The Digital Twin as a Mediator for the Digitalization and Conservation of Expert Knowledge

A Digital Twin is a virtual representation of a physical asset. It reflects the current state of that machine through a model and the data as observed by sensors in the real machine; and enables effective and efficient interaction with the machine, i.e. for monitoring and control purposes. The Digital Twin facilitates the collection of data, as well as its analysis and visualization through its user interfaces, i.e. GUIs such as screens or Mixed Reality that provide intuitive access to the data and facilitates its manipulation. Embedded in Virtual Testbeds the Digital Twin becomes an “Experimentable Digital Twin” (EDT), in which experiments can be performed and the different outcomes can be compared or evaluated. The intuitive representation of the assets allows the experts to interact with the twin, without highly detailed knowledge in computer science. The digital twin observes, records, and benchmarks experiments performed by the operator. This way the operator’s knowledge becomes digitized and thus preserved as an abstract representation of data, formulas, and models inside the digital twin. By introducing the Digital Twin into the processes carried out by different operators (not only the initially observed expert), formerly intuitive decision-making processes of the operators are enhanced based on empirical data. As a result, the Digital Twin serves as an assistance system that can guide future operators and the outcomes of the experiments become reproducible. The specific representations of interactions and outcomes also facilitate collaboration between the machine operators and other stakeholders by providing different operators a common “perspective”.

Subjective and intuitive decision-making by managers

The article describes the characteristics of the mechanisms of managerial judgments and intuition. It is considered how: intuition and judgments are based on extensive experience and knowledge; they can be understood from the point of view of signal recognition and subsequent recall of relevant experiences from memory. It is shown that intuition and judgment are an analysis that is fixated on the ability to react quickly through recognition. Some pathologies that are often found in managerial behavior, caused by emotions and stress, as well as the lack of appropriate habits, are revealed. In conclusion, it is suggested that managers include the «future» in the managerial «style» or habit.

AHP, a Reliable Method for Quality Decision Making: A Case Study in Business

Decision making is a significant responsibility for business managers, their decisions impacting business performance. Managers are therefore interested in acquiring and implementing reliable methods for making decisions both now and in the future. Currently, in the countries in the Albanian-speaking regions of the Western Balkans, intuitive decision-making methods predominate. In order to find appropriate methods for assessing and prioritizing goals, new approaches to decision making should be adopted. Various methods have been developed for multi-criteria decision making. One of these is the Analytic Hierarchy Process (AHP) method—a method which should receive more attention than it has up to now. We would like to show that the AHP method could be of great use in decision making. Through a case study, this paper explores the AHP, a method with three levels in which the identification of decision-making criteria is based on the perceptions of managers and consumers. The paper’s findings offer an important guide for managers to improve decision making and enhance performance in competitive markets.

The relationship of caffeinated beverages with depressive symptoms and decision-making

BACKGROUND: Caffeine can affect depressive symptoms and decision-making. OBJECTIVE: This study aims to examine the relationship between caffeinated beverages consumption with depressive symptoms and decision-making styles. METHODS: This cross-sectional study was conducted with 432 adults working in office environment. The questionnaire consisting of individuals’ socio-demographic attributes, the frequency and the amount of caffeinated beverages consumption, the “Epidemiological Research Center-Depression (CES-D) Scale” and the “Decision-Making Styles Scale” were used. The frequency and amount of caffeinated beverages were determined using the food frequency questionnaire (FFQ). The participants were asked to choose which cup/mug they prefer to drink their caffeinated beverages and what amount they consume that beverage at a time. All the data were collected using online platforms. RESULTS: In the study, 76.7%of the participants were female and the mean age was 31.5±8.0 years. The average daily total caffeine intake of the participants was 425.8±461.4 mg and the total CES-D scale score was 17.7±11.2 points. It was found that as the amount of caffeine consumed increased, intuitive decision-making decreased and depressive symptoms increased (p < 0.05). In linear regression analysis, total caffeine consumption was found to be a significant predictor for the intuitive decision-making score (B: –0.151; p:0.002). When caffeine consumption is controlled, intuitive and rational decision making decreases with increasing depressive symptoms while addiction and avoidance decision making increased (p < 0.05). CONCLUSIONS: As a result, the amount of caffeine consumed daily was related to intuitive decision-making but did not effect depression. It has been observed that depressive symptoms affect decision-making styles in different ways. To our knowledge, our study is the first to examine the effects of caffeine consumption on depression and decision-making styles. Accordingly, future studies may focus on the link between caffeine consumption, depression, and decision-making styles in larger populations and the mechanisms that influence this relationship.

STUCK AT A CROSSROADS? SUBSTANTIVE LEGITIMATE EXPECTATIONS IN ENGLISH LAW

In “The Provenance and Protection of Legitimate Expectations” Forsyth argued that English law should protect substantive legitimate expectations. However, he was concerned that too great an expansion of legitimate expectations could lead to incoherence and intuitive decision-making. I argue that recent case law, and Forsyth's analysis, have clarified some of these inconsistencies. Nevertheless, the doctrine of legitimate expectations stands at a crossroads. Should it adopt a rules-based approach and narrow legitimate expectations, or a principled approach that embraces a broader conception? I argue that English law needs both for legitimate expectations effectively to balance legal certainty and substantive equality.

