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Big data and analytics have become an essential component of organizational operations. The ability to collect and interpret significantly large data sets has provided a wealth of knowledge to guide decision makers in all facets of society. This is no different in sport management where big data has been used on and off the field to guide decision making across the industry. As big data evolves, there are concerns regarding the use of enhanced analytic techniques and their advancement of knowledge and theory. This special issue addresses these concerns by advancing our understanding of the use of big data in sport management research and how it can be used to further scholarship in the sport industry. The six articles in this special issue each play a role in advancing sport analytics theory, producing new knowledge, and developing new inquiries. The implications discussed in these articles provide a foundation for future research on this evolving area within the field of sport management. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence in physical education is a widely debated topic and has an immense potential for the future. This article discusses the benefits of applying AI to physical education and its possible applications. It is argued that AI can be applied in a number of ways, and it can integrate different types of sensors to collect data. This data can then be used to train an autonomous system which will help the users to make intelligent decisions. Tactical behavior was assessed in subgroups based on team lines (goalkeeper -- defenders, defenders -- midfielders and midfielders -- forward) before and after the intervention in an 8-a-side game. The results suggest that short-term differentiated practice seems insufficient to change linear outcomes, but useful to boost tactical-pattern regularity at subgroup level during the team-possession game phase. Youth-sport academy coaches should employ modified games introducing artificial rules to encourage young players to learn and strengthen the acquisition of regular tactical-patterns [ABSTRACT FROM AUTHOR]

Article 22 GDPR imposes a prima facie prohibition on certain forms of automated decision-making. It has been suggested that Article 22 GDPR may prohibit online personalised pricing, as online personalised pricing involves the making of automatic pricing decisions based on customers' personal data. However, this paper will argue that Article 22 GDPR should not be relied upon to constrain online personalised pricing. Two main reasons are given in support of this argument. First, Article 22 GDPR does not in fact pose a significant obstacle to online personalised pricing. Second, there are good reasons why a prohibitory stance against online personalised pricing should not be adopted, and a transparency-centric approach should be preferred instead. [ABSTRACT FROM AUTHOR]

Increasing global competition has forced high-tech companies to focus on their core competences and outsource other activities to maintain their competitive advantages in the supply chains. While most companies rely on domain experts to coordinate strategic outsourcing decisions among a number of qualified vendors with different capabilities, the present problem can be formulated into a complex nonlinear, multi-dimensional, multi-objective combinatorial optimisation problem. Focused on real settings, this study aims to fill the gap via developing a bi-objective genetic algorithm (boGA) for determining the outsourcing order allocation with nonlinear cost structure, while minimising both the total alignment gap and the total allocation cost. The proposed boGA incorporates specific random key representation to facilitate encoding and decoding. This study also develops a bi-objective Pareto solution generation algorithm to enable efficient searching of Pareto solutions in multiple ranks and designs a composite Pareto ranking selection with uniform sum rank weighting for effective selection. To estimate its validity, the proposed boGA was validated with realistic cases from a leading semiconductor company in Hsinchu Science Park in Taiwan. The optimal boGA parameters were tested using a set of experiments. Scenario analyses were conducted to evaluate the performance of the proposed algorithm under different demand conditions using the metrics in the literature. The results have shown the practical viability of the proposed algorithm to solve the present problem of monthly outsourcing decisions for the case company in practicable computation time. This algorithm can determine the near-optimal Pareto front for decision makers to further incorporate with their preferences. This study concludes with discussion of future research directions. [ABSTRACT FROM AUTHOR]

Designing new products based on those features (platforms) that have proven successful or 'fit' is generally termed as 'platform product development (PPD)'. This approach provides a basis for mass customisation (MC) which largely enhances an enterprise's competitiveness in terms of the speed, quality and cost. Currently, however, the PPD approach is heavily dependent on the experience and skills of individual designers and managers. Researchers have been working on platform leveraging strategies at the strategic level, and guidelines for better structuring the approach to facilitate its adoption and implementation. This research project recognises an equal need for scientific decision supports at the tactical level for design practitioners and managers to address fundamental questions such as: (A) how a platform should be established for a family of products in a given industrial and market context; and (B) how a product development team chooses and then customises the most appropriate product platform to meet the customer requirements from a specific market segment with particular manufacturing resources and supply bases. This paper describes a scientific and effective decision - genetic algorithm to assist product development practitioners and managers with major decision activities in the process of platform product development under several focus areas. [ABSTRACT FROM AUTHOR]

