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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.

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Presentation on theme: "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."— Presentation transcript:

1 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 of S&T, Decision Science Dept. P&B Foundation, Kraków, Poland Third International Seville Conference on Future-Oriented Technology Analysis (FTA): Impacts and implications for policy and decision-making 16th- 17th October 2008

2 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, , The Network of 20 institutions led by the DG-JRC – IPTS May 2004: EU enlargement => FISTERAs 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 : verification and implementation at the national and regional IS levels 2. New tools and methodologies 3. Findings, conclusions, and recommendations

3 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?

4 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

5 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

6 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 countrys 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

7 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) green - main elements of IS dark blue - strong direct dependencies, medium blue - average strength of impact, light blue – weak direct dependencies

8 Modelling the Evolution of Information Society and its Technologies Analysis of trends and drivers: an example Table of relations within the NMSs IS resulting from an experts Delphi

9 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: - 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,,Q 0,Q m ) where: Q - the set of the IS states (can be defined verbally, or quantitatively), – the set of admissible actions over the system, =(V 1, V 2,V 3 ) - (controllable actions, actions of other decision-makers, random drivers) : V Q Q - the transition function defining the results of actions, Q 0 - the set of initial states of the IS, Q m – 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,,Q 0,Q m ) where: Q – the set of ISs states (can be defined verbally or quantitatively) V – the set of admissible actions in the IS, V=(V 1, V 2, V 3 ) =(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, Q 0 - the set of initial states of the IS at the beginning of the modeling period (mp) Q m – 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:=(q 1,q 2 ), such that q 2 = (v,q 1 )

10 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

11 Modelling the Evolution of Information Society and its Technologies Benchmarking Analysis of the IS in the NMS: The Polands case

12 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

13 Modelling the Evolution of Information Society and its Technologies An Example: SWOTC Analysis of the NMS IS

14 Modelling the Evolution of Information Society and its Technologies SWOTC Analysis of the NMS IS (2) OpportunitiesThreats 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

15 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

16 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).

17 Modelling the Evolution of Information Society and its Technologies IS Scenario Visualisation Year Optimistic scenario Basic scenario Pessimistic scenario

18 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 FISTERAs 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

19 Modelling the Evolution of Information Society and its Technologies Thank you for your attention! Contact with the author:


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