Path Interdependence, Firm Innovation and Resilience:
A Complex Adaptive Systems Perspective
TRes Working Paper: Theoretical Framework
Phil, Cooke, Gillian Bristow and Julie Porter
Cardiff University
Introduction
The concept of resilience has become increasingly prominent in economic
geography and regional studies as regional economies and societies struggle to
respond and adapt to shocks and change. The concept is developing particular
traction within evolutionary economic geography (EEG) where it resonates strongly
with non-equilibrium and path-dependent notions of adaptive change (Martin, 2012).
Indeed much recent work within EEG views the economic landscape as a ‘complex
adaptive system’ (Martin and Sunley, 2006; p. 573) and asserts that major shocks
exert a formative influence over how this landscape evolves and changes over time
(Boschma and Martin, 2010; Simmie and Martin, 2010; Martin, 2012). This approach
to economic change rejects neo-classical inspired notions of adjustment
mechanisms towards any notion of equilibrium (Dawley et al, 2010). Instead in EEG,
resilience is defined as the adaptive capacity of a local or regional economy or ‘the
ability of the region’s industrial technological, labour force and institutional structures
to adapt to the changing competitive, technological and market pressures and
opportunities that confront its firms and workforce’ (Simmie and Martin, 2010; p. 30).
Much of the emerging debate within evolutionary economic geography around
resilience focuses upon the causal concepts of adaptation – the ability to respond to
an economic shock with a movement back towards a pre-conceived development
path, and adaptability – where a different kind of resilience emerges through
opportunities or a decision to leave a path that may have proven successful in the
past in favour of a new, related or alternative trajectory or niche (Dawley et al, 2010).
This perspective suggests that the adaptive capabilities of a region’s economy are
likely to depend upon the nature of the region’s pre-existing economy – in other
words, adaptation and adaptability are likely to be path-dependent processes shaped
by the region’s industrial legacy and the scope for re-orientating skills, resources and
technologies inherited from that legacy (Martin, 2012).
Path dependency helps us understand what shapes a region’s ability to respond to a
system shock but is arguably less well equipped to help us understand the process
of adaptability or what might cause a region’s endogenous functioning to renew or
atrophy, or how regional competencies and specialisms may change and thus how
‘lock-in’ may be avoided (Boschma and Frenken, 2009). Indeed, path dependence at
the regional level may explain stability but also system stagnation and inertia
(Cooke, 2011).
In seeking to understand the evolutionary trajectories of regions demonstrating
capacities to transition to ‘green’ economic development paradigms, Cooke (2010;
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2011) has drawn attention to the significant role played by instances of knowledge
re-combination between firms. There is indeed a growing body of evidence indicating
that fruitful eco-innovation increasingly occurs at the interfaces between firms,
sectors and clusters where the cross-fertilisation of ideas may either produce new
unforeseen innovations, or result in the application of old knowledge in new ways to
tackle complex or wicked problems (e.g. automotive engineering to biofuels, or milk
coolers to wind turbines). Such unforeseen innovations typically emerge from
‘revealed related variety’ or the ex post relatedness of a region’s economic sectors
and their trajectories (Geels, 2007; Martin and Sunley, 2008). As such, this
represents a form of co-evolution of paths, or ‘path interdependence’ (Liu, 2009;
Cooke, 2011). Understanding path interdependence thus offers the potential to
understand how regional economic development trajectories change and mutate.
To date, however, there have been few empirical investigations into how these path
interdependencies emerge from innovation, spin-offs and the rise of market niches at
the micro or firm level. As such we have little knowledge as to how far they can
effectively change the course of an industrial or development path and subsequently
effect co-adaptation between firms, markets, technology, industry and institution (Liu,
2009). This is thus the principal focus for our research work which will seek to
address two key questions: firstly, what shapes the capacities for knowledge
recombination amongst particular firms, clusters and sectors in different contexts;
and secondly, how do these micro-interdependencies relate to or help facilitate
macro-outcomes in terms of regional resilience.
