Complex Economic and Social Systems


Te Pūnaha Matatini is using methods from complex systems analysis and organisational-level data sets to understand the role of innovation in productivity growth, and to assess the importance of knowledge, network, and supply-chain spillovers on rm behaviour.

Our work


The last decade has seen dramatic advances in our understanding of complex economic networks. Researchers at Te Pūnaha Matatini are applying new methods from complexity science to better understand New Zealand’s economic and innovation performance. New Zealand’s failure to close the gap in GDP with other advanced economies has been attributed to our small scale and distance from major markets, but the manner in which these factors in uence the New Zealand economy’s ability to capture and benefit from knowledge spillovers is largely unexplored. Understanding the potentiality of spillovers from diversity will inform government policy and decision-making, and will assist in the evaluation of the effectiveness and impact of government policies.


Our impact

Our research informs government policy and decision-making, and will assist in the evaluation of the e ectiveness and impact of government policies. We work closely with the Ministry of Social Development, the Ministry for Business, Innovation, and Employment, and the Ministry for the Environment, which are sponsors of much of our work.

Our team


Our Research

Te Pūnaha Matatini researchers are applying new methods from complexity science to better understand New Zealand’s economic performance, the impact of innovation, and issues in social development. This understanding will inform government policy and decision-making, and will assist in the evaluation of the e ectiveness and impact of government policies.


Research highlight: Social development

The Network Science for the Social Sector project applied social network analysis to develop a new model that uses rela- tionship information to assess risk to children. Evidence-based decision making tools are increasingly common in social services provision but few, if any, have used social network data. This programme was directly funded by the Ministry for Social Development (MSD). The results show that information about close family relationships integrated from national data- bases can substantially improve decision making as well as quantifying the importance of family relationships in children’s lives and providing additional information about risk factors
for social workers assessing a case. A working paper was discussed at a Roundtable on Social Investment with Minister Sepuloni in Wellington in July. Laura Black, Director of the Methodist Mission Southern, a prominent social services provider, said “The practical implications if the child network study are quite profound when considering a preventative lens.”

• A. James, J. McLeod, M. Plank, S. C. Hendy, K. Marks, D. Rusu, and S. Nik, “Hidden in plain sight: the e ect of close family relationships on outcomes for children”, Te Pūnaha Matatini working paper (2018).


Research highlight: Knowledge networks

Samin Aref, one of our rst PhD graduates, took up a post-doctoral fellowship at the Max Planck Institute for Complex Systems in 2018. He published nine papers during his PhD, with the last one appearing in the Proceedings of the Austral- asian Computer Science Week Multiconference in 2018. This paper was based on his internship at the Ministry of Business, Innovation, and Employment, where he curated a Scopus bibliometric dataset for identi cation of New Zealand research institutions. This was applied to study co-institutional research collaboration within New Zealand.

• Aref, S., Friggens, D., Hendy, S. “Analysing scienti c collabo- rations of New Zealand institutions using scopus bibliometric data” ACM International Conference Proceeding Series, 3167920, (2018).


Research highlight: Knowledge networks

Demival Vasques and Dion O’Neale published their work on bipartite networks, i.e. networks that connect two types of entity, such as research articles and their authors. These types of networks are important in the analysis of social and economic systems as they explicitly show conceptual links between di erent types of entities. They show that bipartite degree distributions are not the only feature driving topology formation of projected networks (networks of papers or networks of co-authors), in contrast to what is commonly described in the literature.

• Vasques Filho, D., & O’Neale, D. R. “Degree distributions of bipartite networks and their projections” Physical Review E, 98(2), 022307 (2018).


Research highlight: Evidence and policy-making

Suzi Kerr and Ste en Lippert released a working paper that looks at how countries can cooperate to tackle climate change. International agreements addressing climate change must overcome the di culties implied by the absence of an institution with the power to ensure compliance. They have to be self-enforcing: the threat of future punishment must give participants su cient incentives to comply with the agreed reductions in emissions voluntarily. Every country has an incentive to increase their emissions unilaterally, to produce higher economic output, bene tting the individual country. The environmental costs or the increase in greenhouse gas emissions, however, are shared by the community of coun- tries, leading to ine ciently high individual incentives to emit. In contrast, e cient global mitigation – low emissions by all countries – generates the greatest joint gains. In the context of climate change, this means that, for patient countries, the loss of future cooperation is so large that short-run opportun- istic increases in emissions today do not pay. Unfortunately, as we have learned from 30 years of climate negotiations, this theoretical insight does not easily transfer into reality. Because we do not like where the rules of the game take us, we need to change them.

• Suzi Kerr, Ste en Lippert, and Edmund Lou “Transfers, self-enforcing agreements and climate cooperation” From Theory to Application 21.2018 Coalition Theory Network (2018).