Computational social science, sociology of science and science policy, innovation, consumer behaviour, sociology of the environment, privacy and surveillance.
Current projects include:
Whole Systems Energy Modelling Consortium (WholeSEM):
Energy models provide essential quantitative insights into the 21st Century challenges of decarbonisation, energy security and cost-effectiveness. Models provide the integrating language that assists energy policy makers to make improved decisions under conditions of pervasive uncertainty. Whole systems energy modelling also has a central role in helping industrial and wider stakeholders assess future energy technologies and infrastructures, and the potential role of societal and behavioural change.
The key aims of the interdisciplinary wholeSEM consortium are to:
Undertake internationally cutting edge research on prioritised energy system topics;
Integrate whole energy systems modelling approaches across disciplinary boundaries;
Build bilateral engagement mechanisms with the wider UK energy systems community in academia, government and industry.
The Global Dynamics of Extortion Racket Systems
The GLODERS research project is directed towards development of an ICT model for understanding a specific aspect of the dynamics of the global financial system: Extortion Racket Systems (ERSs). ERSs. GLODERS will provide a theory-driven set of computational tools, developed through a process of participatory modelling with stakeholders, to study, monitor, and possibly predict the dynamics of ERSs, as they spread from local through regional into global influence.
The research will draw on expertise already developed in the small, but highly experienced multidisciplinary consortium to use:
• computer-assisted qualitative text mining of documentary evidence;
• guided semi-automatic semantic analysis of stakeholder narratives and other textual data; and
• multi-level, stakeholder-centred agent-based modelling of the distributed negotiations between normative agents.
These methods will advance the state of the art for using data to inform policy decisions.
Throughout, the project will interact with a large, international group of stakeholder representatives from EU Ministries of Justice and police forces. The output will provide a set of ICT tools to facilitate strategic policies that could prevent the further penetration and extension of the global menace posed by ERSs.
The GLODERS project is supported by the European Commission's Framework Programme 7 from October 2012 to September 2015.
ERIE addresses a series of fundamental questions relating to the application of complexity science to social and economic systems. The programme aims to embed cutting-edge complexity science methods and techniques within prototype computational tools that will provide policymakers with realistic and reliable platforms for strategy-testing in real-world socio-economic systems.
Quality Collectives (QLectives)
QLectives aims to combine three recent trends within information systems:
QLectives aims to bring these together to form Quality Collectives, i.e. functional decentralised communities that self-organise and self-maintain for the benefit of the people who comprise them. The project will generate theory at the social level, design algorithms and deploy prototypes targeted towards two application domains:
QLectives is supported by the European Commission 7th Framework Programme (FP7) for Research and Technological Development under the Information and Communication Technologies Theme, Future and Emerging Technologies (FET) Proactive, Call 3: ICT-2007.8.4 Science of Complex Systems for socially intelligent ICT (COSI-ICT).
The SIMIAN (Simulation Innovation: a Node) project is funded by the Economic and Social Research Council to promote and develop social simulation in the UK. The project started in September 2008, and involves a collaboration between the Centre for Research in Social Simulation (CRESS) and Dr Edmund Chattoe-Brown at the University of Leicester. SIMIAN is a node of the National Research Methods Centre. The project involves a programme of training courses and three "demonstrator" simulations chosen to address important social science challenges.
Recently completed projects include:
The NEW TIES project aimed to grow an artificial society using computer programming that develops agents—or adaptive, artificial beings—that have independent behaviours. The project's goal was to evolve an artificial society capable of exploring and understanding its environment through cooperation and interaction. The agents are sufficiently complex and their environment demanding, which enables them to develop a communication system to learn how to cooperate and to adapt.
Emergence in the loop (EMIL)
The main objective of this project was to understand and develop design strategies able to cope with the complex 2-way dynamics of sociality, consisting of emergent and immergent processes: from interaction among individual agents to aggregate level, and immergence of entities (norms) at the aggregate level into agents' minds. In particular, it focused on norm innovation. As research priorities, beside dealing with incompleteness and uncertainty, it contributed to the understanding and description of hierarchic systems by describing agents acting on multiple, i.e. individual, communitarian and institutional levels.