As budgets have tightened, library administrators are looking for more data to help them in making difficult collections decisions. They then often provide these data to higher-level administrators outside the library. When collecting library data, several questions come to mind: Who collects the data? What kinds of data exist? When are data collected? Where does the responsibility of data collection reside? How are data collected and interpreted? In this article, the author aims to address all these questions so as to give the reader a sense of direction in how these data can be applied to drive collections and collection-development policy. [ABSTRACT FROM AUTHOR]

This paper presents an improvement on earlier work on a common weight multi-criteria decision-making (MCDM) approach for technology selection by (Karsak, E.E. and Ahiska, S.S., Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection. Int. J. Prod. Res., 2005, 43, 1537-1554.) benefiting from a bisection search algorithm. The proposed algorithm enables to calculate the values of discriminating parameter, k, which appears in the introduced efficiency measure, in a systematic and robust manner rather than requiring the decision analyst to assign an arbitrary step size value. In addition, the paper presents comments on the model proposed by (Amin, G.R., Toloo, M. and Sohrabis, B., An improved MCDM DEA model for technology selection. Int. J. Prod. Res., 2006, 44, 2681-2686.) for technology selection. Finally, the robustness of the proposed decision-making framework is illustrated via several numerical examples taken from the above-mentioned papers. [ABSTRACT FROM AUTHOR]

Competitive pressures increasingly force companies to consider the adoption of new business improvement methodologies. The multi-criteria decision aid described in this article provides managers with a comprehensive set of selection criteria, within a structured and formalized evaluation process. The decision aid employs multiple criteria decision-making (MCDM) methods and is underpinned by a selection framework which aims to promote rational decision-making and assists decision-makers to structure their evaluation process, compile useful information and reach a consensus decision with confidence. This article describes the theoretical background to the development of both selection framework and decision aid and explains the operation of the decision aid. A case example of a multi-national company is used to illustrate the operation of the decision support process. Practical testing of the decision aid provides evidence of its feasibility, usability, and utility. The article concludes by discussing how this approach can help managers make more rational decisions, when deciding to adopt new improvement methodologies. [ABSTRACT FROM AUTHOR]

For the design of an "intelligent" assistant system aimed at supporting operators' decision in subway control, we modeled operators' activity and know-how. As a result, we introduce the notion of a contextual graph, which appears as a simple solution to describe and manage operational decision-making. [ABSTRACT FROM AUTHOR]

I inspect the relevant literature on data-driven algorithmic decision-making, providing both quantitative evidence on trends and numerous in-depth empirical examples. Building my argument by drawing on data collected from HubSpot, Pew Research Center, and Statista, I performed analyses and made estimates regarding % of users who say they frequently/sometimes see content on social media that makes them feel amused/angry/connected/inspired/depressed/lonely, % of users who say they more often see people being mean or bullying/kind or supportive/trying to be deceptive/trying to point out inaccurate info when using social media sites, and % of users who say it would be very/somewhat difficult/easy for social media sites to figure out their race or ethnicity/hobbies and interests/political affiliation/religious beliefs. Data collected from 4,800 respondents are tested against the research model by using structural equation modeling. [ABSTRACT FROM AUTHOR]

We inspect the relevant literature on the decision-making logic of big data algorithmic analytics, providing both quantitative evidence on trends and numerous in-depth empirical examples. Building our argument by drawing on data collected from Pew Research Center, we performed analyses and made estimates regarding % of users who say they frequently/sometimes see on social media posts that are overly dramatic or exaggerated/people making accusations or starting arguments without having all the facts/posts that teach you something useful you had not known before/posts that appear to be about one thing but turn out to be about something else and % of users who say, after being directed to view their Facebook "ad preferences" page, that they did not know Facebook maintained this list of their interests and traits/they are not comfortable with Facebook compiling this information/the listings do not very or at all accurately represent them. Data collected from 4,200 respondents are tested against the research model by using structural equation modeling. [ABSTRACT FROM AUTHOR]

Employing recent research results covering real-time data-driven technologies, and building our argument by drawing on data collected from Pew Research Center, we performed analyses and made estimates regarding % of Facebook users who say they think users have no/a little/a lot of control over the content that appears in their newsfeed and % of social media users who say it is acceptable for social media sites to use data about them and their online activities to recommend events in their area/recommend someone they might want to know/show them ads for products and services/show them messages from political campaigns (by age group). Structural equation modeling was used to analyze the collected data. [ABSTRACT FROM AUTHOR]

