Third International Seville Conference on
Future-Oriented Technology Analysis (FTA):
Impacts and implications for policy and decision-making
16th- 17th October 2008
MODELLING THE EVOLUTION OF
INFORMATION SOCIETY AND ITS
TECHNOLOGIES:
THE CASE OF THE EU NEW Andrzej
MEMBER
STATES
M.J. Skulimowski
AGH University of S&T, Decision Science Dept.
P&B Foundation, Kraków, Poland
Modelling the Evolution of Information Society and its Technologies
1. Lessons learned from FISTERA
1. The aims of the project
Foresight on the Information Society Technologies in the European Research
Area, 2003-2005, The Network of 20 institutions led by the DG-JRC – IPTS
http://fistera.jrc.es
May 2004: EU enlargement => FISTERA’s scope extension
New issues to be studied:
- trends, processes, and phenomena concerning the Information Society (IS)
in the New Member States (NMS, 2004): Cyprus, Czech Republic, Estonia,
Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia
- focus on cohesion („catch up”) process concerning the IS and the diffusion of
information technologies, strong role played by dynamic phenomena
- impact and IS cohesion processes in the EU Candidate Countries
2. New tools and methodologies
3. Findings, conclusions, and recommendations
2006-2008: verification and implementation at the national
and regional IS levels
Modelling the Evolution of Information Society and its Technologies
2. The Main Research Problems
From the point of view of IS policies:
„How the development of the IS in a country, or a group of countries, does
depend on the global processes of IT development and on integration of IS
around the world, driven by the global trends?”
First step: define, what the Information Society is (Information Society vs.
Knowledge-Based Society)
Second step: Characterise the policy goals and criteria related to the IS
Third step: Find the commonalities among the ISs to enable studying global IS
From the point of view of foresight methodology:
A. „Which variables and indicators characterise the Information Society in a
complete and non-superfluous way?”
B. „Which tools and methods allow to model the evolution of Information
Society in an adequate way?”
Modelling the Evolution of Information Society and its Technologies
3. Analytical Methods and Tools
The definition of an Information Society
Major factors of the Information Society:
- Certain population, not necessarily involved in information processes,
-Information acquisition, trade, storing, processing, applying, consuming
-Technologies that make the above possible
Specific problems:
-Social processes accompanying the production, dissemination and
consumption of information in the society
-Individual and social behaviour and customs related to the use of IT
-Impact and implications for culture and entertainment
-Science and technology: computing science and IT first, but all sectors and
disciplines using IT and producing information for dissemination count as well
-Common and permanent learning
-New issues and phenomena: computer security, fraud, addictions
Modelling the Evolution of Information Society and its Technologies
Analytical Methods and Tools (2)
The main analytical methods elaborated to solve problems arisen
when studying the IS in the NMS:
• Selecting essential elements of variables describing an Information Society
in a complete and non-supefluous way
• Merging quantitative and qualitative dynamical modelling methods in one
model, which:
- applies at the same time symbolic dynamics, dicrete-event processes, trend
analysis, and state-space methods,
- for its calibration uses information from heterogenous sources and models
• IS benchmarking analysis to study the catch-up processes
• Generalised SWOT(C) [SWOT with Challenges], including dynamical
SWOTC, TOWSC, merging SWOTC etc.
