5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 APPLYING ANTICIPATORY NETWORKS TO SCENARIO PLANNING AND BACKCASTING IN TECHNOLOGICAL FORESIGHT Andrzej M.J. Skulimowski1,2 1 AGH University of Science and Technology, Chair of Automatic Control and Biomedical Engineering, Decision Science Laboratory, Kraków, Poland 2 International Centre for Decision Sciences and Forecasting, Progress & Business Foundation, Kraków, Poland Email: [email protected] Abstract Anticipatory networks (AN) are a new tool in researching futures. This tool simultaneously formalises backcasting combined with scenario planning and generalises anticipatory models of decision impact in multicriteria problem solving. A constructive impact analysis is based on the assumption that future decision makers take into account causal relations linking the anticipated outcomes of decision problems they solve with subsequent problems. The decisions are modelled as time-ordered nodes while the anticipated state of the future are assumed to be the consequences of each decision. These are modelled by multiple criteria assessments and edges starting from an appropriate node. The network is supplemented by anticipatory feedback relations, which model decision makers’ will to influence their successors to achieve the specific results of future decision problems. When making a choice, they exploit causal dependences of future constraints and preferences on the decision just made to influence future outcomes in a desired way. Both types of relations as well as forecasts and explorative scenarios regarding future decision problem parameters form an information model termed an Anticipatory Network. This paper outlines the basic properties of ANs in the context of their foresight applications, as well as the methods for computing them. This will allow us to construct and filter foresight scenarios, and perform an assessment process to build a preference structure in a set of normative scenarios. Following that, an AN-based backcasting process can be run taking into account intertemporal preferences. We will assume that all future agents whose decisions are modelled in the network are rational, i.e. their decisions comply with their preference structures. This allows us to apply constructive algorithms to filter plausible explorative scenarios taking into account the preference information contained in an AN. The outcomes of the current decision problem affect future problems by means of multifunctions linking the decisions to be made with future constraints and/or preference structures. The solution selection process with an AN is equivalent to filtering the scenarios that are modelled by causal chains in the network, taking into account the above preference structure. This may be regarded as a rational backcasting. By virtue of multicriteria preference models, the above-outlined procedure does not usually yield a unique solution. Instead, recommendations from a foresight exercise contain a selection of nondominated action variants while the final strategy choice is left to group decision making at the project stakeholders level. We will present an application of the above model to filter scenarios concerning the development of a creativity support system and provide strategic recommendations arising from a recent ICT foresight project (www.ict.foresight.pl). We will also point out how the new tool could benefit from synergy with other analytical foresight methods and IT tools contained in a foresight support system (FSS) that was implemented in the above-mentioned exercise. Keywords: Anticipatory networks, Impact analysis, Scenario planning, AI foresight, Technological evolution, Causality, Backcasting, Creativity, Foresight Support Systems Introduction The principal purpose for introducing and investigating anticipatory networks as a new tool in researching future scenarios was to extract and analyse the general rules and principles that govern the evolution of selected artificial intelligence (AI) technologies. Anticipatory networks (AN) simultaneously formalise backcasting combined with scenario planning and generalise the THEME 3: CUTTING EDGE FTA APPROACHES -1- 5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 anticipatory models of decision impact in multicriteria problem solving, cf. [21] and [18] for further references. A constructive analysis of causal relations is based on the assumption that future decision makers take into account the anticipated outcomes of each future decision problem linked by the causal relations with their own problem. The decision-making process is modelled as linked time-ordered nodes while the future states of the investigated objects are assumed to be the consequences of each decision. These are modelled by the values of the vector criteria which correspond to the decision made and by multiple edges starting from an appropriate node. Scenarios can be interpreted as time-ordered paths along the decision nodes that lead to a given state in the future. Decision nodes that solve an optimisation problem are termed optimisers. The network is supplemented by relations of anticipatory feedback, which model a