A Preliminary Model of Innovation Systems
Mauricio Uriona!, Ricardo Pietrobon!, Gregorio Varvakis”, and Elias
Carvalho!
1RoR Group. Duke University. Durham, NC, 27710, USA
2 Department of Knowledge Engineering. Federal University of Santa Catarina.
P.O. Box 476, Florianopolis, SC, 88040-970, Brazil
'{m.uriona,rpietro,ecc18} @duke.edu
2{ grego} @egc.ufsc.br
Abstract
tion System (IS) approach is a conceptual framework
mtific and tech-
The Innove
used by scholars and policy makers to d
nological structures and processes that influence economic develop-
ment. The current literature offe
‘ibe the
s a wide variety of models represent-
ing IS making difficult to choose the most appropriate one to inform
and formulate policy. This article aims at summarizing and aggregat-
ing the wide range of models into one single meta-model that could
allow a more comprehensive understanding of the structure - compo-
nents and linkages - of an IS and offer rigorous and timely ments
of the evidence base to inform decision and policy makers. Thus, a
preliminary structured model of a Regional Innovation Sy:
veloped and formalized through the use of a system dynamic
tion model that depicts changes in the linkages and its knowledge flows
over time. The model might be used in future research in analyzing
specific Innovation Systems.
Keywords: Innovation Systems, System Dynamics, Simulation Models
em is de-
mula-
1 Introduction
Innovation System (IS) is the term used by scholars and policy makers to de-
scribe the emerging scientific and technological structures and processes that
influence economic development (Ereemanl, [1987; [Lundvalll [1999 [Nelson]
{1993, [199).
As many complex systems, due to the presence of a large number of ac-
tors in the IS, policies which were meant to increase its performance often
produce the opposite, unintended effect. As Liu and White point out, it is
the IS’ inherently endogenous structure i.e. the complex non-linear relation-
ships among the actors and institutions composing the IS, that cause those
failures in policy making
This context is especially problematic for Developing Countries where
there are
“weak liaisons, low trust and poor association between economic
generators (companies) and their supporting institutions (gov-
ernment, federations, banks and academies). Innovation is not
perceived as an asset, and high value entrepreneurship is not a
common finding in the entrepreneurial profiles of most profes-
sionals” (Scheel and Paradal, 2008)
Accordingly, efforts in understanding, explaining and managing IS, have
atistical and econometric approaches or expert-based
2001). Since IS are inherently complex, these methods only
— for partial explanations of IS behavior and performance, and con-
sequently, only offer partial answers on how they should be managed.
This article aims at summarizing and aggregating the wide range of
models into one single meta-model that could allow a more comprehensive
understanding of the structure - components and linkages - of an IS and offer
rigorous and timely assessments of the evidence base to inform decision and
policy makers.
Thus, a preliminary structured model of an Innovation Systems is devel-
oped and formalized through the use of a system dynamics simulation model
that depicts changes in the linkages and its knowledge flows over time and
afterwards, instantiated in a Region in Brazil. This study presents the pre-
liminary results of the research (model building and pre-testing phase:
2 Innovation Systems and System Dynamics mod-
eling
The use of the Innovation Systems framework has brought undoubtedly
several achievements, especially related with the understanding of the inno-
vative process as a em, however, some shortcomings have been identified
in the past few years. These shortcomings can be characterized as twofold:
shortcomings related to the IS framework per se and shortcomings related
to the modeling of IS.
First, because of the mainly economic approach that has driven the
study of innovation for the last sixty years, scholars have had difficulties
in explaining what is in nature an interdisciplinary phenomenon, which not
only accounts for economic variables :
Second, although the IS framework emphasizes that the relationships
among the actors are the drivers that cause changes in the behavior of the
Innovation System (Godin , it does not explain how the components
of the system compete for resources, in other words, how the relationships
between the components are coordinated (Lee and Yoo, )).
Third, due to its static nature, the IS framework lacks in representing
the behavior changes on the system over time (Lee and Yoo, 2007). As Lee
sustains, the systemic and dynamic patterns of interactions among the actors
of an Innovation System are neglected topics of study in the NIS literature
And fourth, because of the complexity of the system (large number of
actors and relationships) the main emphasis of empirical studies carried out
has been on the system’s statistics or comparative statistics, and none going
onward more dynamic analysis (Carson et all 2002).
