Judgments Of Decision Makers Research Articles

GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer.

Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed urine from patients with lung cancer in the last weeks of life by headspace gas chromatography mass spectrometry. Urine was acidified or alkalinised before analysis. VOC changes in the last weeks of life were identified using univariate, multivariate and linear regression analysis; 12 VOCs increased (11 from the acid dataset, 2 from the alkali dataset) and 25 VOCs decreased (23 from the acid dataset and 3 from the alkali dataset). A Cox Lasso prediction model using 8 VOCs predicted dying with an AUC of 0.77, 0.78 and 0.85 at 30, 20 and 10 days and stratified patients into a low (median 10 days), medium (median 50 days) or high risk of survival. Our data supports the hypothesis there are specific metabolic changes associated with the dying. The VOCs identified are potential biomarkers of dying in lung cancer and could be used as a tool to provide additional prognostic information to inform expert clinician judgement and subsequent decision making.

Decision Making Under Impending Regime Shifts

In environments with impending regime shifts, previous research studies probability judgments of decision makers (DMs) regarding a regime shift and finds evidence for the system neglect hypothesis: In unstable environments with precise signals, DMs exhibit relatively more underreaction in their probability judgments, and in stable environments with noisy signals, they exhibit relatively more overreaction. In the present paper we focus on pricing decisions and study how risk attitudes, as well as probability judgments regarding a regime shift, affect these decisions. We develop a model that integrates prospect theory with the system neglect hypothesis and compare changes in empirical pricing decisions to those of a Bayesian risk-neutral agent. We find a pattern of under- and overreaction in pricing decisions that mimics the system neglect hypothesis in probability judgments only when the DM’s prior probability of a regime shift is low; the system neglect pattern vanishes when the DM’s prior probability of a regime shift is high. We argue that in the case of low priors, changes in risk attitude in reaction to signals indicative of a regime shift amplify system neglect in probability judgments. In the case of high priors, on the other hand, changes in risk attitude dampen the effect of system neglect. We test our model predictions in experimental insurance markets and offer various extensions to check for the robustness of our findings. Finally, we discuss implications for managerial practice in organizational contexts that are characterized by continuous change. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis. Funding: This work was funded by Fundação para a Ciência e a Tecnologia (UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab - PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences DataLab, PINFRA/22209/2016). Furthermore, it was supported by the Ministerio de Ciencia e Innovación España [10.13039/501100011033, Grants ECO2014-52925-P, PID2019-111512RB-I00-HMDM, and HDL-HS-280218]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4661 .

Spur gears with contact ratio less than unity

The objective of this research is to evaluate the design of a pair of spur gears and another analogous pair with a contact ratio of less than unity. Considering two pairs of normalized gears with equal diametral pitches and pressure angles, as well as equal pitch radii in their driving gears and equal pitch radii in their driven gears. For the first pair of gears with normalized tooth numbers, the contact ratio greater than unity es obtained. For the second pair of gears, the number of teeth is proportionally reduced, consequently obtaining a contact ratio of less than one. For both pairs, the maximum von Mises stresses are obtained using the finite element method. The pairs are compared qualitatively and quantitatively. This work contributes with novel elements of judgment for a better decision making of the industrialists interested in reducing the problems of normalized spur gears such as noise, abrasive wear, adhesive wear, temperature, and efforts induced by the overlapping relationship between coupled teeth; proposing them a practical solution that will open new avenues of research.

FMEA Based Fuzzy Topsis for Assessment of Quality Problems in Telescopic Platform Production

In order to increase process and product quality fast and least costly, it is very useful to focus on the process that causes the important quality problems. In this study, quality problems and related processes in a company that produces on-vehicle equipment in the automotive industry are examined. The purpose of this study is to provide a quality improvement perspective to reduce process and product defects. Fault Mode and Effects Analysis (FMEA) is applied to detect faults, determine their causes and prioritize them. Prioritization of critical quality errors is revealed using traditional FMEA and FMEA-based fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The FMEA-based fuzzy TOPSIS method has been proposed because of its ability to handle uncertainty arising from the subjective judgments of decision makers. The results are compared and the preventions are suggested to avoid from the most important process that causes the quality problem.

