NEXSUS Overview
NEXSUS was the first Priority Network funded by the ESRC. Over a period of five years, the network had the central task of
developing a collaborative network of research projects. The theme that linked the research of the participants was that of the
sustainability of socio-economic systems, and how the new ideas of complexity science could help us understand and
improve it.
It had six participating research groups. See below for details of individual research projects.
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University of Manchester: CRIC, Prof S Metcalfe and Dr R Ramlogan
This project studied distributed innovation systems and the factors that affected the performance of adaptive
networks of companies.
University of Cambridge: Institute for Manufacturing, Dr E Garnsey
Study of the growth of hi-tech industrial firms in the Cambridge region. The study focussed on the growth
dynamics and the reasons for the different patterns of growth observed.
University of Sheffield: Prof K Ridgway and Dr B Winder
Regeneration of SME manufacturing firms in South Yorkshire.
University College London: Department of Archaeology, Dr J McGlade
The influences of power structures, hierarchies and networks on adaptive capacity over the long term.
University College London: CASA, Prof M Batty
Multi -agent and CA dynamic models of urban change, looked at multi -scalar patterns and impacts of planning etc.
Cranfield University: CSMC, Prof P M Allen and M Strathern
Models of adaptive industrial and business networks were develope d with case studies – Italian districts, UK
Aerospace and other examples.
NEXSUS structure
NEXSUS was made up of six research centres across five UK universities. At the core, the Network Co-ordination centre at
Cranfield linked the different institutions.
A number of other satellite institutions were also linked to NEXSUS and over the lifetime of the project, this led to the creation
of a Network of Networks and the formation of the Complexity Society. The Complexity Society is a network aimed at linking
people interested in the ideas and implications of Complexity. It also provides links through to many other Networks and
Societies working in related areas. For more information please visit the website at: www.complexity-society.com.
Other Networks
NEXSUS was also involved in helping to set up the Complexity Network based at Liverpool University. In addition the
Bologna Colloquium produced such enthusiasm among participants that a Network - Diversity Dynamics - was set up as a
result. All of these different elements were brought together when the Liverpool based Complexity Network organized a major
conference "Complexity, Science and Society", in September 2005, involving among others: NEXSUS partners, the
Complexity Society and Diversity Dynamics. The proceedings were published in the book “Complexity, Science & Society,
edited by Jan Bogg and Robert Geyer, Oxford: Radcliffe Publishing, 2007.
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Main Lines of Communication
NEXSUS Continuation
NEXSUS is continuing with a collaborating project between NEXSUS partners – an ESRC-funded joint project with the
University of Sheffield “Modelling the Evolution of the Aerospace Supply Chain.”
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Individual Research Projects
University of Manchester: CRIC
Project Title: Evolutionary Comple x Systems and Socio-Economic Sustainability
Principal Investigator: Professor Stan Metcalfe
Project Aims and Objectives
The aims and objectives of this Project were:
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The development of complex adaptive systems approaches to the study of Growth Innovation and Competition
The appraisal of complex system concepts in relation to economic and social processes of change
The appraisal of the complex systems concepts in relation to processes of economic and social change
The development of complex systems perspectives on innovation policy
Outcomes
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We have developed an analytical framework grounded in economy theory in which individual but diverse agents
were aggregated in a bottom up fashion to represent an economy operating far from equilibrium; competition was
emphasised as a process involving the co-ordination of diverse behaviours. Within this framework we established
two points:
o That growth and innovation are coupled emergent phenomena.
o That the average rate of progress depends upon the micro -diversity of the population as reflected in the
correlations between efficiency and innovation and efficiency and growth.
We argued that economic transformation is open-ended because the generation of information and its translation
into knowledge is also autocatalytic and open-ended. This is why the future of economic systems is not
predictable, their history is open -ended.
This model demonstrated that competition in the long run may not select the most efficient firm or eliminate all the
inefficient firms in an industry. Neither does a process of competitive selection ensure that the fastest growing
firms are always the most efficient firms. Hence we cannot strictly say that competition always increases the
average efficiency in the utilisation of resources.
Instead competitive selection maximises the rate at which the average efficiency in an industry changes over time.
