Computational Social Science

The use of 'big data' for a wide range of social science research. The term was coined by Lazer et al in Science. 2009 February 6; 323(5915): 721–723.

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Projects

Bayesian Agent-Based Population Studies: Transforming Simulation Models of Human Migration

This is a cutting-edge project in demographic methodology, funded by the European Research Council (ERC), through the Consolidator Grant ERC-CoG-2016-725232. Its aim is to develop a ground-breaking simulation model of international migration, based on a population of intelligent, cognitive agents, their social networks and institutions, all interacting with one another. The project also aims to transform the study of migration – one of the most uncertain population processes and a top-priority policy area – by offering a step change in the way it can be understood, predicted, and managed.

Care Life Cycle

Seth Bullock, Sally Brailsford, Jason Noble, Jakub Bijak (Investigators), Elisabeth zu-Erbach-Schoenberg, Jason Hilton, Jonathan Gray

This research programme brings together teams of researchers from social sciences, management science and complexity science to develop a suite of models representing the socio-economic and demographic processes and organisations implicated in the UK’s health and social care provision. Integral to the project is working with our partners in the public sector and communicating the results of these models to policymakers allowing them to effectively plan for the future.

Census 2022: Transforming Small Area Socio-Economic Indicators through Big Data

Patrick James, Ben Anderson (Investigators)

One of only 20 to be funded under the ESRC’s new ‘Transformative Social Science’ programme, this project will explore the feasibility of estimating small area (neighbourhood) census-like statistics from transactional ‘big data’ including large scale fine grained temporal energy monitoring data held by the Energy & Climate Change Division (FEE).

It takes all sorts: the mathematics of people’s behaviour in financial markets

Valerio Restocchi (Investigator), Frank McGroarty, Enrico Gerding

Agent-based models provide a deeper understanding of financial markets than classic models. We model people's behaviour and use agent-based simulations to study financial markets. By analysing the emerging complex dynamics, we achieve a deeper understanding of market participants' behaviours, which are necessary for a deeper comprehension of financial markets themselves.

SAVE: Solent Achieving Value through Efficiency

Patrick James, Ben Anderson (Investigators), Luke Blunden

Analysis of 15 minute electricity consumption and 10 second instantaneous power data from 4,000+ households in the Solent region collected over 3 years of a randomised control trial study.