Auditing fairness under unawareness through counterfactual reasoning

Artificial intelligence (AI) is rapidly becoming the pivotal solution to support critical judgments in many life-changing decisions. In fact, a biased AI tool can be particularly harmful since these systems can contribute to or demote people’s well-being. Consequently, government regulations are introducing specific rules to prohibit the use of sensitive features (e.g., gender, race, religion) in the algorithm’s decision-making process to avoid unfair outcomes. Unfortunately, such restrictions may not be sufficient to protect people from unfair decisions as algorithms can still behave in a discriminatory manner. Indeed, even when sensitive features are omitted (fairness through unawareness), they could be somehow related to other features, named proxy features. This study shows how to unveil whether a black-box model, complying with the regulations, is still biased or not. We propose an end-to-end bias detection approach exploiting a counterfactual reasoning module and an external classifier for sensitive features. In detail, the counterfactual analysis finds the minimum cost variations that grant a positive outcome, while the classifier detects non-linear patterns of non-sensitive features that proxy sensitive characteristics. The experimental evaluation reveals the proposed method’s efficacy in detecting classifiers that learn from proxy features. We also scrutinize the impact of state-of-the-art debiasing algorithms in alleviating the proxy feature problem.

A New Integrated Multi-Criteria Decision-Making Model for Sustainable Supplier Selection Based on a Novel Grey WISP and Grey BWM Methods

Supplier selection is an important task in supply chain management, as suppliers have a vital role in the success of organisations in a supply chain. Sustainability has emerged as a solution to decreasing resources and increasing environmental and social problems in the past few decades. It has been applied to various industrial operations, one of them is supplier selection, to mitigate unwanted effects in the future. Sustainable supplier selection is a complicated multi-criteria decision making problem, including several criteria from economic, environmental, and social perspectives. To deal with subjective judgements of decision makers, fuzzy and grey methods are widely used in multi-criteria decision making, In the case of small, limited, and incomplete data, the grey theory provides satisfactory results, compared to fuzzy methods. Therefore, this study is an integrated method including grey Best-Worst Method (BWM) and grey Weighted Sum-Product (WISP) for choosing the most sustainable supplier for a textile manufacturer, which includes three main criteria and twelve sub-criteria. According to the result of the proposed model, the supplier with the best performance was determined to be the supplier with the SP2 coded. The results of the developed model were shown to the experts, and the accuracy of the results was confirmed. According to the experts, a higher amount of product can be purchased from the supplier with the SP2 code, and a tighter relationship can be worked with this supplier. The contributions of this study are: (1) Develop a new grey MCDM model called Grey WISP. (2) Create a new integrated MCDM model with grey theory, BWM, and WISP methods that can be applied to assess supplier sustainability using this hybrid model. The proposed model can be used not just for selecting sustainable suppliers, but also for any other decision problems that have multiple criteria and alternatives. The findings suggest that the Grey WISP method achieved accurate results.

Why Don’t Local Governments Buy Catastrophe Insurance? Biased Decision-Makers

Theoretical background: Incorporating behavioral analysis into finance allows for finding responses to many questions that researchers have not been able to answer by relying on assumptions characteristic of the traditional, normative approach. One of the areas where recently the achievements of behavioral finance have been used to analyze selected problems is public finance. In this article, we use the achievements of psychology, incorporating them into public finance, which allows us to shed new light on decisions made by public managers in Poland. Purpose of the article: The aim of this paper is to present the reasons why local governments do not buy insurance that would protect them against potential losses resulting from the occurrence of a catastrophic event. In this paper, we rely on responses from 303 municipalities, following the premise that an obvious way to learn about motives, constraints, and the decision-making process is to ask decision-makers. Research methods: This paper examines the influence of behavioral determinants and the level of tax revenue per capita on the decisions made by public managers. The CART method was used for this purpose. The necessary empirical data were obtained through a CAWI survey conducted in 2020 between August 1 and September 21 by a consortium of two research agencies: Biostat Research & Development Sp. z o.o. and Biostat Sp. z o.o. Main findings: The analysis conducted in this paper demonstrates that behavioral factors significantly impact public managers' decisions not to purchase catastrophe insurance. Our results confirm the significant role of a decision maker's traits, judgments, views, feelings, and experiences in the decision-making process.

