Andrzej M. J. Skulimowski

Freedom of choice and creativity in multicriteria decision making

This paper presents new approaches to formulating and solving complex real-life decision-making problems, making use of the creativity concept. We assume that the decision-making process is embedded in the system of views and mutual relations between the decision-makers and their surrounding environment, so that creativity, as defined formally in Sec. 2, could play a primary role in the decision-making process. We will investigate multicriteria decision problems, where the decision-maker is unable to fully follow decisionmaking rules resulting from a standard mathematical formulation of multicriteria optimization problem. This is either due to external conditions (such as the need to make a quick decision, loss of data, or lack of data processing capabilities) or when the decision-maker can manifest creativity related to the hidden internal states of the decision-making process. We will provide a formal definition of freedom of choice (FOC), specifying three levels of FOC for multicriteria decision-making (MCDM) problems. Then we will point out that creativity in decision-making can be explained within the framework of autonomous and free decisions, and that decision-making freedom is a necessary prerequisite for creativity. The methods presented here can be applied to analyzing human decision-making processes and conditions allowing the expression of creativity as well as to designing pathways leading to creative decision-making in artificial autonomous decision systems (AADS). The applications of the latter include visual information retrieval, financial decision-making with feature identification, intelligent recommenders, to name just a few.

A foresight support system to manage knowledge on information society evolution

In this paper we present an intelligent knowledge fusion and decision support system tailored to manage information on future social and technological trends. It focuses on gathering and managing the rules that govern the evolution of selected information society technologies (IST) and their applications. The main idea of information gathering and processing here presented refers to so-called real-time expert Delphi, where an expert community works on the same research problems by responding to structured questionnaires, elaborating complex dynamical system models, providing recommendations, and verifying the models so arisen. The knowledge base is structured in layers that correspond to the selected kinds of information on the technology and social evolution, uses, markets, and management. An analytical engine uses labelled hypermultigraphs to process the mutual impacts of objects from each layer to elicit the technological evolution rules and calculate future trends and scenarios. The processing rules are represented within discrete-time and discrete-event control models. Multicriteria decision support procedures make it possible to aggregate individual expert recommendations. The resulting foresight support system can process uncertain information using a fuzzy-random-variable-based model, while a coupled reputation management system can verify collective expert judgments and assign trust vectors to experts and other sources of information.

Universal Intelligence, Creativity, and Trust in Emerging Global Expert Systems

This paper presents a hypothesis together with evidence related to the use of global knowledge as a holistic expert system. By global expert system (GES) we mean all knowledge sources, bases, repositories, and processing units, regardless of whether they are human, artificial, animal, or hybrid, such that the relation “able to transfer knowledge on immediate demand” forms a connected graph over the elements of the system. A key requirement is that problem solving using GES is an anytime process with respect to the number of information sources taken into account. We conjecture that due to the high and ever-growing level of interconnection of knowledge units, a universal intelligence emerges, which under specific conditions can outperform the intelligence and creativity of any of its individual elements, including humans. It will be shown that this is possible only if an appropriate level of credibility can be assigned to each element of the system, which ensures that users trust the responses. We will design a hybrid supervised-reinforced learning scheme that makes it possible to achieve a satisfactory level of trust in GES query responses. Query processing will apply knowledge fusion methods such as combinations of recommendations and forecasts.

Future Trends of Intelligent Decision Support Systems and Models

The aim of this paper is to investigate, formulate, and analyse the general rules and principles that govern the evolution of key factors that influence the development of decision support systems (DSS) and models. In order to ela borate a model suitable for medium-term forecasts and recommendations, we have defined eight major elements of Information Society that characterise the evolution of the corresponding digital economy. The evolution of the overall system is described by a discrete-continuous-event system, where the mutual impacts of each of the elements are represented within state-space models. Technological trends and external economic decisions form inputs, while feedback loops allow us to model the influence of technological demand on IT, R&D, production, and supply of DSS. The technological characteristics of the product line evolution modelled in this way can provide clues to software providers about future demand. They can also give R&D and educational institutions some idea on the most likely directions of develop¬ment and demand for IT professionals. As an example, we will model the evolution of decision-support systems and recommenders for 3D-internet-based e-commerce, and their impact on technological progress, consumption patterns and social behaviour. The re sults presented here have been obtained during an IS/IT foresight project carried out in Poland since 2010 and financed by the ERDF.

Web-Based Learning in Remote Areas: An Evaluation of Learning Goals, Scenarios and Bidirectional Satellite Internet Implementation

This paper presents the findings derived from a series of EC-financed projects aimed at supporting web-based learning in rural communities and other remote areas of Europe. The findings are mainly based on the case studies of ten pilot local learning hubs in the Beskidy Mountains, Poland. The results of four subsequent projects supported by different EU programs and conducted from 2006 to 2013 contributed to this study. These projects were conducted in Poland and 16 other countries of European Research Area ERA. We will present the learning and research goals as well as the predominant learning scenarios. We will also show how innovative local social networks emerged to foster web-based and blended learning. A common feature of all the learning hubs was the need to use DVB-RCS bidirectional satellite internet technology for learning. We will present a multicriteria performance evaluation of the learning hubs in connection with their learning goals, adopted scenarios, and future development prospects.

