Download Complex Adaptive Systems and the Evolutionary Triple Helix

Published in Critical Sociology, 36, 4, 2010
Complex Adaptive Systems and the Evolutionary Triple Helix
Riccardo Viale and Andrea Pozzali
Fondazione Rosselli
Abstract
Innovation dynamics are more and more considered to represent one of the most
important processes in the development of modern economic systems. This perspective is
at the root of the development of various innovation concepts. The systematic study of
different systems of innovation raised the awareness that each particular system has its
own characteristics, and that it is not always possible to unilaterally define all the
parameters that play a role in shaping innovation processes. The paper describes the main
obstacles facing public policy decision-making drawing on examples from innovation
and research policy. It argues that Complex Adaptive System (CAS) research can teach
us series of useful lessons, especially, those features necessitating the consideration of
innovation systems as a complex adaptive system.
Keywords:
evolutionary triple helix, neo-corporatist triple helix, triple helix, complex adaptive
system
Introduction
Innovation dynamics are more and more considered to represent one of the most
important processes in the development of modern economic systems. In 1999, the
Economist labelled innovation as the ‘industrial religion of the late 20th century,’ and ever
since, there has been an ever growing interest in innovation. However, studying the
dynamics of innovation is a complex task. It is now almost unanimously recognized that
innovation should be approached in a systemic perspective, as long as it involves many
different actors, structures and interactions. This perspective is at the basis of the
development of concepts such as National Systems of Innovation (Nelson, 1993) and
Regional Systems of Innovation (Braczyk et al., 1998; Cooke et al., 2004).
The systematic study of different systems of innovation has raised the awareness that
each particular system has its own characteristics, and that it is not always possible to
unilaterally define all the parameters that play a role in shaping innovation processes.
What is even more important is that, as long as innovation is by definition a continuous
and ever changing phenomenon, it is not enough to give a static description of the
configuration of a system of innovation at a given time. It is, also, necessary to analyse
the dynamics of the system, in order to forecast the possible evolution in the near future.
To do this, a model that takes into consideration the possible paths of relationships
between the different actors of the systems, and the nexus of causal links between
variables, is needed. One such model is the Triple Helix (Leydesdorff and Etzkowitz,
1998), which “adds to the description of the variety of institutional arrangements and
policy models an explanation of their dynamics” (Etzkowitz and Leydesdorff, 2000:
112).
The triple helix model has been somehow criticized as lacking a solid micro
foundation, as long as the vagueness of the model makes the connection between theory
end empirical data somehow problematic (Shinn, 2002). In this paper we argue that, in
order to explore more in-depth the theoretical and empirical basis of the triple helix,
useful suggestions can come from the literature focusing on complex adaptive systems
(CAS). These suggestions, in particular, can help to highlight the presence of different
interpretations of the triple helix that have already been described as “neo-corporatist” vs.
“evolutionary”1 (Viale and Campodall’Orto, 2002). The paper starts by describing some
of the main obstacles that public policy decision-making has to face, focusing, in
particular, on examples drawn from innovation and research policy. It also introduces
some features necessitating the consideration of innovation systems as a complex
adaptive system.
The latter part of the paper is dedicated to the analysis of some important points
developed in CAS research, and the ways in which they relate to innovation and research
policy. The paper draws upon some stylised facts from well known case studies to show
how the relation between CAS and the triple helix can be empirically substantiated. It
details how the empirical relationship between CAS and Triple Helix allows relevant
consequences to assess the specific features of the different approaches to the triple helix,
the neo-corporatist and the evolutionary models. The intention of the authors is not
directed at discussing specific case studies at length. The aim here is to draw some
general lessons that can guide future research on the triple helix. In this sense, the
examples presented should be considered only as provisional body of evidence directed at
supporting the general analysis explored.
Obstacles to Public Planning
Public policy decision-making is faced with choosing between two opposing principles,
that is, by either: (a) directly generating public goods from the top-down, or (b) creating
the environmental conditions and incentives necessary to lead production from the
bottom-up. The first principle is inspired by the awareness of market failures following
the decentralized and spontaneous origin of public goods (Pigou, 1932; Arrow, 1962).
Without a ‘benevolent dictator’, that is, the ‘public hand’ of government, it seems
impossible to equally allocate goods like health, security, education, environment and
culture, etc. Public goods do not seem to obey market laws and if the ‘visible hand’ does
not take action, no form of economic balance can be accomplished. This irregular market
condition is put under more pressure when ‘intangibles’ such as information and
knowledge are concerned; this is due to the specific characteristics of these goods (Foray,
2000). They cannot be appropriated unless under some sort of vulnerable and insecure
temporary monopoly, such as, Intellectual Property Rights (patents, copyrights, etc.)
They easily generate positive external effects and spill-overs without private
compensation for their producers. They are non-rival goods, that is, they are not totally
consumed by the user.
A theorem, a scientific law or a prototype can be reproduced endlessly just like a
flame can light millions of other flames before going out. These goods can also be
cumulative as they generate other goods while they are being consumed. Eating an apple
does not make another one grow, but applied scientific law helps us discover further
regularities in nature and technology and can, therefore, lead to the development of new
technologies. These characteristics of public goods- in particularly of knowledge- appear
to impose strict limits on innovation policy. In order to generate knowledge that is useful
for technological innovation in manufacturing and services, it may seems that the first
option, that is, direct planning, coordination and control of technological research with
the aim of directing it towards business, would be the only practicable option. When
academic research plays an essential role in the technological competitiveness of
businesses, collaboration between the two cannot be generated spontaneously if pulled by
market forces, but needs to be pushed by direct government involvement. Therefore, if
we want to find a solid theoretical foundation for the triple helix model (Leydesdorff and
Etzkowitz, 1998), we have to consider these market failures in the production and
industrial use of academic-generated knowledge. In order to make the two helixes
interact as much as possible and to generate innovation, coordination and planning, the
third helix, represented by government is essential.
The coordination and planning role of the public in innovation policy might however
be questioned on some equally sound theoretical basis. Such criticism weakens the
importance of the third component of the triple helix although it does not eliminate it. In
fact, government plays an essential role in creating both the environmental conditions and
the structure of the individual selective incentives. This structure is unique in being able
to foster innovation by generating micro-motives through which macro-behaviours
emerge, especially if we want to revisit the title of Schelling's work (1978). Objections,
though, can be put forward on three different levels regarding the simplicity vs.
complexity of the system that is generated by public action. Each level of criticism will
also impact on the next level along a simple-to-complex path. Examples will be drawn
from the Italian situation to explain the complexity.