situation where decision makers take into account the anticipated results of some future decision problems. When making a choice, they use causal dependences of future constraints and preferences on the decision just made to influence its outcomes in a desired way. Both types of relations as well as forecasts and explorative scenarios regarding the parameters of future decision problems form an information model called an Anticipatory Network [18], [21], [22]. This paper presents the basic properties of ANs in the context of their applications in foresight, as well as the methods used for computing them. This, in turn, will allow us to construct and filter foresight scenarios, perform an assessment process to select a subset of normative scenarios, then run an AN-based backcasting process taking into account intertemporal expert assessments. Finally, we will present an application of ANs to construct a causal model of the technological development of decision support systems endowed with creative capabilities together with the corresponding recommendations to R&D policy makers. The above-outlined approach sheds new light on scenario planning and the mutual relations between explorative and normative scenarios. Specifically, each admissible decision path [21] corresponds to an explorative scenario, while the anticipatory paths model the decisions that yield a state describing a normative scenario. Admissible decision chains can also be regarded as elementary scenarios of future trends and events modelled by a class of discrete-timediscrete-event systems studied in [19]. Normative scenarios occur in an AN as target reference points [14] and may be situated in different moments in the future, namely in those which are target nodes of anticipatory feedbacks of a present-time decision maker. Scenarios defined and used in ICT foresight [23] and strategic planning [4], [6], [24] can depend on the choice of a decision in one of the networked optimisation problems as well as be external-trend or event driven. In addition, ANs allow the user to model a situation where future decision makers define their own target values and normative scenarios. The solution process of a given AN checks the feasibility of anticipatory feedbacks, which corresponds directly to reverse planning in backcasting. In addition, the scenario model may be hierarchic, i.e. besides scenarios driven by future decisions, alternative structures of decision nodes can be considered in the network, depending on alternative parameter forecasts or external events. The topology of such alternative networks may also depend on the decisions made at previous nodes. In this paper we will assume that (i) (ii) all decision makers in the network strive to make their decisions in a rational way, by selecting nondominated solutions to their multicriteria optimisation problems [[21]],[Error! Reference source not found.], all decisions are cooperative, without conflicts among any decision makers. These assumptions allow us to reduce the set of plausible elementary explorative scenarios and can be very assistive when building foresight scenarios by clustering elementary scenarios. The above assumed anticipatory behaviour of decision makers corresponds to the definition of an THEME 3: CUTTING EDGE FTA APPROACHES -2- 5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 anticipatory system proposed by Rosen [12] and developed further by other researchers [13], [18]. A bibliographic survey of these ideas can be found in [7]. Methodological approach Based on the above introductory remarks, we discover that an anticipatory network G can be built if the following information about the future is available: • • • explorative scenarios or forecasts concerning the parameters of future decision problems represented by the decision sets U, criteria F, the preference models θ and P, the first one used to select a nondominated subset, the second - to make a final compromise choice by future decision makers, and their anticipated behaviour (rational/partly rational/irrational), parameters of the causal dependence relations r linking the nodes in the network, anticipatory feedback relations pointing out which future outcomes are relevant when making decisions at specified nodes, and specifying the anticipatory feedback conditions. Constructive algorithms for filtering the plausible exploratory scenarios taking into account the preference information contained in an anticipatory network G may be applied if we know that: • • • • All future agents whose decisions are modelled in the network are rational, i.e. they make their decisions complying with their preference structures. An agent can assess to what extent the outcomes of causally dependent future decision problems are desired. This relation is described by multifunctions linking the decisions to be made with the future constraints and/or preference structures. The above assessments are transformed into decision rules for the current decision problem. It should also be assumed that the latter affects the outcomes of future problems in a way known to the agent. There exists a relevancy hierarchy among the anticipatory