Before proceeding with this empirical work however, we need a theoretically
informed understanding of the concept of path interdependence and its evolution at
the micro level. The purpose of this paper is thus to establish a conceptual
framework for our subsequent work. Since path interdependency is a co-evolutionary
study and emphasises the interactions between different entities and their
environment, it fits well with complexity thinking which sees the human world as
complex, unpredictable and dynamically linked to its environment on a range of
temporal and spatial scales. As such, in this paper we explore the potential for
complex adaptive systems analysis to facilitate an enriched understanding of how
and where path interdependencies emerge through innovative knowledge recombinations.
The paper is now organised as follows. The next section introduces the core
elements of complex adaptive systems analysis and reviews its application and
development to date in the EEG and regional innovation literature. The paper then
proceeds to examine in more detail two of its key concepts – preadaptation and the
adjacent possible. These concepts usefully take the evolutionary concept of ‘variety’
and the way in which it translates potential into system re-organisation through the
evolution of novelty much further in analytical terms (Cooke, 2011). They do this by
developing understanding of the creative process and how innovation proceeds in
both a recombinant and incremental fashion. We conclude by outlining in brief our
choice of case studies for the empirical investigation of these issues.
Complex Adaptive Systems Analysis
2
Complexity theory considers that the world is composed of systems that are
constantly adapting. Thus complexity arises from the inter-relationship, interaction
and inter-connectivity of elements with a system and between a system and its
environment. Complex adaptive systems are dynamic systems able to adapt and
change within, or as part of, a changing environment. There is no dichotomy
between a system and its environment in the sense that the system always adapts to
a changing environment. Rather the system is closely linked with all other related
systems making up an ecosystem. Within such a context, change is viewed in terms
of co-evolution with all other related systems, rather than as adaptation to a separate
and distinct environment (Cooke and Eriksson, 2011).
In recent years, significant progress has been made in the EEG literature in applying
complex adaptive systems thinking to regional economies and particularly regional
innovation systems, notably with an evolutionary biology inflection rather than a
physic-chemical systems model (Cooke, 2012). This has established that as
complex adaptive systems, regional innovation systems are characterised by a
number of key features. First, they are characterised by non-linear and nonequilibrium dynamics because of complex feedbacks and self-reinforcing interactions
amongst components. The environment is constantly changing and as such the
recursive nature of the algorithmic or evolutionary processes of variation, selection
and replication never stops. Complex systems are thus always dynamic, always
evolving, prone to crises and characterised by continual processes of adaptation and
innovation (Arthur et al, 1997). In short, ‘in evolutionary systems, statis is a recipe for
extinction’ (Beinhocker, 2007; p. 216).
Second, they exhibit emergence in that they exhibit macro-level structures or
patterns, characteristics of the system as a whole, that arise endogenously and
spontaneously out of the micro-level interactions of agents and their environment. In
effect, the whole is greater than the sum of its parts and applies to many every day
experiences. As Beinhocker (2007; p. 167) observes, for example, ‘what we call a
symphony is a pattern of sound that emerges out of the playing of individual
instruments’. In regional terms, it can be interpreted as the tendency for macro-scale
structures and dynamics (such as cities, industrial districts and clusters) to emerge
spontaneously out of micro-scale behaviours of agents (such as firms, workers,
consumers) (Martin and Sunley, forthcoming). Clusters, for example may be viewed
as ‘emergent’ from the shared interest of firms in higher-order economic activity they
could not achieve acting alone. This emergence may be characterised as having
three orders or layers. The first order emergence is created by the economy in the
region and is found in the people and firms (or entities) in the area. The second order
emergence is created by the knowledge networks and the labour market that
connect the regional entities and compose a configuration based on the level of
connectedness of the entities. Third order emergence would be the cluster itself with
the competitors becoming collaborators, co-locating and co-creating knowledge
spillover and ‘buzz’ (Fornahl et al, 2010).
Thirdly, and following on from the above, complex adaptive regional innovation
systems are imbued with self-organising and co-evolutionary interactions among
their constituent components and elements. They exhibit an adaptive capacity which
enables them to re-arrange their internal structure and dynamics spontaneously,
whether in response to changes in the external environment (e.g. external shocks),
3
or from within through co-evolutionary mechanisms or in response to ‘self-organised
criticality’ (Martin and Sunley, 2007). As such they are autocatalytic (they drive their
own growth) but in ways that cannot be foreseen. Each member of the population or
entity in the system is continually searching for new ways of adapting to the
environment. Thus knowledge about the environment and how it is changing is the
key to self-organisation and the ability of entities to understand how and in what
ways they need to adapt in order to survive (Cooke, 2012).
