International Journal of Information Technology & Decision Making

The production and consumption of crude oil became a major issue with the sharp increase in crude oil prices that took place during the last few months. We investigate the relationship between crude oil consumption and the GDP of the top crude oil consuming countries. The amount of GDP produced per barrel of crude oil varies significantly between different countries; the ratio is in the range of 2% to 10% of the GDP when the price of a barrel of crude oil is $100. The paper attempts to explain the high variability with the aim of learning from high GDP producers as to how they are able to generate a larger GDP per barrel of crude oil consumption. The paper also identifies a hysteresis effect in crude consumption reduction and illustrates how understanding it can lead to better production and conservation policies.

In this paper, we carry out a study about the main themes treated by the International Journal of Information Technology & Decision Making during its first 10 years (2002–2011). The themes are detected, quantified and visualized using an approach that combines performance analysis and science mapping. Bibliometric maps based on co-word analysis will help us to visualize the division of the journal into several subfields and their relationships, providing interesting insight into the main topics being discussed in the journal in these years. In addition, the study will show the most productive themes (according to published papers) and the most impacting ones (according to received citations).

There is an extensive literature on decision making under uncertainty. Unfortunately, up to date there are no valid decision principles. Experimental evidence has repeatedly shown that widely used principle of maximization of expected utility has serious shortcomings. Utility function and nonadditive measures used in nonexpected utility models are mainly considered as real-valued functions whereas in reality decision-relevant information is imprecise and therefore is described in natural language. This applies, in particular, to imprecise probabilities expressed by terms such as likely, unlikely, probable, etc. The principal objective of the paper is the development of computationally effective methods of decision making with imprecise probabilities. We present representation theorems for a nonexpected fuzzy utility function under imprecise probabilities. We develop an effective decision theory when the environment of fuzzy events, fuzzy states, fuzzy relations and fuzzy constraints are characterized by imprecise probabilities. The suggested methodology is applied for a real-life decision-making problem.

An extension of the DS/AHP method is proposed in the paper. It takes into account the fact that the multi-criteria decision problem might have several levels of criteria. Moreover, it is assumed that expert judgments concerning the criteria are imprecise and incomplete. The proposed extension also uses groups of experts or decision makers for comparing decision alternatives and criteria. However, it does not require assigning favorability values for groups of decision alternatives and criteria. The computation procedure for processing and aggregating the incomplete information about criteria and decision alternatives is reduced to solving a finite set of linear programming problems. Numerical examples explain in detail and illustrate the proposed approach.

We introduce a new interactive procedure for multiple objective optimization problems. The identification of the most preferred solution is achieved by means of a systematic dialogue with the decision maker (DM) during which (s)he specifies pairwise comparisons of nondominated solutions from a current sample. We represent this preference information by a compatible form of the achievement scalarizing function, i.e., we are searching for weights of objectives which ensure that the reference solutions are compared by the function in the same way as by the DM. Directions of the isoquants of all compatible achievement scalarizing functions create a cone in the evaluation space, with the origin in a reference point. In successive iterations, each new pairwise comparison of solutions contracts the cone which is zooming on a subregion of nondominated points of greatest interest for the DM. The procedure ends when at least one satisfactory solution is selected or when the DM comes to conclusion that there is no such solution for the current problem setting.

This paper presents an up-to-date comprehensive overview of the MACBETH approach to multicriteria decision-aid. It requires only qualitative judgements about differences of attractiveness to help a decision maker, or a decision-advisory group, quantify the relative value of options. The approach, based on the additive value model, aims to support interactive learning about evaluation problems and the elaboration of recommendations to prioritize and select options in individual or group decision making processes. A case study based on a real-world application of MACBETH for multicriteria value measurement of IT solutions is presented. It shows how the M-MACBETH decision support system can be used in practice to construct an additive evaluation model. The paper addresses key issues related to structuring the model, building value scales, weighting criteria and sensitivity and robustness analyzes. Reference is also made to applications of MACBETH reported in the scientific literature.

We report on the panel discussion held at the ICDM'10 conference on the top 10 data mining case studies in order to provide a snapshot of where and how data mining techniques have made significant real-world impact. The tasks covered by 10 case studies range from the detection of anomalies such as cancer, fraud, and system failures to the optimization of organizational operations, and include the automated extraction of information from unstructured sources. From the 10 cases we find that supervised methods prevail while unsupervised techniques play a supporting role. Further, significant domain knowledge is generally required to achieve a completed solution. Finally, we find that successful applications are more commonly associated with continual improvement rather than by single "aha moments" of knowledge ("nugget") discovery.