feedbacks in the network G (usually the more distant in time an agent is, the less relevant his/her chosen solution). Anticipatory networks which contain only decision makers that solve optimisation problems are termed optimiser networks. An optimiser O is a function that acts on a set of feasible decisions U and on a certain additional preference structure P and selects a subset X⊂U according to P and to a fixed set of optimisation criteria F with values in an ordered space E. For the purposes of generating the multicriteria recommendations in a foresight exercise, we will assume that the optimisation problems solved by the optimisers have the form (F:U→E)→min(θ), (1) where E is a vector space with a partial order ≤θ defined by a convex and closed cone θ, i.e. iff x ≤θ y y-x∈ θ for each x,y∈ E. Usually F=R , = and P may provide further partial information on the order in E but does not eliminate all related uncertainty. The solution to (1) is the set of nondominated points defined as n Rn+ Π(U,F,θ):={u∈ U: [∀ v∈ U: F(v) ≤θ F(u)⇒ v=u]}. The role of the preference structure P is to allow the decision maker to select a best-compromise solution from Π(U,F,θ), to gradually confine the solution set or to build a multicriteria ranking of the elements of U. The above problem formulation makes possible a uniform treatment of the THEME 3: CUTTING EDGE FTA APPROACHES -3- 5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 minimisation and maximisation of the coordinates of F as well as to include constraints on tradeoff coefficients between criteria into the problem solving procedure. Most frequently, the decision maker’s aim is to select just one nondominated solution u to (1) and apply it to generate the outcome F(u). The following definitions 1-3 have been formulated first in [21] and [22]. Definition 1. If O1:=X1(U1,F1,θ1,P1) and O2:=X2(U2,F2,θ2,P2) are multicriteria optimisers with Xi=Π(Ui,Fi,θi), i=1,2, then a constraint influence relation r between O1 and O2 is defined as O1 r O2 ⇔∃ ϕ:X1→2U2: X2=ϕ(X1). Acyclic r are termed causal constraint influence relations – here, in short, causal relations. (2) ■ Causal relations are represented by a (causal) network of optimisers. Def. 1 models the situation where the decision maker anticipating a decision output at a future optimiser can react by creating or forbidding decision alternatives, described by influencing the constraints by multifunctions φ that depend on the outputs from the preceding problems. From this point on, the term causal network will refer to the graph of a causal constraint influence relation. To complete the definition of anticipatory networks, we will now define the anticipatory feedback relation. Definition 2. Suppose that G is a causal network consisting of optimisers and that an optimiser Oi in G precedes another optimiser, Oj, in causal order r. Then the anticipatory feedback between Oj and Oi in G is the requirement concerning the anticipated output from Oj, which will influence the choice of decision at optimiser Oi. This relation will be denoted by fj,i. ■ By the above definition, the existence of an anticipatory information feedback between the optimisers On and Om means that both conditions below apply: • • The decision maker at Om is able to anticipate the decisions to be made at On. The results of this anticipation will be taken into account when selecting a decision at Om. The anticipatory information feedback relation does not need to be transitive. As in the case of causal relations, there may also exist multiple types of anticipatory information feedback in a network, each related to the different way the anticipated future optimisation results are considered at optimiser Om. The multigraph of r and one or more anticipatory feedbacks define an anticipatory network of optimisers: Definition 3. A causal network of optimisers with the starting node O0 and at least one additional anticipatory feedback relation linking O0 with another node in the network will be termed an anticipatory network (of optimisers). ■ A causal graph of optimisers G that can be embedded in a straight line and contains at least one anticipatory feedback fi,0 will be termed an anticipatory chain of optimisers. Examples of different anticipatory structures on chains are shown in Fig.1 and Fig.2 below. The solution process of an AN is equivalent to filtering the scenarios that are modelled by causal chains in the network. It is described in [21] for networks of cooperative agents and in [18] for hybrid networks that may include antagonistic agents modelled by Nash equilibria and random decisions. In both cases, a natural processing order of anticipatory feedbacks has been assumed, according to the rule that the more distant in time the target is, the less relevant it is. To incorporate the normative scenarios with a prescribed planning horizon, the algorithms provided in [21] can be easily modified to regard all anticipatory feedback relations starting from the decision nodes at a given future moment, interpreted as the planning horizon for backcasting, as most important. THEME 3: CUTTING EDGE FTA APPROACHES -4- 5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 