Computational social science, sociology of science and science policy, innovation, sociology of the environment, energy policy, the use of models in the policy process.
Current projects include:
Centre for the Evaluation of Complexity Across the Nexus (CECAN)
The Centre for the Evaluation of Complexity Across the Nexus, a £3m research centre hosted by the University of Surrey, brings together a unique coalition of experts to address some of the greatest issues in policy making and evaluation. Nexus issues are complex, with many diverse, interconnected factors involved. This presents a major challenge to policy making because changing one factor can often have unexpected knock-on effects in seemingly unrelated areas. We need new ways to evaluate policy in these situations. CECAN will pioneer, test and promote innovative evaluation approaches and methods across nexus problem domains, such as biofuel production or climate change, where food, energy, water and environmental issues intersect. The Centre will promote ‘evidence based policymaking’ by finding ways for the results of evaluation to both inform policy, and reflect back onto future policy design. Embracing an 'open research' culture of knowledge exchange, CECAN benefits from a growing network of policymakers, practitioners and researchers and a core group of academic and non-academic experts.
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. Our contribution to this major four-year EPSRC funded project is to develop models of household energy demand.
Evolution and Resilience of Industrial Ecosystems (ERIE)
ERIE addresses a series of questions relating to the application of complexity science to social and economic systems. The programme aims to embed 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. The programme is supported by EPSRC and will be completed by May 2016.
Collective Reasoning as a Moral Point of View
Everybody knows a free rider: the flatmate that does not do the dishes, the friend who never invites back for dinner, the work colleague who pushes work your way. If honest, everyone can probably point to a situation where they did something like this. The same person might be a free-rider in one situation and a collective contributor in the next. What are the triggers of making one choice or the other? How do these triggers depend on social factors such as observations of behaviours in ones network or the overall population? What are the population outcomes of different social and personal dynamics? And what kinds of social structures strengthen or weaken cooperative behaviour? This AHRC funded project (July 2014 – July 2016) investigates the triggers and social settings of collective or individual choices and the resulting dynamics of cooperation using agent-based modelling.
Techno-social platform for sustainable models and value generation in commons-based peer production in the Future Internet (P2PValue)
Commons-based peer production (CBPP) is a new and increasingly significant model of social innovation based on collaborative production by citizens through the Internet. This project, partially funded by the European Commission’s Framework 7 Programme, will foster the CBPP phenomenon by providing a techno-social software platform specifically designed to facilitate the creation of resilient and sustainable CBPP communities. The design of the P2Pvalue platform will be empirically and experimentally grounded. Through a triangulation of qualitative and quantitative methods, we will elaborate guidelines for the institutional and technical features that favour value creation in CBPP.
HomeSense: digital sensors in social research
HomeSense will develop and demonstrate how digital sensors can be used to advantage in social research. The project is a collaboration between the Centre for Research in Social Simulation (CRESS) and the 5G Innovation Centre. Drawing on recent developments in the use of fixed and mobile sensors to measure location, movement, noise levels, air quality, temperature, energy use and a range of physical states, the project team will trial the use of such sensors in a sample of UK households. Households come in all sorts of configurations and they vary in their ways of organising the use of rooms, household devices and energy sources, as well as in the extent of communications amongst household members and the purposes for which members spend their time at home. Observations of households to-date have relied on self-reporting and on-site observations. With HomeSense we will demonstrate how to collect data from fixed and mobile sensors, and how to manage, technologically and responsibly, the intensive measuring of state, location, activity and interaction. We will show how this method affects respondent burden, consent, privacy and data security, and how the data can be converted into meaningful descriptions of socially relevant activities in conjunction with time-use diaries, questionnaires and walking interviews/observations.