This article reviews and advances existing literature concerning big data-driven algorithmic decision-making. Using and replicating data from Deloitte and Pew Research Center, I performed analyses and made estimates regarding % of Facebook users who have not/have intentionally tried to influence the content that appears on their news feed (by age group), % of U.S. adults who say the content on social media does/does not provide an accurate picture of how society feels about important issues, % of social media users who say it is not at all acceptable/not very acceptable/somewhat acceptable/very acceptable for social media sites to use data about them and their online activities to recommend events in their area/recommend someone they might want to know/show them ads for products and services/show them messages from political campaigns, and among U.S. social media users, the % of who say it would be hard to give up/not hard to give up social media (by age group). Data were analyzed using structural equation modeling. [ABSTRACT FROM AUTHOR]

I draw on a substantial body of theoretical and empirical research on the inherent regulatory capacity of data-driven automated decision-making, and to explore this, I inspected, used, and replicated survey data from Pew Research Center, performing analyses and making estimates regarding % of Facebook users who say they understand not at all/not very/somewhat/very well why certain posts are included in their news feed and others are not, % of U.S. adults who say that it is possible for computer programs to make decisions without human bias/computer programs will always reflect bias of designers (by age group), and % of Facebook users with no assigned category/fewer than 10 categories/10-20 categories/21+ categories listed on their "ad preferences" page. Structural equation modeling was used to analyze the data and test the proposed conceptual model. [ABSTRACT FROM AUTHOR]

Abstract Aiming at the lack of public concern about the attributes of large-group emergency decision making in major emergencies, a large-group emergency risk decision method based on data mining of public attribute preferences is proposed. First, according to historical data from a similar emergency, an attribute-keyword lexicon is established using text mining and latent semantic analysis. Meanwhile, fuzzy association rule mining and a fuzzy cognitive graph are applied to obtain information regarding public opinion about the attributes. Then, a risk measurement model of degree indicators based on information entropy is presented, and according to the linguistic preference for information given by large-group members, the decision risk of large-group members is measured. On this basis, a group member clustering algorithm is constructed to cluster large groups, and then the best alternative is acquired by comparing score function and precise function of the interval intuitionistic fuzzy number. Finally, a case application using historical data is conducted to verify the method's rationality and feasibility. Highlights • A method for obtaining the public opinion on attributes of emergency decision-making alternatives is proposed. • A model of decision risk measurement is proposed. • A clustering algorithm is proposed to deal with large number of decision makers. • With full consideration of controlling decision risk, the large-group emergency risk decision method is proposed. [ABSTRACT FROM AUTHOR]

An executive information system (ETS) is a computer-based information system designed to provide senior managers access to information relevant to their management activities. With such trends as globalization and intense competition increasing the importance of fast and accurate decision making, the use of these systems by executives may become a particularly important component of their decision-making behavior. Previous research on EIS has focused on descriptive studies of how and why EIS are used. This research empirically examines the effects of EIS use on aspects of the decision-making process by surveying 46 executive users of US. The frequency and duration of EIS use are shown to increase problem identification speed, decision-making speed, and the extent of analysis in decision making. [ABSTRACT FROM AUTHOR]

Maintaining the temporal validity of real-time data in cyber-physical systems (CPSs) is of critical importance to ensure correct decision making and appropriate system operation. Most existing work on real-time data retrieval assumes that the real-time data under study are always available, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity (freshness) constraints. This assumption, however does not hold in many real-life CPS applications with intermittent data availability, such as in energy harvesting-based sensing systems. In this paper, we study the multi-interval availability-constrained fresh data retrieval (MAFDR) problem, which aims to retrieve all required real-time data on time for a set of decision tasks while taking both the temporal validity and data availability constraints into consideration. We present the formulation of the MAFDR problem and study its complexity under different settings. For the scenario of single decision task with unit-size data retrieval time, we propose a polynomial-time optimal data retrieval algorithm, which comprises a task finish time selection phase and an optimal retrieval schedule construction phase. For the general scenario of multiple decision tasks with nonunit-size data retrieval time, we provide an integer linear programming formulation for the MAFDR problem and propose a fast heuristic algorithm based on max flow. The effectiveness of the proposed algorithms has been validated through extensive experiments by comparing to the optimal solution and the state-of-the-art approach. [ABSTRACT FROM AUTHOR]

