In: Benmoussa, R., De Guio, R., Dubois, S., Koziołek, S. (eds) New Opportunities for Innovation Breakthroughs for Developing Countries and Emerging Economies. TFC 2019. IFIP Advances in Information and Communication Technology, vol 572. Springer, Cham. https://doi.org/10.1007/978-3-030-32497-1_38

Linked Open Data for Digital Humanities provides insights into how digital technologies can enrich and diversify humanities scholarship and make it pioneering in the digital age. Written in non-specialist language, the book illustrates how information is captured, published, represented, accessed, and interpreted using computational systems and, in doing so, shows how technologies actively shape the way we understand what we encounter. Focusing as it does on underlying Web architecture and projects accessible online, the book has an inherently international focus.

The interdisciplinary case study in this book examples include bibliographic data from works published in England between 1470 and 1700; literature from ancient Iraq; jazz performances, predominantly from the USA in the 1930s; and even reach as far as an alien, fictional future. Whilst these case study examples span vast spatio-temporal distances, they all share a common thread in the use of the Linked Data information publication paradigm. Using existing computer science methods, as well as processes such as ontology development and database design, the book also includes reflections on practical considerations and offers advice about how to take institutional policies, socio-cultural sensitivities and economic models into consideration when implementing Linked Data projects. Linked Open Data for Digital Humanities discusses technological issues in the context of Humanities scholarship, bridging disciplines and enabling informed conversations across disciplinary boundaries. It will be of interest to humanities scholars, computer and data scientists, and library and information scientists.

The Encyclopedia of Social Psychology is designed as a road map to this rapidly growing and important field and provides individuals with a simple, clear, jargon-free introduction. These two volumes include more than 600 entries chosen by a diverse team of experts to comprise an exhaustive list of the most important concepts. Entries provide brief, clear, and readable explanations to the vast number of ideas and concepts that make up the intellectual and scientific content in the area of social psychology.

The principle of bounded rationality suggests that humans have limited resources to deal with the problems they face. In this context, cognitive bias has historically been used to describe people's systematic but flawed judgments and decisions because of their reliance on simple heuristics that require little cognitive resources. Recent ecological conceptions of rationality, however, emphasize the need to go beyond explanations based on motivational and cognitive limitations and consider the adaptive character of cognitive bias as it reflects informed bets about the structure of past and current environments.

Aims and objectives.  The aim of this paper was to review the current literature clinical decision‐making models and the educational application of models to clinical practice. This was achieved by exploring the function and related research of the three available models of clinical decision making: information‐processing model, the intuitive‐humanist model and the clinical decision‐making model. Background.  Clinical decision making is a unique process that involves the interplay between knowledge of pre‐existing pathological conditions, explicit patient information, nursing care and experiential learning. Historically, two models of clinical decision making are recognized from the literature; the information‐processing model and the intuitive‐humanist model. The usefulness and application of both models has been examined in relation the provision of nursing care and care related outcomes. More recently a third model of clinical decision making has been proposed. This new multidimensional model contains elements of the information‐processing model but also examines patient specific elements that are necessary for cue and pattern recognition. Design.  Literature review. Methods.  Evaluation of the literature generated from MEDLINE, CINAHL, OVID, PUBMED and EBESCO systems and the Internet from 1980 to November 2005. Results.  The characteristics of the three models of decision making were identified and the related research discussed. Conclusions.  Three approaches to clinical decision making were identified, each having its own attributes and uses. The most recent addition to the clinical decision making is a theoretical, multidimensional model which was developed through an evaluation of current literature and the assessment of a limited number of research studies that focused on the clinical decision‐making skills of inexperienced nurses in pseudoclinical settings. The components of this model and the relative merits to clinical practice are discussed. Relevance to clinical practice.  It is proposed that clinical decision making improves as the nurse gains experience of nursing patients within a specific speciality and with experience, nurses gain a sense of saliency in relation to decision making. Experienced nurses may use all three forms of clinical decision making both independently and concurrently to solve nursing‐related problems. It is suggested that O'Neill's clinical decision‐making model could be tested by educators and experienced nurses to assess the efficacy of this hybrid approach to decision making.

There are upsides and downsides to making decisions in a group. The main risks include falling into groupthink or other biases that will distort the process and the ultimate outcome. But bringing more minds together to solve a problem has its advantages. To make use of those upsides and increase the chances your team will land on a successful solution, the authors recommend using seven strategies, which have been backed by behavioral science research: Keep the group small, especially when you need to make an important decision. Bring a diverse group together. Appoint a devil’s advocate. Collect opinions independently. Provide a safe space to speak up. Don’t over-rely on experts. And share collective responsibility for the outcome.

