An Adaptive Stigmergy-based System for Evaluating Technological

Indicator Dynamics in the Context of Smart Specialization

Antonio L. Alfeo

, Francesco P. Appio

, Mario G. C. A. Cimino

, Alessandro Lazzeri

Antonella Martini

and Gigliola Vaglini

Department of Information Engineering, Università di Pisa, Largo Lazzarino 1, Pisa, Italy

Department of Energy, System, Territory and Construction Engineering, Università di Pisa, Largo Lazzarino 1, Pisa, Italy

Keywords: Smart Specialization, Regional Innovation, Trend Analysis, Patent-based Indicators, Marker-based

Stigmergy, Parametric Adaptation, Differential Evolution.

Abstract: Regional innovation is more and more considered an important enabler of welfare. It is no coincidence that

the European Commission has started looking at regional peculiarities and dynamics, in order to focus

Research and Innovation Strategies for Smart Specialization towards effective investment policies. In this

context, this work aims to support policy makers in the analysis of innovation-relevant trends. We exploit a

European database of the regional patent application to determine the dynamics of a set of technological

innovation indicators. For this purpose, we design and develop a software system for assessing unfolding

trends in such indicators. In contrast with conventional knowledge-based design, our approach is

biologically-inspired and based on self-organization of information. This means that a functional structure,

called track, appears and stays spontaneous at runtime when local dynamism in data occurs. A further

prototyping of tracks allows a better distinction of the critical phenomena during unfolding events, with a

better assessment of the progressing levels. The proposed mechanism works if structural parameters are

correctly tuned for the given historical context. Determining such correct parameters is not a simple task

since different indicators may have different dynamics. For this purpose, we adopt an adaptation mechanism

based on differential evolution. The study includes the problem statement and its characterization in the

literature, as well as the proposed solving approach, experimental setting and results.

1 INTRODUCTION AND

MOTIVATION

After years of economic crisis and the resulting

reduction of resources available for research and

development investments, Smart Specialization has

immediately become a very relevant concept to get

these two questions answered (Foray, 2013). It

represents an important chance for a progressive

economical restart. In order to develop a policy-

prioritization logic to foster regional growth is

important to have a deep knowledge of the potential

evolutionary pathways related with the existing

dynamics and the structures at regional level

(McCann and Ortega-Argilès, 2013). In this light,

each region should start this process using as

standpoints the knowledge-based sectors in which

already presents a consistent ‘critical mass’ or, at

least, capabilities that refer to a future potential

exploitable with right and focused investments.

In the last decade, several causes have

determined the increasing need for rationalization of

resources within regions. The crucial ones are the

increased globalization, mainly pursued by

multinational enterprises, the economic crisis

involving all EU regions with different magnitudes

and the diffusion of a new wave of general purpose

technologies. This situation calls for a deep

rethinking of the overall approach to regional

development; policy-makers and experts largely

agree on the fact that the new economic boost should

originate exploiting and enhancing the specific

potential and competitive advantage of each region

through focused innovation policies. On this line, the

European Commission has established a program

labelled ‘Smart Specialization’, consisting in a set of

policies and guidelines aimed to promote the

Alfeo, A., Appio, F., Cimino, M., Lazzeri, A., Martini, A. and Vaglini, G.

An Adaptive Stigmergy-based System for Evaluating Technological Indicator Dynamics in the Context of Smart Specialization.

DOI: 10.5220/0005645204970502

In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2016), pages 497-502

ISBN: 978-989-758-173-1

497

efficient and effective use of public investment in

research and development (R&D).

Smart Specialization is defined as “an industrial

and innovation framework for regional economies

that aims to illustrate how public policies,

framework conditions, but especially R&D and

innovation investment policies can influence

economic, scientific and technological specialization

of a region and consequently its productivity,

competitiveness and economic growth path. It is a

logical continuation in the process of deepening,

diversifying and specializing of more general

innovation strategies, taking into account regional

specificities and inter-regional aspects, and thus a

possible way to help advanced economies (as well as

emerging economies) to restart economic growth by

leveraging innovation led / knowledge-based

investments in regions” (OECD 2013, p.17). From

one hand, this approach requires the concentration of

R&D resources in few domains; from the other

hand, a consisting part of literature underlines the

importance of industry diversification in promoting

innovation. In this light, the dichotomy

specialization-diversification has become topical.