This raises questions about the more general view that competition optimises the allocation of resources. The
implication of this is that a more nuanced appraisal of the institutions of the competitive process becomes
necessary.
From an evolutionary standpoint, the outcomes of competition are always contingent on the nature of the selection
environment and the characteristics of the whole population of firms that are being selected. All that was needed
to establish these conclusions was to recognise that the competitive performance of firms is multi dimensional,
and that a theory of competition requires a treatment of firm expansion as well as firm efficiency in the traditional
sense.
We have drawn a distinction between knowledge, which is private and social understanding. We conceived of
understanding as the social shared reflection of knowledge and suggested that it evolves as a socially distributed
process and its growth depends on institutions for sharing and common understanding. Recognising that
understanding is necessarily distributed leads to the insight that economic activity, which is necessarily social,
depends not on shared knowledge, but rather on shared understandings. This provides a clue as to the
unpredictability and unevenness of knowledge accumulation, and of course, the unpredictability of the capitalism
as a knowledge-driven system.
As an example of the complex dynamics of the interplay between knowledge and understanding we have carried
out detailed analysis of medical innovations in ophthalmology and cardiology, tracing the growth of communities of
practitioners and their relation with industry and the distributed innovation process. Figure 1 shows the angioplasty
medical network over the whole phase of its evolution from origins (bottom right) to the current state (bottom left).
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Figure 1 Network of Coronary Angioplasty Publications 1979-2003
The results of the study are expected to be of considerable interest to innovation policy makers and managers of technology
and innovation in industry and in the health sector. In particular, the idea of market failure is to be compared with the idea of
systems failure as a basis for policy.
University of Cambridge – Institute for Manufacturing, Department of Engineering
Project Title: Growth and Co-evolution as Complex Dynamic Processes in High-tech Enterprise –
Principal Investigator: Dr Elizabeth Garnsey
The Project
The core of this investigation has been ventures that show early promise but run into difficulties, revealed in case studies
(Acorn, Ionica, LaserScan, CIS). This process requires conceptual mapping. The causes are not captured by standard
statistical data that lack causal models to support the analysis.
Project Outcomes
Complexity offers a multi-level approach that has enabled us to trace connections between the science base, entrepreneurs,
high tech ventures and clusters of innovative activity.
Our research involved 1500 high tech firms in the Cambridge area, followed over ten years from 1990. Comparisons with
Sophia Antipolis and US cases help us identify features specific to the Cambridge experience. Cambridge clusters in IT and
the life sciences reflect the resource pool represented by the university science base.
We have identified why it is that entrepreneurs are agents of change and propagators of innovation, and how their
experiments are a source of diversity.
Figure (1) The Entrepreneurs are agents of change and propagators of innovation
Entrepreneurs seek to pursue opportunities despite resource shortages and without incurring dependence. Problem solving
within constraints is a source of creativity. Many business experiments fail.
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Early success does not guarantee sustainable products and markets, hence growth crises. But our longitudinal case studies
how that knowledge is recycled through spin-outs and clustering.
High tech entrepreneurs adapt their business models by learning from experience. The UK environment rewards flexibility
and penalizes irrecoverable investment. But the locality gains fro m investment in downstream competence as well as design
and leading edge knowledge.
The example is the industrial Ink Jet Print sector, the only Cambridge sector to achieve world market share. Local
competence in IJP is providing the basis for innovations in a new industry, plastronics.
In Silicon Valley and Massachusetts upstream and downstream activity have co-evolved. To achieve this in the UK requires
joined up thinking on policy.
University of Sheffield – Advanced Manufacturing Research Centre
Project Title: Industrial Regeneration: Sustainable Improvement Programmes
Professor Keith Ridgway
Dr. Belinda Winder
Mr Robert Murray
Dr James Baldwin
Project Summary
This research attempted to create an integrated framework for analysing
and stimulating the regeneration of regions. Individual organisations and
the populations of organisations that make up industrial economies were
considered as complex adaptive systems. To understand the behaviour
and existence of such systems, the research used knowledge and models
based on complex systems, sustainability, population ecology and
biological systematics.
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the development of complex systems’ models of industrial
development and regeneration
simulations of the cladistic evolution of manufacturing form
the development of a co-evolutionary framework for
understanding industrial decline
an investigation into entrepreneurial networks and the
psychology of entrepreneurs
Aims and Objectives
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To create definitions, models and a framework to assist the regeneration of industrial communities.