Fig.1. Anticipatory structures along a chain of optimisers O0, O2,… Red arrows denote causal influences expressed by the multifunctions φi, blue arrows denote anticipatory feedback relations, where the target node requires that the future state of the starting node belongs to the set Vk. The analysis of the chain (a) can be decomposed into two separate subchains that are not linked by any anticipatory feedback It may happen that for a given network, no anticipatory chain exists. In such a case, it is possible to solve a relaxed problem termed an Anticipatory Decision-Making Problem (ADMP, cf. [21],[22]), where the admissible sequences of decisions maximise a certain proximity function to the target states at prescribed time moments. Fig.2. A causal network of seven optimisers, where O0, O2 and O3 influence two future problems each, while both, O4 and O5 are each influenced by the two predecessors. The shaded area between t4’ and t4” on the time axis denotes the synchronization interval for the simultaneous influence of O1 and O3 on the decisions of O4. The corresponding interval for O5 is contained in [t4’, t4”]. The dotted arrow between O6 and O4 denotes an irrelevant anticipatory feedback, because there is no causal relation between them [22] THEME 3: CUTTING EDGE FTA APPROACHES -5- 5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 Fig.2 shows a general network of optimisers, where each node may be influenced by, or itself influences several other nodes. The solution to decision problems described by such networks consists in decomposing the network into chains, analysing each of them separately and intersecting the sets of admissible paths for each of the diverging paths in the AN. However, there is no unique way to analyse two or more causal relations influencing one node. Their synergy may be either: - restrictive, which corresponds to the intersection of constraints at the target node, permissive, allowing us to choose a solution from the union of values of φ, exclusive, based on time priority: only the relation whose influence started first is valid. In this way, asynchronous analysis which is possible for anticipatory chains and trees may become insufficient in the case of general networks. If the preference structure of the present-time decision maker is regarded as superordinated with respect to the future decision makers modelled by the AN, then the selection of decision variants at each node corresponds to the choice of a planning strategy that yields the desired outcome at each intermediate and at the final assessment moment. This procedure may be regarded as a general backcasting process based on rational future decision makers behaviour expectations. Since the multicriteria or other partial-order-based preference structures may occur at each AN node, including the present-time decision maker, the above-outlined procedure does not usually yield a unique solution. Therefore, the recommendations from such an exercise contain a selection of nondominated action variants that forms a subgraph in the network of all anticipatory chains. The final strategy choice – understood as a sequence of actions leading to the target may be left to group decision making procedures performed by the stakeholders of the foresight project. However, AN-based formulation of the strategy choice problem allows the stakeholders to create a composite strategy, i.e. an algorithm that chooses the action out of all those admissible, according to the external states and ex-post evaluation of outcomes. When depicted on the graph of anticipatory chains, this composite strategy appears as a route selection on a roadmap (always forward), an interesting coincidence with classical roadmapping [24]. Results, discussion and implications The AN model built for the technologies investigated within the recent project [23] has benefitted from a synergy with a number of other analytical foresight methods and IT tools. They have all been developed and combined in one novel foresight support system (FSS) [17]. These include: • • • an ontological knowledge base which stores raw technological, economic and social data together with evolution models, trends and scenarios, automated data acquisition and analytic tools to estimate and continually update patent, bibliometric and product-parameter trends in selected AI areas, algorithms of multicriteria rankings suitable for ICT management and capable of generating constructive recommendations for decision makers as regards strategic technological priorities. Thus the overall foresight process in the above-mentioned exercise [23] has been organised within an expert system framework, with IT-supported activities. The FSS has allowed us to take into account in a uniform way the expert knowledge acquired in a Delphi exercise and other kinds of quantitative and qualitative information. They have all been processed together in the analytic engines of an ontological knowledge base. Technological evolution has been modelled THEME 3: CUTTING EDGE FTA APPROACHES -6- 5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 as a discrete-continuous-event system [19]. Technological trends, as