Furthermore, the study of IS does not give any attention to ”delay ef-
fects” or "time lags”, i.e. there are long time delays between the moment
a policy is launched and the moment the actual results of that policy are
seen. In most cases it would take decades to see the effects of a policy or
decision taken by one the innovation actors. Although the time lags have
been studied in themselves, they have not been fully discussed in the context
of a IS (Led, {2002).
The second type of shortcomings arises from the latter statement. So far,
the current modeling of IS has been based on mainly quantitative (statistical,
econometric) and discursive approaches, such as expert-based judgments
(2003). The OECD for example, has focused mainly
on applying large scale surveys among their member countries in order to
obtain data and measure input and output indicators, however, those static
measures have fail in fully representing some of the behaviors changes seen
on Innovation Systems, especially those that depend on time.
I do not state that one approach may be better than another, certainly,
statistical analysis may help in having a scientific way to cluster data, so
it can be analyzed by several alternative methods; on the other hand, the
advantage of opinion surveys is that they can estimate factors where hard
data is not available by using the intuitive insights of experts on subjective
aspects, improving the qualitative analysis. We simply claim that joint
approaches - when used in parallel - can substantially improve decision and
strategy-making.
Thus, I sustain that traditional approaches, such as those cited above,
have many times failed in representing the complexity of Innovation Sys-
tems due to their inability to deal with the non-linearity and dynamicity
found on the NIS structures and relationships. As Lundvall emphasized “a
system of innovation...is also a dynamic system, characterized both by pos-
itive feedback and by reproductioncumulative causation, and virtuous and
vicious circles, are characteristics of systems and sub-systems of innovation”
Statistical models are based on past conditions and depend on time-series
analysis and regression forecasts; econometric models on the other hand are
built upon chang cio-economic conditions (GDP growth, demograph-
ics, etc.) often including ”judgmental” adjustments to their
models in order to account for some non-linear behaviors.
As Lyneis points out, these statistical approaches have not done a good
job in explaining the complexity of non-linear, dynamic systems because
they dont account for capturing the structure of the system which creates
behavior over time ari
On the other hand, the risks of using discursive approaches, such as
expert-based judgments, is that they are influenced by subjective elements,
limited knowledge and narrowness in mental models combined with respon-
dent biases, resulting likely in a reflection of a nation's reputation rather
than its actual state and since it lacks of a hard quantitative basis for sus-
taining their proposal, they may also fail in explaining the behavior changes
over time (van Raanl, 2003; [Atkinson and Andes, |2009).
Structural modeling, through the use of System Dynamics helps in un-
derstanding patterns of behavior on complex systems in a more rigorous,
scientific and consistent fashion by identifying the key-elements of dynamic
dLvneid mneis,
complexity: time lags/delays, feedback loops and stocks and flows (
[2000 [Stermanl, 2000).
System Dynamics enable us to understand complex non-linear social sys-
tems by describing the causal explanations that produce their performance
and behavior through causal maps (qualitative approach) and by propos-
ing alternate strategies through rigorous modeling and simulation methods
for improving their performance by constructing scenarios and by designing
more effective policies Forrester, frooal termi 2000)
The proper use of System Dynamics for structural modeling provides
advantages over other measuring and modeling approaches
: ay
e More reliable forecasts of short to mid-term trends than statistical
models, since in may complex systems, “structural momentum” dom-
inates over “noise” iw the short term (nei, (2000).
e Means of understanding the causes of complex non-linear social sys-
tems behavior — as seen on Innovation Systems — and thereby changes
on its structure as part of trend forecasting.
e Models that allow the determination of reasonable scenarios as inputs
to decisions, strategies and policies.
e Takes account of a system’s dynamic complexity — the counterintuitive
behavior of complex systems that arises from the interactions of the
agents over time the unanticipated events or side effects that policy
makers face when the system behaves in a hardly predictable way.
Moreover, System Dynamics has been used in a variety of applications
since its beginnings in the 1960's, taken special attention for this research, on
macro-models on science, technology and innovation policies (Soto-Torres et al,
[2008 [Lee, [20025 |Galanakis, 2000; |Stamboulis, [2008).
3 Research Design
The research is developed in two phases: Model Building and Model Pre-
Testing.
3.1 Phase 1 - Model Building
The purpose of this phase is to develop a system dynamics model for a
generic Innovation System, based on the theoretical meta-model previously
proposed by |Uriona Maldonado et_al ming . Based on their work, we will
use the meta-model depicted in Figure 1 to drive our system dynamics model
building process.