The objective of NEMO was to investigate the interplay between political governance, structure and function of politically induced R&D collaboration networks, in particular the networks that have emerged in the European Framework Programmes. The ultimate goal was to identify ways to create and to appraise desirable ('optimal') network structures for typical functions of such R&D collaboration networks (e.g. knowledge creation, transfer and (distribution). This will aid policymakers at all political levels in improving the effectiveness and efficiency of network-based policy instruments at promoting the knowledge economy in Europe.
Pattern Resilience (PATRES)
The project developed methods and prototype software tools for modelling and managing pattern resilience in complex systems. Pattern resilience is understood as the capacity of the system to maintain or to recover some desired pattern dynamics (which are related to useful functions) in a changing environment. The pattern dynamics are evolving statistical regularities which are generated by the interconnected components of the system. The methods will be tested on a set of applications, including: bacteria dynamics, land-use in semi-arid savannas, learning of sequences in basal ganglia, language variety, and biotech firm networks.
SIMWEB: aimed to provide European businesses in the digital contents sector with insights and tools which will enable them to take informed business strategy decisions and become more competitive by adapting their traditional business models. To achieve this objective, SimWeb has designed and implemented sector models based on innovative, reusable, and highly scalable multi-agent simulation technology. These computer-based models, calibrated to market data extracted from sector surveys, allow market participants in the digital contents sector to run through a variety of social and economic scenarios, and observe the impact they have on their businesses in particular, and on the competitive digital contents landscape in general.
EICSTES: European Indicators, Cyberspace and the Science-Technology-Economy System, was a project funded by the European Union to develop indicators of how the Science-Technology-Economy system is being affected by the growth of the Internet. Our contribution is an analysis of how the web is used, looking at it from the point of view of the user, rather than the technology.
FIRMA: Freshwater Integrated Resource Management with Agents was an EU project that brought together environmental scientists and social scientists to develop simulations to help manage drinking water at the local level in Europe.
PETRAS was an EU project that compared public and business reactions to proposals to implement ecological tax reforms in Europe (project completed).
SEIN: Simulation of self-organising Innovation Networks was an EU project that developed a theory of innovation networks, expressed as a computational model. The project also carried out case studies of biotechnology, web designers, combined heat and power, and mobile communications research to examine the role of innovation networks (project completed).
IMAGES: This EU project developed a simulation model for EU policymakers to help them design better 'Agri-Environmental Measures' (contracts with farmers that pay them to farm in a more environmentally desirable way) (project completed).
SOEIS: the Self-organisation of the European Information Society, an EU project for which the contribution from the University of Surrey has been to carry out a comparative study of the research funding systems in European states.
EPRESS: this JISC funded project has developed tools for publishing electronic journals on the internet.
Research methods, computational social science
Chair, Management Board, Sociological Research Online
Editor, Journal of Artificial Societies and Social Simulation
Editor, Social Research Update
Director, Centre for Research in Social Simulation
Director, University of Surrey Institute of Advanced Studies
Find me on campus Room: 20 AD 03
Agent-based simulation can model simple micro-level mechanisms capable of generating macro-level patterns, such as frequency distributions and network structures found in bibliometric data. Agent-based simulations of organisational learning have provided analogies for collective problem solving by boundedly rational agents employing heuristics. This paper brings these two areas together in one model of knowledge seeking through scientific publication. It describes a computer simulation in which academic papers are generated with authors, references, contents, and an extrinsic value, and must pass through peer review to become published. We demonstrate that the model can fit bibliometric data for a token journal, Research Policy. Different practices for generating authors and references produce different distributions of papers per author and citations per paper, including the scale-free distributions typical of cumulative advantage processes. We also demonstrate the model’s ability to simulate collective learning or problem solving, for which we use Kauffman’s NK fitness landscape. The model provides evidence that those practices leading to cumulative advantage in citations, that is, papers with many citations becoming even more cited, do not improve scientists’ ability to find good solutions to scientific problems, compared to those practices that ignore past citations. By contrast, what does make a difference is referring only to publications that have successfully passed peer review. Citation practice is one of many issues that a simulation model of science can address when the data-rich literature on scientometrics is connected to the analogy-rich literature on organisations and heuristic search.