The expansion of digital data is transforming the way decision making is implemented. However, the management of vast amounts of continuous data is a challenge that public bodies are not equipped to face yet. Here we will explore the field to clarify what kind of Big Data are now available for policy making and at what policy stages they can be used, following two different theoretical models. Then, we will elaborate about the need to unpack Big Data assemblages, proposing a research agenda on the three dimensions: datascape, datasphere and data culture.The expansion of digital data is transforming the way decision making is implemented. However, the management of vast amounts of continuous data is a challenge that public bodies are not equipped to face yet. Here we will explore the field to clarify what kind of Big Data are now available for policy making and at what policy stages they can be used, following two different theoretical models. Then, we will elaborate about the need to unpack Big Data assemblages, proposing a research agenda on the three dimensions: datascape, datasphere and data culture. [ABSTRACT FROM AUTHOR]

Sentiment analysis has applications in diverse contexts such as in the gathering and analysis of opinions of individuals about various products, issues, social, and political events. Understanding public opinion can help improve decision making. Opinion mining is a way of retrieving information via search engines, blogs, microblogs and social networks. Individual opinions are unique to each person, and Twitter tweets are an invaluable source of this type of data. However, the huge volume and unstructured nature of text/opinion data pose a challenge to analyzing the data efficiently. Accordingly, proficient algorithms/computational strategies are required for mining and condensing tweets as well as finding sentiment bearing words. Most existing computational methods/models/algorithms in the literature for identifying sentiments from such unstructured data rely on machine learning techniques with the bag-of-word approach as their basis. In this work, we use both unsupervised and supervised approaches on various datasets. Unsupervised approach is being used for the automatic identification of sentiment for tweets acquired from Twitter public domain. Different machine learning algorithms such as Multinomial Naive Bayes (MNB), Maximum Entropy and Support Vector Machines are applied for sentiment identification of tweets as well as to examine the effectiveness of various feature combinations. In our experiment on tweets, we achieve an accuracy of 80.68% using the proposed unsupervised approach, in comparison to the lexicon based approach (the latter gives an accuracy of 75.20%). In our experiments, the supervised approach where we combine unigram, bigram and Part-of-Speech as feature is efficient in finding emotion and sentiment of unstructured data. For short message services, using the unigram feature with MNB classifier allows us to achieve an accuracy of 67%. [ABSTRACT FROM AUTHOR]

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The paper discusses fuzzy group analytic hierarchy process. In the literature, there are found several approaches for determining weights either directly from the individual judgments or via group comparison matrix. However, the quality of the group weights was not studied yet. In the paper, we propose two new measures for the evaluation of the group weights that are adapted from classical analytic hierarchy process. We selected six approaches for deriving weights and use them in an application from the literature. We evaluated the gained weights. Our results show that the weights derived by the most popular extent analysis method are questionable in their reliability. [ABSTRACT FROM AUTHOR]

The advent of sports technology has led to large, high-dimensional, performance data-sets, which pose decision-making challenges for coaches and performance analysts. If large data-sets are managed poorly inaccurate and biased decision-making may actually be enabled. This paper outlines a process for capturing, organising and analysing a large performance data-set in professional netball. Two hundred and fifty ANZ Championship matches, from the 2012 to 2015 seasons, where analysed. Self-organising maps and a k-means clustering algorithm were used to describe seven game styles, which were used in a case study to devise a strategy for an upcoming opponent. The team implemented a centre-pass (CP) defence strategy based on the opponent’s previous successful and unsuccessful performances. This strategy involved allowing the oppositions Wing-attack to receive the CP while allowing their Goal attack to take the second pass. The strategy was monitored live by the coaches on a tablet computer via a custom-built dashboard, which tracks each component of the strategy. The process provides an alternative to use of conventional performance indicators and demonstrates a method for handling large high-dimensional performance data-sets. Further work is needed to identify an ecologically valid method for variable selection. [ABSTRACT FROM AUTHOR]

