analysis of social and new media. I am interested in how organisations use and monitor social and new media, such as Twitter, Facebook and online web forums. I also research how information flows through social media and new trends and fashions come and go.

  • Modelling and simulation of organisational behaviour. How do the interactions of connect organisations shape the development of economic sectors? What are the significance of hidden and explicit connections between businesses, such as shared directorships? This research uses network analysis techniques and modelling to investigate now relationships between business and the people running them influence those businesses and the wider economy.

  • Data Mining and Analytics. Business (and society at large) generates huge amounts of information. Leveraging this mountain of information to extract value is becoming increasingly challenging. We use modelling and analytical techniques to help mine information out of Big Data.

  • The effect of algorithms Big Data analytics on privacy and liberty. Information can be both a defender liberty, as it can increase transparency in society. It however can also be be used to erode our civil liberties and freedom, by increased surveillance by both the state and private sector.

  • Systems of systems. Increasing dealing with the complexity of physical, natural and human systems, and how they are connected demands new approaches to their study. We use the notion of systems of systems to model different aspects of society, such as the distribution of risk in financial systems.

  • Business history. Analysis of historical data can often lead to insights into the presence and the future. We apply all the techniques we use to model and analyse contemporary data to historical business history. As a way of learning from the past, and overturning misconceptions.


I am a complex systems scientist and lecturer of operations management and business analytics based at the University of York UK. I am also a visitor at the Institute of Hazards, Risk and Resilience (IHRR) in Durham University, and a member of theYork Centre for Complex Systems Analysis (University of York UK).

My scientific background is in the modelling and simulation of biological systems. I started my academic career as a geneticist (BSc Genetics University of Nottingham, UK) and quickly realised that it was the modelling aspects of this field that I was most interested in. For example, the modelling of drift and selection in populations. I then went on to study for an MSc Information Processing (University of York, UK), and eventually did a PhD in Computational Biology (University of York, UK). Over time, my interest in modelling social systems also developed. Now as a lecturer in Operations Management and Business Analytics my research still combines aspects of modelling and simulation, along with the analysis of complex or difficult data.

I am interested in how simple interactions between agents (such as people or organisations) can produce complex and often unexpected (emergent) behaviours in a system. My PhD work focused on the modelling and simulation of Auxin Transport Canalisation (for review see, Computer simulation: The imaginary friend of auxin transport biology). My current research interests are focused around applying systems theory, complex systems theory, and network analysis techniques to a wide range of problems, largely focused on the processing of information. Combined with modelling and simulation techniques I am interested in what the analysis of information can tell us about how organisations and society works. I am also interested in the power of information and its consequences for our privacy and liberty.

Collaborations and top research areas from the last five years

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