Agency Effect
If the social system is simple, has few actors whose behaviour can be directly monitored
and is transparent, that is, detailed information on actors’ normal behaviour is easily
available- the collective decision-maker could then calculate and plan the best social
decisions in order to reach a specific goal. Such ideal conditions are difficult to find even
among micro-societies or institutions like families, with the exception of some tribal
communities where behaviour is uniform, involve well-known problems. As we learn
from Public Choice Theory (Buchanan, 1999), the collective decision-maker is
responsible for making choices on behalf of others. If the firm he makes a decision for is
his own–like with private companies-or if he has a moral or affective tie with the firm–as
with his family–his utility flows will be internalized. If he makes a mistake, he will have
to bear the consequences. However, when a company is state-owned–like in the agency
model–the utility flows of the decision-maker are mainly directed towards the outside
because the cost-opportunities of his decision mainly affect the organization rather than
him. If there is no external tie, for example, independent performance assessment, social
accountability and sanctions for the decision-maker, he will not have any selective
incentives for assessing or planning the best solutions for the system he manages. Perhaps
his decisions would be more geared to satisfying his own individual interests rather than
collective ones. As an example of a similar situation, we could refer to fund-raising in
most academic departments of Italian public universities.
Until 2007, even though resources for research were very low, most Italian
university departments failed when applying for European Commission’s Framework
Programmes, despite the slight improvement registered after 2005, when some
universities began to introduce some forms of assessment of the departments’ economic
performance. As a result, Italy can claim a constant credit from the Commission in terms
of resources for research.2 Such poor performance has mostly been due to a lack of
cooperation and even exclusion by department decision-makers rather than to a failure of
Italian academics to design suitable projects. Although an academic, the department
manager was often held back by bureaucrats’ negative utility flows towards timeconsuming and difficult drafting of projects and application forms for the Commission
that would involve further tiresome reporting of activities, if successful. Department
public decision-makers would theoretically have been in an excellent position to
coordinate the organization’s collective choices, which could roughly be considered
simple and straightforward. However, this was not feasible due to inconsistency between
managers and bureaucrats’ utility and agency subordinates (professors).
Bounded Rationality Effect
If the social system is complicated rather than simple, that is, if it is made up of not few
but many individuals whose behaviour, in principle, can be monitored, and if it is
transparent, that is to say, we know each one’s action models, then the public decisionmaker could schedule and coordinate favourable collective choices for the organization
(setting aside the negative effects of the already mentioned agency model) thanks to
excellent information flows and calculation capacity. This condition involves many
objective and subjective problems (Simon, 1990:79). As for the objective problems, it is
obviously difficult to monitor the behaviour of a many-actor system, even when no
complex phenomena arising from interaction emerge. The transparency clause is also
difficult to respect. Even if behaviour models are stable and do not change with
experience, having detailed knowledge of many of them would still be practically
unfeasible.
However, the biggest problems arise from subjective issues concerning the decisionmaker. Firstly, if information sources were available, it would be necessary to direct them
to decision-makers who, in turn, should process them and make the necessary decisions.
The subjective bounded rationality of any human decision-maker does not allow for such
a possibility. The bottleneck of short term memory and information processing, reasoning
and decision-making capacities make any centralized decision model, that calculates the
collective rational selection models on behalf of decentralized individual rationalities,
highly unfeasible. This is difficult to achieve even with the help of sophisticated
calculation capacities and efficient decision-making organization, as the responsibility of
the final choice always rests on the individual decision-maker.
Thus, either the decision is based on a biased and incomplete simplification of
reality, made by the delegated organization, or it should be based on a large number of
significant variables. In the latter case, we would have the same well-known phenomena
of information overload and of sub-optimal information processing, reasoning and
decision-making. As a result, collective decision-maker rationality will often override the
organization members. Social and organizational approaches that are far from the tastes
and motivations of the behaviour of individuals would then be applied. An example is the
failure of many Italian university liaison offices. They were established in the late 1990s
to fill the communication and cooperation gap between academic and business research.
On one hand, academic researchers had no incentive, whether social (i.e. career and
prestige),3 cultural (i.e. discovery of new phenomena),4 or economic (i.e. salary increase)5
to direct their research programmes towards industrial innovation goals. The incentives
are, in fact, negative. On the other hand, industrial researchers do not intend to waste
their time in ‘talking to a brick wall’ with research bodies that are very far removed from
business sales targets and tight deadlines. Some universities have tried to fill the gap by
setting up offices to record and monitor their professors’ research and match them with
the potential technological requirements of some local businesses. The failure of this
approach is due to different reasons. A major one lies in the difficulty of establishing
relations between academic and business values, cultures and knowledge that are quite
distinct between them (Viale, in press; Viale and Pozzali, forthcoming).
Tacit knowledge, which makes the merging and sharing of meanings more
complicated, especially when a third actor is involved in the transfer process, may also
prevent business from understanding the opportunities to make academic research more
valuable (Balconi, Pozzali and Viale, 2007). Many liaison offices also failed because it
was impossible for a centralized university administrative department to focus on
behaviour models of the actors involved. They were expected to operate on the basis of
unrealistic models of academic and industrial researchers rationality, and as such, they
could not collect substantial information from within the university, or outside it and
process it centrally. Academic researchers’ real interests and priorities were not taken
into account and potential needs of business were not identified. As a result, there is
hardly any significant collaboration between universities and business, and university
production of technologies, know-how and patents is negligible.
Complexity Effect
When a system has many actors that frequently interact and frequently change, we cannot
talk of complicated systems, but reason, instead, in terms of complex systems. An
analogy could be between the complication of an internal combustion engine and the
complexity of an anthill (Terna, 2005). The internal combustion engine consists of many
parts that interact without changing, that is, a piston is always a piston, even after it has
stopped working. Therefore, it is possible to understand and anticipate the engine’s
operation by examining all its parts and their causal relationship. On the other hand, even
if an anthill includes a limited and countable number of subjects doing elementary
actions, its operation cannot be explained by describing each individual ant’s behaviour.
Their continuous interaction, the feedback phenomena and the change brought about do
not provide a comprehensive representation of the anthill operation through causal linear
patterns. An anthill represents an example of a CAS-Complex Adaptive System (Arthur et
al., 1997; Kauffman, 1995a; Miller and Page, 2007). The variety and density of
interaction and the heterogeneous character of the interacting subjects increase the
complexity of the system.
