Wisdom Of Experts Research Articles

Cross-functional group decision making with heterogeneous cooperation for digital transformation in supply chain resilience

Supply chain resilience plays a critical role in gaining competitive advantages for companies. The resilience of supply chains can be achieved by leveraging emerging digital technologies to realize digital transformation. It is necessary to select an appropriate digitalization technology under such background. The wide-spanning of digital transformation and technology selection needs cross-functional integration of various expertise. However, in the process of making decisions by leveraging expert wisdom, differences in experts’ willingness to cooperate lead to difficulties in reaching a consensus. The existing literature fails to incorporate both non-cooperation and proactive-cooperation into the consensus reaching process. Thus, in this study, we introduce a cross-functional multi-attribute group decision making model for digitalization technology selection. To manage potential non-cooperative behaviors in the group consensus reaching process, the proposed model allows experts to have proactive cooperation, i.e., making more contributions than recommended feedback suggestions provided by the moderator. Proactive cooperation can make up for the loss caused by the non-cooperative behaviors of experts. A knowledge mining method is proposed to mine academic and practical preferences for attributes. Two consensus mechanisms are put forward for the meso decision-making process in functional teams and the macro decision-making process in the whole group, respectively. An illustrative example regarding the technology selection in shipbuilding industry is provided to verify the applicability of our model. Numerical experiments suggest that our model will improve the efficiency of consensus reaching process.

Cavernous Malformations: What They Have Taught Us.

Cavernous Malformations: What They Have Taught Us.

Principles of Problem-Based Learning (PBL) in STEM Education: Using Expert Wisdom and Research to Frame Educational Practice

Developing teacher knowledge, skills, and confidence in Science, Technology, Engineering, and Mathematics (STEM) education is critical to supporting a culture of innovation and productivity across the population. Such capacity building is also necessary for the development of STEM literacies involving the ability to identify, apply, and integrate concepts from STEM domains toward understanding complex problems, and innovating to solve them. However, a lack of visible models of STEM integration has been highlighted by teachers as a challenge to successfully implementing integrated STEM education in schools. Problem Based Learning (PBL) has been well-established in higher education contexts as an approach to learning in the STEM disciplines and may present an effective way to integrate knowledge and skills across STEM disciplines in school-based STEM education and support the development of students as capable, self-directed learners. However, if PBL is to effectively contribute to STEM education in schools and build teacher capacity to teach STEM, then this approach needs to be better understood. This paper aims to generate a set of principles for supporting a PBL model of STEM education in schools based on insights from the literature and expert focus groups of PBL professionals. Four principles of PBL emerged from the data analysis: (a) flexible knowledge, skills, and capabilities; (b) active and strategic metacognitive reasoning; (c) collaboration based on intrinsic motivation; and (d) problems embedded in real and rich contexts. The study outcomes provide evidence-informed support for teachers who may be considering the value of adopting a PBL approach in school-based STEM education.

It’s Time to Reconsider How We Make the Best Use of the Wisdom of Experts

It’s Time to Reconsider How We Make the Best Use of the Wisdom of Experts

Socratic Open-mindedness

A philosophical conception of open-minded inquiry first emerges in western philosophy in the work of Socrates. This paper develops an interpretation of Socratic open-mindedness drawing primarily on Socratic ideas about (i) the requirements of serious argument, and (ii) the nature of human wisdom. This account is defended against a number of objections which mistakenly interpret Socrates as defending, teaching, or inducing skepticism, and neglecting the value of expert wisdom. The ongoing significance of Socratic open-mindedness as an ideal of inquiry is brought out through examination of a notorious Canadian case in the context of forensic pathology.

Leveraging Our Strengths to Achieve Sustainable Healthcare.

We are fortunate to have the reflections and wisdom of experts from multiple disciplines in this issue's discussion of healthcare sustainability. In the lead paper of this issue of Healthcare Papers, we issued a call to action drawing on the capacity of the Canadian health sector to address climate change (Miller and Xie 2020). Our colleagues agree on the need to deliver sustainable care and raise important questions about how such an aim can be achieved. In this response, we consider and revisit three themes: feasibility of a sustainable healthcare system, theories of change that support our recommendations and capacities that enable change.

Optimal design of experiments to identify latent behavioral types

Bayesian optimal experiments that maximize the information gained from collected data are critical to efficiently identify behavioral models. We extend a seminal method for designing Bayesian optimal experiments by introducing two computational improvements that make the procedure tractable: (1) a search algorithm from artificial intelligence that efficiently explores the space of possible design parameters, and (2) a sampling procedure which evaluates each design parameter combination more efficiently. We apply our procedure to a game of imperfect information to evaluate and quantify the computational improvements. We then collect data across five different experimental designs to compare the ability of the optimal experimental design to discriminate among competing behavioral models against the experimental designs chosen by a "wisdom of experts" prediction experiment. We find that data from the experiment suggested by the optimal design approach requires significantly less data to distinguish behavioral models (i.e., test hypotheses) than data from the experiment suggested by experts. Substantively, we find that reinforcement learning best explains human decision-making in the imperfect information game and that behavior is not adequately described by the Bayesian Nash equilibrium. Our procedure is general and computationally efficient and can be applied to dynamically optimize online experiments.

