Understanding the Emergence of Pro-Social Behaviourin Dynamic Social Networks

In social dilemmas, where individuals make use of collective resources, "co-operators" pay a cost to improve public goods whereas "defectors" free-ride, reaping benefit without incurring any costs. While defection may be the individually rational strategy in one-shot interactions, we know that cooperation can be sustained in repeated games, where complex strategies emerge to counter defectors. In this project we want to understand what happens in dynamic social networks, where individuals facing a social dilemma can choose who to interact with, and can observe(and potentially imitate) others'
behaviour. So, when do social networks promote cooperation? is it the ease of making connections, the type of interaction, or the similarity we have with others that motivate us to look beyond our self-interest? In recent theoretical analysis we found that the ratio between the speed of imitation and partner selection is key to promote cooperation. Our results are however still limited to simple partner selection and imitation rules and are still not linked with empirical findings i.e., what humans do in practice. The objective of this project is to devise a theoretical model of learning-based cooperation on social networks and link it to behavioural data we have collected, with the goal of identifying explanations for the patterns of real cooperation and defection we see in the human data. This is an interdisciplinary challenge that uses both analytical and experimental methodologies to come up with a rigorous and empirically backed theoretical model explaining when pro-social behaviour can emerge in dynamic social networks. The project undertaker will employ theoretical analysis, i.e., through the study of dynamical systems and/or their algorithmic complexity, computer-aided analysis, i.e., through agent-based modelling and simulation, and empirical analysis, i.e., through experiments with humans.

In the 2018 Government Office for Science report, 'Computational Modelling: Technological Futures', Greg Clarke, the Secretary of State for Business Energy and Industrial Strategy, wrote "Computational modelling is essential to our future productivity and competitiveness, for businesses of all sizes and across all sectors of the economy". With its focus on computational models, the mathematics that underpin them, and their integration with complex data, the MathSys II CDT will generate diverse impacts beyond academia. This includes impacts on skills, on the economy, on policy and on society.

Impacts on skills.
MathSys II will produce a minimum of 50 PhD graduates to support the growing national demand for advanced mathematical modelling and data analysis skills. The CDT will provide each of them with broad core skills in the MSc, a deep knowledge of their chosen research specialisation in the PhD and a complementary qualification in transferable skills integrated throughout. Graduates will thus acquire the profiles needed to form the next generation of leaders in business, government and academia. They will be supported by an integrated pastoral support framework, including a diverse group of accessible leadership role models. The cohort based environment of the CDT provides a multiplier effect by encouraging cohorts to forge long-lasting professional networks whose value and influence will long outlast the CDT itself. MathSys II will seek to maximise the influence of these networks by providing topical training in Responsible Research and Innovation, by maintaining a robust Equality, Diversity & Inclusion policy, and by integration with Warwick's global network of international partnerships.

Economic impacts.
The research outputs from many MathSys II PhD projects will be of direct economic value to commercial, public sector and charitable external partners. Engagement with CDT partners will facilitate these impacts. This includes co-supervision of PhD and MSc projects, co-creation of Research Study Groups, and a strong commitment to provide placements/internships for CDT students. When commercial innovations or IP are generated, we will work with Warwick Ventures, the commercial arm of the University of Warwick, to commercialise/license IP where appropriate. Economic impact may also come from the creation of new companies by CDT graduates. MathSys II will present entrepreneurship as a viable career option to students. One external partner, Spectra Analytics, was founded by graduates of the preceding Complexity Science CDT, thus providing accessible role models. We will also provide in-house entrepreneurship training via Warwick Ventures and host events by external start-up accelerator Entrepreneur First.

Impacts on policy.
The CDT will influence policy at the national and international level by working with external partners operating in policy. UK examples include Department of Health, Public Health England and DEFRA. International examples include World Health Organisation (WHO) and the European Commission for the Control of Foot-and-mouth Disease (EuFMD). MathSys students will also utilise the recently announced UKRI policy internships scheme.

Impacts on society.
Public engagement will allow CDT students to promote the value of their research to society at large. Aside from social media, suitable local events include DataBeers, Cafe Scientifique, and the Big Bang Fair. MathSys will also promote a socially-oriented ethos of technology for the common good. Concretely, this includes the creation of open-source software, integration of software and data carpentry into our computational and data driven research training and championing open-access to research. We will also contribute to the 'innovation culture and science' strand of Coventry's 2021 City of Culture programme.