Regions, Innovations, and the North–South Divide in Italy

Using firm-level data collected by Statistics Italy for 2008, 2011, and 2015, the Triple-Helix synergy among geographical and size distributions of firms and technology classes is analyzed both regionally and nationally. The Italian system is both knowledge-based and knowledge-intensive, and therefore an interesting case. The contributions to national synergy of the twenty regions in Italy have increased between 2008 and 2015, but synergy generation at levels above the regions has remained relatively stable at approximately 45%. As against the statistical classification into twenty regions, or into Northern, Central, and Southern Italy, the greatest synergy is retrieved by defining the country in terms of Northern and Southern Italy as two sub-systems, with Tuscany included as part of Northern Italy. Different innovation strategies could be developed for these two parts of the country. However, the current focus on twenty regions for innovation policies may to some extent be an artefact of the statistics and EU policies. In terms of sectors, both medium- and high-tech manufacturing (MHTM) and knowledge-intensive services (KIS) are integrated proportionally in the various regions.

Summary and Conclusions

Three themes have been central to my research program: (1) the dynamics of science, technology, and innovation; (2) the scientometric operationalization and measurement of these dynamics; and (3) the Triple Helix (TH) of university-industry-government relations. University-industry-government relations provide an institutional infrastructure carrying the potential of self-organization in the knowledge base of an economy. I elaborated these themes into the problem of relating (i) Luhmann’s sociological theory about meaning-processing in communications with (ii) information-theoretical operationalizations of the possible synergies in Triple-Helix relations, and (iii) anticipatory mechanisms in cultural evolutions.

Towards a Calculus of Redundancy

In this chapter, I extend Shannon’s linear model of communication into a model in which communication is differentiated both vertically and horizontally (Simon, 1973). Following Weaver (1949), three layers are distinguished operating in relation to one another: (i) at level A, the events are sequenced historically along the arrow of time, generating Shannon-type information (that is, uncertainty); (ii) the incursion of meanings at level B is referential to (iii) horizons of meaning spanned by codes in the communication at level C. In other words, relations at level A are first distinguished from correlations among patterns of relations and non-relations at level B. The correlations span a vector space on top of the network of relations. Relations are positioned in this vector space and can then be provided with meaning. Different positions provide other perspectives and horizons of meaning. Perspectives can overlap, for example, in Triple-Helix relations. Overlapping perspectives can generate redundancies—that is, new options—as a result of synergies.

Knowledge-Based Innovations and Social Coordination

Three themes have been central to my research program: (1) the dynamics of science, tech-nology, and innovation; (2) the scientometric operationalization and measurement of these dynamics; and (3) the Triple Helix (TH) of university-industry-government relations. In this introductory chapter, I relate these three themes first from an autobiographical perspective to (i)) Luhmann’s sociological theory about meaning-processing in communications with (ii) information-theoretical operationalizations of the possible synergies in Triple-Helix relations, and with (iii) anticipation as a selection mechanism in cultural evolutions different from “natural selection.” Interacting selection mechanisms can drive the development of redundancy; that is, options that are available, but have not yet been used. An increasing number of options is crucial for the viability of innovation systems more than is past performance. A calculus of redundancy different from and complementary to information calculus is envisaged.

Subdynamics in Knowledge-Based Systems

Using a set of six equations, I propose to model “interactions,” the “organization of meaning,” and “self-organization” as three coordination mechanisms among expectations; three further equations can be derived to operationalize “double contingency,” “identity,” and “reflection.” One can expect that the subdynamics update one another in co-evolutions as feedbacks and feed-forwards. Interfaces among two (sub)dynamics can be expected to operate with time differences (At). Interactions among horizontal and vertical time differences can generate hyper-incursivity in interhuman communications. Hyper-incursion enables us to reconstruct expectations. The social system is probably the only system which can be expected to carry “strong” anticipation while being reproduced as expectations. A system of expectations is not alive, is not constrained by a life-cycle, and does not need to “exist” otherwise than as expectations. The dynamics against the arrow of time are “cultural”: they rest on codes as the pillars of discursive knowledge driven upward into horizons of meaning.