Human systems are the most complex. Any change in a social system made up of a
large number of individuals–such as a medium-size city or an organization supplying
public goods–brought about by the introduction of a positive or negative incentive,
produces both direct and indirect effects resulting from actors’ interaction, that cannot be
significantly anticipated. Quite often a public decision-maker introduces a direct
incentive or sanction in order to achieve a social goal, but the final outcome is not as
expected. There is plenty of social and economic literature on this topic where
unintentional consequences (Popper, 1966; Hayek, 1952) and perverse effects of social
action (Boudon, 1977) approaches have been developed. Thus, when a public decisionmaker intends to generate a social and economic phenomenon–for instance social
upgrading–by creating incentives and organizations to reach this target–for example, by
making universities more easily accessible, lowering university fees, abolishing the fixed
maximum number and making university less selective–he will often fail–as university
degrees may loose their value in terms of job placements.
Setting aside the agency and limited rationality effect, a public decision-maker
cannot thus reach all his expected goals due to the complexity effect. Even if he
internalizes all his utility flows, by cancelling the agency effect, and correctly
understanding and processing the behaviour of everyone involved (thus cancelling the
bounded rationality effect) he could not be effective in his coordination or planning
because of the complexity effect, that is, the consequences induced by the interaction,
learning, adapting and changing of the social actors his action addresses. An example is
the failure of the initiative called Questio promoted by the Lombardy Region in 2000 to
foster cooperation between business and academic research. It involved introducing
technological vouchers, that is, direct financial contribution to firms who put forward
technological research projects to be developed in cooperation with academic research
labs certified by the Regional Council. According to the plan of the Region, a firm could
choose from a list, drawn up by regional evaluators, the most reliable research lab with
the most suitable skills required for the completion of the project. Regional financing
could only be spent, though, on labs included in the list. In this way, two targets could be
reached at the same time. On the one hand, industrial innovation projects could be
fostered, while on the other universities and public research centres like CNR and ENEA
were economically encouraged to cooperate with local firms.
The public decision-maker had made his decision as if he was acting in a simple
social system. Therefore, his decision had been made with no knowledge or processing of
actors behaviour patterns although they were, in fact, essential to planning the effects
(bounded rationality effect). Most of all, he had not considered the system of learning and
adjustment in results induced by the interaction of rational actors (complexity effect).
Basically, he ignored the fact that a modest economic incentive could only stimulate a
very low interest in actual cooperation between academic research and business, and that
small companies do not normally have the technological culture required to make
research a real priority. In addition, he overlooks the fact that many actors do not have in
their tacit background knowledge, and ethical values connected either with the
responsibility towards public decisions or goods. The rational interaction among the
actors’ behaviour models has thus generated phenomena of perverse cooperation between
firms and academic labs, as the two subjects agreed to share the resources and continued
their individual planned activities exactly as before.
What Complex Adaptive System (CAS) Can Teach to Triple Helix
Complex Adaptive System (CAS) research can teach us series of useful lessons. These
general suggestions can in turn be applied to the triple helix model, in order to understand
more critically the forces that can drive the evolution of a given innovation system. We
detailed just a few hints as well as suggestions for future research. The first point to be
underlined is related to one of the greatest contributions of the complex system approach:
the development of the new network theory (Newman, 2003). Every actor in a social
system plays a role within a grid of relationships that are either active or that could be
activated. Therefore, any change in the way an actor’s incentives are structured will
generate actions that may affect his social network. A change in his surrounding
environment will follow and therefore his representation of the world and the tools
necessary to achieve his goals will change; even the goals themselves will change. For
example, the 1980 Bayh-Dole Act introduces significant incentives to technological
research and collaboration with the industrial world in the American university system.
Professors and academic structures are encouraged to generate patentable technologies to
be sold to firms or used to create academic hi-tech spin-offs. The change in the economic
incentives structure for academic IPR impacts the university organization as well as the
national and local economic communities. The role of the applied researcher acquires
academic value as companies start appreciating the university as a strategic business
partner. This entails a change in the values and merit criteria of academic careers and
slowly creates the dual-career model.
The second point concerns the way in which specific public goods can be produced
via bottom-up emergence and self-organization from poorly structured and ordered
situations. There are many examples that can be taken from CAS research, including the
achievement of social welfare through decentralized means like citizen heterogeneity,
multiple towns and different voting mechanisms (Miller and Page, 2007:20-25). In this
case, where the Tiebout’s concept (1956) of ‘voting with your feet’ is extended, the use
of simple institutional mechanisms like voting rules and possible migration to a different
town, generates the emerging result of more homogeneous towns, where social welfare
policy is consistent with the needs of the majority of the population. Computational
models of market behaviour have emphasized the key characteristics for price and trading
patterns emergence in markets. Simple institutional rules are enough to generate such
phenomena (Rust, Miller and Palmer, 1994). The same emergence has been recorded in
other phenomena such as workers seeking jobs, individuals forming social groups and
clubs, industries sorting out standards and geographic locations (Miller and Page, 2007,
p. 25).
In terms of the triple helix, the emergence of the entrepreneurial university can be
taken as an example. In the traditional framework of American universities, the MIT is
the best example of innovative change that results in a new type of university. On the
edge of bankruptcy after the First World War, the MIT had a new mission with the ‘1920
Technology Plan’ bringing companies, where former university students were employed,
and its research lab together, aim to promote technology transfer between university and
industry. This change of role was successful and was reinforced with what the MIT
managed to do during the New England crisis–both before and after the Second World
War. Its technological and entrepreneurial mission was fulfilled when President Compton
created the first venture capital company, the American Research and Development
Corporation (Etzkowitz, 1990). The emergence of MIT as the world’s first
entrepreneurial university was a response to its economic problems, and New England’s
crisis. This example is repeated over and over again in the United States and replicated in
other parts of the world, to overcome universities lack of financial resources following
cuts in public funds. It is also due to the increasing interest from industry in academic
research as a consequence of greater international technological competition and the
introduction of institutional rules like the Bayh-Dole Act that support university
incentives towards innovation.
Another important concept, that comes from CAS research and can be usefully
applied to the triple helix model, is feedback. When the interactions are not independent,
feedback can enter the system and alter its dynamics. When feedback is negative,
changes get absorbed and the system tends towards stability. When it is positive, changes
get amplified leading to instability (Miller and Page, 2007:50). Systems that settle into
equilibrium tend to include negative feedback. If drivers know that a motorway is full of
cars they will choose alternative roads, thus decreasing the traffic jam. On the contrary,
systems that generate complexity tend to include positive feedback. An example is the
phenomenon of network externality. The value of the Stock Exchange or the Internet
increases as more actors take part in them. There is positive feedback from actors already
involved towards those who are outside the network. The more companies on the Stock
Exchange, the greater the possibility for my company that is listed on the Stock Exchange
to enjoy the flow of money invested there. The more people investing in the Stock
Exchange, the more easily my company shares will be sold on the market. The more
people connected to the Internet, the more useful it is for one to join the network and
interact with the users. This way the system becomes more unstable and complex through
positive feedback.