To apply these ideas to the region of South Yorkshire and Sheffield.
To develop models of sustainable complex systems and complex sustainable industrial development.
Using complexity theory, explore factors in the decline of South Yorkshire’s metals manufacturers.
To explore and empirically investigate the role of evolutionary complex systems’ thinking in manufacturing
cladistics
To apply evolutionary complex systems’ thinking in manufacturing functional studies.
The integration of the above objectives culminating in individualistic sustainable improvement programmes for the
use of manufacturing organisations.
Research Results
Simulating the Cladistic Evolution of Manufacturing Form (with P Allen, Cranfield)
This project was based on earlier research concerning “cladistic diagrams” (representing evolutionary history) showing the
occurrence of successive new practices and innovative ideas in the manufacturing sector. With the exploration of
organisational transformations, important insights were provided into commitment, the decision-making process, and risk and
unpredictability in management. The model demonstrated its ability to explore certain decisions, the consequences, and
potential new organisational structures that emerged. The main conclusion was that research in this new field and
information and tools that it would generate would be valuable for manufacturing organisations. It will facilitate a reflection of
the possible innovations, disrupting technologies, and threats and opportunities that they face.
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The N-ET Model of Industrial Development and Regeneration
The model examined the industrial process from a theoretical framework derived from the non-equilibrium thermodynamic
paradigm of systemic development. The case study for the N-ET model was South Yorkshire; the region’s development, its
metals manufacturing, clustering phenomenon and industrial decline.
The model was developed to meet several goals and aimed to extend and support the concept of Industrial Ecology.
As well as enhancing insight into the issues involved in the sustainable development and regeneration of industrial regions,
the novelty of this approach also introduces scope for increasing understanding of the transitions needed for existing
companies to remain competitive in mature metabolic systems.
A Cladistic Guide for Manufacturing Sustainability
The outcome of this project was a cladogram (‘evolutionary tree’) mapping the evolution of manufacturing sustainability. This
work continues to help provide guidelines in the form of sustainable improvement programmes which manufacturers may use
to help with the adoption of sustainable practices.
Co-Evolutionary Framework for understanding Industrial Decline (with J McGlade, UCL)
The failure of socio-economic systems to absorb change is demonstrated as a lack of system resilience, using the
spectacular example of the demise of the South Yorkshire coalfield. It is this ability to manage under conditions of uncertainty
and to create flexible planning strategies – those designed to withstand the impact of unintended consequences – that is the
hallmark of system resilience.
Entrepreneurial Networks and the Psychology of Entrepreneurs (with E Garnsey, Cambridge)
The research aimed to address the role of entrepreneurial networks and to identify factors that both hinder and allow
networks to prosper. Discussion outlines the significance of trust, social capita, and network effects in influencing the
development and evolution of enterprise networks.
We outline a proposed network for Sheffield, the primary objective of which is to develop and foster symbiotic relationships
between academics, industrialists, innovators, environmental and socially oriented organisation s, to aid in the transposition of
innovative technology for sustainable industrial regeneration. The ultimate aim of the network is to introduce innovative
perturbations, pushing the traditional manufacturing network system, via self organisation, to new system
University College London – Department of Geography, CASA.
Project Title: Multi-Agent Approaches to Urban Development Dynamics
Principal Investigator: Professor Mike Batty
Project Aim
To develop new models of urban development based on agent or object-based approaches which will tie urban development
to the actors and the processes which determine decisions affecting development change in cities.
Urban Complexity: Visual Simulation Models of Urban Change
The UCL group at CASA has been involved in a series of projects involving new approaches to understanding the city using
complexity theory. We have been developing models of urban change at the finest level possible using new small scale data
sets concerning the location of individual populations and firms in cities. The models we have been building are based on
new computational methods which enable individual objects to be handled consistently, and we have been developing
methods for communicating the results of this understanding using different forms of computer visualization. All these
projects are dominated by ‘bottom-up thinking’ in which ordered patterns in cities ‘emerge’ as the result of the relatively
independent actions of individuals. These are four related projects.