Scientific Communication and Codification

In the sociology of scientific knowledge and the sociology of translation, heterogeneous networks have been studied in terms of practices and so-called actor-networks. However, scientific practices are intellectually structured by codes. Cognitive structures interact and co-construct the organization of scholars and discourses into research programs, specialties, and disciplines. The intellectual organization of the sciences adds to and feeds back on the configurations of authors and texts. The social, textual, and cognitive sub-dynamics select upon each other asymmetrically. Selections can further be selected for stabilization along trajectories and then also be globalized—symbolically generalized—into regimes of expectations.

The Communication Turn in Philosophy of Science

Whereas knowledge has often been attributed to individuals or, from a sociological perspective, to communities, a communications perspective on the sciences enables us to proceed to the measurement of the discursive knowledge contents. Knowledge claims are organized into texts which are entrained in evolving structures. The aggregated citation relations among journals, for example, can be used to visualize disciplinary structures. The structures are reproduced as “ecosystems” which differ among them in terms of using specific codes in the communications (e.g., jargons). Unlike biological DNA, these codes are not hard-wired; they can be changed in the communication. The sciences develop historically along trajectories embedded in regimes of expectations. Regimes exert selection pressure on the historical manifestations. The evolutionary dynamics at the regime level induce crises, bifurcations, etc., as historical events.

Evolutionary and Institutional Triple Helix Models

The institutional TH model focuses on relations of universities, industries, and governments in networks. Institutional arrangements develop over time along trajectories. The Triple-Helix metaphor of university-industry-government relations can also be elaborated into a neo-evolutionary model combining the vertical differentiation among the levels (in terms of relations, correlations, perspectives, and horizons of meaning) with the options for horizontal differentiation among the codes (e.g., markets, technologies, politics, etc., oper-ating in parallel). The neo-evolutionary model focuses on the interactions among selection mechanisms (markets, technologies, endowments) at the regime level. The historical and evolutionary dynamics feedback on each other. The relative weights of the historical versus evolutionary dynamics can be measured as a trade-off. Among three or more selection environments, synergy can be generated as redundancy on top of the aggregates of bilateral and unilateral contributions to the information flows. The number of new options available to an innovation system for realization may be as decisive for its survival more than the historical record of past performance.

The Measurement of Synergy

When policy-makers call for “interdisciplinarity,” they often mean “synergy.” Problem-solving requires crossing boundaries, such as those between disciplines. However, synergy can also be generated ininter-sectorial or geographical collaborations. Synergy is indicated when the whole offers more possibilities than the sum of its parts; “interdisciplinarity” can be an instrument for creating “synergy.” Synergy can be measured as an increase of redundancy; that is, the number of options which are available, but not-yet used. Instead of asking for the synergy among pre-defined categories, such as regions, sectors, size-classes, or nations, etc., I propose to let the most synergetic combinations among (potentially heterogenous) variables emerge from the data matrix. A synergy map can be drawn showing (cluster of) available but not-yet-realized options. A computer routine is made available at https://www.leydesdorff.net/software/synergy.triads which compares all possible triads in a data matrix in terms of their contributions to the synergy in a configuration.

Anticipation and the Dynamics of Expectations

The operationalization of socio-cognitive structures in terms of observables such as texts (e.g., in discourse analysis and scientometrics) or the behavior of agents (e.g., in the sociology of scientific knowledge) may inadvertedly lead to reification. The dynamics of knowledge are not directly observable, but knowledge contents can be reconstructed. The reconstructions have the status of hypotheses; hypotheses can be tested against observations. Whereas agent-based modelling (ABM) focuses on observable behavior, simulations based on algorithms developed in the theory and computation of anticipatory systems (CASYS) enable us to visualize the incursive and recursive dynamics of knowledge at the individual level as different from the potentially hyper-incursive dynamics at the intersubjective level. The sciences can be considered as “strongly anticipatory” at this supra-individual level: expectations are discursively reconstructed in terms of next generations of expectations. This reflexive restructuring is embedded in historical dynamics on which it feeds back as a selection environment. The agents and texts entertain discursive models and thus be considered “weakly anticipatory” participants in the communication.