The long term aim of this work is exploring

whether - and to what extent - different policies of

‘technological specialization’ and ‘technological

diversification’ pays off in term of wealth creation at

regional level. Then, we want to provide policy

makers with computerized support in the analysis of

innovation-relevant trends (Jin, 2014). To properly

move into that direction, we start looking at this

problem by analysing the trends of the

aforementioned indicators for 268 EU-27 regions

over 35 technological domains in the period 1990-

2012, in order to obtain a model that can efficiently

recognize significant events. For this purpose, we

have designed and developed a software system. In

contrast with conventional knowledge-based design,

our approach is biologically-inspired and based on

stigmergy as a mechanism of self-organization of

information. Moreover, the performance of such a

model is contrasted with a supervised adaptation

based on the Differential Evolution (DE hereafter).

2 BIOLOGICALLY-INSPIRED

DATA ANALYSIS

In this paper we propose to use the principles of the

stigmergy for assessing unfolding trends in time

variant indicators. In biology, stigmergy is an

indirect communication mechanism between

individuals of an insect society. In marker-based

stigmergy (Parunak, 2006) volatile substances, such

as pheromones, maintain the information locally for

other individual to perceive. In computer science,

marker-based stigmergy can be employed as a

powerful computing paradigm exploiting both

spatial and temporal dynamics, because it

intrinsically embodies the time domain (Cimino et

al., 2015). Moreover, marker-based stigmergy can

be considered a computational black box modelling

approach, because no domain model is assumed at

design time and then results are not directly

interpretable.

In Figure 1 we present the terminology via an

ontology diagram. Concepts are enclosed in white

ovals and connected by properties (represented as

black directed edges). A property that cannot be

directly sensed (i.e., instantiated) is represented as

an abstract property, shown by a dashed edge.

More specifically, it is known that diversification

and specialization of Patents applied in a Region

measure the Innovation of the region itself. Thus, it

is important for a Policy Maker to analyse Trends of

Innovation, to properly address the investments.

Such trends cannot be directly sensed nor associated

to the Innovation. For this purpose, there are three

important indicators which quantify Innovation:

specialization (S), related variety (R), and unrelated

variety (U). The study of such Trends by the Policy

Maker is fundamental to recognize scenarios of

interest, i.e., the ways in which special situations

may develop. Example of scenarios of interest are:

(i) R or U decreases, while S increases; (ii) R or U

decreases, while S is stable; (iii) R or U increases,

while S is stable;(iv) R or U increases, while S

increases.

The problem is to detect variations of an

indicator in terms of increase, decrease or stability.

In this paper, we adopt an emergent modeling

perspective. With an emergent approach, the focus is

on the low level processing. In Figure 1 we also

present the terminology related to the approach.

More specifically, an Indicator Value enables the

release of a Mark. Marks aggregate in Tracks,

depending on their spatiotemporal local dynamics.

Emergent paradigms are based on the principle of

the self-organization of the data, which means that a

functional structure, the Track, appears and stays

spontaneous at runtime when local dynamism

occurs. A particular Track representing only the

main characteristics of such local dynamics is the

Prototype. It is the Dissimilarity which compares

Prototypes generated at different times, in order to

assess the Trend. Finally, the Evolution process

ICPRAM 2016 - International Conference on Pattern Recognition Applications and Methods

498

adapts Mark, Track, Prototype and Dissimilarity to

properly fit the temporal dynamics of the indicators.

The Evolution process represents the application of

biologically-inspired patterns to adapt parameters.

The approach iteratively tries to improve a

population of candidate parameters with regard to a

given measure of quality, or fitness. Solutions are

found by means of transformation mechanisms

inspired by biology, such as reproduction, mutation,

recombination, selection, in an environment where

competition is represented by the quality measure.

Figure 1: Ontological view of the approach.

3 ARCHITECTURAL DESIGN

Figure 3 illustrates the system architecture, made of

four main subsystems, i.e., marking, trailing,

prototyping, and dissimilarity. At the input/output

interfaces of each subsystem, an unbiasing module

is used for a better efficiency and alignment of the

processing layers. This lets an input signal to reach a

certain level before a processing layer passes it to

the next layer, and allows a better distinction of the

critical phenomena during unfolding events, with a

better detection of the progressing levels.

The marking subsystem transforms input data

into marks, whereas the trailing subsystem

aggregates and evaporates marks as a track in the

stigmergic space. The prototyping subsystem

provides a simplified version of the track. It is a

vehicle of abstraction, leading to the emergence of

high-level information. The dissimilarity subsystem

evaluates the difference between consecutive

prototypes in order to extract trend information of

the indicators. The proposed mechanism works if

structural parameters are correctly adapted for the

given application context. Determining such correct

parameters is not a simple task since different

indicators may have different dynamics. For this

purpose, we adopt a tuning mechanism based on the

DE. In the next subsections each module and

subsystem is precisely described, by using a pilot

data sample.