The Town Centre Project
The definition of town centres, using new data sets are enabling us to model the density of employment, floor space and
retail turnover at the scale of the unit post code (circa 50 metre resolution). Such models give us a new understanding of
retailing and the leakage of trade from town centres into unconventional outlets like airports and stations, letting us evaluate
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the sustainability and health of different town centres. This is of central importance to planning policy. Our work has focused
so far on Greater London for ODPM.
Simulating Crowds at the Notting Hill Carnival and the Tate Britain
Models of crowds at organised events have been developed to assess issues of congestion, security and crime. We have
simulated models of visitors at the Notting Hill Carnival where we combine ideas about flocking, panic, crowd control and
security so as to improve the Carnival management. These ideas are being extended to crowd control in subway stations,
high buildings and airports, and we have developed the model to simulate visitor movements in the Tate Britain.
Notting Hill Crowd Movements
Tate Britain Crowd Movements
Self-organization of Latin-American Cities
These models simulate how low income groups are attracted to cities and develop informal housing, often called squatter
housing on the edge of large cities. This is a classic process of bottom up action which is one of the constructs of complexity
theory, and these models show how such informal developments become more formal as their residents gradually improve
their living standards.
Models of informal development in typical Latin-American cities
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Gentrification
We are simulating gentrification in cities. The process is one of invasion and succession, ghettoisation of a kind, as one
social group eventually di splaces another. In these models, we have incorporated ideas about social networks.
Cranfield University – Complex Systems Management Centre
Industrial Networks Project Principal Investigator: Professor Peter Allen
Aims and Objectives
The main aim of this project was to develop new, self-organizing and evolutionary complex systems models of industrial and
business networks, based on learning, interacting multi -agent interactions. In order to achieve this there were five subsidiary
objectives, which were:
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To study Italian District Manufacturing Networks, in order to understand the mechanisms by which they are able to
adapt to rapidly changing demand
To use the information arising from a study on the supply chain of the aerospace industry in order to understand
the source of their capacity to create new products
To use the results of the Cambridge study of local high tech firms and of regeneration in specific the small
manufacturing sector of South Yorkshire to explore the reasons for growth and decline, and to sources of, and
barriers to, their adaptability
To use these four different examples as case studies for the conceptual framework offered by Complex Dynamic
systems. The particular aim here would be to establish the methodology required for a comm on representation of
the mechanisms related to the establishment and exploration of option space, as well as the capacity to create
new, appropriate products or services, together with the new knowledge required.
To develop models of dynamic, self-organizing and evolutionary networks of inter and intra -industrial and business
activities, capable of anticipating the possible impacts of new telecommunication technologies
Outcomes
The main outcomes were to find that socio-economic systems evolve through successive instabilities, from one period of
structural stability to another – from one “structural attractor” to another and that they correspond to successive regimes of
operation or organisational forms that are not simply re -configurations of the same components, but occupy new dimensions,
include new entities and possess new attributes. This demonstrated how to develop quantitative mathematical models that
can describe a qualitative evolutionary process in which innovation and learning play a fundamental role. In so doing, the
models showed that what matters most for resilience and sustainability is not short term efficiency, which takes away the
adaptive capacity of the system, but instead the ability of the system to transform itself to new, better adapted structural
attractors. This research therefore situates the adaptive capacity of an organisation/structure in the diversity and non consensus of its constituent elements, which for long-term sustainability must be maintained, despite selection choosing a
particular path into the future. Also, because any successful new product, structure or organisation must have positive
internal synergy between its internal components these new models allow the anticipation of which new practices or
innovations can be adopted in an organisation or product, and which cannot, providing a much richer basis for decisionmaking than “best practice”.
The models developed were used to demonstrate how complex systems such as – evolving markets with learning agents,
networks resembling Italian District Industrial networks, manufacturing organizations, etc.- could be successfully modelled,
and how new insights could be gained from this.
University College London – Institute of Archaeology
Project title: Self-organising Networks: Historical Perspectives on the Resilience of Societal Systems
Principal Investigator: Dr James McGlade
Project Aim
To identify the self-organizing processes involved in the structuring of social networks within urban/rural systems and to
assess their contribution to the maintenance of resilience and the long-term persistence of complex societal systems.