According to Figure 1 the meta-model is made of a Financial component,
a Scientific-Technological component, a Production-Industrial component,
Market Component and a Workforce component helps in the sense that it
relates flows rather than actors or agents by linking processes that occur in-
side the IS (Uriona Maldonado et alJ,|ming). Despite the over-simplification
of Figure 1, the closed-loop relationships in the macro-model are arguably
showing the dynamic nature of this complex system.
[EEE
‘Sil mon apt evestmens inhuman capital
Se HUMAN
ETAL |
. »
SCIENTIFIC. Gapabity PRODUCTION- products
TECHNOLOGICAL — INDUSTRIAL — CONSUMER
a \ q ¥ s
Investments in scence Funding fom Fis
‘od technology Prot
FINANCIAL
Figure 1: Meta-Model of a generic Innovation System - Source: Uri-
ona et al (Forthcoming)
Afterwards, we will use causal-loop diagraming (CLD) to represent the
most relevant interactions among the five building blocks of the meta-model
and then we will translate it to Stock and Flow programming language in
order to run different simulations in our pilot study.
3.2. Phase 2 Model Pre-Testing
Once the model has been built, it will be compared against real Innovation
System data, taken out of a Federal State in Brazil, in terms of the com-
ponents of the meta-model and its relationship with CLD and stock and
flow diagrams. The pilot study will use data from Santa Catarina State,
in Southern Brazil, which has been increasing its investments on science,
technology and innovation in the past years.
4 Model Building
The model building phase started with the CLD design, which was based on
literature review and on the use of the meta-model proposed by[Uriona Maldonado et al]
(ming). Figure 2 shows the main CLD which includes 9 reinforcing loops
(represented by the R letter) and 9 balancing loops (represented by the B
letter).
A detailed explanation has been omitted due the space constrains, how-
ever, in a nutshell the CLD represents the major relationships between in-
vestments (private and public), its use on Research and Development and its
results as scientific andor technological outputs, which eventually produce a
certain level of innovative capability.
As (2011) sustains the innovative capability is the ability of a
country to produce and commercialize a flow of innovative technology over
the long term”, concept borrowed from (2002).
We consider the “Innovative Capability” variable essential to the under-
standing of the dynamics of innovation systems, since as more innovative
capability is built, the number of technological innovations increases bring-
ing with it increases on revenues for private and public funding sources which
then feedback through the engagement on R&D activities.
This innovative capability produces later process and product innova-
tions, which eventually enhances the attractiveness of the product and helps
in increasing market share as well. At his point, the model only accounts
for STI knowledge disregarding (at least momentarily) the effects of DUI
Knowledge, an additional_source of knowledge as stated by Jensen et al.
(For a broader review see . STI knowledge is depicted in
sIoyNY :20IM0g - sutayshg UOTPEAouUT JOJ [OPOY GIO *Z enB1q
ag is for Private R&D
Private Invetsmenis in
Private finding sources for Qualitied Human Capital _aPobli fa F
ible funding sources for
- Public Investments in-—— Public R&D
ts)
Public Investmen ja Qualified Human Cepital
Quang Haman > rom Public Investments
_
Pressure from private
sector to invest n other
‘apital ~
areas aa he Public RED
= ves cay
oo Science and Tech =~ Public Investments in
Ay Public Research —\— Public Fundi
nic Funding sure
\ Tor Pull Rescuch \ he
ther eome \
\ 2)
2
he te) t=) & |
bi Rcach la
Private Investments in ——————— Actiy ‘Technological
Private Research PP Private Research Outpt
‘Activity yi
am 2
a
Production Casts ‘Knowledge
~
i Poss iad
products te) = tance Chany
our model of Figure 2 as the Science and Technology Knowledge variable,
which feeds up from scientific and technological activities.
Following the CLD, the next step was to develop a stock and flow model
that could allow for simulation modeling. Figure 3 shows the stock and flow
diagram which has been translated from the previous CLD.
Once the model has been built we go through the next phase, Model
Pre-Testing.
5 Pre-Testing
In the second phase, the stock and flow model was instantiated to represent
the situation of the Santa Catarina State Innovation System in Southern
Brazil.
Santa Catarina (SC) is a Federal State in the southern region of the
Federal Republic of Brazil, in terms of GDP it is the sixth in the country.
In terms of GDP per capita, it is the fourth with 12.800USD approximately.