The Unbiasing Module

Figure 2 shows the U indicator of a region for 4

years, in dashed line. To unbias the input signal the

s-shaped function is used, having the following

behaviour: input values smaller\larger than (β - α)/2

are lowered\raised; values smaller\larger than α\β

assume the minimum\maximum value, i.e., 0\1. In

biologically inspired subsystems, this function

models the active zone of a signal generated by a

subsystem (Avvenuti et al., 2013). Figure 2 shows in

thick line the unbiasing output, with α

=0.2, β

=0.8.

Figure 2: An example of application of unbiasing.

The Marking Subsystem

The Marking takes an unbiased sample 



(t) of a

normalized input time series D, and releases a mark

in a marking space whose codomain is called

intensity (Cimino et al., 2015). The mark has four

structural attributes: the center position 



(t), the

intensity I, the mark extension ε, and the mark

evaporation θ. Figure 4 (a) shows, in thick line, the

mark released by the sample 



(2) of our pilot time

series. The mark shape is an isosceles triangle: its

center is 



(2)=0.25, its height I=1, and its base has

length 2ε=0.5.

The Trailing Subsystem

The evaporation θ is the temporal decay of the

mark. After each step the mark intensity decreases

by a percentage θ. Thus, evaporation leads towards a

progressive disappearance of the mark. Anyway,

subsequent marks can reinforce previous mark in the

environment if their shapes overlap. In Figure 4 (b)

we also show the mark 



(2) after an evaporation

step, in thin line. In Figure 4 (right) we show in thin

line three consecutive marks, their apex coordinates

(x, y). We also show the final track, T

at time t=3,

in thick line, as the sum of the track intensities

The Prototyping Subsystem

The Prototyping subsystem takes as input the

output track of the trailing subsystem, T

. This input

is first unbiased, as 



. The prototype P

is then

generated as a triangular shape, with base width 2ε,

An Adaptive Stigmergy-based System for Evaluating Technological Indicator Dynamics in the Context of Smart Specialization

499

Figure 3: Architectural overview of our data analysis system based on Stigmergy.

saturation height I

max

=I/(1-θ) (Avvenuti et al., 2013),

and center p

. Figure 5 shows in dotted line the track

of Figure 4 (b), the unbiased track 



in dashed

line, and the corresponding prototype P

centered in

in solid line. The center p

of the prototype is the

position that maximizes the similarity between the

unbiased track and the prototype itself.

(a) (b)

Figure 4: Single mark shape (a) and aggregation of three

marks (b) in a track.

Figure 5: An example of prototyping.

Figure 6: Similarity between two prototypes sampled at

the 24

and 48

months of the time series, respectively.

The similarity between two shapes is



(





,



)

=



∩







∪



⁄

. It is the ratio

between the intersection and the union of the shapes.

In Figure 6, 

(





,



)

= 0.0097.

The Dissimilarity Subsystem

This subsystem calculates the complement of the

similarity between two prototypes generated at two

instant of times, with a positive (negative) sign if the

barycenter of the most recent track is larger

(smaller) than the previous: ∆ = (1 − (



,



)∙

(



−



). In Figure 6, ∆ = 0.9903. An

unbiasing module is finally used, in order to provide

one of three different classes as output: -1, +1, or 0,

when the time series decreases, increases, or it is

otherwise considered stable, respectively. In Figure

6, ∆



=1, meaning that the indicator increases.

The Adapting Subsystem

The overall system uses 8 structural parameters

(summarised in Table 1) to be appropriately adapted.

Since different indicators in different Regions have

different dynamics, manual adaptation is very time-

consuming, human-intensive and error-prone

(Ciaramella, 2010). In this section, we first describe

the role of each parameter in the processing, and

then we adopt a supervised optimization based on

DE, an evolutionary technique for numerical

optimization problems (Cimino et al., 2015).

Table 1: System Parameters.

Module Params Human Expert Range

marking

(0.2; 0.8; 0.2) (0, 1)

trailing

(0.65) (0, 1)

prototyping

(0.15; 0.75) (0, I

max

)

dissimilarity

(0.35; 0.65) (0, 1)

The mark extension (

) controls the distance of

interaction between marks. If it is close to 0, the

marks cannot interact with each other, and there is

no patterns reinforcement. If the mark extension is

close to 1, all marks reinforce each other without

distinction between patterns. The mark evaporation

(

) affects the lifetime of a mark. Short-life marks

evaporate to fast preventing aggregation and pattern

reinforcement. Long-life marks cause an early

ICPRAM 2016 - International Conference on Pattern Recognition Applications and Methods

500

saturation of the track, thus all tracks become similar

to each other. Finally, α and β affect the system as

previously described in the unbiasing module.