A public decision-maker can thus generate public goods or new social behaviour by
introducing economic or symbolic individual or organizational incentives that generate
public goods or behaviour production-oriented feedback rather than via top-down
promotion. The introduction of positive or negative incentives in a social system must
therefore be balanced by taking care of positive or negative feedback mechanisms
generated by the incentives impact on social actors’ behaviour. Incentives should help
direct the actor’s behaviour towards a specific goal. The critical factor in fulfilling the
public decision-maker’s goal is the effect of such behaviour via its feedback on the
motivation of the actors. If feedback is negative, that is, the outcome of the actor’s
stimulated behaviour does not motivate either the latter or the others to repeat or reinforce
this same behaviour–the social phenomenon will decline or become steady. If, on the
other hand, feedback is positive–that is, the effect of the stimulated behaviour motivates
the actor and others to continue–a dynamic situation will be generated resulting in the
public decision-maker achieving his goal. The social system will tend to change and
quickly adjust unexpectedly and in a complex way. Thus, the public decision-maker
should be able to constantly monitor the feedback dynamics following the introduction of
the incentive and adjust the instrumental use of the incentive as if he were pulling at the
reins of a wild horse.
If we analyze the development of some phenomena linked to the triple helix like the
Second Academic Revolution, the dual career and the entrepreneurial university
(Etzkowitz, 1999) we could point out that this change took place in competitive
university systems, like the American and British ones, where feedback dynamics are
crucial and not in the centralized and state owned universities of continental Europe,
where there is a lack of feedback mechanisms. In the US system, the government
introduces institutional rules to stimulate relationships with industry, and strengthens or
changes them according to the feedback generated. In the British system, the government
sets the criteria to assess university merit, including the relationship with industry, and
gives financial grants that are proportional to the degree of achievement of such goals via
the well-known Research Assessment Exercise (RAE). In this case positive feedback is
also generated that leads more universities to imitate successful behaviour in order to be
entitled to grants. Whereas, in centralized and public systems the non merit-based and
non-competitive direct financing does not lead to any positive feedback system that could
encourage universities to adjust or change. Cooperation with industry therefore, does not
get off the ground or is very feeble despite government direct promotional action.
Very often centralized decisions for direct creation of public goods or behaviour do
not attain their goals and the adaptive bottom-up dynamics of complex systems prevail
against any top-down planning. When government goals only focus on final results rather
than motivating actors to achieve such goals, the result is either the goal is not reached or
it is insubstantial or temporary and over-ridden by behaviour linked to the actors’ real
motivations and the emerging phenomena of social system’s adaptive complexity (Miller
and Page, 2007). There is a wealth of related examples. The most striking one is the
failure of the economic planning of centralized systems like the ex-Soviet Union. In the
framework of the Triple Helix model, we should mention the failure of a number of
initiatives by some European countries to promote university-industry cooperation topdown, like the setting up of technological parks and of technological districts in Italy over
the last years (Balconi and Passananti, 2006). Even the French attempt to create
Genopole, a large biotechnological development cluster at Evry, does not seem to have
fostered entrepreneurial development dynamics comparable with the most advanced
American biotechnological clusters (Fuhrer, 2003). Sophia-Antipolis is another case of
the insufficiency of state intervention to create a high-technology milieu. Public
deliberate policies aimed at organizing a high-technology complex have not had success
because “they have not set off reinforcing processes capable of rendering developments
self-sustaining” (Garnsey, 1998:367).
Finally, an important lesson that triple helix can draw from research on complex
adaptive systems is linked to the necessity of taking into account the heterogeneity of the
different actors. Every central decision-maker makes a mistake when he considers the
social system formed of homogeneous actors. The most common mistake is to consider
them all as constrained expected utility maximizers. This approach is misleading for two
reasons: first of all, it does not correspond to reality (see the bounded rationality theory
tradition, Simon et al., 1992). Secondly, it is desirable that it does not correspond with
reality (Page, 2007). In fact, the research tradition within CAS has proved that the
difference in knowledge and cognitive skills among social system members is one of the
most critical factors of innovation, adaptation and productivity capability. The more
actors have different perspectives, interpretations, heuristics and predictive models (Page,
2007:7), the more the social system they belong to is able to generate output–that is,
goods, behaviour, organizations, and institutions-more inclined to adaptation, that
represent more effective solutions compared to those resulting from uniform systems or
centralized decisions.
In fact, it is well known that the cognitive difference is also a major factor to
overcome the inactivity deriving from ‘paradigmatic’ conservatism and from the ‘path
dependence’ (David, 1975; Arthur, 1989; Kauffman, 1995b) of the R&D and innovation
programmes of companies. If a territory features a high degree of cognitive difference
and is able to push it into industrial companies through the communication channels with
universities, research centres, and other companies, it will have a greater propensity to
technological change and to the creation and development of innovative companies. For
example the endogenous and bottom-up development in Cambridge was based on initial
conditions characterized by rich and diversified scientific environment, multiple spin-offs
and spin-outs, different financial resources and strong interactive effects (Garnsey and
Lawton Smith, 1998; Garnsey and Hefferman, 2005).
How possible is it to incorporate these suggestions, coming from CAS research, into
the analysis of the triple helix model? Some suggestions can be found by looking at some
well-known innovation phenomena. We can take as an example the development of the
Silicon Valley technological area. This is the major prototypical case of successful
cooperation between university and industry for high-tech development. Its origin is the
result of all the above mentioned elements: a territory with a high rate of knowledge and
cultural difference; creation of many and various social networks; self-organization and
emergence of bottom-up relationships that favours self-reinforcing mechanisms based on
the continuous creation of new firms as spin-offs from universities labs or spin-outs from
existing firms; a limited direct intervention on the side of government, that mainly
consisted in the setting up of a few institutional rules and selective incentives, in order to
enhance strong positive feedback to promote the system’s adaptive and expansive
dynamics (for a more detailed empirical description see Castells and Hall, 1994). The
same conclusion can be taken according to a recent research (Lester, 2005) on how
universities can support local economic development through their contributions to local
industrial innovation processes.