Nigel Gilbert is interested in supervising doctoral students wishing to study innovative ways of using computational models in the social sciences, and interdisciplinary topics bridging engineering (especially computer science) and the social sciences. For more more details about PhDs in CRESS, please go to: http://www.surrey.ac.uk/sociology/study/postgraduateresearch/index.htm
Research methods, computational social science
Chair, Management Board, Sociological Research Online
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
Computational models are increasingly being used to assist in developing, implementing and evaluating public policy. This paper reports on the experience of the authors in designing and using computational models of public policy (‘policy models’, for short). The paper considers the role of computational models in policy making, and some of the challenges that need to be overcome if policy models are to make an effective contribution. It suggests that policy models can have an important place in the policy process because they could allow policy makers to experiment in a virtual world, and have many advantages compared with randomised control trials and policy pilots. The paper then summarises some general lessons that can be extracted from the authors’ experience with policy modelling. These general lessons include the observation that often the main benefit of designing and using a model is that it provides an understanding of the policy domain, rather than the numbers it generates; that care needs to be taken that models are designed at an appropriate level of abstraction; that although appropriate data for calibration and validation may sometimes be in short supply, modelling is often still valuable; that modelling collaboratively and involving a range of stakeholders from the outset increases the likelihood that the model will be used and will be fit for purpose; that attention needs to be paid to effective communication between modellers and stakeholders; and that modelling for public policy involves ethical issues that need careful consideration. The paper concludes that policy modelling will continue to grow in importance as a component of public policy making processes, but if its potential is to be fully realised, there will need to be a melding of the cultures of computational modelling and policy making.
Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries.
This paper investigates the fate of manuscripts that were rejected from JASSS-The Journal of Artificial Societies and Social Simulation, the flagship journal of social simulation. We tracked 456 manuscripts that were rejected from 1997 to 2011 and traced their subsequent publication as journal articles, conference papers or working papers. We compared the impact factor of the publishing journal and the citations of those manuscripts that were eventually published against the yearly impact factor of JASSS and the number of citations achieved by the JASSS mean and top cited articles. Only 10% of the rejected manuscripts were eventually published in a journal that was indexed in the Web of Science (WoS), although most of the rejected manuscripts were published elsewhere. Being exposed to more than one round of reviews before rejection, having received a more detailed reviewer report and being subjected to higher inter-reviewer disagreement were all associated with the number of citations received when the manuscript was eventually published. This indicates that peer review could contribute to increasing the quality even of rejected manuscripts.
Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals’ protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.
Models are used to inform policymaking and underpin large amounts of government expenditure. Several authors have observed a discrepancy between the actual and potential use of models in government. While there have been several studies investigating model acceptance in government, it remains unclear under what conditions models are accepted. In this paper, we address the question ‘‘What criteria affect model acceptance in policymaking?’’, the answer to which will contribute to the wider understanding of model use in government. We employ a thematic coding approach to identify the acceptance criteria for the eight models in our sample. Subsequently, we compare our findings with existing literature and use qualitative comparative analysis to explore what configurations of the criteria are observed in instances of model acceptance. We conclude that model acceptance is affected by a combination of the model’s characteristics, the supporting infrastructure and organizational factors.
This paper looks at 10 years of reviews in a multidisciplinary journal, The Journal of Artificial Societies and Social Simulation (JASSS), which is the flagship journal of social simulation. We measured referee behavior and referees' agreement. We found that the disciplinary background and the academic status of the referee have an influence on the report time, the type of recommendation and the acceptance of the reviewing task. Referees from the humanities tend to be more generous in their recommendations than other referees, especially economists and environmental scientists. Second, we found that senior researchers are harsher in their judgments than junior researchers, and the latter accept requests to review more often and are faster in reporting. Finally, we found that articles that had been refereed and recommended for publication by a multidisciplinary set of referees were subsequently more likely to receive citations than those that had been reviewed by referees from the same discipline. Our results show that common standards of evaluation can be established even in multidisciplinary communities.
Missing data frequently occurs in quantitative social research. For example, in a survey of individuals, some of those selected for interview will not agree to participate (unit non-response) and others who do agree to be interviewed will not always answer all the questions (item non-response).