What distinguishes economic and human systems from biological ones, is the role
played by the deliberate acquisition of knowledge. Economic agents are pro-active
as well as reactive in implementing novel plans to access new energy sources or
increase exploitation of old ones. In economic self-organisation both physio-chemical
and biological limits on economic development can be transcended. The decay of
old investments can variously be planned for (through planned depreciation) and
overlooked (depending on commitment levels to specialist organisational structures
or market niches). In short, economic self-organisation brings immense complexity
through acquired energy and acquired knowledge which in combination yield
creativity in economic evolution (Foster, 1997). Creativity and innovation thus play an
integral role in system dynamics and indeed, may be regarded as the ‘evolutionary
fuel’ of complex systems (Cooke, 2012).
These characteristics create an imperative to understand how knowledge is
acquired, spread and transmitted in complex systems. This leads into to the fourth
key characteristic of complex adaptive systems which is that they are not uniform
and homogenous. Rather, they are characterised by an ontogenetic topology.
This reflects the understanding that complex systems have a particular ‘fitness
landscape’ that in visual terms represents a mountainous (or ontogenetic) terrain
between peaks of high fitness (where entities are well-designed to fit the needs of
their environment and from which all paths are downhill to lower fitness), and low
fitness valleys (from which most paths lead uphill).
These fitness landscapes may take various forms. They may be a rugged
topography of hills and valleys, or sleek, or have variations in between. The sleeker
the landscape, the more stable the system because there are fewer sources of
perturbance and little opportunity for communication between system entities. The
more rugged the landscape, the more conduits and tributaries that exist that act as
communication lines between centres of energy or potentially interacting entities,
and so the more unstable the system. This means it is more prone to collisions of
economic and ecological activities or their sub-elements (Cooke, 2012). When such
interactions are abundant, the system is said to be operating at the ‘edge of chaos’
or to be highly susceptible to system change or shocks (Kauffman, 1995; 2008). The
greater the interactions between entities, the greater the likelihood of emergent
properties and phenomena (Cooke, 2012). In essence, ‘too much structure creates
gridlock…too little structure creates chaos’ (Brown & Eisenhardt, 1998; p. 14, cited in
Baum & Silverman, 2001). If the system can remain in this chaotic mode, sustainable
adaptive functioning can be achieved which supports learning, flexibility and
adaptation amongst the system’s entities.
Path dependence and interdependence
4
Complexity theory thus highlights ‘the tension between exploitation of knowledge
gained (path dependence) and exploration of novel actions (path creation) and
illustrates how innovation processes characterised by ‘edge of chaos’ behaviour
balance these tensions, permitting adaptive functioning of technological-organisation
systems’ (Baum and Silverman, 2001; p. 198). As such, complex adaptive system
thinking also provides interesting insights into the nature and form of interactions that
can occur and their resulting innovations. The system’s fitness landscape has certain
constrained pathways which are known as ‘attractors’. These are the routine or
‘normal’ self-organised behaviours or interactions in the system between entities and
with the system and its environment (such as cyclical or neighbourhood effects).
These create normal path interactions that in economic systems we understand as
path dependence and which emerge from routine related variety (Cooke, 2012). One
variant of these interactions are ‘strange attractors’ where there is no a priori reason
why such paths might coalesce and where unexpected combinations of pathways
occur to produce innovative new pathways. As such strange attractors display
‘revealed relatedness’ rather than obvious relatedness. Furthermore, the more unlike
or strange they are, the more radical the innovation conceived by the interdependent
paths will be (Kauffman, 2008; Cooke, 2011).
Cooke and Eriksson (2011) depict the outcome of strange attractors visually (Figure
1). This demonstrates a rugged system landscape where there are multiple fitness
peaks or mountains (depicted by the triangles), and a range of paths between them.
