M ODELLING THE E VOLUTION OF I NFORMATION S OCIETY AND ITS T ECHNOLOGIES : THE C ASE OF THE EU N EW M EMBER S TATES Andrzej M.J. Skulimowski AGH University

MODELLING THE EVOLUTION OF INFORMATION SOCIETY AND ITS

TECHNOLOGIES: THE CASE OF THE EU NEW MEMBER

STATESAndrzej M.J. Skulimowski

AGH University of S&T, Decision Science Dept.

P&B Foundation, Kraków, Poland

Third International Seville Conference onFuture-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

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

2006-2008: verification and implementation at the national

and regional IS levels

2. New tools and methodologies3. Findings, conclusions, and recommendations

http://fistera.jrc.es/


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?”


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


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


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 general2. IT (and overall) education system3. R&D sector producing and consuming IT4. 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 availability6. IT at use by the population and the industry, including the IT infrastructure, consumer IT and telecommunications7. IST relations to the other sectors of economy: their IST absorption capacity, overall GDP growth, and sustainability of country’s economical system8. Relations to the outer IS & IT world: close EU neighbours, EU-27, most relevant IT non-EU foreign partners, and global IS society


5. Analysis of Trends and Drivers

An impact graph: the relations between the elements of IS (the case of NMS & CC until 2020)

IT infrastructure and equipment at use

Legal system and IST policies

IT sector

R&D sector

IT education system

Economic development(GDP)

The population and its structure

Global trends and terms of trade

green - main elements of ISdark blue - strong direct dependencies, medium blue - average strength of impact, light blue – weak direct dependencies


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

FDI-dependence Policy/legal

system dependence

Mobile phones - weak strongly weakly e-health weak diversified

dependence weak strong

e-government medium weakly - strong e-learning medium - - medium e-commerce medium strongly weak weak e-advertising weakly medium weak weak e-banking and brokerage

medium medium medium weakly

e-entertainment diversified dependence

diversified dependence

- weak

Digital TV - medium weak medium Hot spots medium medium - weak


Analysis of Trends, Drivers and Events

New methods to cope with trends, drivers and events in one model

Motivations:- Search for objective methods to handle information about events and trends- Search for database architectures allowing to store information about trends, drivers and events in an efficient way (temporal, object-oriented database)

The proposed model of an IS and its external environment (discrete-event system):

P=(Q,V,,Q0,Qm) where: Q - the set of the IS states (can be defined verbally, or quantitatively) , – the set of admissible actions over the system,=(V1, V2,V3) - (controllable actions, actions of other decision-makers, random drivers): V Q Q - the transition function defining the results of actions,Q0 - the set of initial states of the IS, Qm – the set of anticipated final) states.An event e:

A pair of states e:=(q1,q2), such that q2= (v,q1)

A model of an Information Society and its external environment (a discrete-event system):

P=(Q,V,,Q0,Qm)where: Q – the set of IS’s states (can be defined verbally or quantitatively)V – the set of admissible actions in the IS, V=(V1, V2, V3) =(planned operations, actions of other decision-makers, random drivers) : V Q Q – the transition function governing the results of actions at each state of the IS,Q0 - the set of initial states of the IS at the beginning of the modeling period (mp)Qm – set of anticipated or recommended final states at the end of the mp.

An event e caused by the action v:

A pair of IS states e:=(q1,q2), such that q2=(v,q1)


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


Benchmarking Analysis of the IS in the NMS: The Poland’s case


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


An Example: SWOTC Analysis of the NMS’ IS

Strengths Weaknesses

-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

-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


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


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


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).


IS Scenario Visualisation

2010

2015

2020

Year

Optimistic scenarioBasic scenarioPessimistic scenario


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


Thank you for your attention!

Contact with the author:

[email protected],

[email protected]

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M ODELLING THE E VOLUTION OF I NFORMATION S OCIETY AND ITS T ECHNOLOGIES : THE C ASE OF THE EU N EW M EMBER S TATES Andrzej M.J. Skulimowski AGH University