Anchoring Effect in Capital Market

Anchoring effect refers to a bias phenomenon in which an individual's decision-making in an uncertain situation is influenced by the initial irrelevant information (anchor), which biases subsequent judgment decisions to this information (anchor). As one of the most robust cognitive biases, the anchoring effect is pervasive in people's decision-making process. This paper systematically reviews and sorts out the domestic and foreign research literature on the impact of anchoring effect in the field of economic management, and summarizes the application of anchoring effect in domestic and foreign financial markets, in order to inspire future research.

A road maintenance management strategy evaluation and selection model by integrating Fuzzy AHP and Fuzzy TOPSIS methods: The case of Ethiopian Roads Authority

The construction industry is the most important for the social, economic, and political development of countries. The road construction sector is the largest in the construction industry. Inappropriate road maintenance leads to an inefficient and random way of spending maintenance costs. Therefore, road maintenance needs to prevent deterioration that shortens the service life of the road. Thus, the study was focused on a road maintenance strategy evaluation and selection model by integrating Fuzzy AHP and Fuzzy TOPSIS methods. The evaluation was a multiple-criteria decision-making problem. The fuzzy AHP technique was used to determine criterion weight. To deal with the uncertain judgment of decision makers, a fuzzy TOPSIS method is applied as an evaluation tool in a fuzzy environment where the uncertain, subjectivity, and imprecise judgments of decision makers are handled with linguistic values parameterized by triangular fuzzy numbers. To achieve the objective, the data were collected from primary and secondary sources. The method of data analysis for this study was made by integrated methodology, and the analysis was made by using Microsoft Excel. The study revealed that quality, maintenance time, environment, production cost, and profit are the most important selection criteria. The finding of the study revealed that periodic maintenance was the best relative closeness value among the considered alternatives. This study recommends that the analysis method can be implemented by the Ethiopian Roads Authority to demonstrate its practicality in selecting the appropriate strategy.

Effects of video-based training on anticipation and decision-making in football players: A systematic review.

The training of athletes' anticipation and decision-making skills has received increasing attention from researchers, who developed and implemented training programs to achieve this. Video-based training (VBT) has become a popular method in anticipation and decision-making skills training. However, little is known about the benefits of implementing VBT in soccer. This systematic review considered the results of studies on VBT aiming to develop decision-making and anticipation skills in football players, and analyzed its effects. Literature published up to March 2022 was systematically searched on the scientific electronic databases Web of Science, PubMed, Scopus, SportDiscus, and Google Scholar. In total, 5,749 articles were identified. After screening the records according to the set exclusion and inclusion criteria, ten articles were considered eligible, including six longitudinal studies and four acute studies. Eight of the ten included studies (80%) showed that VBT group performance in anticipation or decision-making skills was significantly better at post-test than at pre-test, as evidenced by improvements in response accuracy (RA), response times (RT), mean distance scores (MDS) and passing decision-making performance. In six studies that included the no video-based training (NVBT) group, results showed that athletes in the VBT group performed better in anticipation or decision-making skills than in the NVBT group, as evidenced by improvements in RA and RT performance. The studies used different methods for VBT, both explicit and implicit training effectively improved participants' anticipation and decision-making skills. In addition, the implementation of the "first-person" perspective (i.e., the player's perspective) and virtual reality (VR) improved the presentation of video stimuli, effectively improving anticipation and decision-making. The findings of this review suggest that VBT is beneficial in developing anticipation and decision-making judgments in football players. However, some findings were inconsistent with previous studies due to differences in intervention duration and experimental protocols, and further studies are needed. Furthermore, future research should actively seek to design appropriate retention tests and transfer tests to truly understand the benefits of VBT for athletes.