Purpose: Technology could transform routine decision making by anticipating patients' information needs, assessing where patients are with decisions and preferences, personalizing educational experiences, facilitating patient-clinician information exchange, and supporting follow-up. This study evaluated whether patients and clinicians will use such a decision module and its impact on care, using 3 cancer screening decisions as test cases.Methods: Twelve practices with 55,453 patients using a patient portal participated in this prospective observational cohort study. Participation was open to patients who might face a cancer screening decision: women aged 40 to 49 who had not had a mammogram in 2 years, men aged 55 to 69 who had not had a prostate-specific antigen test in 2 years, and adults aged 50 to 74 overdue for colorectal cancer screening. Data sources included module responses, electronic health record data, and a postencounter survey.Results: In 1 year, one-fifth of the portal users (11,458 patients) faced a potential cancer screening decision. Among these patients, 20.6% started and 7.9% completed the decision module. Fully 47.2% of module completers shared responses with their clinician. After their next office visit, 57.8% of those surveyed thought their clinician had seen their responses, and many reported the module made their appointment more productive (40.7%), helped engage them in the decision (47.7%), broadened their knowledge (48.1%), and improved communication (37.5%).Conclusions: Many patients face decisions that can be anticipated and proactively facilitated through technology. Although use of technology has the potential to make visits more efficient and effective, cultural, workflow, and technical changes are needed before it could be widely disseminated. [ABSTRACT FROM AUTHOR]

The virtual linguistic model is a good technique for linguistic decision making and has been widely used in applications including linguistic information fusion. The main purpose of this paper is to define and specify the syntax and semantics of virtual linguistic terms (VLTs) in detail, and then to serve as the theoretical foundation of the computational models based on VLTs. The syntactical rule generates VLTs by a symbolic transformation, and then the semantic rule presents the semantics of VLTs by means of linguistic modifiers. Based on the syntax and semantics, VLTs could be a possible alternative for solving some current challenges of qualitative information fusion in decision making. [ABSTRACT FROM AUTHOR]

The attraction effect is a well-studied cognitive bias in decision making research, where one's choice between two alternatives is influenced by the presence of an irrelevant (dominated) third alternative. We examine whether this cognitive bias, so far only tested with three alternatives and simple presentation formats such as numerical tables, text and pictures, also appears in visualizations. Since visualizations can be used to support decision making — e.g., when choosing a house to buy or an employee to hire — a systematic bias could have important implications. In a first crowdsource experiment, we indeed partially replicated the attraction effect with three alternatives presented as a numerical table, and observed similar effects when they were presented as a scatterplot. In a second experiment, we investigated if the effect extends to larger sets of alternatives, where the number of alternatives is too large for numerical tables to be practical. Our findings indicate that the bias persists for larger sets of alternatives presented as scatterplots. We discuss implications for future research on how to further study and possibly alleviate the attraction effect. [ABSTRACT FROM PUBLISHER]

Within the visualization community there are some well-known techniques for visualizing 3D spatial data and some general assumptions about how perception affects the performance of these techniques in practice. However, there is a lack of empirical research backing up the possible performance differences among the basic techniques for general tasks. One such assumption is that 3D renderings are better for obtaining an overview, whereas cross sectional visualizations such as the commonly used Multi-Planar Reformation (MPR) are better for supporting detailed analysis tasks. In the present study we investigated this common assumption by examining the difference in performance between MPR and 3D rendering for correctly identifying a known surface. We also examined whether prior experience working with image data affects the participant's performance, and whether there was any difference between interactive or static versions of the visualizations. Answering this question is important because it can be used as part of a scientific and empirical basis for determining when to use which of the two techniques. An advantage of the present study compared to other studies is that several factors were taken into account to compare the two techniques. The problem was examined through an experiment with 45 participants, where physical objects were used as the known surface (ground truth). Our findings showed that: 1. The 3D renderings largely outperformed the cross sections; 2. Interactive visualizations were partially more effective than static visualizations; and 3. The high experience group did not generally outperform the low experience group. [ABSTRACT FROM PUBLISHER]

This paper presents the main results of a study about the influence of the web on decision-making processes of women during pregnancy. It aims to understand how search practices, peer influence and opinion sharing are carried out and specifically focusing on the decisions concerning “prenatal screening” and “type of birth”. Data was collected using a questionnaire filled by 178 Portuguese pregnant women that focused on baseline demographics, Internet use and its comparison to traditional forms of information (medical personnel and family). The results are mainly descriptive and show that, although internet information was often sought and used, face to face contact, especially with the medical doctor, is most influential in decision making. Contact with other pregnant women, relatives and friends were relevant but less influential. The main conclusions point out that currently the physician's opinion is still the primary factor in decision making. The importance of Internet data is secondary when compared to direct interaction with the physicians and care givers. [ABSTRACT FROM AUTHOR]