The higher the connectivity the more rugged the system becomes i.e. a system that
has low connectivity will only have one peak. However, a system with high
connectivity will have many peaks which is the case here. The catalyst for the
intersection is the ‘blockage’. The blockage can represent different events such as
an external (e.g. recessionary) shock to the system. At the point where the path
runs into the blockage it can do four things: (1) end, (2) redirect temporarily and
return to its path over time, (3) go in another direction, or (4) intersect with another
path in the valley. The fourth scenario, where at least two paths intersect, results in
path interdependence. However, not all paths are destined to intersect.
Figure 1:
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Source: Cooke & Eriksson, 2011
Preadaptation and the adjacent possible
There are two main types of innovation that can result from the meeting of strange
attractors: preadaptation and the adjacent possible. Preadaptation is rooted in
evolutionary biology and is a type of technology transfer where an innovation from
one industry setting is adapted for wholly different industry solutions in another.
The adjacent possible is an incremental innovation that is closely related to an
existing product and where a design effort must be effected to execute an innovative
improvement (Cooke, 2011). This simple innovation becomes radical when it is
recombined with existing resources (Kauffman, 2008). Complex adaptive systems
thinking suggests that both types of innovation are more conducive to a region with
related variety and connectedness as there is more potential technology transfer and
recombination than in a specialised region. Kauffman (2008; p. 151) asserts ‘the
more diverse the economic web, the easier is the creation of still further novelty’.
Research Questions
The application of complex adaptive systems thinking to regional economies and
regional innovation systems thinking in particular is still in its infancy and as a result,
there is a clear need for more detailed empirical work. This review of the existing
literature suggests a number of key research questions require further testing
through empirical research.
Firstly, there are a set of questions around what factors shape the capacities for and
likely success of knowledge recombination amongst particular firms, clusters and
sectors, or how do different vectors of activity successfully combine and co-evolve in
some places and contexts but not others? Do they, for instance, emerge in response
to institutional or market developments (such as economic shocks) or are they
largely endogenous self-organising processes (a product of individual
6
entrepreneurship and agency, or self-organised knowledge networks)? How
important is related variety – does greater variety in the number of entities (firms,
institutions and nodes for interaction) lead to greater potential for the creation of
further novelty? What forms of knowledge recombination are emerging in practice,
between what interfaces between sectors, and through what combination (or
interdependence) of paths? And how is knowledge recombined? What role is played
by preadaptation and/or the adjacent possible (i.e. how does innovation proceed –
what role is played by chance or other factors in prompting key interactions, and how
is preadaptation being exploited? What is the relative importance of endogenous
system topology, or a resilience effect (or shock)?
A second set of questions surround how these micro-interdependencies relate to or
help facilitate macro-outcomes in terms of adaptive capacities and resilience for
regions? How and in what ways do they help promote the renewal of regional
development trajectories?
Introduction to case studies
We propose to address these questions through two UK based case studies. The
first case is the diversified cluster in the North Wales region of Denbighshire that has
high related variety with industries such as photonics, aerospace, solar energy and
fibre optics. The second case is the specialised cluster in the East England region of
North Suffolk that has low related variety with the main industry of
telecommunications centred around British Telecommunications (BT) research and
development centre. Both cases have experienced the shock of the recent
recession. However, interesting questions surround whether and how they have
responded and reacted differently to develop greater resilience.
North Wales
Since its inception in the 1950s, the North Wales cluster has evolved significantly to
its current state. The original hub firm was Pilkington Glass which became
specialised in photonics in the 1970s. As a result, spinoffs were created but unlike
other areas that specialised in photonics, the spinoffs in North Wales retained their
linkages with the hub firm creating the specialised cluster base. The aerospace
sector was simultaneously growing in the region, having been embedded there since
post-WW2. In the late 80s, through government emphasis on growing the sector,
aerospace became a major economic contributor in North Wales (Cooke & Ehret,
2009). Around the same time, the Thales group acquired Pilkington Photonics, then
the hub firm in the photonics clusters and part of the larger Pilkington Group which
included Pilkington Glass. This acquisition led to increased linkages between
people, firms, and the larger labour market in the photonics cluster and the growing
aerospace agglomeration. The linkages were exacerbated by the Thales military
and technology emphasis to the point where collaborations were created. Several
years later, in the late-90s, Pilkington Group, along with Pilkington Photonics, which
belonged to Thales, was acquired by the global conglomerate Qioptiq. Through this
latest acquisition, the cluster formed more external linkages while also retaining the
intra-regional linkages with the aerospace sector. Given the socio-economic
transition that was experienced in the mid-2000s with an increased emphasis on a
7
post-hydrocarbon society, another path emerged, largely through regional University
involvement, specialising in solar energy.