The need for a comprehensive facility decision-making process

Purpose The facility decision-making process (FDMP), as described in this paper, is a framework based on decades of proven effectiveness in analogous military applications. The purpose of this paper is to evaluate key factors which justify the need for the FDMP before describing the FDMP. Design/methodology/approach This paper conducts a literature review as it relates to facilities specific decision-making, puts forward a new concept for those processes and provides an abridged case study of the concept in application. Findings Facilities management is a key function for nearly every type of business or enterprise. As part of facilities management, many decisions are made about physical assets that a business or enterprise owns or maintains. Currently, there is no uniform decision-making framework, in literature or in practice, which enables consistency, robustness and scalability for facility management decisions. Such a framework would enable facility managers to effectively justify decisions related to capital and operational expenses. Practical implications The FDMP provides a robust procedure for facilities managers and engineers that enables enhanced strength in the face of business enterprise scrutiny as compared with the often-intuitive decision-making processes currently used in practice. Originality/value This novel conceptual process articulates the opportunity to provide for a comprehensive facilities decision-making process that enables better decisions especially as it relates to what are often ill-defined problem sets in facilities management.

Creation of clinical algorithms for decision-making in oncology: an example with dose prescription in radiation oncology

In oncology, decision-making in individual situations is often very complex. To deal with such complexity, people tend to reduce it by relying on their initial intuition. The downside of this intuitive, subjective way of decision-making is that it is prone to cognitive and emotional biases such as overestimating the quality of its judgements or being influenced by one’s current mood. Hence, clinical predictions based on intuition often turn out to be wrong and to be outperformed by statistical predictions. Structuring and objectivizing oncological decision-making may thus overcome some of these issues and have advantages such as avoidance of unwarranted clinical practice variance or error-prevention. Even for uncertain situations with limited medical evidence available or controversies about the best treatment option, structured decision-making approaches like clinical algorithms could outperform intuitive decision-making. However, the idea of such algorithms is not to prescribe the clinician which decision to make nor to abolish medical judgement, but to support physicians in making decisions in a systematic and structured manner. An example for a use-case scenario where such an approach may be feasible is the selection of treatment dose in radiation oncology. In this paper, we will describe how a clinical algorithm for selection of a fractionation scheme for palliative irradiation of bone metastases can be created. We explain which steps in the creation process of a clinical algorithm for supporting decision-making need to be performed and which challenges and limitations have to be considered.

Thinking Fast and Slow: A CBR Perspective

In a path-breaking work, Kahneman characterized human cognition as a result of two modes of operation, Fast Thinking and Slow Thinking. Fast thinking involves quick, intuitive decision making and slow thinking is deliberative conscious reasoning. In this paper, for the first time, we draw parallels between this dichotomous model of human cognition and decision making in Case-based Reasoning (CBR). We observe that fast thinking can be operationalized computationally as the fast decision making by a trained machine learning model, or a parsimonious CBR system that uses few attributes. On the other hand, a full-fledged CBR system may be seen as similar to the slow thinking process. We operationalize such computational models of fast and slow thinking and switching strategies, as Models 1 and 2. Further, we explore the adaptation process in CBR as a slow thinking manifestation, leading to Model 3. Through an extensive set of experiments on real-world datasets, we show that such realizations of fast and slow thinking are useful in practice, leading to improved accuracies in decision-making tasks.

Intuitive decision-making promotes rewarding prosocial others independent of the personality trait Honesty-Humility

Although past research has convincingly shown that rewarding prosocial individuals helps to establish high levels of cooperation, research investigating factors that promote rewarding others has been surprisingly rare. The present research addresses this gap and examines two factors that were shown in past research to play a role in prosocial behaviour. In a well-powered study (total N = 1003), we tested the impact of (a) a basic prosocial personality trait (the Honesty-Humility dimension from the HEXACO personality model) and (b) intuitive decision-making, as well as (c) their interaction, in rewarding prosocial individuals. We found that (1) intuition promotes rewarding prosocial others; (2) Honesty-Humility was not significantly related to rewarding prosocial others; and (3) that Honesty-Humility did not significantly moderate the effect of intuition on reward. Implications for the understanding of reciprocating others’ prosocial behaviour are discussed.

Decision Making in Organizations: Intuition, Information, and Religiosity

Previous research in experimental psychology suggests that religious belief is influenced by one’s general tendency to rely on intuition rather than information. A corollary emerging from this based on balance theory is that managers who are religious might make more intuition-based decisions than their counterparts who are not religious. The latter group might tend to make more information-based decisions. Recent research also indicates that the use of scientific method, a close cousin of information-based decision making, triggers moral behavior. Employing critical incident technique, the present researchers test this potential relationship among business executives at various ranks, various cultural contexts, and holding various religious beliefs. Our analysis indicates that theist managers, both gnostic and agnostic, preferred intuitive decision making. Likewise, both gnostic and agnostic atheist managers preferred information-based decision making. Also, atheist managers articulated better logical explanations as to why their decisions were morally correct.