The mainstream research into project management has been criticized in the recent years for its heavy reliance on the functionalist view of projects characterized by linearity of the life cycle. Considering that instrumental approaches for project management are not sufficient for a flexible and uncertain product as software, the project success depends on how software project managers deal with the problems and make decisions. Different from the normative decision theory, which focus on how decisions should be made in order to be rational, the descriptive decision theory focus on how decisions are actually made and includes the naturalistic decision making models. Based on a systematic literature review, this paper aims to synthetize empirical studies published on journals and conferences proceedings that analyze the decision-making phenomenon in the software project management context from a naturalistic perspective. The search were conducted in five search engines: ScienceDirect, Scopus, Wiley, IEEE Xplore, and ACM Digital Library. The 27 selected papers were categorized and then synthesized into a thematic map. The categories included: agile development practices, stakeholders involvement, escalation and de-escalation of commitment factors, participatory decision-making, communication, use of rational methods, emotion, and cognitive biases. The results indicate that more studies are needed to better understand the decision-making phenomenon from this perspective. [ABSTRACT FROM AUTHOR]

Innovation portfolio management (IPM) is a dynamic decision-making process, in which projects are evaluated and selected, and resources are allocated. Previous research has developed an understanding of IPM success and its influencing factors. However, little research investigated the quality of the decision-making process and the ability to quickly adapt the portfolio. This study focuses on the antecedents of decision-making quality and agility (i.e., responsiveness to changes in the environment). Based on a decision-making framework, five structural and cultural IPM components are derived as important antecedents of decision-making quality, which in turn influences agility. The structural components (1) clarity of strategic goals, (2) formality of the IPM processes, and (3) controlling intensity serve a coordinating function. The cultural components (4) innovation climate and (5) risk climate serve a motivating function in IPM. An analysis of a sample of 179 firms and their innovation portfolios through structural equation modeling using a double-informant design documents that these five components all positively influence portfolio decision-making quality, which in turn positively influences agility. Results further show that environmental turbulence moderates some of these relationships. While the positive effect of process formality is weakened under increasing turbulence, the effects of controlling intensity and climate for innovation are strengthened by environmental turbulence. The findings have theoretical implications for the understanding of IPM as a dynamic capability and practical implications for the management of portfolios in turbulent environments. [ABSTRACT FROM AUTHOR]

Agent-based models (ABMs) are increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire-Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the 'brains' of an agent can be modelled in the BDI system in the usual way, while the 'body' exists in the ABM system. The architecture is flexible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration of off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community. [ABSTRACT FROM AUTHOR]

We present a new intuitionistic fuzzy rule-based decision-making system based on intuitionistic fuzzy sets for a process scheduler of a batch operating system. Our proposed intuitionistic fuzzy scheduling algorithm, inputs the nice value and burst time of all available processes in the ready queue, intuitionistically fuzzify the input values, triggers appropriate rules of our intuitionistic fuzzy inference engine and finally calculates the dynamic priority (dp) of all the processes in the ready queue. Once the dp of every process is calculated the ready queue is sorted in decreasing order of dp of every process. The process with maximum dp value is sent to the central processing unit for execution. Finally, we show complete working of our algorithm on two different data sets and give comparisons with some standard non-preemptive process schedulers. [ABSTRACT FROM AUTHOR]

This paper presents some models and algorithms for social choice in agent networks. Agent networks are graphical models used to represent systems involving multiple, locally interacting, agents. They allow the representation of complex decision-making situations where the utility function of every agent depends on the actions of its neighbors. In this context, coordination requires some optimization method able to determine a combination of individual actions that maximizes social efficiency. We study here the maximization of different social welfare functions covering various attitudes ranging from utilitarianism to egalitarianism. For all these models we propose graph-based algorithms as well as MIP formulations to find optimal sets of actions. We also provide numerical tests to assess their practical efficiency. [ABSTRACT FROM AUTHOR]

