Complexity, Risk, and Uncertainty

Today, society and the economy generate a complex torrent of data. If this unprecedented flow of information is to be made useful, we require new tools and methods for its analysis.


Our work

The measurement, interpretation, and communication of complexity and risk is a key part of modern science. Te Pūnaha Matatini researchers working within the Complexity, Risk, and Uncertainty theme (formerly called Complex Data Analytics) are developing tools for understanding and dealing with complex systems by developing the underlying theory. This includes work on optimising stochastic systems from supply chains to healthcare, inferring numbers of New Zealand birds from their calls (AviaNZ), and building a library of New Zealand soils from their spectral signatures. Public engagement with science is also a key part of Te Pūnaha Matatini’s work, and the researchers in this theme are working on ways to improve scientist-public interactions.


Our impact

Our work is both fundamental and applied. The theory that we develop is used, by ourselves and others, to support applications for the benefit of New Zealand, and international research. This theme is outward-looking, and combines with the other two themes to form integrated projects within Te Pūnaha Matatini, as well as externally.


Our team

Our research

The Complexity, Risk and Uncertainty theme is particularly diverse. We have research interests covering all parts of the process of using data, from developing visualisations of data (particularly networks), through theoretical developments in game theory, dynamical systems, machine learning, to investigating public engagement with science.

Research highlight: Networks

The mathematical theory of networks combines graph theory, statistics, and data analytics. We are extending this theory in a variety of ways, such as by treating them as dynamical systems, and developing new ways to transform them, and to find useful structure within them. In addition, we are turning these new methods to transform data into real information in a variety of application areas, including post-marital residence patterns of human societies. Combining theory and practice gives us a unique perspective on many of the problems that we investigate.


  • Vasques Filho, D., & O’Neale, D. R. “Degree distributions of bipartite networks and their projections”, Physical Review E, 98(2), 022307 (2018).
  • Ashwin P., & Postlethwaite, C. “Sensitive finite-state computations using a distributed network with a noisy network attractor”, IEEE Transactions on Neural Networks and Learning Systems, 1–12 (2018).
  • Moravec J.C., Atkinson Q., Bowern C., Greenhill S.J., Jordan F.M., Ross R.M., Gray R., Marsland S., Cox M.P. “Post-marital residence patterns show lineage-specific evolution”, Evolution and Human Behavior, (39), 594–601 (2018).
  • Whaanga H., Wehi P., Cox M.P., Roa T., Kusabs I. “Māori oral traditions record and convey indigenous knowledge of marine and freshwater resources”, New Zealand Journal of Marine and Freshwater Research, (52), 487–496 (2018).


Research highlight: Data analysis

It is common to hear that modern societies are data rich, but information poor. In Te Pūnaha Matatini, we are seeking to redress this balance by developing new data analysis methods hand-in-hand with the problems where the data arise. Examples from 2018 come from geology, where a team from Landcare Research have analysed soil carbon stocks, and statistical ecology, where the AviaNZ project based at Victoria University and Massey has been complemented by research from Auck land, looking at ways to extend the statistical models of animal abundance that are currently used to take into account some of the human errors that are inherent in their application.

  • Malone B., Hedley C., Roudier P., Minasny B., Jones E., McBratney A. “Auditing on-farm soil carbon stocks using downscaled national mapping products: Examples from Australia and New Zealand”, Geoderma Regional (13), 01–014 (2018).
  • Hamilton O.N.P., Kincaid S.E., Constantine R., Kozmian- Ledward L., Walker C., Fewster R.M. “Accounting for uncertainty in duplicate identification and group size judgments in mark-recapture distance sampling”, Methods in Ecology and Evolution, (9), 354-362 (2018).
  • Priyadarshani N., Castro I., Marsland S. “The Impact of Environmental Factors in Birdsong Acquisition using Automated Recorders”, Ecology and Evolution (8), 5016-5033 (2018).

Research highlight: Simulation and medical data

Hospitals are rich sources of complex data. Within Te Pūnaha Matatini our focus in this sphere is on projects that involve people, which generally require our expertise in complex systems analysis. Examples from 2018 of this are centred on modelling: simulating the emergency department, using simulating to evaluation proposed community health interventions, and looking for changes that could work to improve health equity.

  • Furian N., Neubacher D., O’Sullivan M., Walker C. “GED- Mod – Towards a generic toolkit for emergency department modelling”, Simulation Modelling Practice and Theory, (87), 239–273 (2018).
  • Matheson A., Bourke C., Verhoeven A., Khan M.I., Nkunda D., Dahar Z., Ellison-Loschmann L. “Lowering hospital walls to achieve health equity”, BMJ, (362), k3597 (2018).
  • Matheson A., Walton M., Gray R., Lindberg K., Shanthakumar M., Fyfe C., Wehipeihana N., Borman B. “Evaluating a community-based public health intervention using a complex systems approach”, Journal of Public Health, (40), 606–613 (2018).


Research highlight: Supply chain optimisation

Supply chains are a key component of production, from obtaining materials from suppliers through to delivering completed products. Optimisation of this chain can substantially reduce the costs of doing business, by reducing storage needs, ensuring that nothing is wasted, and that time is not spent waiting. While this is important for any business, in agribusiness, where products can spoil if they are not harvest- ed on time, or take too long in transit, these matters are even more critical. Te Pūnaha Matatini researchers are combining statistical and business expertise to study how to deal with the risks inherent in such business management practices.

• Behzadi G., O’Sullivan M.J., Olsen T.L., Zhang A. “Agribusiness supply chain risk management: A review of quantitative decision models”, Omega, (79), 21–42 (2018).

• Behzadi G., O’Sullivan M.J., Olsen T.L., Zhang A. “Allocation flexibility for agribusiness supply chains under market demand disruption”, International Journal of Production Research, (56), 3524–3546 (2018).