Adaptation requires a focus on the societal context of decision processes. Decision contexts are described as coevolving values-rules-knowledge (vrk) systems. A focus on vrk links analyses of constraints on decisions and dynamics of contexts. We illustrate the use of vrk to analyse complex coastal adaptation problems. A vrk perspective can help develop strategic goals for adaptation projects. Responding to global change represents an unprecedented challenge for society. Decision makers tend to address this challenge by framing adaptation as a decision problem, whereby the responses to impacts of change are addressed within existing decision processes centred on defining the decision problem and selecting options. However, this ‘decision-making perspective’ is constrained by societal values and principles, regulations and norms and the state of knowledge. It is therefore unsuitable for addressing complex, contested, cross-scale problems. In this paper we argue that simply broadening the decision-making perspective to account for institutions and values is not enough. We contend the decision-making perspective needs to be connected with a broader ‘decision-context perspective’ that focuses on how the societal system of decision processes affects the manner in which a particular problem is addressed. We describe the decision context as an interconnected system of values, rules and knowledge (vrk). The interaction of systems of vrk both creates and limits the set of practical, permissible decisions; the types of values, rules and knowledge that influence the decision and the capacity for change and transformation in the decision context. We developed a framework to analyse the interactions between values, rules and knowledge and their influence on decision making and decision contexts of adaptation initiatives, and applied it retrospectively to three projects on adaptation to sea-level rise. Our analysis revealed: (1) specific examples of how interactions between vrk systems constrained existing framings of decision making and the development of options for coastal adaptation; (2) limitations in the adaptive management strategies that underpinned the projects and (3) how the linked systems of vrk can allow adaptation practitioners to structure adaptation as a process of co-evolutionary change that enables a broader set of social issues and change processes to be considered. Adaptation projects that focus on the decision context represent a pragmatic alternative to existing decision-focused adaptation. By using the vrk model to diagnose constraints in decision processes, we show how the reframing of adaptation initiatives can reveal new approaches to developing adaptation responses to complex global change problems.

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision‐making, and decision‐support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade‐offs). This is best achieved by applying a rapid‐prototyping approach. At each step, decision‐support tools can provide additional insight and clarity, whereas decision‐support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision‐support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply.

This article describes the origins and contributions of the naturalistic decision making (NDM) research approach. Background: NDM research emerged in the 1980s to study how people make decisions in real-world settings. Method: The findings and methods used by NDM researchers are presented along with their implications. Results: The NDM framework emphasizes the role of experience in enabling people to rapidly categorize situations to make effective decisions. Conclusion: The NDM focus on field settings and its interest in complex conditions provide insights for human factors practitioners about ways to improve performance. Application: The NDM approach has been used to improve performance through revisions of military doctrine, training that is focused on decision requirements, and the development of information technologies to support decision making and related cognitive functions.

The formal mathematical structure for decision making under uncertainty was first expressed in Savage's axioms over 60 years ago. But while the underlying normative concepts for decision making under uncertainty remain constant, the practice of applying these concepts in real‐world settings, as conducted by decision analysis (DA) specialists working with agencies and interested parties, has seen a major transformation in recent decades. The purpose of this article is to provide perspectives that characterize and interpret how DA practice for societal risk management questions has grown and is being transformed over the last 40 years. It addresses a series of themes for parsing changes in how DA has evolved toward more flexible approaches, moving beyond strict theoretical assumptions and constrained settings, and addresses multiple interested parties to provide insights rather than a single correct answer. The article clarifies the path from the initial DA formulation as a set of normative axioms, through gradual change into what is now the most flexible and least restrictive form of policy analysis. The article shows how the practice of DA for societal risks has become more attuned to a wide array of interests and perspectives, more behaviorally informed, more creative, and more informative for governance process. It addresses the following themes: the evolution in the basic orientation of DA, the increasingly important role of stakeholders in DA practice, the importance and value of key problem‐structuring techniques, and evolution in approaches for eliciting values and technical judgments.

This article compares and contrasts the various perspectives on rationality and bounded rationality, and in doing so, advances two claims. The central one is that the definition of rationality depends on bounded rationality. This is reminiscent of debates in philosophy concerning the definition of concepts in terms of their opposites, which has led to efforts to destabilise dichotomies. In addition, as argued in this article, there is a related connection between the (bounded) rationality of economists and the agents they study.

Many real‐world planning and decision problems are far too uncertain, too variable, and too complicated to support realistic mathematical models. Nonetheless, we explain the usefulness, in these situations, of qualitative insights from mathematical decision theory. We demonstrate the integration of info‐gap robustness in decision problems in which surprise and ignorance are predominant and where personal and collective psychological factors are critical. We present practical guidelines for employing adaptable‐choice strategies as a proxy for robustness against uncertainty. These guidelines include being prepared for more surprises than we intuitively expect, retaining sufficiently many options to avoid premature closure and conflicts among preferences, and prioritizing outcomes that are steerable, whose consequences are observable, and that do not entail sunk costs, resource depletion, or high transition costs. We illustrate these concepts and guidelines with the example of the medical management of the 2003 SARS outbreak in Vietnam.