The paper presents a focused survey about the presence and the use of the concept of 'preferences' in Artificial Intelligence. Preferences are a central concept for decision making and have extensively been studied in disciplines such as economy, operational research, decision analysis, psychology and philosophy. However, in the recent years it has also become an important topic both for research and applications in Computer Science and more specifically in Artificial Intelligence, in fields spanning from recommender systems to automatic planning, from non monotonic reasoning to computational social choice and algorithmic decision theory. The survey essentially covers the basics of preference modelling, the use of preference in reasoning and argumentation, the problem of compact representations of preferences, preference learning and the use of non conventional preference models based on extended logical languages. It aims at providing a general reference for all researchers both in Artificial Intelligence and Decision Analysis interested in this exciting interdisciplinary topic. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new formulation of computational trust based on quantum decision theory (QDT). By using this new formulation, we can divide the assigned trustworthiness values to objective and subjective parts. First, we create a mapping between the QDT definitions and the trustworthiness constructions. Then, we demonstrate that it is possible for the quantum interference terms to appear in the trust decision making process. By using the interference terms, we can quantify the emotions and subjective preferences of the trustor in various contexts with different amounts of uncertainty and risk. The non-commutative nature of quantum probabilities is a valuable mathematical tool to model the relative nature of trust. In relative trust models, the evaluation of a trustee candidate is not only dependent on the trustee itself, but on the other existing competitors. In other words, the first evaluation is performed in an isolated context whereas the rest of the evaluations are performed in a comparative one. It is shown that a QDT-based model of trust can account for these order effects in the trust decision making process. Finally, based on the principles of risk and uncertainty aversion, interference alternation theorem and interference quarter law, quantitative values are assigned to interference terms. By performing empirical evaluations, we have demonstrated that various scenarios can be better explained by a quantum model of trust rather than the commonly used classical models. [ABSTRACT FROM AUTHOR]

Both feature selection and online learning have been studied extensively in machine learning and pattern recognition. Most existing studies of feature selection are using the batch learning methods. Such methods are not appropriate for real-world applications especially when data arrive sequentially. Recently, this problem is addressed by some feature selection techniques using online learning: this is the problem of Online Feature Selection (OFS). The key challenge of OFS is how to make accurate prediction using a small number of active features. Despite the advantages in efficiency of online feature selection methods, they are not always accurate enough when handling real world data. In this paper, we address this limitation by the integration of automated negotiation process. We present a novel online feature selection method based on negotiation theory (ANOFS) and demonstrate its application to several public datasets with ultra-high dimension and massive training data. [ABSTRACT FROM AUTHOR]

Despite growing international interest in the use of data to improve education, few studies examining the effects on student achievement are yet available. In the present study, the effects of a two-year data-based decision-making intervention on student achievement growth were investigated. Fifty-three primary schools participated in the project, and student achievement data were collected over the two years before and two years during the intervention. Linear mixed models were used to analyze the differential effect of data use on student achievement. A positive mean intervention effect was estimated, with an average effect of approximately one extra month of schooling. Furthermore, the results suggest that the intervention especially significantly improved the performances of students in low socioeconomic status schools. [ABSTRACT FROM AUTHOR]

The use of data-based decision making (DBDM) in schools to drive educational improvement and success has been strongly promoted by educational experts and policymakers, yet very little is documented about the actual DBDM practices used in schools. This study examines DBDM practices in 25 middle schools through 80 standardized observations of data team meetings and through survey responses. DBDM practices in terms of structure, process, content, quality, and alignment between self-report and observation are described. Key findings include the following: (a) The average amount of time spent discussing an individual student was brief, less than 2 min; (b) on average, actionable decisions following discussions of behavior or reading issues were made 34% to 40% of the time; and (c) there was weak alignment on key topics between self-reported and observed DBDM practices. These findings underscore the need for additional studies of DBDM practices in school and of the impact of DBDM practices on important student outcomes. [ABSTRACT FROM AUTHOR]

Network modeling and analysis has become a fundamental tool for studying various complex systems. This paper proposes an extension of statistical monitoring to network streams, which is crucial for effective decision-making in various applications. To this end, a model for the probability of edge existence as a function of vertex attributes is constructed and a likelihood method is developed to monitor the underlying network model. The method is flexible to detect any form of anomaly that arises from different network edge-formation mechanisms. Experiments on simulated and real network streams depict the properties and benefits of the method compared with existing methods in the literature. [ABSTRACT FROM AUTHOR]

Although of high relevance to political science, the interaction between technological change and political change in the era of Big Data remains somewhat of a neglected topic. Most studies focus on the concept of e-government and e-governance, and on how already existing government activities performed through the bureaucratic body of public administration could be improved by technology. This article attempts to build a bridge between the field of e-governance and theories of public administration that goes beyond the service delivery approach that dominates a large part of e-government research. Using the policy cycle as a generic model for policy processes and policy development, a new look on how policy decision making could be conducted on the basis of ICT and Big Data is presented in this article. [ABSTRACT FROM PUBLISHER]

