White Spaces Innovation in Sweden - Innovation policy for ... - Vinnova
White Spaces Innovation in Sweden - Innovation policy for ... - Vinnova White Spaces Innovation in Sweden - Innovation policy for ... - Vinnova
WHITE SPACES INNOVATION IN SWEDENthis micro-macro relationship is of practical importance since it determines the role ofintentionality and governance in a complex social system.In practical innovation policy terms this can be exemplified by analyses being donein relation to Strategic Niche Management and Multi Level Governance. In that Dutchschool of research on transition (e.g. from a hydrocarbons to a post-hydrocarbons sociotechnical„landscape‟) a distinction is being made between landscapes, regimes andniches in a nested system of self-organisation that is constrained and/or enabled betweenlevels (MLP – multi-level perspective). History/time constrains the development ofregimes via path-dependencies being reflected in a dominant logic (that acts as an „attractor‟between them). Slowly changing cultural patterns, regulation and institutionalconditions enable/constrain change of regimes and the way niches are developed. Andover time changed practice affects institutions and culture and changes the landscape.An example is the way shipyards now house creative industry and its incubators. This isbecause the requirements of the former – waterfronts, grand office spaces – act as(„strange‟) attractors to the design-driven „urban pioneer‟ and „creative edge‟ ethic ofthe creative „socio-cultural regime‟. Independent path dependencies have become pathinterdependencies. Rents are also low and subsidies available, which helps „protect theniche‟.However it is important to note that hierarchy does not have same connotation oftop-down control in complexity analysis as in everyday language. Neologisms likeheterarchy and panarchy (Gunderson & Holling, 2002) have been suggested instead ofhierarchy to allow inter-level causal relations to flow in both directions, part to whole(bottom-up) and whole to part (top-down). By delimiting the parts´ initial repertoire ofbehaviour, the structured whole in which the elements are suddenly embedded alsoredefines them. They are now something they were not before, nodes in a network,components of a system. This may mean that they might be restricted compared to beingindependent. The evolutionary advantage is that the system can reach states that theindependent parts cannot. This is the point when the term co-specialisation is used inrelation to dynamic capabilities of clusters and business ecosystems.EvolutionThinking about complexity has evolved from the study of natural systems or biologicalsystems to social systems. Along this path it has picked up influences from systemsthinking by reference to Ashby. In a similar way evolutionary thinking has been influentialin framing the issue of the survival and development of systems as a question ofevolutionary fitness between the system and its environment. This means that definingthe boundary between a system and its environment is very important unless the systemis truly universally defined both for analytical purposes and for systemic intervention.The analysis of fitness is seen as an optimization problem and the solution to this problemis dependent on the topology of a fitness landscape. Fitness landscapes are oftenconceived of as ranges of mountains. There exist local peaks (points from which all28
WHITE SPACES INNOVATION IN SWEDENpaths are downhill, i.e. to lower fitness) and valleys (regions from which most pathslead uphill). A fitness landscape with many local peaks surrounded by deep valleys iscalled rugged. The Dolomites is a good illustration of the topology.A useful way of visualizing this is as ontogenetic landscapes depicting a “series ofchanges of relative stability and instability” over time (Thelen & Smith, 1994). Figure 2.If a system accessed every point or region in change over time with the same frequencyas every other (that is, randomly), its ontogenetic landscape would be smooth and flat.A completely flat, smooth initial landscape would portray an object with no propensitiesor dispositions; that is, with no attractors. It would describe a “system” with no identity,a logical impossibility. The deeper the valley, the greater the propensity of its beingvisited and the stronger the entrainment that its attractor represents.Ontogenetic landscapes are constantly modified, dynamical portraits of the interactionsbetween a system and its environment over time: they capture, in short, a timelapseportrait of individual systems. Attractors embody the system‟s current controlparameters (its self-organized controls), which have been constructed and continue to bemodified as a result of the persistent interactions between the dynamical system and itsenvironment.Attractors can be of several kinds. Of special interest for our purposes are thosecalled strange attractors. All attractors represent characteristic behaviours or states thattend to draw the system toward themselves, but strange attractors are “thick” (Juarrero,2002) allowing individual behaviours to fluctuate so widely that even though capturedby the attractor´s basin they appear unique. Strange attractors describe ordered globalpatterns with such a high degree of local fluctuations, that is, that individual trajectoriesappear random. Complex adaptive systems are often characterized by strange attractors.The strange attractors of seemingly “chaotic” phenomena are therefore often not chaoticat