Through this brief overview of the evolution of the cluster and the North Wales
region, four key points can be drawn in relation to the theory. First, possibly due to
the shock of external competition or the recession in the 80s1, the aerospace path
and the photonics path intersected in the late 80s. Even though the technological
relatedness between photonics and aerospace is obvious now, this was an
innovative step at that time and exemplifies revealed related variety. Second, even
with the higher resistance to external shocks provided by related variety in the
region, the photonics path intersected the solar energy path 20 years after
intersecting the aerospace path. This intersection was the result of the shock of
competition and climate change. The resulting incremental innovations could be
used as preadaptation in the future. Third, although aerospace and photonics are
historically linked to the area, highly skilled labour market as well as the extensive
knowledge networks, attributes of second-level emergence that affect third level
emergence, in the region have evolved over time to provide a fertile area for industry
and new path creation. Fourth, given the highly connected nature of this cluster,
which is largely based around photonics, is it approaching the limits of the edge of
chaos where too much connectivity stifles change? The level of connectivity is also
mentioned in the fitness landscapes where the higher the connectivity the more
mountains there are; however, if there are more mountains, it would be more difficult
for the paths to meet.
East England
In comparison to the North Wales cluster, the East England cluster has evolved but
not in the same, highly diversified, way. The East England cluster can be attributed
to the 1960s relocation of the British Telecommunication research and commercial
headquarters from Dollis Hill, London to Ipswich. This is the major hub firm in the
region, the centre of the cluster and the attractor of other firms, both MNCs and
SMEs, to the region. Over time, little has changed on the site in terms of what the
focus of the innovation is: telecommunications. Due to this, the cluster is highly
specialised in telecommunications. Nonetheless, this cluster has recovered from
shocks such as privatisation of the industry (ie. sector specific), competitiveness, and
previous recessions (ie. large external shocks) and has, despite the contradictions in
the literature, still managed to flourish.
Due to the high level of specialisation in this cluster, it is difficult to analyse this case
in relation to the approach illustrated in figure 1 as there is only one path in the
cluster making the intersection of paths impossible. Instead of concentrating on the
path, this section will focus on the evolution of regional policy in regards to the
cluster. One major difference between this specialised cluster and other diversified
cluster is the role of regional governance. The East of England Development
Agency (EEDA), greatly supports the cluster through collaborative policies, finance
and networking. The evolution of the policy and support at the regional level towards
the cluster may be the reason why it has more resilient properties than the average
8
specialised cluster. For example, several of the main initiatives within the cluster,
such as Innovation Martlesham, University collaboration projects, and the cluster
incubator are all either solely or partially owned by the EEDA. It will be interesting,
given the EEDA’s prominent role in the cluster, when the EEDA closes in 2012 as
part of the larger UK government plan to replace the regional development agencies
(RDAs) with local economic associations (LEAs).
9
Bibliography
Ablowitz, R. 1939. ‘The Theory of Emergence’, Philosophy of Science. 16, pps. 1-16.
Adger, N. 2000. ‘Social and ecological resilience: are they related?’. Progress in
Human Geography 24, pp. 347- 64.
Arthur, B., Durlauf, S. And Lane, D. (2005) The Economy as an Evolving Complex
System II. Reading, MA: Addison-Wesley.
Ashby, J., Cox, D., McInroy, N. and Southworth, D. 2009. An International
Perspective of Local Government as Steward of Local Economic Resilience. Report
by the Centre for Local Economic Strategies: Manchester.
Baum, J. & Silverman, B. 2001. ‘Complexity, Attractors and Path Dependence and
Creation in Technological Evolution’, in Garud, R. & Karnoe, P (eds.) Path
Dependence and Creation, New Jersey: Lawrence Erlbaum Associates.
Beinhocker, E. D. (2007) The Origin of Wealth: Evolution, Complexity and the
Radical Remaking of Economics. Random House: London.