The Hybrid Factor Analysis and Analytic Network Process (F'ANP) model modified: Assessing community social resilience in Tehran metropolis

The F'ANP model was proposed to overcome one of the inherent drawbacks of the Analytic Network Process (ANP): the subjective judgements of decision makers in the construction of pairwise comparison matrices. The model introduced construction of data-driven matrices instead of the ANP subjective judgements. The model is now modified to overcome the second main drawback of the ANP model: the construction of great number of pairwise comparison matrices. Also, the F'ANP model utilizes the inherent features of Factor Analysis (FA) to streamline the construction of the ANP network model systematically. The proposed model could be used to weigh the indicators that depict a multi-dimensional phenomenon and also be used for composite index construction. It is applied to assess the social resilience to disasters at the neighborhoods level in Tehran. The results show that four correlated factors represent community social resilience: social structure, social equity, social values, and social capital. The composite social resilience index shows that the northern parts of the city have higher levels of social resilience, whereas the southern parts of the city exhibit lower social resilience capacity towards disasters. The model's application in Tehran shows that the modified F'ANP model is a concise and robust model to use.

Hesitant fuzzy linguistic iterative method for consistency and consensus-driven group decision making

Hesitant fuzzy linguistic information has been used as a powerful expression to present the judgments under uncertainty. To improve its decision-making theory and enhance its application ability, we propose a decision-making method that considers the individual acceptable consistency and the group consensus of hesitant fuzzy linguistic preference relations (HFLPRs). Firstly, we define a multiplicative consistency of a HFLPR and make hypothesis testing analyses for the consistency threshold. Then, we establish a group consensus procedure to achieve comprehensive decision-making results that possibly reserve the original judgments of decision makers. Some experiments are conducted to show the values of the consistency threshold in different scenarios, and make support for using the proposed method. An illustrative example is finally given to perform the method.

First language translation involvement in second language word processing.

Studies on bilingual word processing have demonstrated that the two languages in a mental lexicon can be parallelly activated. However, it is under discussion whether the activated, non-target language gets involved in the target language. The present study aimed to investigate the role of the first language (L1, the non-target one) translation in the second language (L2, the target one) word processing. The tasks of semantic relatedness judgment and lexical decision were both adopted, to explore the relation of the possible L1 involvement and the task demand. Besides, bilinguals with relatively higher and lower L2 proficiency were recruited, to clarify the potential influence of L2 proficiency. Results showed that the manipulation of L1 translation exerted an influence on bilinguals’ task performances, indicating that L1 translation was involved, but did not just serve as a by-product when bilinguals were processing L2 words. And about the influence of L2 proficiency, the higher proficiency bilinguals performed better than the lower proficiency ones when the L1 translations could be taken advantage of, indicating a better access to L1 translation in L2 word processing, as bilinguals’ L2 proficiency increased. As for the task demands, the L1 translation was partially involved in Experiment 1 while a full involvement was observed in Experiment 2, suggesting a differed depth of L1 translation involvement, if the task demands allowed. The present study supplemented the previous ones due to its participants (the intermediate bilinguals) and tasks (the tasks of semantic relatedness judgment and lexical decision); besides, it provided an interesting view into interpreting the “task schema” of the BIA+ model.

Rapidly diminishing sensitivity and discrete sensitivity: Explaining people's sensitivity to outcomes across decisions.

Classic theories of perception, cognition, judgment, and decision making assert that people exhibit diminishing sensitivity to magnitude changes. For instance, according to the value function of prospect theory (Kahneman & Tversky, 1979), the pleasure of moving from 0 to $1000 is larger than the pleasure of moving from $1000 to $2000, whereas the pleasure of moving from $1000 to $2000 is larger than the pleasure of moving from $2000 to $3000. In this article, I call into question how much diminishing sensitivity actually impacts choice and the nature of the value function. I advance a special form of diminishing sensitivity that I have termed rapidly diminishing sensitivity, which postulates that people are sensitive to the presence versus the absence of outcomes but are less sensitive to the scope of the outcomes when the latter differ from zero. Rapidly diminishing sensitivity yields a value function that flattens out rapidly as values move away from zero. In the special case where the value function flattens out immediately after zero, rapidly diminishing sensitivity assumes a discrete form that I have termed discrete sensitivity. I propose that rapidly diminishing sensitivity and discrete sensitivity are typically observed in experimental studies that involve extensive manipulations of the size of the outcomes across decisions, holding everything else constant. I provide evidence for my hypothesis in eight well-powered preregistered experiments across a wide range of domains, such as financial decisions, valuations of human lives, purchase decisions, judgments of goal-related performance, intertemporal choices, and moral decisions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Genetic algorithm optimised Hadamard product method for inconsistency judgement matrix adjustment in AHP and automatic analysis system development