Many executives are surprised when previously successful leadership approaches fail in new situations, but different contexts call for different kinds of responses. Before addressing a situation, leaders need to recognize which context governs it -and tailor their actions accordingly. Snowden and Boone have formed a new perspective on leadership and decision making that's based on complexity science. The result is the Cynefin framework, which helps executives sort issues into five contexts: Simple contexts are characterized by stability and cause-and-effect relationships that are clear to everyone. Often, the right answer is self-evident. In this realm of "known knowns," leaders must first assess the facts of a situation -that is, "sense" it -then categorize and respond to it. Complicated contexts may contain multiple right answers, and though there is a clear relationship between cause and effect, not everyone can see it. This is the realm of "known unknowns." Here, leaders must sense, analyze, and respond. In a complex context, right answers can't be ferreted out at all; rather, instructive patterns emerge if the leader conducts experiments that can safely fail. This is the realm of "unknown unknowns," where much of contemporary business operates. Leaders in this context need to probe first, then sense, and then respond. In a chaotic context, searching for right answers is pointless. The relationships between cause and effect are impossible to determine because they shift constantly and no manageable patterns exist. This is the realm of unknowables (the events of September 11, 2001, fall into this category). In this domain, a leader must first act to establish order, sense where stability is present, and then work to transform the situation from chaos to complexity. The fifth context, disorder, applies when it is unclear which of the other four contexts is predominant. The way out is to break the situation into its constituent parts and assign each to one of the other four realms. Leaders can then make decisions and intervene in contextually appropriate ways.

This article described three heuristics that are employed in making judgments under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty.

Developing optimum solutions to engineering problems typically relies on structured and complex thought processes that require evaluation, interpretation and opinion. Well-developed critical thinking (CT) skills are essential for dealing with the multi-dimensional nature of these problems. CT in an engineering context is well reported in teaching and learning academic literature. However, much of this is framed within theoretical and conceptual frameworks. Practical approaches of how CT skills are best promoted in engineering curricula are less common. A state-of-art review of practical interventions that target the development of CT in engineering students is presented. The review draws on 25 selected peer-reviewed journal articles in established engineering databases and focusses on teaching strategies where their effects in promoting CT skills in students are measured. Considerable variability in the reviewed literature was apparent. CT interventions and strategies are often reported, but metrics of their success in enhancing students' CT is often limited to qualitative, subjective inferences. To more robustly and holistically ensure that CT is clearly embedded in university curricula, there needs to be well-funded research programmes that allow different methods to be developed and trialled over extended periods in higher education engineering programmes.

Importance of the development of skills in critical thinking for law students - review of critical thinking literature - criteria for the measurement of an ability to engage in critical thinking about law - application of criteria in designing a marking rubric that facilitates a whole-of-curriculum approach to the development of critical thinking.

Probably never before in the history of educational practice has there been a greater push to teach children to think critically. The signs are everywhere: multiple alternative programs to teach critical thinking at a variety of ages,'tomes that review in some detail the numerous programs available, 2 work shops for teachers and administrators sponsored by such prestigious organiza tions as the Association for Supervision and Curriculum Development, and an outpouring of articles on teaching criti cal thinking in such journals as Educa tional Researcher, Educational Leader ship, and the Phi Delta Kappan. It would be difficult to read anything at all in the contemporary literature of education without becoming aware of this new interest in teaching critical thinking.

In the first part of this two-part discussion of critical thinking, I examined 10 ways in which the teaching of critical thinking, as it is usually done, inadequately prepares students for the kinds of problems they will face in everyday life. I ended by posing the question, How then are we to train students for the demands with which life will present them? Now, as I promised, I will try to answer that question. I do not believe that the problem we face is
insoluble? though it is complicated, and, like most real-life problems, there is no one right …

Much diagnostic error is caused by cognitive bias. More than 100 biases affecting clinical decision making have been described, and many medical disciplines acknowledge their pervasive influence on our thinking. Training in critical thinking may ameliorate the problem. The two major products of clinical decision making are diagnoses and treatment plans. If the first is correct, the second has a greater chance of being correct too. Surprisingly, we don't make correct diagnoses as often as we think: the diagnostic failure rate is estimated to be 10 to 15%. The rate is highest among specialties in which patients are diagnostically undifferentiated, such as emergency medicine, family medicine, and internal medicine. Error in the visual specialties, such as radiology and pathology, is considerably lower, probably around 2%. 1 Diagnostic error has multiple causes, but principal among them are cognitive errors.

Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a hypothesis in hand. The author reviews evidence of such a bias in a variety of guises and gives examples of its operation in several practical contexts. Possible explanations are considered, and the question of its utility or disutility is discussed.