Boschma, R. & Iammarino, S. 2009. "Related Variety, Trade Linkages, and Regional
Growth in Italy," Economic Geography, Clark University, vol. 85(3), pages 289-311, 07
Boschma, R. & Frenken, A. 2006. ‘Why is evolutionary economic geography not an
exact science?’ Journal of Economic Geography. 6, pps.273-302.
Boschma, R. and Frenken, K. (2009) ‘Some notes on institutions in evolutionary
economic geography’, Economic Geography, 85 (2), pp. 151-158.
Boschma, R. & Martin, R. 2010. ‘The Aims and Scope of Evolutionary Economic
Geography’, in R. Boschma & R. Martin (eds.) The Handbook of Evolutionary
Economic Geography, Cheltenham: Edward Elgar.
Centre for Local Economic Strategies 2010. Productive Local Economies: Creating
Resilient Places. CLES: Manchester; December 2010.
Christopherson, S., Michie, J. & Tyler, P. 2010. ‘Regional Resilience: Theoretical and
empirical perspectives’. Cambridge Journal of Regions, Economy and Society, 3(3),
pps.3-10.
Clark, J., Huang, H.I. and Walsh, J.P. 2010. ‘A typology of ‘innovation districts’: what
it means for regional resilience’. Cambridge Journal of Regions, Economy and
Society, 3 (1), pp. 121 – 137.
Cooke, P. 2012. Complex Adaptive Innovation Systems: Relatedness and
transversality in the evolving region. Cheltenham, Edward Elgar.
Cooke, P. 2011, ‘Transition Regions: Regional-national eco-innovation systems and
strategies’. Progress in Planning, 76(3), pps. 106-134.
10
Cooke, P. 2010. ‘Transversality and Transition: Branching to new regional path
dependence’. Prepared for the New Path Creation workshop at Trinity College,
Oxford, September 5-7, 2010.
Cooke, P. & Ehret, O. 2009. ‘Proximity and Procurement: A study of agglomeration
in the Welsh aerospace industry’. European Planning Studies, 17 (4) pps. 549-567.
Cooke, P. & Eriksson, A. forthcoming. ‘Resilience, Innovative ‘White Spaces’ &
Cluster-Platforms as a Response to Globalisation Shocks’ in P. Cooke & D. Parrilli
Innovation, Global Change and Territorial Resilience.
Cooke, P. & Eriksson, A. 2011. White Spaces Innovation in Sweden: Innovation
Policy for Exploring the Adjacent Possible. Sweden: VINNOVA.
Dawley, S. Pike, A. & Tomaney, J. 2010. ‘Towards the Resilient Region?’. Local
Economy, 25(8) pp. 650-667.
Duit A, Galaz V, Eckerberg K and Ebbeson J. 2010. ‘Introduction: governance,
complexity, and resilience’, Global Environmental Change 20, 363-368.
Fornahl, D., Henn, S. And Menzel, M. (eds) (2010) Emerging Clusters. Cheltenham:
Edward Elgar.
Foster, J. (1997) ‘The analytical foundations of evolutionary economics: from
biological analogy to economic self-organisation’, Structural Change and Economic
Dynamics, vol 8, pp. 427 – 451.
Foster, K. A. 2007. A case study approach to understanding regional resilience.
Working Paper 2007-08, Institution of Urban and Regional Development, Berkeley.
Frenken, K. And Boschma, R. 2007. ‘A theoretical framework for economic
geography: industrial dynamics and urban growth as a branching process’ Journal of
Economic Geography, 7, pp. 635 – 649.
Geels, F. 2007. ‘Analysing the breakthrough of rock ‘n’ roll (1930-1970): Multi-regime
interaction and reconfiguration in the multi-level perspective’ Technological
Forecasting & Social Change, 74, pps. 1141-1431.
Gunderson, L. & Holding, C.S. 2002. Panarchy: Understanding Transformations in
Human and Natural Systems. Washington: Island Press.
Hill, E., Wial, H. & Wolman, H. 2008. ‘Exploring Regional Economic Resilience’,
Working Paper for the Institute of Urban and Regional Development, University of
California, Berkeley.
