Skip to main content
University of Bath

Driving socio-economic development

This research project seeks to uncover ways policy makers can stimulate socio-economic development, and aims to deliver policy intervention evaluation tools.

Ariel view of Silicon Valley
Silicon Valley in the US is an iconic example of social and economic development based on self-reinforcing processes.

From Silicon Valley to the proposed 'Northern Powerhouse' and beyond

Social and economic development is often driven by self-reinforcing processes which evolve within a geographical cluster. An iconic example of this is Silicon Valley in the US.

In the United Kingdom, the government is seeking to stimulate similar socio-economic clusters such as the 'Northern Powerhouse' by promoting a self-sustaining process of transformation.

Dynamics of Cumulative Innovation in Complex Social Systems (DCICSS)

Using models of co-evolving systems to understand these social and economic dynamics, the DCICSS research project seeks to identify how policymakers can intervene to stimulate socio-economic development, and provide tools for evaluating policy interventions.

Our central questions are:

  • How can we conceptualise and model self-reinforcing processes of social and economic change?
  • How can we evaluate corresponding policy interventions?

We will address these questions by drawing on mathematical models of non-linear systems and apply them to a range of well-chosen empirical data sets, using carefully crafted statistical tools.

Enquiries

Institute for Mathematical Innovation (IMI) imi@bath.ac.uk 01225 385185

The project

Using mathematics to better understand co-evolving networked systems and innovation

The aim is to develop a novel approach to improve our understanding of co-evolving networked systems, modelled in mathematically precise terms and applied to empirical data sets.

We will explore how such mathematical models can illuminate processes of cumulative innovation within different institutional settings. To test competing theoretical accounts, we will also develop statistical methods that can be used to investigate empirical data.

Delivering a toolset for evaluating policy interventions and private initiatives

By studying how policymakers can intervene in complex social and economic dynamics, our research aims to provide tools for evaluating policy interventions and private initiatives. We will use a series of empirical case studies to develop and test the toolset.

The project will focus on public policies aimed at enhancing the self-reinforcing dynamics of social and economic development. We also seek to understand the role of corporations and other actors as they intervene in these socio-economic transformations.

The dynamics of co-evolution

Jain and Krishna (2003) provided our theoretical starting point. They are interested in the dynamics of co-evolution and use network theory to explore autocatalytic sets (ACS) which are sets of simple molecular organisms that are unable to self-replicate, but each providing a catalyst for its fellows.

Likewise, insects and flowering plants have co-evolved, thriving disproportionately by virtue of their mutual adaptations. Jain and Krishna use computational models to simulate such a dynamic system. This reveals ‘punctuated equilibria’, with processes of growth and then partial collapse.

Two evolutionary scales: the fast and slow dynamics

One interesting feature of the Jain and Krishna model is the two evolutionary scales. The fast (or short) dynamics correspond to the fitness propagation within the network, and the slow (or long) dynamics correspond to the update of the network. These features have interpretations in social networks. For example, the fast and slow dynamics can be interpreted respectively as local innovation (transfer of knowledge between similar technologies) and global innovation (updates to the global technology system).

Studying self-reinforcing processes of social and economic change

The two evolutionary scales provide our distinctive focus and vantage point, for studying self-reinforcing processes of social and economic change. We do so across a range of diverse empirical problems:

  • we develop groundbreaking mathematical variations on the basic Jain and Krishna model, appropriate to the various case studies
  • we link our empirical data analysis to the theoretical economic and social science literature on cumulative causation and innovation

The Jain and Krishna model has attracted a good amount of attention in the literature on complex network dynamics, and they have themselves discussed its potential application to patterns of technological innovation. Nevertheless this is, to our knowledge, the first and most ambitious attempt to apply their model on a multi-disciplinary basis and with a strong empirical and policy emphasis.

Empirical case studies

Patents: The initial empirical case study is on patents, and we have been given access to the PATSTAT datasets we require. Using the case study of patents as our template, we will extend the research across further social and economic case studies with a view to expanding the mathematical, statistical and empirical scope of the Jain and Krishna model.

Additional case studies are likely to include some of the following:

  • Mergers and Acquisitions to track the development and co-evolution of different capabilities in firms
  • Financial system to model the co-evolution of risks within connected financial systems
  • Welfare regimes to model how institutions concerned with social security, vocational training, employment, etc. co-evolve to produce a number of distinct regimes typical of particular countries
  • Developing countries to link science and engineering innovations with the institutional contexts and ‘soft technologies’ of the developing world
  • Digital social networks to examine the dynamics of social media, and the insights offered by our modelling for data analytics and digital governance

Research overview, data and papers

We are pleased to make available our research data and first findings on co-evolving systems, technology networks, and innovation patterns.

Project team

This research is led by a multi-disciplinary team of mathematicians and social scientists at the University:

External research associates

  • Dr Emanuele Pugliese, Fellow at the Institute for Complex Systems, National Research Council (Italy)
  • Dr Lorenzo Napolitano, post-doctoral Fellow, Institute for Complex Systems, National Research Council (Italy).

Other colleagues will join the research team as new empirical case studies are undertaken.

Acknowledgements

We gratefully acknowledge the assistance of PATSTAT at the European Patent Office, who kindly supplied the datasets we are using for our patent case study.