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Summary of Outcomes
This project has demonstrated the viability of applying the concepts and tools of complexity science to the non-trivial
problems posed by socio-economic evolution. Research has shown that societal systems and the human-modified
landscapes that they construct, can usefully be analysed as emergent, self-organizing entities, subject to phenomena such
as bifurcation and phase transitions. Indeed, our research has shown that the language of complexity is particularly suited to
the description and interpretation of long-term structural change.
The Emporda project has, moreover, exposed the conventional gradualist archaeological chronology as potentially
misleading it its focus on incremental progressive change at the expense of the essentially discontinuous nature of the
societal trajectory.
An examination of the social, political, economic, cultural and environmental data has lead to the construction of an
alternative model of the long-term, viewed in terms of a sequence of relatively discrete human eco -dynamic episodes, ie,
spatio-temporal attractors or eco-historical regimes. These are defined by sets of co-evolutionary relationships in which the
interactions between resources (material and ideological), agency (environmental and social) and trade/exchange
transactions (local and global) are seen to provide a minimal model of structural change in societal systems.
Outcomes of the Emporda Project
The Emporda region of north-east Spain has seen many successive societies over the millennia.
Long-term evolution
Social systems are usefully described as complex dynamic systems and underline the importance of history in understanding
the evolutionary trajectory. The Emporda landscapes can profitably be viewed as a sequence of topological transformations
brought about by the interaction of determined and contingent processes. The long term is punctuated by distinctive human
eco-dynamic ‘signatures’ that define a meta-stable landscape comprising a series of structural attractors. These are
conceived as discrete spatio-temporal entities, or eco-historical regimes.
Eco-historical regimes
These multi -dimensional structures are characterized by the following dimensions.
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Geo-biophysical space: boundary conditions represented by the physical and ecological environment.
Production space: agricultural/hunting territories, human modification of rivers, littoral, wetlands.
Communication space: tracks, roads, rivers, coast.
Settlement space: location, topography, population.
Political space: territory, property, the space of power and control, the space of conflict.
Transactional space: barter exchange, symbolic alliances, market economies.
Symbolic space: ritual, burials, cemeteries, monuments, churches, monasteries.
Cognitive space: decision space, culturally modified by values, ideology and history.
History
Societal systems do not necessarily learn from history, eg, from past failures or successes. This, ironically, is the real lesson
of history. Human socio-natural systems are subject to unanticipated outcomes on account of inherent non -linearities; this is
the source of emergent behaviour.
Co-evolution
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Eco-historical regimes are driven by co-evolutionary processes, linking knowledge structures, resources and power. These
dimensions form a variety of non-linear couplings that generate self-organizing dynamics. The role of contingent events
(political, social and cultural) means that unlike ‘fitness landscapes’, eco-historical regimes do not seek to maximize any
stated ‘evolutionary potential’, or pursuit of increasing complexity, but rather follow a trajectory that is essentially contingent.
Adaptation
In essence, human societies are not adapted to any pre-existing niche. Rather, humans are opportunistic, able to insert
themselves into the ecological dynamic and to construct the type of environmental conditions that will allow them to prosper –
at least over the short term.
Temporalities
Human eco-dynamic structure emerges as a result of the intersection of temporalities, ranging from the slowest processes
such as tectonic movements (107), climatic cycles (105), all the wa y to population dynamics (10²) and other micro -level
phenomena (10 -1). Research shows that discontinuity – and frequently catastrophic outcomes – can be the result of the
conjuncture of ‘fast’ and ‘slow’ variables.
Resilience
The resilience of each eco-historical regime depends crucially on the ability to enhance the stocks of knowledge. From an
evolutionary perspective, this can lead to non -linear transformations (regime switches). Thus, the slow accumulation of
knowledge over millennia can be followed by a discontinuous change to a new intensive agricultural exploitation of the land
along with the emergence of power structures and territoriality, ie a movement to a new structural attractor.
Trade/exchange
Transactional processes play a key role in converti ng resources to power. An important aspect of trade -exchange systems in
ore-industrial economies is their inherent instability as new goods and information constantly probe the system. Non-linear
interactions at the heart of these transactional networks can even induce chaotic dynamics across the network, inducing
significant socio-political transformations
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