The paper draws on studies of innovation-enabled industrial change in twenty two
locations in six countries including both high-tech and less favoured regions, mature and
new industries, and first-tier and second-tier universities. The roles of universities in
supporting the local development are variegated and depend from their autonomous
emerging adaptation to the specific demands of the territory. Little role has been found
for a direct public planning and coordination of university-industry relations. The same
conclusions are drawn by Garnsey (1998) and Garnsey and Lawton Smith (1998). There
are no recipes to create high-tech milieu. Even when there are similar initial conditions
small differences are sufficient to generate divergent path-dependence and accumulation
effects. Oxford and Cambridge are a good example. The role of policy makers is not to
design and plan the birth of an industrial innovative milieu. On the contrary its role is to
supply initial endowments of infrastructures, normative environment and human
resources. Moreover, policy-makers “must anticipate and counteract local congestion,
resources shortages and the impact of external shocks” (Garnsey, 1998:375). In other
words they should play and anti-cyclic role in order to help the milieu to overcome the
exogenous and endogenous crises.
The above considerations, taken from research into complex systems, also highly
favour the Evolutionary Triple Helix-ETH-(Viale and Campodall’Orto, 2002:154-56). In
this, the crucial role of governments is restricted to defining a legal context capable of
structuring the individual incentives in order to redirect academic and industrial actors
towards higher degree of interaction. Its main function is to create a normative
framework that sets up an environment of selective incentives that can be effective in
inducing the evolution of collaboration in research and innovation between firms and
university. On the contrary the usual concept of triple helix, that we call Neo-Corporatist
Triple Helix-NCTH-(Viale and Campodall’Orto, 2002:155) is based on the active role of
national and regional government to plan and to promote this kind of collaboration. The
privileged instruments to achieve this goal are mostly tied to public planning,
coordination, agreement, interfacing, and to the public direct economic support of the
collaboration between university and industry.
Different rates of innovation in USA and Europe seem to be explained, partly, by the
two different models of triple helix (Viale and Campodall’Orto, 2002:157-60). The same
argument seems to be valid in analyzing the different rates of innovation in the regional
innovation systems. In Braczyk et al. (1998) a set of variables is introduced according to
three different categories: the governance of enterprise innovation support; the business
innovation dimension; the technological competence. The regions that are most
innovative, that is that are “pioneer or top manufacturer” in new industries are those that
are open to global competition and that have decentralized governance. California,
Massachusetts and other American regions are some examples. They are regions that
satisfy the model of ‘evolutionary triple helix’ (ETH).
The Support for ETH from Some Regional Case Studies
The theoretical considerations we have developed so far can be corroborated empirically
by looking at how they helped explain differences in the innovative performances of
countries and regions all over the world. We have already mentioned with a brief
reference to the example of Silicon Valley, but since we are constraint by space, we
cannot engage in an in-depth analysis of different innovation systems all over the world.
We will, therefore, limit ourselves to presenting few reflections that can also be taken as
suggestions to guide future research. The empirical insights we briefly detailed here
concern three different regional systems of innovation: the Lombardy Region in Italy, the
County of Goteborg in Sweden and the State of New York in the United States.6
A methodological caveat must be borne in mind, before we proceed: we are perfectly
aware of the fact that we are looking at three very different realities, both for what
concern the mere physical and geographical dimension7 and for what concerns the
administrative and political system;8 anyway, in the perspective we are taking, all these
three realities can be considered as local systems of innovation, as long as they enjoy a
territorial, political and administrative unit. We will see that differences in dimension and
in the degree of autonomous decision-making power can indeed have an influence on
innovation performances.
Even though succeeding in accurately determining and measuring the degree of
propensity towards innovation in any given local context is a tough challenge, all three
systems under consideration appear to be characterised by significant innovative
performance. The State of New York alone accounts for 10% of all American PhD
students and nearly 10% of the members of the National Academy of Sciences. It is also
one of the American States with the highest number of patents issued each year and the
highest level of R&D funding. The County of Goteborg is part of the Västsverige region
which is one of the most advanced in Sweden and is in second place (just behind the area
of Stockholm) in the rankings of Europe’s most innovative regions according to the
European Commission’s Regional Innovation Scoreboard (see www.trendchart.org).
Lombardy occupies the top slot among all Italian regions, concerning most of the
indicators normally associated with the measurement of innovative performance–from
the level of human capital qualification and R&D spending to the number of new patents
and innovative businesses (Fondazione Rosselli and Finlombarda, 2005).
Though basic data and indicators can sometimes present a somewhat simplified
assessment, is still obvious that to understand the way in which innovative activities are
performed in the three contexts under consideration, it is necessary to develop a more indepth analysis. In particular, it may prove useful to focus attention on three specific
factors: local government policies, the role of local universities within the regional
innovation system and the degree of involvement of private actors. As far as the structure
of the innovation system is concerned, in all the three cases we isolated significant forms
of collaboration amongst the three main institutions involved: government (particularly
local government), academia and private sector. Signs of convergence towards a Triple
Helix are therefore evident, but with some important qualifications.
As already mentioned, it is possible to isolate two versions of the Triple Helix
model. The “neo-corporatist” Triple Helix (NCTH) is based on a strong intervention of
governmental bodies and on the proliferation of co-ordination and planning committees,
while individual actions are still weak. The “evolutionary” Triple Helix (ETH), by
contrast, is based on strong individual initiatives of innovative actors and on a limited
role by government, whose main function is “to define a legal context capable of
structuring the individual incentives grid to redirect academic and industrial actors
towards a higher degree of interaction” (Viale and Campodall’Orto, 2002:156). As we
noted earlier, the ETH corresponds to the social features of a CAS. The different versions
of Triple Helix can thus be more or less integrated: the integration of the model rests on
the coordination of efforts and actions between the three strands.
With the State of New York, we can see a system that is both evolutionary and
closely inter-connected. Policies supporting innovation and scientific and technological
research are managed by NYSTAR, (New York State Strategically Targeted Academic
Research) a state agency set up in 1999, which recently became a foundation. NYSTAR
was created during a historically critical stage for the innovation system of New York
State, as it risked losing ground to more dynamic areas such as California and
Massachusetts. The setting up of a single body responsible for all innovation measures
and policies was a fundamental element for the adoption of a policy that strongly
favoured innovation.
Since its creation, NYSTAR has financed a series of different development
programs, together with a wide range of instruments which help in promoting all aspects
of innovation: from basic research to technology transfer, from legal and administrative
advice to access to venture capital, from technology brokering to tax relief. A network of
ten Regional Technology Development Centers (RTDCs) that provide product and
process enhancement to smaller manufacturing and high technology companies has been
put in place, together with a whole series of programmes to support the processes of
technology transfer from universities to local firms. Among other things, funding from
NYSTAR led to the setting up of eight Strategically Targeted Academic Research
(STAR) Centers and of five Advanced Research Centers (ARCs) for the realization of
basic research and of fifteen Centers for Advanced Technology (CATs), where the focus
is more on applied science, new technology and technology transfer. All these structure
are located among local universities and the funding is granted on a purely competitive
basis.