The adaptation uses the DE algorithm to

optimize the parameters of the system with respect

to the fitness computed using a training set. Let Z be

the set of Regions in the dataset, D

) the input

signal for Region z, c

) ∈ {-1,0,1} the output of

the system. Let us consider {D

)*, C

)*} the

training set. We compute the average fitness among

Regions as the Mean Squared Error (MSE) between

the outputs of the system calculated for the training

inputs and their corresponding training outputs:



(



)

∑∑

(



(





)

∗

−



(



))











∙

⁄

In the DE algorithm, a solution is represented by

a real n-dimensional vector, where n is the number

of parameters to tune. The DE starts with a

population of N candidate solutions, injected or

randomly generated. In the literature different ranges

of population are suggested (Mallipeddi et al.,

2011). Population size spread can vary from a

minimum of 2n to a maximum of 40n. A higher

number increases the chance to find an optimal

solution but it is more time consuming. To balance

speed and reliability we use N=20. At each iteration

and for each member (target) of the population, a

mutant vector is created by mutation of selected

members and then a trial vector is created by

crossover of mutant and target. Finally, the best

fitting among trial and target replaces the target.

Many strategies of the DE algorithm have been

designed, by combining different structure and

parameterization of mutation and crossover

operators (Mezura et al., 2006 and Zaharie, 2007).

We adopted the DE/1/rand-to-best/bin version,

which places the perturbation at a location between a

randomly chosen population member and the best

population member. The differential weight F ϵ [0,2]

mediates the generation of the mutant vector. F is

usually set in [0.4-1) (Mezura et al., 2006). There are

different crossover methods in DE. Results show

that a competitive approach can be based on

binomial crossover (Zaharie, 2007). With binomial

crossover, a component of the offspring is taken

with probability CR from the mutant vector and with

probability 1-CR from the target vector. A good

value for CR is between 0.3 and 0.9 (Mallipeddi et

al., 2011).

4 CASE STUDY AND RESULTS

The case study is based on a data set that contains

the three annual indicators S, U, R, (described in

Section 1) monitored for 15 years for 200 European

Regions. The dataset contains 9000 samples. In

order to reduce data to a canonical size, the

following normalization is first applied: 



(



−



)(



−



)

⁄

. Since the original data

samples are subject to significant sampling error, we

also performed a granulation process (Cimino et al.,

2014). More specifically, for each year we grouped

regions via k-nearest neighbour algorithm. For each

group we computed the annual mean μ and the

standard deviation σ. We also determined that the

resulting indicator samples are well-modelled by a

normal distribution, using a graphical normality test.

Finally, monthly samples have been derived

considering normal distribution with mean and

variance μ/12 and σ

/12, respectively.

To choose the best value of CR and F, we first

performed trials with CR in [0.3, 0.6, 0.9] and F in

[0.4, 0.6, 0.8]. For each experiment, 5 trials have

been carried out, by using the 20% of the dataset as

a training set, and the remaining 80% as a testing

set. We also determined that the resulting MSE

samples are well-modelled by a normal distribution,

using a graphical normality test. Hence, we

calculated the 95% confidence intervals. Table 2

shows the results in the form “mean ± confidence

interval”. The best performance has been with

CR=0.6 and F=0.6. In general, we observed that

fitness function gets stable after 15 generations only.

Table 2: 95% confidence interval of the MSE for the best

setting of differential weight (F) and crossover rate (CR).

0.4 0.6 0.8

0.3

0.022 ±

0.0002

0.012 ±

0.00002

0.018 ±

0.0002

0.6

0.019 ±

0.0002

0.009±

0.0001

0.011 ±

0.00001

0.9

0.014 ±

0.00006

0.013 ±

0.00007

0.013 ±

0.00007

In order to assess the effectiveness of the

approach, we adopted a 5-fold cross-validation.

Indeed, each evaluation is also dependent on the data

points, which end up in the training and test sets. For

each trial, the training and test sets consist,

respectively, of randomly extracted 20% and 80% of

the original data. We carried out each trial 5 times.

Table 3 summarizes, for indicator U, the results in

terms of mean and standard deviation of the MSE

for each trial. The low values of the MSE, for all

trials and for both training and testing sets, highlight

the effectiveness of the system in terms of both

performance and generalization properties. We

An Adaptive Stigmergy-based System for Evaluating Technological Indicator Dynamics in the Context of Smart Specialization

501

replicated the same experiments and achieved

similar performances for the other indicators.