well as industrial and social demand, formed the inputs, while feedback loops make it possible to model the impact of technological demand on R&D, production, and supply. The anticipatory networks modelled future decisions that might eventually split the predicted trends into several variants, then – after a clustering – create a given number of explorative technological scenarios. These in turn could provide clues to defining normative scenarios and set policy goals. The variants of actions resulting from backcasting have been assessed by experts using several performance criteria at the final foresight horizon taken into account (2030) as well as at the intermediate periods (2015 – to check whether the official forecasts for the coming years are real; 2020 – the end of the current EU financial perspective; 2025 – the reference foresight horizon set in the contract). The temporal preference structure so arisen was used in a backcasting procedure to establish a selection of best-compromise (in a multicriteria decision-making meaning) action plans for the decision makers at regional (until 2020) and national levels (until 2025). The technological focus areas of the above-mentioned project are: • expert systems, including decision-support, recommenders, and diagnostic systems, • machine vision, neurocognitive systems and technologies, including Brain-Computer Interfaces (BCI) and artificial creativity reseearch, • key AI application areas (e-government, e-health, m-health, e-commerce), • molecular and quantum computing. such as expert systems, decision-support systems. Out of these areas, the ANs have been built and applied to the selected topics selected from the first two fields. They were applied to filter out dominated scenarios and in reverse planning/backcasting at regional and corporate level. The latter methodology is outlined in the following table. Table 1. Standard strategic backcasting [10] vs. anticipatory networks applied in the project SCETIST [23] Specification A typical backcasting procedure Anticipatory modelling Common goals ● To assess the feasibility of given visions of the future ● To support strategic planning projects that involve multiple strategies Time scale Determine the reverse planning horizon based on information provided by experts and stakeholders Core target analysis Define future ideal states (normative Define the criteria and preference structures scenarios), and feasible actions that may be as well as reference points for relaxing applied to reach them anticipatory feedback conditions Strategic planning Engage experts and stakeholders to take part in the action planning. Use panels, workshops, brainstorming, etc. to elicit their opinions Use AN algorithms to calculate compromise solutions along anticipatory chains. The information provided while building the AN generates the solution Interaction All steps in the procedure should be repeated with updated information and forecasts until the preference thresholds set by stakeholders and decision makers are met Once built, the AN remains stable during the analysis. The interaction touches upon the presentation of different nondominated anticipatory chains until the present-time decision makers are satisfied THEME 3: CUTTING EDGE FTA APPROACHES -7- In addition to expert information: AN allows the modeller to determine the maximum grade of the network and derive the anticipation horizon out of it 5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 The procedure outlined above was preceded by a pre-analysis of technological priorities, where a few technological areas have been preselected for further analysis taking into account the existing capabilities in the region concerned, as well as the needs of the industrial stakeholders. Regional policy priorities have been taken into account as a starting point only, as one of the foresight goals was to verify the existing policies and provide improvement recommendations. Thus, as an example, creativity support systems have been identified as a prospective advanced IT/AI development area, with the following focus subareas: • • • learning support systems (LSS) employing creativity measurements and stimulation, creative design support systems, strategic decision support systems employing creativity techniques to support group decisions. The anticipatory network consists of the following main decision-making units (cf. Fig.3 below): • • • • • a national research authority, assigning funds to industry-oriented creativity research. regional government, responsible for strategic planning and providing appropriate legislation for each 7-year budgetary perspective separately (2014-2020 and the subsequent periods), a regional creativity support centre responsible for the coordination of creativity-related research and the distribution of the EU, national, and regional aid to the creative industry sector, generic IT companies, one for each subarea, based on typical software and other creative industry profiles in the southern macroregion of Poland, generic research and training institutions involved in creativity-related issues. The other nodes in the network correspond to the EU-level