At its most benign, missing data reduces the achieved sample size, and consequently the precision of estimates. However, missing data can also result in biased inferences about outcomes and relationships of interest. Broadly, if the underlying, unseen, responses from those individuals in the survey frame who have one or more missing responses differ systematically from those individuals in the survey frame whose responses are all observed, then any analysis restricted to the subset of individuals whose responses are all observed runs the risk of producing biased inferences for the target population.
Thus every researcher needs to take seriously the potential consequences of missing data. This paper describes the use of Multiple Imputation (MI) to correct estimates for missing data, under a general assumption about the cause, or reason for missing data. This is generally termed the missingness mechanism. MI has robust theoretical properties while being flexible, generalisable and readily available in a range of statistical software.
Computer simulation models have been proposed as a tool for understanding innovation, including models of organisational learning, technological evolution, knowledge dynamics and the emergence of innovation networks. By representing micro-level interactions they provide insight into the mechanisms by which are generated various stylised facts about innovation phenomena. This paper summarises work carried out as part of the SIMIAN project and to be covered in more detail in a forthcoming book. A critical review of existing innovation- related models is performed. Models compared include a model of collective learning in networks , a model of technological evolution based around percolation on a grid [2, 3], a model of technological evolution that uses Boolean logic gate designs , the SKIN model , a model of emergent innovation networks , and the hypercycles model of economic production . The models are compared for the ways they represent knowledge and/or technologies, how novelty enters the system, the degree to which they represent open-ended systems, their use of networks, landscapes and other pre-defined structures, and the patterns that emerge from their operations, including networks and scale-free frequency distributions. Suggestions are then made as to what features future innovation models might contain. © Springer-Verlag Berlin Heidelberg 2014.
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.
This paper reports the results of a multi-agent simulation designed to study the emergence and evolution of symbolic communication. The novelty of this model is that it considers some interactional and spatial constraints to this process that have been disregarded by previous research. The model is used to give an account of the implications of differences in the agents' behavior, which are embodied in a spatial environment. Two communicational dimensions are identified: the frequency with which agents refer to different topics over time and the spatial limitations on reaching recipients. We use the model to point out some interesting emergent communicational properties when the agents' behavior is altered by considering those two dimensions. We show the group of agents able to reach more recipients and less prone to changing the topic have the highest likelihood of driving the emergence and evolution of symbolic communication.
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.
None of the standard network models fit well with sociological observations of real social networks. This paper presents a simple structure for use in agent-based models of large social networks. Taking the idea of social circles, it incorporates key aspects of large social networks such as low density, high clustering and assortativity of degree of connectivity. The model is very flexible and can be used to create a wide variety of artificial social worlds.
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.
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.
According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning. (c) 2006 Elsevier B.V. All rights reserved.
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.
What activities take place at home? When do they occur, for how long do they last and who is involved? Asking such questions is important in social research on households, e.g., to study energyrelated practices, assisted living arrangements and various aspects of family and home life. Common ways of seeking the answers rest on self-reporting which is provoked by researchers (interviews, questionnaires, surveys) or non-provoked (time use diaries). Longitudinal observations are also common, but all of these methods are expensive and time-consuming for both the participants and the researchers. The advances of digital sensors may provide an alternative. For example, temperature, humidity and light sensors report on the physical environment where activities occur, while energy monitors report information on the electrical devices that are used to assist the activities. Using sensor-generated data for the purposes of activity recognition is potentially a very powerful means to study activities at home. However, how can we quantify the agreement between what we detect in sensor-generated data and what we know from self-reported data, especially nonprovoked data? To give a partial answer, we conduct a trial in a household in which we collect data from a suite of sensors, as well as from a time use diary completed by one of the two occupants. For activity recognition using sensor-generated data, we investigate the application of mean shift clustering and change points detection for constructing features that are used to train a Hidden Markov Model. Furthermore, we propose a method for agreement evaluation between the activities detected in the sensor data and that reported by the participants based on the Levenshtein distance. Finally, we analyse the use of different features for recognising different types of activities.