The analytic hierarchy process (AHP) is an important method to solve the multi-objective decision-making weight problem. However, due to the subjective judgement and selection preference of decision-makers, the consistency of the judgement matrix is inevitably poor. Hence, to improve the reliability of decision-making results, it is necessary to adjust the consistency of the judgement matrix. We propose a genetic algorithm optimised Hadamard product (GAOHP) to judge the consistency of the matrix. This method converts the original matrix consistency adjustment problem into an optimisation solution problem and uses a meta-heuristic algorithm to search the global optimisation quickly. Our method, which significantly improves the computing efficiency and realises that the judgement matrix after adjustment satisfies the basic consistency, has fully retained the judgement intention of the decision-makers. It offers the dual advantages of preserving the original intention of decision-makers and greater efficiency of operation. Finally, we developed an automatic analysis system based on MATLAB app designer to realise the rapid adjustment of the consistency of judgement matrix, which is conducive to providing an analysis system with simple and stable operation for AHP analysis.

An optimization model for rescuer assignments under an uncertain environment by using Dempster–Shafer theory

Emergency decision making and disposal are significant challenges faced by the international community. To minimize the emergency casualties, and reduce probable secondary disasters, it is necessary to immediately dispatch rescuers for emergency rescue in calamity prone areas. The abruptness, destructiveness, and uncertainty of emergencies, the rescue team often faces challenges of pressing time, scattered calamity locations, and diverse tasks. This necessitates the effective organization of rescuers, for their swift dispatch to the areas requiring rescue. A valuable research problem is how to group and dispatch rescuers reasonably and effectively according to the actual needs of the emergency rescue task and the situation to achieve the best rescue effect. This study establishes a dispatch model for rescuers in multiple disaster areas and rescue points. First, this paper combines the Dempster–Shafer theory (DST) and linguistic term set, to propose the concept of an evidential linguistic term set (ELTS), that can flexibly and accurately describe the subjective judgment of emergency decision-makers. It not only lays a theoretical foundation for establishing the rescuers’ dispatch model, but also aids in expressing information in uncertain linguistic environments of decision-making and evaluation. Second, to determine the weight of ability-based rescuer evaluation criteria, this study adopted the evidential best–worst method, combining it with DST to compensate for the limitations of the traditional weightage calculation method in expressing uncertainty. Third, to effectively dispatch rescuers to multiple disaster areas, modeling is carried out based on the above methods to maximize the competence of rescuers and the satisfaction of rescue time, and the best scheme for the allocation of rescuers is determined by solving the model. Finally, the advantages of the constructed model in emergency multitasking group decision-making are demonstrated through an empirical analysis.

Intentional bounded rationality methodology to assess the quality of decision-making approaches with latent alternative performances

Expert's judgments have been crucial in the development of decision theory; however, what criterion to use in the selection of experts remains an issue to address. Decision support techniques proposed to improve the quality of expert judgment decision making consider a demonstrated inconsistency of the judgments expressed by an expert as a criterion of exclusion in the decision-making process of such expert. Although consistency appears to be a desirable condition to qualify as “expert”, little is known about the quality of the decisions made imposing consistency as the expert qualifying condition. This paper proposes a simulation methodology, based on an automaton programmed to make decisions in an intended but bounded rational way, to assess the cost-benefit of different aspects of decision support techniques. Within this methodology, the imposition of the consistency condition in the selection of experts is studied. In particular, the paper shows with a case study example that the Analytical hierarchy process (AHP) decision support technique expected payoff is at most 5% higher when implementing Saaty's consistency criterion of the expert's judgments than when the consistency criterion is not considered.