all. Such intricate behaviour patterns are evidence of highly complex dynamic organisation.This is essential if innovation processes are to be thoroughly understood as „recombinations‟of knowledge, new and old.Quantitative research has articulated the strange attractors that shape a variety of dynamicalhuman systems. Such quantitative analysis requires that the systems incorporatea small number of deterministic variables (dimensions). If the dimensionality of thesystem is too high (the commonly-used limit is eight variables), the system is consideredto be random because the pattern cannot be discerned by current manipulativepractices and analytical algorithms. Qualitatively, however, the strange attractor hasbeen used as a metaphor to describe highly complex, but patterned, behavior in humansystems. Whenever the behavior of the system is bounded, includes infinite freedomwithin the bounds, and generates coherent patterns over time, the human system can bemetaphorically described as a strange attractor regime. Examples of human systemaspects that fit this qualitative description include organizational culture, patterns ofprofessional practice, or the behaviors of firms within a given industry. In each case,29
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WHITE SPACES INNOVATION IN SWEDENthis micro-macro relationship is of practical importance s<strong>in</strong>ce it determ<strong>in</strong>es the role of<strong>in</strong>tentionality and governance <strong>in</strong> a complex social system.In practical <strong>in</strong>novation <strong>policy</strong> terms this can be exemplified by analyses be<strong>in</strong>g done<strong>in</strong> relation to Strategic Niche Management and Multi Level Governance. In that Dutchschool of research on transition (e.g. from a hydrocarbons to a post-hydrocarbons sociotechnical„landscape‟) a dist<strong>in</strong>ction is be<strong>in</strong>g made between landscapes, regimes andniches <strong>in</strong> a nested system of self-organisation that is constra<strong>in</strong>ed and/or enabled betweenlevels (MLP – multi-level perspective). History/time constra<strong>in</strong>s the development ofregimes via path-dependencies be<strong>in</strong>g reflected <strong>in</strong> a dom<strong>in</strong>ant logic (that acts as an „attractor‟between them). Slowly chang<strong>in</strong>g cultural patterns, regulation and <strong>in</strong>stitutionalconditions enable/constra<strong>in</strong> change of regimes and the way niches are developed. Andover time changed practice affects <strong>in</strong>stitutions and culture and changes the landscape.An example is the way shipyards now house creative <strong>in</strong>dustry and its <strong>in</strong>cubators. This isbecause the requirements of the <strong>for</strong>mer – waterfronts, grand office spaces – act as(„strange‟) attractors to the design-driven „urban pioneer‟ and „creative edge‟ ethic ofthe creative „socio-cultural regime‟. Independent path dependencies have become path<strong>in</strong>terdependencies. Rents are also low and subsidies available, which helps „protect theniche‟.However it is important to note that hierarchy does not have same connotation oftop-down control <strong>in</strong> complexity analysis as <strong>in</strong> everyday language. Neologisms likeheterarchy and panarchy (Gunderson & Holl<strong>in</strong>g, 2002) have been suggested <strong>in</strong>stead ofhierarchy to allow <strong>in</strong>ter-level causal relations to flow <strong>in</strong> both directions, part to whole(bottom-up) and whole to part (top-down). By delimit<strong>in</strong>g the parts´ <strong>in</strong>itial repertoire ofbehaviour, the structured whole <strong>in</strong> which the elements are suddenly embedded alsoredef<strong>in</strong>es them. They are now someth<strong>in</strong>g they were not be<strong>for</strong>e, nodes <strong>in</strong> a network,components of a system. This may mean that they might be restricted compared to be<strong>in</strong>g<strong>in</strong>dependent. The evolutionary advantage is that the system can reach states that the<strong>in</strong>dependent parts cannot. This is the po<strong>in</strong>t when the term co-specialisation is used <strong>in</strong>relation to dynamic capabilities of clusters and bus<strong>in</strong>ess ecosystems.EvolutionTh<strong>in</strong>k<strong>in</strong>g about complexity has evolved from the study of natural systems or biologicalsystems to social systems. Along this path it has picked up <strong>in</strong>fluences from systemsth<strong>in</strong>k<strong>in</strong>g by reference to Ashby. In a similar way evolutionary th<strong>in</strong>k<strong>in</strong>g has been <strong>in</strong>fluential<strong>in</strong> fram<strong>in</strong>g the issue of the survival and development of systems as a question ofevolutionary fitness between the system and its environment. This means that def<strong>in</strong><strong>in</strong>gthe boundary between a system and its environment is very important unless the systemis truly universally def<strong>in</strong>ed both <strong>for</strong> analytical purposes and <strong>for</strong> systemic <strong>in</strong>tervention.The analysis of fitness is seen as an optimization problem and the solution to this problemis dependent on the topology of a fitness landscape. Fitness landscapes are oftenconceived of as ranges of mounta<strong>in</strong>s. There exist local peaks (po<strong>in</strong>ts from which all28