legislation fostering creativity research and industry, the institution responsible for conducting the foresight exercise that provides recommendations to the authorities, as well as external factors and drivers that may influence the overall system. The recommendations arising from a foresight exercise do not have an obligatory impact on policy makers. This is why they are modelled as separate causal influence relations (dotted lines in Fig.3). The policy makers above the regional level are not concerned directly with the activities at lower level, so no anticipatory feedback edge reaches them. Fig.3. An anticipatory network modelling the strategic planning and backcasting process aimed at the development of regional creativity fostering system THEME 3: CUTTING EDGE FTA APPROACHES -8- 5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 Legislation and regional innovation support measures influence the scope of investment and new product development at the companies. The financing of research influences the innovation potential at the companies and their new product development capabilities. The company activities and anticipated tax revenues and employment growth may influence, in turn, the readiness of the regional authority to support specific industries. Uncertainties within the creativity support system include the emergence of efficient creativity measurements online [25] with the BCI and otherwise. The wild cards as well as external trends and drivers include a.o. the possibility of disruptive discoveries in cognitive creativity research [3], [5], [9], [15], [20], economic decline, altered terms of trade and social preferences concerning creativity-related products and services. Recommendations to policy makers were generated making use of multicriteria outranking methods, some of them developed solely for AI technology prioritisation problems. As an example, for each evolution scenario of decision-support systems (DSS) [16] endowed with creativity support capabilities, the AN allowed the foresight analyst to better assess the DSS impact on other technological areas and the education sector as well as on consumers’ social behaviour. Conclusions This paper presents a new area for researching futures with a novel scenario planning and backcasting tool, termed anticipatory networks. We have shown how this could be applied to elicit future scenarios of AI technologies as exemplified by a creativity R&D support system. AN-based backcasting can provide constructive clues to ICT and innovation policy makers as well as to companies interested in the development of advanced technological products such as createvity support systems, autonomous mobile rescue robots etc. The underlying AN methodology, although it has been developing over the past few decades [14] and despite theoretical links to Stackelberg games [6], [21] and multi-level programming [8], has not yet been applied in Future Technological Analysis. However, related recent research concerning the multicriteria approach [2] and control-theoretical methods [1] in scenario planning, can provide further fruitful synergies in the development of ANs and their applications in innovation management [11] and FTA. AN application areas are not restricted to ICT. ANs can also be a convenient tool to model sustainability, as has been shown in a recent foresight project on inorganic waste flows until 2030 (www.inorganicwaste.eu). In the case of the project SCETIST [23], the main client and supervising body was the Polish Ministry of Science and Higher Education. Regional authorities and software companies were additional, yet relevant, stakeholders. The IT tools developed [17], [19], including an AN modelling application, form an integrated online foresight support system [17],[26] that makes possible continuous updates of input information and resulting trends, scenarios and recommendations. This FSS is a universal tool in the sense that it can be applied in foresight research in different technological or social areas, with different time horizons and end users, allowing them to extend technology analysis to anticipatory planning and backcasting. References [1]. J.L. Amezcua-Martínez, D. Güemes-Castorena. Strategic Foresight Methodology to Identifying Technology Trends and Business Opportunities. in: Technology Management for Global Economic Growth (PICMET), Proceedings of PICMET '10, Phuket, 18-22 July 2010, p.6, IEEE, 2010 [2]. T. Comes, N. Wijngaards, B. Van de Walle, Exploring the future: Runtime scenario selection for complex and time-bound decisions, Technol. Forecast. Soc. Change,p.18, http://dx.doi.org/10.1016/j.techfore.2014.03.009 (Articles in Press), 2014 THEME 3: CUTTING EDGE FTA APPROACHES -9- 5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014 [3]. J.S. Gero, Future Directions for Design Creativity Research. In: T. Taura, Z. Nagai (eds.), Design Creativity 2010, Springer-Verlag, pp.15-22, 2011 [4]. M. Godet M., Creating Futures – Scenario Planning as a Strategic Management Tool. Economica, London, 2001 [5]. T. Iba, An Autopoietic Systems Theory for Creativity. In: K. Riopelle, P. Gloor, C. Miller, J. Gluesing (eds.): 1st