Holling, C.S. 1973. ‘Resilience and stability of ecological systems’. Annual Review of
Ecology and Systematics 4, pp. 390 – 405.
11
Hopkins, R. 2008. The Transition Handbook: From Oil Dependency to Local
Resilience. Chelsea: Green Books.
Hudson, R. 2010. ‘Resilient regions in an uncertain world: wishful thinking or a
practical reality?’ Cambridge Journal of Regions. Economy and Society, 3(1), pp. 11
– 25.
Kauffman, S. 1995. At Home in the Universe. Oxford: Oxford University Press.
Kauffman, S. 2008. Reinventing the Sacred. New York: Basic Books.
Kechidi M., Talbot D. 2010. ‘Institutions and coordination: what is the contribution of
a proximity-based analysis? The case of Airbus and its relations with the
subcontracting network’, International Journal of Technology Management, 50(3/4),
pp. 285-299.
Lang, T. 2010. ‘Urban resilience and new institutional theory – a happy couple for
urban and regional studies?’ in Muller, B. (ed) German Annual of Spatial Research
and Policy 2010, Springer: Berlin (pp. 15 – 22).
Larkin, K. and Cooper, M. 2009. Into Recession: Vulnerability and Resilience in
Leeds, Brighton and Bristol. Centre for Cities: London. January 2009.
Levin et al 1998. ‘Resilience in natural and socioeconomic systems’, Environment
and Development Economics, 3, pp. 222 – 234.
Lui, Z. (2009) ‘Bringing History into Evolutionary Economic Geography for a Better
Understanding of Evolution’, Papers in Evolutionary Economic Geography,09/01,
Utrecht University (http://econ.geo.uu.nl/peeg/peeg0901.pdf - accessed May 2012).
Martin, R. 2012. ‘Regional economic resilience, hysteresis and recessionary shocks’,
Journal of Economic Geography, 12 (1), pp. 1 – 32.
Martin, R. & Sunley, P. forthcoming. ‘Forms of Emergence and the Evolution of
Economic Landscapes’. Journal of Economics of Business and Organisation,
submitted and forthcoming.
Martin, R. & Sunley, P. 2008. ‘The Place of Path Dependence in an Evolutionary
Perspective on the Economic Landscape’. In R. Boschma & R. Martin (eds.)
Handbook of Evolutionary Economic Geography. Cheltenham: Edward Elgar.
Martin, R. & Sunley, P. 2007. ‘Complexity Thinking and Evolutionary Economic
Geography’, Journal of Economic Geography. 7, pps. 573-602.
Martin, R. & Sunley, P. 2006. ‘Path Dependence and Regional Economic Evolution’,
Journal of Economic Geography. 7, pps. 573-602.
Muller, B. 2010. ‘Urban and Regional Resilience – A new catchword or a consistent
concept for research and practice? In Muller, B,. (ed) German Annual of Spatial
12
Research and Policy 2010: Urban Regional Resilience: How do Cities and Regions
Deal with Change? Springer: Berlin.
Newman, P., Beatley, T. And Boyer, H. 2009. Resilient Cities. Responding to Peak
Oil and Climate Change. Washington DC: Island Press.
Pendall, R., Foster, K. & Cowell, M. 2008. Resilience and Regions: Building
understanding of the metaphor. Working Paper.
Resilience Alliance 2007. Research Prospectus: A Resilience Alliance Initiative for
Transitioning Urban Systems towards Sustainable Futures. Retrieved from:
http://resalliance.org/files
Rose, A. and Liao, S-Y, 2005. ‘Modelling regional economic resilience to disasters: a
computable general equilibrium model of water service disruptions’. Journal of
Regional Science, 45, pp. 75 – 112.
Setterfield, M. 2010. Hysteresis, Working Paper 10-04, Department of Economics,
Trinity College, Hartford, Connecticut.
Simmie, J. & Martin, R. 2010. ‘The economic resilience of regions: towards an
evolutionary approach’. The Cambridge Journal of Regions, Economy & Society,3(3)
pps. 27-43.
Suire R., Vicente J. 2009. ‘Why Do Some Places Succeed When Others Decline? A
Social Interaction Model of Cluster Viability’, Journal of Economic Geography, 9(3):
381-404
13