WHITE SPACES INNOVATION IN SWEDEN<strong>in</strong> evolutionary economic geography as a key characteristic of „emergence‟ (see Mart<strong>in</strong>& Sunley, 2011). Such processes are <strong>in</strong> Kauffman‟s (2008) terms, essentially „lawless‟.In Fig. 4.1 a scheme is elaborated of the complex „emergence‟ of <strong>in</strong>novation through„preadaptation‟ and/or the „adjacent possible‟ <strong>in</strong> relation to „attractors‟ and especially„strange attractors‟ after Kauffman (2008).Figure 4.1 The Nature of Emergence of <strong>Innovation</strong>: A Complexity PerspectiveNOVELTYADAPTATION BYIMITATIONCLUSTERINTERACTIONCOGNITIVEREVERSALPREADAPTATION’EDGE OFCHAOS’ADJACENT POSSIBLESTRANGE ATTRACTORSEMERGENCESource: Centre <strong>for</strong> Advanced StudiesWhat occurs <strong>in</strong> Fig. 4.1 (compare Fig. 3.3) is that Mart<strong>in</strong> & Sunley‟s path <strong>in</strong>terdependenceevolves on the plane of a complex adaptive system. In analogue <strong>for</strong>m, thisis a regional economy that is <strong>in</strong>vested with a topology (Fig. 2.2). The topological routeways(path dependencies) favour certa<strong>in</strong> deviations and disfavour others. At a givenpo<strong>in</strong>t they meet as the convergence of socio-technical systems (STS). This conceptcomes from the co-evolutionary multi-level perspective (MLP), which demonstrateshow <strong>in</strong>novation occurs through the <strong>in</strong>teraction of STS that were hitherto path dependent(Geels, 2007). This occurs not only when such trajectories are related or natural attractorsbut particularly when they are „strange attractors‟. Strange attractors display „revealedrelatedness‟ rather than obvious relatedness. While both can facilitate <strong>in</strong>novation,that caused by „strange attractors‟ has the possibility to be of the most radical k<strong>in</strong>d. Thisis because an adjacent possible that is utterly unknown is be<strong>in</strong>g explored. This meansthe possibilities <strong>for</strong> secondary <strong>in</strong>novations are great, as can be demonstrated <strong>in</strong> relationto „<strong>in</strong><strong>for</strong>matisation‟ which has released cascades of <strong>in</strong>novation – rang<strong>in</strong>g from graphic<strong>in</strong>terface and „mouse‟ to e-mail, Internet and Facebook. Contrariwise <strong>in</strong> Fig.4.1 (unlikeFig. 3.3) the „preadaptation‟ route is either moderately surpris<strong>in</strong>g because it <strong>in</strong>volves a„cognitive reversal‟ of an exist<strong>in</strong>g <strong>in</strong>novation – as <strong>in</strong> Kauffman‟s favourite metaphor ofthe tractor chassis that always broke due to the weight of the eng<strong>in</strong>e, be<strong>in</strong>g replaced bythe eng<strong>in</strong>e itself be<strong>in</strong>g bolted to the back axle and drive-tra<strong>in</strong>. Alternatively „preadapta-76
WHITE SPACES INNOVATION IN SWEDENtion‟ is <strong>in</strong>cremental <strong>in</strong>novation and quite close to „imitation‟ because it takes an <strong>in</strong>novationfrom one field and applies it to another. <strong>Innovation</strong> agencies sometimes facilitatethis by mount<strong>in</strong>g <strong>in</strong>novation „fashion shows‟ where a „smart textile‟ <strong>in</strong> automotive seatscan be a solution to the quest <strong>for</strong> stay-clean medical uni<strong>for</strong>ms <strong>in</strong> hospitals (Chapter 3).The harder, more reward<strong>in</strong>g <strong>in</strong>novation route comes where strange attractors merge atwhat complexity theorists call „the edge of chaos‟ which is both stable and unstable withmuch <strong>in</strong>teraction, communication and „buzz‟ go<strong>in</strong>g on between, <strong>for</strong> example, clustersor, more precisely, <strong>in</strong>novation-spott<strong>in</strong>g members of two or more clusters. A breakthroughhere among say mobile telephony, <strong>in</strong>ternet media and life sciences may lead tomany big leaps <strong>for</strong>ward <strong>in</strong> mobile diagnostics and even therapeutic treatment deliveredby „smartphone‟.In what follows, we shall proceed to an explication and exemplification of regionalchange by direct<strong>in</strong>g discussion towards the concept of „strange attractors‟ which, <strong>for</strong> thepurposes of this report show, with startl<strong>in</strong>g illum<strong>in</strong>ation, how related variety and relatednessof the unpredictable k<strong>in</strong>d, occur as emergent features of complex systems likeregional economies. On occasion, reference is made to the substance of Chapter 2 <strong>in</strong>relation to such concepts as „dialogical‟ reason<strong>in</strong>g and „narrative discourses‟ <strong>in</strong>volv<strong>in</strong>gstorytell<strong>in</strong>g and theatre as means of sense-mak<strong>in</strong>g (Weick, 1995) about complex organisationalprocesses. This is because communication and connectivity are key to understand<strong>in</strong>ghow <strong>in</strong>novation is made. Elsewhere, deeper analysis of the operation of varietyupon regional <strong>in</strong>novation, pr<strong>in</strong>cipally by firms as system agents, is provided. Althoughthe whole tenor of this report concerns the function<strong>in</strong>g of variety <strong>in</strong> relation to <strong>in</strong>novation<strong>in</strong> externalised and complex system adaptation and organisation, elements of theexplanation offered are assisted by the complexity science critique of theories of <strong>in</strong>ternalisedsystems by which learn<strong>in</strong>g organisations are presumed, wrongly, it is shown byStacey (2001) to function. Thus the chapter proceeds with <strong>in</strong>itial explanations of „emergence‟as the process by which transition (trans<strong>for</strong>mation or <strong>in</strong>novation) occurs from<strong>in</strong>teraction between diverse entities (i.e. the <strong>in</strong>teraction of entities display<strong>in</strong>g variety). InMart<strong>in</strong> & Sunley (2011) these are organised <strong>in</strong> an MLP way. Here they are more geographical(i.e. spatially <strong>in</strong>teractive). It then moves <strong>in</strong>to a discussion of the role of „attractors‟of path <strong>in</strong>teraction that are better-known to regional scientists as (regional) „pathdependences‟. That is, an <strong>in</strong>dustry <strong>in</strong> a region evolves with an historical trajectorywhich, possibly after a regional or <strong>in</strong>dustrial „shock‟, deviates to an <strong>in</strong>tersection with adifferent <strong>in</strong>dustrial path dependence <strong>in</strong> the same region (proximity effect). One variantof these k<strong>in</strong>ds of <strong>in</strong>teraction is „strange attractors‟ where there is no a priori reason <strong>for</strong>even imag<strong>in</strong><strong>in</strong>g their trajectories might converge and coalesce to produce <strong>in</strong>novation. Inpass<strong>in</strong>g, mention is made of „normal attractors‟. These are less surpris<strong>in</strong>g, as, <strong>for</strong> example,when the pre-existence of a certa<strong>in</strong> eng<strong>in</strong>eer<strong>in</strong>g knowledge allows <strong>for</strong> <strong>in</strong>novation <strong>in</strong>a neighbour<strong>in</strong>g eng<strong>in</strong>eer<strong>in</strong>g field. In complexity science, such „neighbourhood effects‟,facilitate „learn<strong>in</strong>g curve‟ th<strong>in</strong>k<strong>in</strong>g, like „scale-effects‟ the relative predictability of77