• Quantifying cross-impact between events, policies, and trends in discrete
event-based models
• Generate scenarios as an output of the previous methods
• Generate recommendations
Modelling the Evolution of Information Society and its Technologies
4. Modelling an Information Society
Major elements of an Information Society
1. The population and its structure according to age, sex, education, welfare,
relation to the labour market, professional background, psychological
characteristics influencing the attitudes towards IT and innovation in general
2. IT (and overall) education system
3. R&D sector producing and consuming IT
4. IT sector (industry and services)
5. Legal system and policies governing the production, trade, supply, and use
of IT as well as migration and social policies influencing the IST HR
development and availability
6. IT at use by the population and the industry, including the IT infrastructure,
consumer IT and telecommunications
7. IST relations to the other sectors of economy: their IST absorption capacity,
overall GDP growth, and sustainability of country’s economical system
8. Relations to the outer IS & IT world: close EU neighbours, EU-27, most
relevant IT non-EU foreign partners, and global IS society
Modelling the Evolution of Information Society and its Technologies
5. Analysis of Trends and Drivers
An impact graph: the relations between the elements of IS
(the case of NMS & CC until 2020)
IT education system
The population
and its structure
R&D sector
Global trends
and terms of trade
IT sector
Economic development
(GDP)
Legal system
and IST policies
IT infrastructure
and equipment at use
green - main elements of IS
dark blue - strong direct dependencies,
medium blue - average strength of impact,
light blue – weak direct dependencies
Modelling the Evolution of Information Society and its Technologies
Analysis of trends and drivers: an example
Table of relations within the NMS’s IS resulting from an experts’ Delphi
Dependence
Service/technology
IT-skills
dependence
GDP (welfare)
dependence
Mobile phones
e-health
weak
e-government
e-learning
e-commerce
e-advertising
e-banking and
brokerage
e-entertainment
medium
medium
medium
weakly
medium
weak
diversified
dependence
weakly
strongly
medium
medium
diversified
dependence
medium
diversified
dependence
medium
medium
Digital TV
Hot spots
FDI-dependence
Policy/legal
system
dependence
strongly
weak
weakly
strong
weak
weak
medium
strong
medium
weak
weak
weakly
-
weak
weak
-
medium
weak
Modelling the Evolution of Information Society and its Technologies
Analysis of Trends, Drivers and Events
New methods to cope with trends, drivers and events in one model
Motivations:
A model of an Information Society and its external environment (a
- Search for objective methods to handle information about events and trends
discrete-event
system):
- Search
for database
architectures allowing to store information about trends,
P=(Q,V,
,Q0,Qm) object-oriented database)
drivers and events in an efficient
way (temporal,
where:
Q –proposed
the set ofmodel
IS’s states
beits
defined
verbally
or quantitatively)
The
of an (can
IS and
external
environment
(discrete-event
system):
V – the set of admissible actions in the IS, V=(V1, V2, V3) =(planned
P=(Q,V,,Q0,Qrandom
operations, actions of other decision-makers,
drivers)
m)
where:
 : V  Q  Q – the transition function governing the results of actions at
Qeach
- the state
set ofofthe
states (can be defined verbally, or quantitatively) ,
theISIS,
–Qthe-set
admissible
actions
theof
set
of initial states
of over
the ISthe
at system,
the beginning of the modeling
0
=(V1, V2,V3) - (controllable actions, actions of other decision-makers, random
period (mp)
drivers)
or recommended
finalthe
states
at the
end of the mp.
:QVm–Qset
ofQanticipated
- the transition
function defining
results
of actions,
Q0 - the set of initial states of the IS,
event e caused by the action v:
QAn
m – the set of anticipated final) states.
An event e: A pair of IS states e:=(q ,q ), such that q =(v,q )
1 2
2
A pair of states e:=(q1,q2),
such that q2=
(v1,q1)
Modelling the Evolution of Information Society and its Technologies
6. Benchmarking Models for the IS
Benchmarking vs. IS Rankings and Indices
Motivations:
Allow comparisons with the single countries or groups of EU countries to study
the cohesion („catch-up”) processes
Properties:
- Unlike when using rankings based on composite indices, this analysis allows
to identify the causal relations between the (mostly) external or random trends,
drivers, events on one hand, and their consequences in the IS under study,
Then – to compare this reaction with reactions in the reference countru, or
reference group of countries
-Allows quantitative comparisons
-Gives input to SWOTC analysis
-Allows to formulate recommendations to the decision-makers
Modelling the Evolution of Information Society and its Technologies
Benchmarking Analysis of the IS in the NMS:
The Poland’s case
Modelling the Evolution of Information Society and its Technologies
7. SWOTC - Generalised SWOT
Main features:
We have introduced the fifth element in SWOT: Challenges, that may play the
role of Opportunities or Threats, depending on the attitude of the object under
study, external events and actions.