There is also a very detailed scrutiny of the scientific and economical returns that the
activities carried out in these structures can be determined (Bessette, 2003) and this in
some cases is considered as a prerequisite for the allocation of new funds. Other
programmes (for example the Centre of Excellence program) are based on the presence
of a three to one match between private and public funding: this is a good example of a
policy that is able to strengthen the propensity to innovate of local actors by creating a
structure of incentives based on the involvement of both universities and private firms
sector and on a careful balance between competition and cooperation. Universities
compete against each other to obtain funding for the setting up of a new centre (maybe a
STAR, an ARC or a Centre of Excellence) but still they are able to work together once a
project is underway in order to create the “critical mass” of knowledge and skills
necessary to set up real initiatives of excellence.9
The County of Goteborg presents a different way of interpreting the Triple Helix
model. In this case, the prime mover is in fact a single actor, that is, the Chalmers
University. Despite its somewhat modest size, over time this University has managed to
consolidate its role as leader within Goteborg’s innovation system, thanks also to the
setting up a series of structures and “outreach centres” (Clark, 1998) which have allowed
it to successfully deal directly with local businesses and firms. Chalmers University
created two scientific parks: the Chalmers Science Park, founded in 1984 in partnership
with the city of Goteborg and the regional Chamber of Commerce and the Lindholmen
Science Park, set up in 1999 that has a business incubator aimed at creating innovative
spin-offs. Together with local businesses, including Volvo, Chalmers is partner of one of
Sweden’s largest venture capital company10 and has built up a wide network of contacts
with entrepreneurs to sustain-among others-training and professional development.
Chalmers University has always paid close attention also to all economic and legal
aspects that could influence the innovation process. This is demonstrated by the setting
up of a dedicated centre for intellectual property rights studies and by the launch of the
Chalmers School of Entrepreneurship, an advanced studies programme aimed at
developing business plans to create new high-tech spin offs. Over the years, Chalmers
has acquired all the tools necessary to independently manage each phase of the
innovation process, from developing a scientific idea and protecting the invention to
getting funds for the pre-competitive development phases, from setting up new
businesses to developing sales strategies in partnership with others. Many of these
activities were possible thanks to the status of “foundation university”, which, in 1994,
Chalmers was able to take thanks to an opportunity offered by national legislation. This
still represents a significant exception in a national system where universities are public
bodies. The main reason Chalmers made this institutional change was its desire for more
freedom to manage its own sources of funding and properties, organize its administration
and strategies, and determine recruitment and career advancement procedures.
In fact, before 1994, all decisions were based on national law and official documents
had to be approved by qualified bodies. Today Chalmers is governed by private law and
its relationship with the state is defined by a general agreement. Some aspects of public
legislation, however, continue to affect Chalmers (e.g. aspects concerning access to
study, organizing exams, equal opportunities, etc.). Chalmers also undergoes the same
assessment procedures as other public Swedish universities. Setting up the foundation has
certainly allowed Chalmers to increase its level of independence and made adopting and
carrying out investment policies simpler (including infrastructure); this is a prerequisite
to sustain the innovation and technological transfer processes. As Burton Clark pointed
out in his review of models of entrepreneurial universities all over the world, this
experience
... is worthy of international attention. As a hybrid institutional form that leaves a
university broadly influenced by state authority, and partially supported by state money
under long-term contracts, it takes up a quasi-private status that guarantees more fiscal
and managerial autonomy (Clark, 1998:102).
However, it should not be lost on us that Chalmers showed a strong entrepreneurial
tendency even before establishing a Foundation. The experience gained by the University
throughout its history that helped seized the opportunity to change its legal status and to
succeed in completing the transition from public legal body to a Foundation. Goteborg
local authorities have had a less proactive role compared to NYSTAR in the State of New
York. Also, the level of independence and the availability of direct financial investment
are obviously very different in the two contexts, yet still they had an important role in
facilitating the processes of interaction between Chalmers and other actors within the
innovative system, namely large companies and the University of Goteborg.
Relying on the typologies of Triple Helix we put forward, we could define the Triple
Helix model in the area of Goteborg as an evolutionary model-as the initiative comes
from each single actor’s independent actions-even though it is less integrated compared
to New York. Less integration is basically related to smaller size11-leading to less
complexity-and less degree of independent decision making and financing by local
authorities.
The Lombardy Region represents yet another different Triple Helix model, more
inclined to the neo-corporatist version, and it is no coincidence that it is, also,
internationally less successful. We should, in fact, remember that although Lombardy is a
leader in Italy in terms of innovation performance, it ranked seventy in the regional area
rankings of the European Innovation Scoreboard. The Lombardy context is definitely
more complex than Goteborg, but far less independent compared to New York. The
Triple Helix model as we see it is still, not evolutionary or integrated, despite concerted
effort by regional governments over the last few years to organize several important
initiatives supporting innovation processes. Indeed, but for its own regional financing
agency (Finlombarda), Lombardy was the first Italian region to launch its own venture
capital funding, thus, moving into a field where the Italian system, compared to other
advanced countries, is completely inadequate. This is mainly attributable to the lack of
private initiatives. A network of Centres of Excellence, promoting avant-garde research
has also been established in the region. It has also set up a pioneering initiative to monitor
and evaluate the Centres of Research and Technological Transfer with the already
mentioned Questio system.
Consequently, the low levels of success attained by policies promoted by the Region
are not due to insufficient push or implementation mistakes, but to an underlying factor
that has, so far, proved almost impossible to overcome. Despite all efforts, the policies
undertaken failed to substantially change any single actor’s incentives structure and thus
were ineffective in modifying the overall functioning of the system (except for some
praiseworthy exceptions). The fundamental problem seems to lie in the insufficient level
of useful collaborations between local universities and entrepreneurs and this, in turn,
goes back to the specific characteristics of the national academic system, defined by
national laws that are especially difficult to change (Orsenigo, 2001).