Incorporating soft information from financial news media for management decisions in dynamic business environments

Because the nature of numerical information is intuitive and comprehensible, it has been widely used to form a basis for decision making, yet numerical information based on historical principle does not reflect messages about future corporate performance. To confront this issue, one may consider textual information that can transmit future corporate potential without any hysteresis. The key point is how to digest an extensive amount of textual information and identify those topics most likely to precede changes in operation status. Topic modeling can categorize these textual disclosures based on their underlying content and help examine which topics have a strong relevance to corporate operations. To extract decisive words from textual information, we set up a statistical-based approach with objectivity as opposed to frequently used heuristics (i.e., dictionary-based approaches with human involvement). Joint utilization of topic modelling and a statistical-based approach can compress an excessive amount of textual information into a manageable size in a timely manner and further realize a discrepancy among various topics in terms of relevance and influence on corporate operations. Our results benefit managers and current and future investors in how to structure regulatory filings and how word choices are decisive to them in their decision judgments.

The effect of personalized audit communication and evidence inconsistency on auditor judgments and decision-making

PurposeThe purpose of this study is to explore the effects of personalized task communication and examine auditor willingness to rely on and factor into their judgments and decisions historically accurate internal evidence suggesting a less conservative collectability judgment and audit adjustment decision than unfavorable external and less credible internal evidence.Design/methodology/approachThis study uses a 2 × 2 between-subjects experimental design manipulating whether participants receive source credibility insights leading to an evidence set consisting of mixed internal evidence signals, where historically accurate internal evidence suggests either a less conservative or similar collectability signal as unfavorable external evidence; and receive personalized or non-personalized task communication.FindingsThrough experimental analysis of 103 Big Four audit senior associates, the author found auditors factor into their collectability judgments and audit adjustment decisions historically accurate internal evidence suggesting less conservative collectability judgments and audit adjustment decisions than unfavorable external and less credible internal evidence. The author also found evidence that communication personalization elicits affect and increases effort levels for purposes of improving pre-task information processing and subsequent collectability judgments. The results also indicate that the influence of perceived personalization may not incrementally influence reliance on internal evidence that is consistent with unfavorable external evidence.Originality/valueTo the best of the author’s knowledge, this is the first study to examine auditor reliance behaviors when internal evidence suggests a less conservative collectability judgment and audit adjustment decision than unfavorable external evidence and is the first to explore communication personalization as a tool for effective audit knowledge transfer.

International consensus conference recommendations on ultrasound education for undergraduate medical students

ObjectivesThe purpose of this study is to provide expert consensus recommendations to establish a global ultrasound curriculum for undergraduate medical students.Methods64 multi-disciplinary ultrasound experts from 16 countries, 50 multi-disciplinary ultrasound consultants, and 21 medical students and residents contributed to these recommendations. A modified Delphi consensus method was used that included a systematic literature search, evaluation of the quality of literature by the GRADE system, and the RAND appropriateness method for panel judgment and consensus decisions. The process included four in-person international discussion sessions and two rounds of online voting.ResultsA total of 332 consensus conference statements in four curricular domains were considered: (1) curricular scope (4 statements), (2) curricular rationale (10 statements), (3) curricular characteristics (14 statements), and (4) curricular content (304 statements). Of these 332 statements, 145 were recommended, 126 were strongly recommended, and 61 were not recommended. Important aspects of an undergraduate ultrasound curriculum identified include curricular integration across the basic and clinical sciences and a competency and entrustable professional activity-based model. The curriculum should form the foundation of a life-long continuum of ultrasound education that prepares students for advanced training and patient care. In addition, the curriculum should complement and support the medical school curriculum as a whole with enhanced understanding of anatomy, physiology, pathophysiological processes and clinical practice without displacing other important undergraduate learning. The content of the curriculum should be appropriate for the medical student level of training, evidence and expert opinion based, and include ongoing collaborative research and development to ensure optimum educational value and patient care.ConclusionsThe international consensus conference has provided the first comprehensive document of recommendations for a basic ultrasound curriculum. The document reflects the opinion of a diverse and representative group of international expert ultrasound practitioners, educators, and learners. These recommendations can standardize undergraduate medical student ultrasound education while serving as a basis for additional research in medical education and the application of ultrasound in clinical practice.