This article presents an approach for decision-making in support of the control of an autonomous system of multiple tiers of robots (e.g., satellite, aerial and ground) based on the Blackboard architectural style. Under the proposed approach, the system evaluates prospective approaches for goal satisfaction (identified by user selected final rules), identifies the lowest-cost solution and determines the best path to achieving the goal, via the analysis of the Blackboard rule and action set. Two different approaches to this rule and action path generation are discussed. This article presents the proposed Blackboard-style architecture for autonomous multi-tier control and describes its implementation. The benefits and drawbacks of the Blackboard-style approach are analysed, its extrapolation to the control of multiple heterogeneous craft is presented and the tradeoffs between the two approaches to rule-path generation are assessed. [ABSTRACT FROM AUTHOR]

This paper offers a paradigmatic analysis of digital application marketplaces for advancing information systems research on digital platforms and ecosystems. We refer to the notion of digital application marketplace, colloquially called 'appstores,' as a platform component that offers a venue for exchanging applications between developers and end users belonging to a single or multiple ecosystems. Such marketplaces exhibit diversity in features and assumptions, and we propose that examining this diversity, and its ideal types, will help us to further understand the relationship between application marketplaces, platforms, and platform ecosystems. To this end, we generate a typology that distinguishes four kinds of digital application marketplaces: closed, censored, focused, and open marketplaces. The paper also offers implications for actors wishing to make informed decisions about their relationship to a particular digital application marketplace. [ABSTRACT FROM AUTHOR]

Failed or partially failed information systems (IS) investments continue to be a problem in both public and private sectors. Although there are many causes of such failure, lack of clarity about the goals and benefits expected and how they are to be realized is one of the major contributors. This is particularly so in the public sector where the purpose of IS investment is not to increase profits, but to accomplish social and political goals. One way of reducing failure and wastage rates is to have ex-ante evaluation. Over the past three decades IS research has proposed a plethora of new theoretical approaches to improving ex-ante evaluation, but with a few exceptions, these have had little impact on practice. This paper presents a way to address this gap between theory and practice. It proposes a way in which managers can improve IS investment evaluation by changing perspective from a focus on traditional analytic tools towards a design attitude that seeks to develop multi-criteria IS evaluation approach based on contextual experience and prior knowledge. The proposed approach enables organizations to design and develop a value case using value dials as a common language that defines what constitutes the value of particular IS investments for a specific context. To assess its effectiveness, this approach was applied using a multilevel dialogical action research project within a Swedish public organization. The findings show that managers in this organization significantly improved the effectiveness of their evaluation of IS investments. [ABSTRACT FROM AUTHOR]

This paper deals with inverse DEA from both theoretical and applied viewpoints. First, some methodological approaches and theoretical results are outlined; and then an application of DEA with real-world data (for assessing educational departments in a university) is addressed. Afterwards, possible extensions and applications of the existing approaches in the presence of fuzzy data are developed. The final theoretical part of the paper contains a main theorem which provides a sufficient condition for efficiency maintaining in the presence of fuzzy data. To do this, we have used some notions/results from multi-objective decision-making theory. [ABSTRACT FROM AUTHOR]

Rapid advances in communication technology are making access to information faster, more reliable, and cheaper. At the same time, hydrological and meteorological monitoring technologies continue to improve significantly. These technological advances can be exploited to promote regional cooperation for flood risk reduction in the Hindu Kush Himalayas by providing an end-to-end flood information system. The system will function as a decision support tool for decision makers to alert vulnerable communities in a timely and accurate manner. This article provides an example of how regional cooperation has been achieved and is being promoted in the Hindu Kush Himalayas through the development of a regional flood information system. [ABSTRACT FROM PUBLISHER]

The results of an experimental study of retail investors' use of eXtensible Business Reporting Language tagged (interactive) data and PDF format for making investment decisions are reported. The main finding is that data format made no difference to participants' ability to locate and integrate information from statement footnotes to improve investment decisions. Interactive data were perceived by participants as quick and 'accurate', but it failed to facilitate the identification of the adjustment needed to make the ratios accurate for comparison. An important implication is that regulators and software designers should work to reduce user reliance on the comparability of ratios generated automatically using interactive data. [ABSTRACT FROM AUTHOR]