•Challenges enrich the analysis and are especially useful when analysing
heterogeneous complex objects (e.g. a group of countries, like the NMS),
•Challenges eliminate putting the same factor as an Opportunity and a Threat
in SWOT, stimulate an in-depth analysis of causal relations in an object under
study
•Dynamical model allows to generate future SWOTC based on an initial
analysis and evolution rules
•It is possible to merge SWOT or SWOTC of individual components of a large
entity in one analysis (the case of NMS’ SWOTC build of SWOTC tables for
individual countries
•This generalisation applies to TOWC tables in a natural way, resulting in
TOWSC
Modelling the Evolution of Information Society and its Technologies
An Example: SWOTC Analysis of the NMS’ IS
Strengths
-Strong basic IST research
-Availability of qualified IT experts
-National policies and
programmes to make available
the broadband infrastructure to
most of the population
-Availability of qualified IST
immigrants from NIS countries
-The size of the domestic online
market exceeded already the
profitability threshold
-High potential of IT services
exports
-National policies strongly
support e-services
-Immediate availability of all
modern IS technologies
Weaknesses
 -Slowing-down economic
development expected in 2009
and later
 -Foreign investors use often
transfer prices for IT services
and omit domestic suppliers
 -High level of digital divide in
agriculture and construction
sector employees
 -Digital divide between the youth
and the older population with
lower education level
 -Mental, and digital divide gaps
caused by informational isolation
during the communist rule
 -Protectionism on the public IT
market in some EU countries
Modelling the Evolution of Information Society and its Technologies
SWOTC Analysis of the NMS’ IS (2)
Opportunities
Threats






Development of specialised SMEs
meeting the niche IST needs
throughout the EU, based on the
local specialists and international
cooperation;
EU membership facilitates the
attraction of foreign high-tech
investors;
Appropriate use of ERDF and SF
subventions may increase the
competitiveness and capital strength
of the NMS IST-sector
Emergence of new high-quality and
affordable IST services, e.g. in
health care;
Development of transport
infrastructure makes the overall
business in the NMS easier;




Subvention-mentality hampers
entrepreneurship,
Too-high taxes and labour costs
endanger the development of
innovative SMEs,
Scarcity of top IT experts and their
high mobility (both: in-country and
abroad) makes long-term SME
development projects difficult
Rising e-criminality becomes
hampering factors for the IS
development
The outsourcing of IST services
to South-East Asia lowers the
economic standing of the affected
domestic IST companies
Modelling the Evolution of Information Society and its Technologies
SWOTC Analysis of the NMS’ IS (3)
Challenges
The EU membership allows the domestic companies to compete on the EU
market but - at the same time - removes any protection from the domestic IT
market
Globalisation opens new markets, but - at the same time - allows for growing
competition in the areas of strengths of NMS IT companies
Growing IT literacy facilitates the common use of IT among all groups in the
society, but – at the same time - creates negative trends and phenomena,
such as reduces the
The legislation concerning the intellectual property protection may negatively
affect a part of software producers and IST service providers from the NMS,
but - at the same time - may help to achieve extraordinary income for a few
domestic companies
Mono- or oligopolisation concerning some basic information technologies may
slow down the development of the end-user application producers, but - at the
same time – is a challenge to open source software initiatives
Modelling the Evolution of Information Society and its Technologies
8. Building IS Scenarios
Main steps in IS scenario building:
1. Establish causal relations between drivers, trends, events and actions
2. Specify the potential random events, external actions, uncertainties in the
model
3. Specify the relevant variables and indicators that characterise the IS under
study
4. Build the event-based model using the causal relations found previously
5. Specify the number of base scenarios to be elaborated
6. Construct the elementary scenarios defined as chains of events influenced
by all factors included in the model
7. Cluster the elementary scenarios in the specified number of base
scenarios
8. Visualise the scenarios found (example on the next page).
Modelling the Evolution of Information Society and its Technologies
IS Scenario Visualisation
2020
Year
2015
2010
Optimistic scenario
Basic scenario
Pessimistic scenario
Modelling the Evolution of Information Society and its Technologies
9. Conclusions
The features of the modelling approach
1. The presented set of methods is self-contained and can be applied to new
problems, beyond the original FISTERA’s scope
2. The quantitative data come characterise usually the IT and
telecommunications sector, IT infrastructure , and some social variables.
Those qualitative describe usually new phenomena, where the number of
observations is insufficient to derive quantitative characteristics, the quality (of
research results, convenience of using products and technologies etc.).
When applied in a single model, appropriate modelling rules allow to derive
qualitative results from pairs of qualitative and quantitative characteristics
3. The recommendations to the decision-makers can be directly derived from
the model, assuming that the decision-makers have expressed their
preferences in from of criteria to be optimised, sets of reference values and
states, and results of pairwise comparisons. They may have a form of priority
rankings, as well as of recommended actions, including the descriptions of
legislative frameworks
Modelling the Evolution of Information Society and its Technologies
Thank you for your attention!
Contact with the author:
[email protected],
[email protected]