Collaborative Innovation Networks Conference – COINS’2009. Procedia Social and Behavioral Sciences, vol. 2(4), Elsevier Science BV, Amsterdam, pp. 6610-6625, 2010 [6]. K Kijima, Ch. Xu, Incentive Strategies Dealing with Uncertainty about the Followers MCDM Behavior. Int. J. Systems Sci. 25(9), 1427-1436, 1994 [7]. M. Nadin, Annotated Bibliography. Anticipation. Int. J. General Sys. 39(1), 35-133, 2010 [8]. I. Nishizaki, M. Sakawa, Cooperative and Noncooperative Multi-Level Program¬ming. OR/ CS Interfaces Series Vol. 48, Springer-Verlag, Dordrecht, Heidelberg, London, New York, 2009 [9]. C.F. Pereira, Creativity and Artificial Intelligence. A Conceptual Blending Approach. Mouton de Gruyter, Berlin, New York, p.253, 2007 [10]. J. Quist, P. Vergragt, Past and future of backcasting: The shift to stakeholder participation and a proposal for a methodological framework. Futures, 38, 1027–1045, 2006 [11]. J.M. Ramos, Forging the Synergy between Anticipation and Innovation: The Futures Action Model. Journal of Futures Studies, 18(1), 85-106, 2013 [12]. R. Rosen, Anticipatory Systems - Philosophical, Mathematical and Methodological Foun¬dations. Pergamon Press, London (1985), 2nd Ed. Springer-Verlag, 2012 [13]. A.M.J. Skulimowski, Solving Vector Optimization Problems via Multilevel Analysis of Foreseen Consequences. Found. Control Engrg., 10(1), 25-38, 1985 [14]. A.M.J. Skulimowski, Methods of Multicriteria Decision Support Based on Reference Sets, In: R. Caballero, F. Ruiz, R.E. Steuer (eds.) Advances in Multiple Objective and Goal Programming, Lecture Notes in Economics and Mathematical Systems, vol. 455, Springer-Verlag, Berlin-Heidelberg-New York, pp. 282-290, 1997 [15]. A.M.J.: Skulimowski, Freedom of Choice and Creativity in Multicriteria Decision Making. In: T. Theeramunkong, S. Kunifuji, C. Nattee, V. Sornlertlamva¬nich (Eds.) Knowledge, Information, and Creativity Support Systems: KICSS2010 Revised Selected Papers, Lecture Notes in Artificial Intelligence, vol. 6746, Springer-Verlag, Berlin; Heidelberg, pp. 190—203, 2011 [16]. A.M.J. Skulimowski, Future Trends of Intelligent Decision Support Systems and Models, in FutureTech 2011, Part I. Park, J.J., Yang, L.T., Lee, C. Eds., CCIS, vol. 184, pp. 11-20, Berlin-Heidelberg, Springer-Verlag, 2011 [17]. A.M.J. Skulimowski, A Foresight Support System to Manage Knowledge on Information Society Evolution. In: K. Aberer et al., Eds., Social Informatics. 4th International Conference, SocInfo 2012, Lausanne, Switzerland, December 5-7, 2012. Proceedings. Lecture Notes in Computer Science, vol. 7710, pp. 246-259, Berlin – Heidelberg, Springer-Verlag, 2012 [18]. A.M.J. Skulimowski, Hybrid anticipatory networks. In: L. Rutkowski et al. (eds.), Proc. ICAISC 2012, Lecture Notes in Artificial Intelligence, vol. 7268, pp.706-715, Berlin – Heidelberg, Springer-Verlag, 2012 [19]. A.M.J. Skulimowski, Discovering Complex System Dynamics with Intelligent Data Retrieval Tools, in Sinoforeign-interchange workshop on Intelligent Science and Intelligent Data Engineering IScIDE 2011, Xi'an, China : Oct. 23-26, 2011, Y. Zhang et al., Eds., Lecture Notes in Computer Science, vol. 7202, pp. 614-626, Berlin – Heidelberg, Springer-Verlag, 2012 [20]. A.M.J. Skulimowski, Universal intelligence, creativity, and trust in emerging global expert systems. In: L. Rutkowski et al. (Eds.), 12th International Conference on Artificial Intelligence and Soft Computing, Zakopane, 2013, Proceedings, Part II. Lecture Notes in Artificial Intelligence, vol. 7895, Springer-Verlag, Berlin–Heidelberg, pp. 582-592, 2013 [21]. A.M.J. Skulimowski, Anticipatory Network Models of Multicriteria Decision-Making Processes, Int. J. Systems Sci., 45(1) 39-59, DOI:10.1080/00207721.2012.670308, 2014 [22]. A.M.J. Skulimowski, The Art of Anticipatory Decision Making. In: G.A. Papadopoulos (ed.), Proceedings of the 9th International Conference on Knowledge, Information and Creativity Support Systems, Limassol, Cyprus, November 6-8, 2014, pp. 14-25, University of Cyprus, Nicosia, Cyprus, 2014 [23]. A.M.J. Skulimowski (ed.), A. Ligęza, P. Pukocz, P. Rotter, R. Wisła, et al., Scenarios and development trends of selected IST until 2025 (SCETIST), Final Report, Contract No. UDA-POIG.01.01.01-00-021/09-00, Progress & Business Publishers, Kraków, 2014, www.ict.foresight.pl [24]. A.M.J. Skulimowski, P. Pukocz, Enhancing creativity of strategic decision processes by technological roadmapping and foresight. In: Lee, V.C.S., Ong, K.L. (eds.) KICSS 2012: seventh international conference on Knowledge, Information and Creativity Support Systems: Melbourne, Victoria, Australia, 8–10 Nov. 2012. IEEE Computer Society. CPS Conference Publishing Services, pp. 223–230, 2012 [25]. D. Villani, A. Antonietti, Measurement of Creativity. In: E. G. Carayannis (ed.), Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship, Springer-Verlag, pp. 1234-1238, 2013 [26]. H.A. von der Gracht, V.A. Bañuls, M. Turoff, A.M.J. Skulimowski, T.J. Gordon, Foresight support systems: The future role of ICT for foresight. Technol. Forecast. Soc. Change, p.6, DOI:10.1016/j.techfore.2014.08.010 2014 THEME 3: CUTTING EDGE FTA APPROACHES - 10 -
© Copyright 2024 ExpyDoc