Abstract This chapter addresses the relationship between sociology and Non- Equilibrium Social Science (NESS). Sociology is a multiparadigmatic discipline with significant disagreement regarding its goals and status as a scientific discipline. Different theories and methods coexist temporally and geographically. However, it has always aimed at identifying the main factors that explain the temporal stability of norms, institutions and individuals’ practices; and the dynamics of institutional change and the conflicts brought about by power relations, economic and cultural inequality and class struggle. Sociologists considered equilibrium could not sufficiently explain the constitutive, maintaining and dissolving dynamics of society as a whole. As a move from the formal apparatus for the study of equilibrium, NESS does not imply a major shift from traditional sociological theory. Complex features have long been articulated in sociological theorization, and sociology embraces the complexity principles of NESS through its growing attention to complex adaptive systems and non-equilibrium sciences, with human societies seen as highly complex, path-dependent, far-from equilibrium, and selforganising systems. In particular, Agent-BasedModelling provides a more coherent inclusion of NESS and complexity principles into sociology. Agent-based sociology uses data and statistics to gauge the ‘generative sufficiency’ of a given microspecification by testing the agreement between ‘real-world’ and computer generated macrostructures.When the model cannot generate the outcome to be explained, the microspecification is not a viable candidate explanation. The separation between the explanatory and pragmatic aspects of social science has led sociologists to be highly critical about the implementation of social science in policy. However, ABM allows systematic exploration of the consequences of modelling assumptions and makes it possible to model much more complex phenomena than previously. ABM has proved particularly useful in representing socio-technical and socio-ecological systems, with the potential to be of use in policy. ABM offers formalized knowledge that can appear familiar to policymakers versed in the methods and language of economics, with the prospect of sociology becoming more influential in policy.
The concept of self-organization in social science is reviewed. In the first two sections, some basic features of self-organizing dynamical systems in general science are presented and the origin of the concept is reconstructed, paying special attention to social science accounts of self-organization. Then, theoretical and methodological considerations regarding the current application of the concept and prospective challenges are examined.
Agent-based modeling and social simulation have emerged as both developments of and challenges to the social sciences.
According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning. This chapter is a reprint of an article published as Gilbert, GN, Ahrweiler, P. & Pyka, A. (2007). Learning in innovation networks: Some simulation experiments. Physica A, 378 (1): 100–109 DOI:10.1016/j.physa.2006.11.050. Available online at: http://www.sciencedirect.com/science/article/pii/S0378437106012714
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.
Commons-Based Peer Production (CBPP) is a new model of socio-economic production in which groups of individuals cooperate with each other without a traditional hierarchical organisation to produce common and public goods, such as Wikipedia or GNU/Linux. There is a need to understand how these communities govern and organise themselves as they grow in size and complexity. Following an ethnographic approach, this thesis explores the emergence of and changes in the organisational structures and processes of Drupal: a large and global CBBP community which, over the past fifteen years, has coordinated the work of hundreds of thousands of participants to develop a technology which currently powers more than 2% of websites worldwide. Firstly, this thesis questions and studies the notion of contribution in CBPP communities, arguing that contribution should be understood as a set of meanings which are under constant negotiation between the participants according to their own internal logics of value. Following a constructivist approach, it shows the relevance played by less visible contribution activities such as the organisation of events. Secondly, this thesis explores the emergence and inner workings of the socio-technical systems which surround contributions related to the development of projects and the organisation of events. Two intertwined organisational dynamics were identified: formalisation in the organisational processes and decentralisation in decision-making. Finally, this thesis brings together the empirical data from this exploration of socio-technical systems with previous literature on self-organisation and organisation studies, to offer an account of how the organisational changes resulted in the emergence of a polycentric model of governance, in which different forms of organisation varying in their degree of organicity co-exist and influence each other.
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