Enhancing Creativity of Strategic Decision Processes by Technological Roadmapping and Foresight

The quality of a companys strategic planning is a key factor influencing its competitiveness and development prospects. We will demonstrate how an appropriate choice of user inter¬faces, knowledge acquisition tools and analytic decision support methods can stimulate the creativity of the strategic planners taking part in technological road mapping. With strategic planning formalized as a multicriteria decision problem, the usual process of debating and brainstorming is better focused on reaching a consensus solution in an efficient way. An intelligent road mapping support tool and publicly available technological foresight results assure a high quality of data gathering and interaction of experts with stakeholders. Real options are used to evaluate the opportunities, threats, challenges, and flexibility during the planning period. The above approach has been implemented as an intranet application and used to apply information technology (IT) foresight outcomes to establish IT investment plans in innovative companies that develop new products and launch them on the market.

Hybrid anticipatory networks

This paper presents a theory of hybrid anticipatory networks that generalizes earlier models of consequence anticipation in multicriteria decision problems. We assume that the decision maker takes into account the anticipated outcomes of future decision problems linked by the causal relations with the present decision problem. This can be represented by a multigraph, where decision problems are modeled as nodes linked causally and by one or more additional anticipation relations. These types of multigraphs are termed anticipatory networks. Hybrid anticipatory systems may contain additional models of random and non-cooperative game decisions. Constructive solution methods for decision problems modeled by anticipatory networks are discussed as well. Further, we present a generalization of hybrid anticipatory networks, known as superanticipatory systems. In the final section we discuss some of their applications in the design of decision-making rules in autonomous robotic systems and in filtering technology development scenarios.

On-Line Technological Roadmapping as a Tool to Implement Foresight Results in IT Enterprises

Technological roadmapping (TR) is a complex business intelligence and strategic planning process. Due to its relatively high costs, duration, mobilisation of human resources and know-how, its hitherto applications were attributed mainly to long-term strategy formation in large corporations and as a tool supporting policy making. This paper will show how the recent development of web technologies and a public availability of foresight results have allowed to provide access to high-level roadmapping even for small and medium-sized enterprises. We will describe an implementation of the roadmapping process in .NET that may serve as an on-line interactive strategic planning and decision support system applying information technology foresight results. The system may be used as a base to solving IT investment planning problems and new product development and market placement problems (NPD-MP) in innovative companies. A web-based analytic machine that supports roadmapping is able to generate optimal technological investment strategies, visualised as multicriteria shortest paths in classical roadmapping diagrams. The system uses the integrated capacity offered by ontologies and semantic networks is a complex collaborative process of knowledge acquisition from heterogeneous information sources, its managing, sharing and processing. An example of the roadmapping process applied to develop a new computer based on IT foresight results will also be presented.

The Art of Anticipatory Decision Making

This paper presents the recent advances of the theory of anticipatory networks and its applications in future-oriented decision-making. Anticipatory networks generalize earlier models of consequence anticipation in multicriteria decision problem solving. This theory is based on the assumption that the decision maker takes into account the anticipated outcomes of future decision problems linked in a prescribed manner by the causal relations with the present problem. Thus arises a multigraph of decision problems linked causally (the first relation) and representing one or more additional anticipation relations. Such multigraphs will be termed anticipatory networks. We will also present the notion of a superanticipatory system, which is an anticipatory system that contains a future model of at least one anticipatory system besides itself. It will be shown that non-trivial anticipatory networks are superanticipatory systems. Finally, we will discuss several real-life applications of anticipatory networks, including an application to establish efficient collaboration of human and robot teams.

Future Prospects of Human Interaction with Artificial Autonomous Systems

The growing complexity of intelligent systems and technologies raises questions concerning their interaction with human intelligence. The loss of an ability to control artificial intelligent and autonomous decision systems (AADS) due to their high level of sophistication exceeding human analytic capabilities may be referred to as one aspect of ‘singularity’. The latter term is often used to describe potential threats to the mankind coming from the development of AADS that may outperform human intelligence in its most relevant aspects. This paper presents some results of a recent foresight project SCETIST which shed a new light on the above ‘singularity’ dilemma. The project aimed at building the scenarios and trends of selected advanced information technologies. Based on a classification of AI enabling technologies, two basic scenarios concerning the development of AADS have been built. The first one points out that the newly emerging global expert systems (GES) coupled with the Brain-Computer Interfaces (BCI) will allow the human societies to explore in full all data streams and knowledge repositories available. The global knowledge from all sources will be further processed by GES so that rational human decisions will not be outperformed by those made by AADS. In the second scenario, the AADS enabling technologies will develop faster than BCI and GES, so that autonomous decision systems can dominate. The third scenario, ranked as least probable, indicates a possibility of slowing down the development of ICT and AI, so that the singularity problem is deferred. The time horizon of the above scenarios was 2030. Finally, we will present the recommendations arising from SCETIST from the point of view of shaping the R&D policies.