Electronic journal databases allow efficient retrieval and processing of bibliometric data, making possible enhanced literature reviews called research profiling studies. We have conducted such a research profiling study of Multiple Criteria Decision Making (MCDM) using the ISI Web of Science. The ISI database covers close to 9000 publications, mainly journals. We report statistics regarding how the MCDM field has developed based on variations of a set of rather broad search words. We have also produced detailed correlation maps based on most cited authors for different decades, showing the birth and evolution of different schools of thought. We seek to provide the "big picture" of MCDM. Our study shows that the field has experienced exponential growth. At the same time it has penetrated other neighboring domains of knowledge, such as Information and Communication Technologies and engineering.

The collaborative innovation of industry, academia, and government is an effective way to enhance the innovative task and Triple helix approach can be used for its investigation. Bilateral relations can be expanded into triadic relationships and the complex dynamic process will produce a wide range of possible evolutionary trajectories. The essential feature of innovation is knowledge integration and creation. From different dimensions the knowledge will be integrated at knowledge-flow level and organizational level. In this paper, a self-organizing model of knowledge integration in collaborative innovation and its working processes including the nucleation and autopoiesis processes are introduced. Problem about degree of knowledge integration in collaborative innovation is discussed and a new comprehensive categorization method is suggested. Finally a case study is described.

We live in an era when a remarkable number of new information and other technologies are successfully bypassing the main obstacle to technological advance: technology support network (TSN). Technology support net, when fully established and fixed, erects significant barriers to innovation. Innovation is not free and autonomous process of applied creativity, but is technically, economically and politically subservient to the "holders and owners" of the support net. We present operational definitions of technology and high technology which explain past and current barriers to innovation shifts from one technological paradigm to another. The TSN-bypass technologies are causing the process of globalization to slow down and revert itself into a process of relocalization, bringing forth the economy of autonomous, self-reliant and self-sustainable individuals, communities, localities and regions.

Challenging decision problems in changeable spaces are characterized by existence of complex decision parameters that are changing with time and situations, including criteria and alternatives. Some of these parameters may be critical for their effective solutions, but hidden in the depth of potential domains. In this rapidly changing world, including technology and attitude, without paying attention to the problems in changeable spaces, we could easily commit serious mistakes due to decision blinds, decision traps and/or decision shocks. The article starts with a brief description of the evolution of MCDM toward challenging problems in changeable spaces. Then it briefly sketches a dynamic human behavior mechanism and habitual domain theory which provide an effective list for us to search relevant decision parameters and pave the way for latter discussion. Competence set analysis, derived from habitual domain, is then introduced to exemplify decision blinds, decision traps and decision shocks in challenging decision problems. Checking lists and methods for discovering blinds and traps and for dealing with shocks are also provided. Innovation dynamics, a systematic network of thoughts, is introduced to further look out relevant key parameters in dynamic challenging problems. The related academic subjects in each link of the innovation dynamics are also explained, which allow us to see the complexity and interconnectivities among different challenging problems in changeable spaces. Finally we introduce three habitual domain tool boxes to empower ourselves to expand and enrich our thoughts into the depth of the potential domains of the challenging problems, which allows us to more effectively identify hidden parameters, problems and competence sets to reduce decision blinds, avoid decision traps and solve the problems, or dissolve the problems before they occur.

As an active research field, database keyword search (KWS) has put much emphasis on the performance issues, due to its high computational cost. However, a closer examination on KWS reveals that there are some other interesting aspects worth noting. In this paper, we examine KWS from a broader perspective, analyzing its profound implications. Freed from syntax-related considerations, KWS users now have better opportunities to explore the data in the way as they wish, and such exploration may reveal useful hindsight for understanding the hidden nature of the data, thus benefiting the exploitation on the use of the data. In particular, we examine the potential of KWS to data mining from the behavior mining perspective. This examination also leads us to a discussion of viewing KWS in the "web of life" context. We further discuss connections of KWS with other related concepts, including dataspace. Based on our findings and critical examination, we also provide a brief overview of an integrated environment which facilitates the interplay of KWS and data mining.

Construction processes planning and effective management are extremely important for success in construction business. Head of a design must be well experienced in initiating, planning, and executing of construction projects. Therefore, proper assessment of design projects' managers is a vital part of construction process. The paper deals with an effective methodology that might serve as a decision support aid in assessing project managers. Project managers' different characteristics are considered to be more or less important for the effective management of the project. Qualifying of managers is based on laws in force and sustainability of project management involving determination of attributes value and weights by applying analytic hierarchy process (AHP) and expert judgement methods. For managers' assessment and decision supporting is used additive ratio assessment method (ARAS). The model, presented in this study, shows that the three different methods combined (ARAS method aggregated together with the AHP method and the expert judgement method) is an effective tool for multiple criteria decision aiding. As a tool for the assessment of the developed model, was developed multiple criteria decision support system (MCDSS) weighting and assessment of ratios (WEAR) software. The solution results show that the created model, selected methods and MCDSS WEAR can be applied in practice as an effective decision aid.