The Human Development Index (HDI) of the SC State equals 0.840, the
second best in Brazil, and the percentage of literacy is 95.6% (3rd better in
Brazil).
In terms of composition of its industry, 48.5% is manufacturing, 37.2%
is services and 14.3% in agriculture. The most important sectors in the
SC State are Agribusiness (38% of all SC exports and around 1 bi USD),
Furniture/Lumber (800 Million USD in exports), Electro/metal/mechanical
(700 Million USD in exports), Textile (260 Million USD in exports), Mining
(141 Million USD in exports), Technology (630 Million USD in revenues)
and Tourism (780 Million USD in revenues).
These sectors are supported by Federal and State funds, which have
increased in the last years. Specifically, expenditures on Science and Tech-
nology have seen an increase of 131% between 2000 and 2008 for State
Expenditures, and of 175% for Federal expenditures on the same period
(artord oti)
In this phase, we will run three different scenarios, considering changes
in public funding (from Federal or State sources). Scenario 1 considers no
changes in the investment capacity of Federal and State entities in SC Inno-
vation System; Scenario 2 considers reductions in the investment capacity
of innovative activity in SC and Scenario 3 considers an increase in the
investment capacity.
Figure 4 shows the comparison of three scenarios in terms of Innovative
Capability. As show non Figure 4, the increase on public investments on Sci-
i: ofc]
]
inact /
= —
,
“uid Han Cain
Figure 3: Stock and Flow Model for Innovation Systems - Source:
Authors
10
ence and Technology produces an increase on innovative capability (scenario
3. blue line).
innovative capability
as a
Figure 4: Comparative results for Innovative Capability - Source:
Authors
Accordingly, a decrease of public investments produces a decrease on
innovative capability (scenario 2 red line) and finally, if previous conditions
are kept, innovative capability produces an intermediate outcome as well.
It is worth noting that for the first 15 years there is no difference between
three scenarios, which means that even when reducing investments, the out-
come would not be evident in short term, reinforcing the idea of investment
cuts.
As the results of Figure 4 shows, the outcomes of reducing or even in-
creasing investments in Science and Technology are visible only in the long
term (20+ years). Similar results are evident for Scientific Output and
Technological Output, as shown in Figures 5 and 6 respectively.
In Figures 5 and 6 the simulation results of scientific outputs and tech-
nological outputs respectively, show the same behavioral patterns previously
described in innovative capability. Changes in the growth pattern are also
evident after the first decade has passed, which in terms of policy making
represents that decisions that could endanger the innovative capability of
i
Scientific Output
os 1015202530384
Time (Year)
Scientific Output : scenario3
Scientific Output : scenario2
Scientific Output : scenario
Figure 5: Comparative results for Scientific Output - Source: Authors
12
Technological Output
028
os 0 1 20 25 30 35 40 45 30
Time (Year)
Technological Output : scenario3
Technological Output : scenario
Technological Output : scenario!
Figure 6: Comparative results for Technological Output - Source:
Authors
the SC State will only be visible 10+ years after they are implemented.
It is also worth noting that in terms of the dimensionless scale used in our
simulation runs, Scientific Output grows more than Technological Output for
all three scenarios, which depicts a better performance of scientific activities
over technological ones.
6 Conclusions
This research proposal deals with one of the main subjects inside IS litera-
ture, which is to find ways to properly manage innovation and its impact of
economic growth, considering their complexity in terms of dynamics, non-
linear relationships and complex behavior.
Until now, the current development of this research has shown that al-
though there is a lack of agreement regarding which components are part of
the IS and how to model them, there are commonalities among the models
that we can use to propose an integrated model articulated with mathemati-
cal simulation tools, specifically System Dynamics tools, in order to properly
address the management of Innovation Systems. Although the model has
not yet passed through validation, its initial results based on the simulation
13
graphs — show that there are interesting links between the macro-elements
of an IS.
The further step will be to calibrate the model based on the model valida-
tion techniques. Furthermore, the theoretical contribution of this research is
that methods such as System Dynamics: a) bring together a whole group of
quantifiable indices (creating an explicit relation among them and address-
ing the single quantitative index problem), (b) quantify qualitative values
(rather than disregarding them as being too soft), and (c) bring in both
metrics together in an environment where they can be disc
at least some modest degree of consensus; might improve the management
and policy making of Innovation Systems, ultimately leading to welfare and
economic growth.
ed to reach
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