In addition, the private business system also operates within certain system logics,
where incentive to innovate is not always rewarded. With this sort of framework the
Region’s initiatives frequently were forced to pull from above, in an attempt to
compensate for the insufficient push coming from local actors. In so doing, however, they
came up against all the obstacles that we have outlined at the beginning of the paper. It is
no coincidence that among the few initiatives worth noting we find the ones from the
health care sector, that have somehow succeeded in by-passing the public research
system, involving private organizations such as the San Raffaele Science Park, the
European Institute of Oncology (Istituto Europeo di Oncologia-IEO) and the Italian
Foundation for Research on Cancer (Fondazione Italiana per la Ricerca sul Cancro).12
It should be said, however, that even these projects sometimes have to deal with
insufficient interest from local businesses. But, the Polytechnic of Milan, that has a
historical tradition of close contact with local firms, has been able to launch a series of
significant initiatives. In this case too, as we saw with Chalmers University, the
opportunity to use the Foundation’s legal status helped significantly. One difference,
however, was that the Polytechnic could not independently set itself up as a Foundation,
so it created a separate Foundation to manage activities concerned with the valorization
of research and the technology transfer processes. The example of the Lombardy Region
shows how innovation policies that theoretically appear likely to stimulate virtuous
behaviour can determine suboptimal results if unfavourable secondary conditions exist
and single actors can implement opportunistic bottom-up adaptive strategies. For
instance, not having access to useful tools to change incentives for university researchers
that are still tied to a national legal framework was a major obstacle for generating those
positive feedbacks that, as we saw, represent the basic element of CAS.
As single actors react negatively to any attempt of change, the innovation system
cannot pull itself out of its low level equilibrium, despite all the effort of the local
government. We revealed, a series of stop and go that is linked to the adoption of specific
measures, but the system is unable, from within, to feed an evolutionary dynamics. In
New York State, on the other hand, government action was successful because the
proactive policies supporting innovation are part of a system where single actors have the
incentive to adopt new behaviour that reinforces the effect of state provisions. The New
York system also is much more differentiated than its Lombardy counterpart, and as we
noted, this also represents a positive characteristic in the CAS perspective.
The case of Goteborg is an unusual example, as the role of government bodies
appears marginal compared to the capacity of bottom-up emergence and self organization
of a single actor, Chalmers University, that established itself as a successful
entrepreneurial university over the years. This was also possible as the Goteborg County
is a relatively small area, and therefore, the system of innovation is quite simple. Still, it
seems quite significant how, in order to carry on successfully, Chalmers University had
to finally “cut loose” from the norms of the Swedish public university system and made
the most of the opportunity to become a Foundation-based university. This confirms the
fact that if a system is too homogenous internally, like a system with only public
universities, it cannot generate diversity and knowledge differentiation over a long
period. It cannot also make for authentic evolutionary attitudes to emerge.
Notes
1. It must be underlined that in the context of our paper, the use of the term “evolutionary” to
label a specific approach to the triple helix model does not share any kind of references with
the tradition of evolutionary economics following the approach of Nelson and Winter and
other Neo-Schumpeterian authors. The use of this concept must be intended in a rather loose
manner and is mainly linked to the economic tradition following the work of von Hayek, to
define all those process that are mainly the product of bottom-up processes that are not
guided from above, but are left to the individual action. For a more detailed discussion of the
differences between “neo-corporatist” and “evolutionary” triple helix, see Viale and
Campodall’Orto, 2002.
2. If we consider the balance between the VI Framework Programme percent financial return
and the Community balance expenditure contribution percentage, Italy records the highest
negative balance equal to -3.62% (Cotec on MIUR data, 2008).
3. In Italy, cooperation between academia and industry is not uncontrovertibly considered as a
positive thing. Evaluating a curriculum very rarely involves any acknowledgement of patents,
technological transfer or creation of spin-offs. What really matters in Italian universities are
the professional’s scientific publications, even in the most technological faculties such as
engineering.
4. The epistemological and ethical values of Italian academia are close to those of the Merton
ethos (Merton, 1977). The most important ones are generating knowledge of new aspects of
reality without any practical goal, and protecting independent free decision-making from any
external conditioning.
5. The national contract of university professors may hinder the possibilities of external
collaboration and remuneration. In the University system, the role of full-time professor is
favoured either formally, when assigning academic positions, or informally when allocating
resources for teaching and research. The full-time professor cannot carry out a professional
activity or become an associate in a profit-oriented business or get payment from outside,
apart from publications or through department activity but this is often slow and expensive.
6. Data on these three systems mainly come from a series of empirical compared research we
have performed with Rosselli Foundation in order to assess the innovative potential of
Lombardy Region in an international perspective.
7. New York State covers an area of 141.205 km² and has a total population of 19.227.088,
Lombardy covers an area of 23.861 km² and has a total population of 9.604.434, while the
county of Goteborg covers an area of 23.945 km² and has a total population of 1.528.455.
8.
9.
10.
11.
12.
New York is a State, Goteborg is a county and Lombardy is a Region and the differences
between these bodies, in terms of degree of political autonomy, decisional power, and
financial capabilities are quite relevant.
A certain level of collaboration is still necessary, as the State of New York does not have a
university that could take on the role that is sometimes taken on by the MIT or Stanford, i.e.
acting as a pole of scientific, technological and entrepreneurial development which
innovative dynamics can evolve around.
Another venture company called Chalmers Invest, totally owned by the university,
furthermore offers start-up funding for new businesses.
As has already been said, the State of New York has a population of over 19 million; the
County of Goteborg has around one million and a half.
The San Raffaele Science Park is one of the largest Science Parks exclusively dedicated to
biotechnological research in the healthcare sector and is world leader in gene therapy
research. The IEO has promoted the foundation of the Centro Europeo per la Ricerca
Biomedica Avanzata – CERBA (European Center for Advanced Biomedical Research), a
frontier centre that integrates research, training and therapy processes. FIRC has financed the
opening of the IFOM (Istituto Firc di Oncologia Molecolare), a no profit high-tech research
centre dedicated to studying the formation and development of tumors.
References
Arrow, K. (1962) Economic Welfare and the Allocation of Resources for Inventions.
R.R. Nelson (ed.) The Rate and Direction of Inventive Activity: Economic and Social
Factors, pp. 165-182. Princeton University Press: Princeton.
Arthur, W. (1989) Competing Technologies, Increasing Returns and Lock-in by
Historical Events. The Economic Journal 394: 116-31.
Arthur, W.B., Durlauff, S. and Lane, D. eds. (1997) The Economy as an Evolving
Complex System. Addison Wesley: Reading.
Balconi, M. and Passannanti, A. (2006) I Parchi Scientifici e Tecnologici nel Nord Italia.
Franco Angeli: Milano.
Balconi, M., Pozzali, A. and Viale, R. (2007) The ‘Codification Debate’ Revisited: A
Conceptual Framework to Analyze the Role of Tacit Knowledge in Economics.