Collective representations of the quality of artifacts are produced by human societies in a variety of contexts. These representations of quality emerge from a broad range of social interactions, from the uncoordinated behaviour of large collectives of individuals, to the interaction between individuals and organizations, to complex socio-technical processes such as those enabled by online peer production systems. This special issue brings together contributions from sociology, social psychology and social simulation to shed light on the nature of these representations and the social processes that produce them.
Modern knowledge-intensive economies are complex social systems where intertwining factors are responsible for the shaping of emerging industries: the self-organising interaction patterns and strategies of the individual actors (an agency-oriented pattern) and the institutional frameworks of different innovation systems (a structure-oriented pattern). In this paper, we examine the relative primacy of the two patterns in the development of innovation networks, and find that both are important. In order to investigate the relative significance of strategic decision making by innovation network actors and the roles played by national institutional settings, we use an agent-based model of knowledge-intensive innovation networks, SKIN. We experiment with the simulation of different actor strategies and different access conditions to capital in order to study the resulting effects on innovation performance and size of the industry. Our analysis suggests that actors are able to compensate for structural limitations through strategic collaborations. The implications for public policy are outlined.
The relationship between social segregation and workplace segregation has been traditionally studied as a one-way causal relationship mediated by referral hiring. In this paper we introduce an alternative framework which describes the dynamic relationships between social segregation, workplace segregation, individuals’ homophily levels, and referral hiring. An agent-based simulation model was developed based on this framework. The model describes the process of continuous change in composition of workplaces and social networks of agents, and how this process affects levels of workplace segregation and the segregation of social networks of the agents (people). It is concluded that: (1) social segregation and workplace segregation may co-evolve even when hiring of workers occurs mainly through formal channels and the population is initially integrated (2) majority groups tend to be more homophilous than minority groups, and (3) referral hiring may be beneficial for minority groups when the population is highly segregated.
This paper assesses the content- and population-dynamics of a large sample of wikis, over a timespan of several months, in order to identify basic features that may predict or induce different types of fate. We analyze and discuss, in particular, the correlation of various macroscopic indicators, structural features and governance policies with wiki growth patterns. While recent analyses of wiki dynamics have mostly focused on popular projects such as Wikipe-dia, we suggest research directions towards a more general theory of the dynamics of such communities. © 2008 ACM.
Les communautés eén ligne collaboratives ont connu un succés massif avec l’émergence des services et des plates-formes Web 2.0. Les wikis, et notamment la Wikipedia sont un des exemples les plus saillants de ce type de communautés de construction collective de contenus. La Wikipedia a á cet égard jusqu’ici concentré l’essentiel des efforts de recherche au sujet de ces communautés, même si l’ensemble des wikis constitue un écosystème possédant une très grande diversité de contenus, de populations, d’usages, de systèmes de gouvernance. Au contraire de la Wikipedia qui a probablement atteint la masse critique lui permettant d’être viable, la plupart des wikis luttent pour survivre et sont en compétition afin d’attirer contributeurs et articles de qualit é, connaissant ainsi des destinées variées, vertueuses – croissance en population et en contenu – ou fatales – inactivité et vandalisme.
Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appear-it is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools. © 2007 Springer Science+Business Media B.V.
The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs.
Agent-based modeling and social simulation have emerged as both developments of and challenges to the social sciences.
An agent-based computational model, based on longitudinal ethnographic data about the dynamics of intra-group behaviour and work group performance, has been developed from observing an organizational group in the service sector. The model, in which the agents represent workers and tasks, is used to assess the effect of emotional expressions on the dynamics of interpersonal behaviour in work groups, particularly for groups that have recent newcomers. The model simulates the gradual socialization of newcomers into the work group. Through experimenting with the model, conclusions about the factors that influence the socialization process were studied in order to obtain a better understanding of the effect of emotional expressions. It is shown that although positive emotional display accelerates the socialization process, it can have negative effects on work group performance.
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