Finally, to highlight the great benefits of the

adaptation subsystem, we also computed the MSE

for the worst case of Table 2 (i.e., Trial 5), by using

manual adaptation: this implied an MSE of 0.106,

which is very higher than 0.022.

Table 3: MSE for each trial extracted via 5-fold cross-

validation, averaged over 5 repetitions.

MSE (mean ± std dev)

Trial Training Set Testing Set

0.011 ± 0.010 0.018 ± 0.004

0.010 ± 0.010 0.020 ± 0.003

0.009 ± 0.006 0.020 ± 0.008

0.008 ± 0.008 0.020 ± 0.005

0.010 ± 0.007 0.022 ± 0.008

5 CONCLUSIONS

In this paper, we designed and developed a software

system for assessing unfolding trends in innovation

indicators. The core processing is based on

stigmergy, a biologically inspired computational

mechanism. Since the emergent character of

stigmergy depends on biases and scale factors that

can vary for different application contexts, an

essential module is the parametric adaptation. For

this purpose, we adopted the Differential Evolution.

Experiments show the effectiveness of the approach

and the relevant improvements with respect to a

human parameterization.

More precisely the proposed system has been

used to detect the trends of three different patent-

based indicators within 35 technological domains,

belonging to 268 European regions, in the period

1990-2012. The experimental results show that using

20% of the data set as training set to recognize

trends ranging from -1 to 1, the system achieved an

MSE of 0.02. Nevertheless, to ensure high-quality

and robust design, the system should be cross-

validated against other case studies and compared

with existing approaches suitable for the same

purpose. An important future development will be to

adopt benchmark data and to carry out a

comparative analysis of our approach with

alternative techniques available in the literature.

ACKNOWLEDGEMENTS

This work is supported by the University of Pisa, via

the research project entitled “Stigmergic Footprint of

Radical Innovations for Smart Specialisation in

North-American and European Regions”.

REFERENCES

Avvenuti, M., Cesarini, D., and Cimino, M.G.C.A., 2013,

‘MARS, a Multi-Agent System for Assessing Rowers

Coordination via Motion-Based Stigmergy’, Sensors,

vol. 13, pp. 12218-12243.

Ciaramella, A., Cimino, M.G.C.A., Lazzerini, B., and

Marcelloni, F., 2010, ‘Using Context History to

Personalize a Resource Recommender via a Genetic

Algorithm’, in Proceeding of the International

Conference on Intelligent Systems Design and

Applications, ISDA’10, IEEE, pp. 965-970.

Cimino, M.G.C.A., Lazzeri, A., and Vaglini, G., 2015,

‘Improving the Analysis of Context-Aware

Information via Marker-Based Stigmergy and

Differential Evolution’, Artificial Intelligence and Soft

Computing, vol. 9210, pp. 341-352.

Cimino, M.G.C.A., Lazzerini, B., Marcelloni, F., and

Pedrycz, W, 2014, ‘Genetic interval neural networks

for granular data regression’, Information Sciences,

vol. 257, pp. 313-330.

Foray, D., 2013, ‘The economic fundamentals of Smart

Specialisation’, Ekonomiaz, vol. 83, pp. 55-82.

Jin, B., Ge, Y., Zhu, H., Guo, L., Xiong, H., and Zhang,

C., 2014, ‘Technology Prospecting for High Tech

Companies through Patent Mining’, in Proceedings of

the International Conference of Data Mining (ICDM),

IEEE, pp. 220-229.

Mallipeddi, R., Suganthan, P.N., Pan, Q.K. and

Tasgetiren, M., 2011, ‘Differential evolution algorithm

with ensemble of parameters and mutation strategies,’

Applied Soft Computing, vol. 11, pp.1679-1696.

McCann, P., and Ortega-Argilés, R., 2013, ‘Smart

specialization, regional growth and applications to

European union cohesion policy’, Regional Studies,

vol. 49, pp. 1291-1302.

Mezura-Montes, E., Velázquez-Reyes, J., and Coello,

C.A., 2006, ‘A comparative study of differential

evolution variants for global optimization,’

Proceedings of the 8th annual conference on Genetic

and evolutionary computation, ACM, pp. 485-482.

Organization for Economic Co-operation and

Development (OECD), 2013, Innovation-driven

growth in regions: the role of Smart Specialization.

OECD, Paris.

Parunak, V. H., 2006, ‘A survey of environments and

mechanisms for human-human stigmergy’,

Environments for Multi-Agent Systems II, Springer

Berlin Heidelberg, pp. 163-186.

Zaharie, D., 2007, ‘A comparative analysis of crossover

variants in differential evolution’, Proceedings of

IMCSIT, 2nd International Symposium Advances in

Artificial Intelligence and Applications, pp. 171-181.

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