Industrial and Corporate Change 16(5): 823-49.
Bessette, R.W. (2003) Measuring the Economic Impact of University-Based Research.
Journal of Technology Transfer 28(3-4): 355–61.
Boudon, R. (1977), Effects Pervers et Ordre social, Presses Universitaires de France:
Paris.
Braczyk, H.J., Cooke, P. and Heidenreich, M. eds. (1998) Regional Innovation Systems.
The Role of Governances in a Globalized World. UCL Press: London.
Buchanan, J. (1997) La Scelta Individuale nei Ruoli Decisionali. A.M. Petroni e R. Viale
(eds.) Individuale e Collettivo. Decisione e Razionalità, pp.83-97. Raffaele Cortina
Editore: Milano.
Castells, M. and Hall, P. (1994), Technopoles of the World: The Making of 21st Century
Industrial Complexes. Routledge: London.
Clark, B.R. (1998) Creating Entrepreneurial Universities: Organizational Pathways of
Transformation. Pergamon-Elsevier Science: Oxford.
Cooke, P., Heidenreich, M. and Braczyk, H. eds. (2004) Regional Innovation Systems:
The Role of Governance in a Globalized World, 2nd ed. Routledge: London.
Cotec, (2008). Rapporto Annuale sull’Innovazione. Fondazione Cotec: Roma. URL
(consulted 13 July 2009): www.cotec.it.
David, P.A. (1975) Technical Choice Innovation and Economic Growth. Cambridge
University Press: Cambridge.
Etzkowitz, H. (1990) The Second Academic Revolution: The Role of the Research
University in Economic Development. S.E. Cozzens, P. Healey, A. Rip and J. Ziman
(eds.) The Research System in Transition, pp. 11-22. Kluwer: Dordrecht.
Etzkowitz, H. and Leydesdorff, L. (2000) The Dynamics of Innovation: From National
Systems and “Mode 2” to a Triple Helix of University-Industry-Government Relations.
Research Policy 29(2): 109-23.
Fondazione Rosselli and Finlombarda, (2005), Scoreboard Regionale dell’Innovazione
per la Comparazione delle Performance del Sistema Innovativo Lombardo. Fondazione
Rosselli: Torino. URL (consulted 13 July 2009): www.fondazionerosselli.it.
Foray, D. (2000) L’Economie de la Connaisance. La Découverte: Paris.
Fuhrer, B. (2003) Biotech Clusters. A comparison of Genetown™ (U.S.) and Genopole®.
Paper presented at the Medtech Perspectives Symposium in Paris on 5th December 2003.
Garnsey, E. (1998) The Genesis of the High Technology Milieu: A Study in Complexity.
International Journal of Urban and Regional Research 22: 361-77.
Garnsey, E. and Lawton Smith, H. (1998) Proximity and Complexity in the Emergence of
High Technology Industry: The Oxbridge Comparison. Geoforum 29: 433-50.
Garnsey, E. and Hefferman, P. (2005) High-technology Clustering through Spin-out and
Attraction: The Cambridge Case. Regional Studies 39: 1127-44.
Hayek, F.A. (1952) The Counter-Revolution of Science: Studies on the Abuse of Reason.
The Free Press: Illinois.
Kauffman, S. (1995a) At Home in the Universe: The Search for the Laws of Self
Organization and Complexity. Oxford University Press: New York.
Kauffman, S. (1995b) Technology and Evolution: Escaping the Red Queen effect.
McKinsey Quarterly 1: 119-29.
Lester, K.R. (2005) Universities, Innovation, and the Competiveness of Local
Economies: A Summary Report from the Local Innovation Systems Project-Phase I. MIT
IPC Working Paper IPC-05-010.
Leydesdorff, L. and Etzkowitz, H. (1998) The Triple Helix as a Model for Innovation
Studies. Science and Public Policy 25: 195-203.
Merton, R. (1973) The Sociology of Science. Chicago University Press: Chicago.
Miller, J.H. and Page, S.E. (2007) Complex Adaptive Systems, Princeton University
Press: Princeton.
Nelson, R.R., ed. (1993) National Innovations Systems: A Comparative Study. Oxford
University Press: Oxford.
Newman, M. (2003) The Structure and Function of Complex Networks. SIAM Review 45:
167-256.
Orsenigo, L. (2001) The (Failed) Development of a Biotechnology Cluster: The Case of
Lombardy. Small Business Economics 17: 77-92.
Page, S.E. (2007) The Difference. Princeton University Press: Princeton.
Pigou, A.C. (1932) The Economics of Welfare. Macmillan: London.
Popper, K. (1966) The Open Society and its Enemies. Routledge: London.
Rust, J., Miller, J. and Palmer, R. (1994) Characterized Effective Trading Strategies:
Insights from Computerized Double Auction Tournament. Journal of Economic
Dynamics and Control 18: 61-96.
Schelling, T. (1978) Micromotives and Macrobehavior. Norton: New York.
Shinn, T. (2002) The Triple Helix and New Production of Knowledge: Prepackaged
Thinking on Science and Technology. Social Studies of Science 32: 599-614.
Simon, H. (1990) Invariants of Human Behavior. Annual Review of Psychology 41: 1-19.
Simon, A.H., Egidi, M., Viale, R. and Marris R. (2008 [1982]), Economics, Bounded
Rationality and the Cognitive Revolution. Edward Elgar: Cheltenham.
Terna, P. (2005) Economia e Sistemi Complessi. R. Viale (ed.) Le Nuove Economie, pp.
255-274. IlSole24Ore: Milano.
Tiebout, C.M. (1956) A Pure Theory of Local Expenditures. Journal of Political
Economy 64:416-24.
Viale R. (in press) Knowledge Driven Capitalization of Knowledge. R. Viale and H.
Etzkowitz (eds.) The Capitalization of Knowledge: A Triple Helix of University-IndustryGovernment. Edward Elgar: Cheltenham.
Viale, R. and Campodall’Orto, S. (2002) An Evolutionary Triple Helix to Strengthen
Academy-Industry Relations: Suggestions from European Regions. Science and Public
Policy 29: 154-68.
Viale, R. and Pozzali, A. (forthcoming) Different Cognitive Styles among Industrial and
Academic Researchers.
For correspondence
Riccardo Viale, Fondazione Rosselli, Corso Giulio Cesare 4 bis/b, 10152 Torino, Italy,
email: [email protected]
Andrea Pozzali, Fondazione Rosselli, Corso Giulio Cesare 4 bis/b, 10152 Torino, Italy,
email: [email protected]