Modelling Burglary Susceptibility – An Expert Judgement Approach

Developing a model allow a better understanding of the nature of a complicated phenomenon. With advancement of tools and technology, model development has been applied widely to mimic the phenomena of interest, spatial or non-spatial wise, allowing a guided decision making to be made. In this paper, the phenomena of burglary vulnerability and susceptibility are modelled based on expert opinion input to create a model that imitates the expert profiling of burglary occurrences, which is dependent on individual expert wisdom and experience in handling the burglary investigation. Due to seriousness of burglary crime offences in Malaysia, especially the urban areas, a prediction model is needed to correlates the factor of crime and further estimates the spatial susceptibility to work hand in hand with other government initiatives in reducing crime. Eighteen (18) indicators and 63 sub-indicators has been identified to be significant in defining the susceptibility of burglary. Apart from input of rating and ranking of indicators and sub-indicators obtained from questionnaire distribution to expert in handling burglary, the geospatial based data were also incorporated into the model to add the element of spatial accuracy in susceptibility prediction. The geospatial data includes the distribution of burglary incidence from 2010 – 2016, the census data, the building footprint data and the demarcation area. For the collected questionnaire feedback, the procedure of Analytical Hierarchy Process (AHP) were adapted to determine the weight value considering the rating input of expert from the distributed questionnaire. The input of weight and scoring were applied to the corresponding spatial features and combined with the operation of weighted sum to yield the total burglary susceptibility of a place. The results of the model were validated with the real reported burglary frequency based on True Positive Rate correlation matrix. The model validation finds that the model have a sensitivity of 82% in classifying the burglary susceptibility of the building polygon inside the study area. However this model still requires some improvement as it is still lacking to perform the classification of incidence intensity correctly.

Developing an Injury Prevention Advocacy Program: Learning From the Experts

While injury is the leading cause of death for children, unlike many other fatal disease states injury is also particularly amenable to prevention efforts to decrease its morbidity and mortality. Pediatric emergency medicine physicians have a particularly important vantage point from which to craft and deliver injury prevention interventions. Here we interviewed successful physician advocates in order to summarize collective wisdom for those interested in increasing their own advocacy work or simply wanting to best support those who are. Key themes included the importance of each of the following; clinical experience informing advocacy efforts, use of multiple streams of data to drive research, frequent reassessments of efforts, overcoming funding obstacles, combined interventions, building trust in the community, and learning from previous successes. In addition, experts offered specific advice for advocacy at the individual, community, and national level. All physicians should seek to include advocacy, or the support of advocacy efforts, in their own practice. By seeking out the wisdom of experts we can identify the methods most likely to be effective in changing behaviors and ultimately decrease the impact of injury on our pediatric patients.

Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making support for evaluation of clustering algorithms (DMSECA), to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process. The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18,310 instances and 313 attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets. Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment.

Deep ensemble learning for Alzheimer's disease classification.

Ensemble learning uses multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With the growing popularity of deep learning technologies, researchers have started to ensemble these technologies for various purposes. Few, if any, however, have used the deep learning approach as a means to ensemble Alzheimer's disease classification algorithms. This paper presents a deep ensemble learning framework that aims to harness deep learning algorithms to integrate multisource data and tap the 'wisdom of experts'. At the voting layer, two sparse autoencoders are trained for feature learning to reduce the correlation of attributes and diversify the base classifiers ultimately. At the stacking layer, a nonlinear feature-weighted method based on a deep belief network is proposed to rank the base classifiers, which may violate the conditional independence. The neural network is used as a meta classifier. At the optimizing layer, over-sampling and threshold-moving are used to cope with the cost-sensitive problem. Optimized predictions are obtained based on an ensemble of probabilistic predictions by similarity calculation. The proposed deep ensemble learning framework is used for Alzheimer's disease classification. Experiments with the clinical dataset from National Alzheimer's Coordinating Center demonstrate that the classification accuracy of our proposed framework is 4% better than six well-known ensemble approaches, including the standard stacking algorithm as well. Adequate coverage of more accurate diagnostic services can be provided by utilizing the wisdom of averaged physicians. This paper points out a new way to boost the primary care of Alzheimer's disease from the view of machine learning.

Stock Selection Model Based on Machine Learning with Wisdom of Experts and Crowds

Both stock recommendations from sell-side analysts and online user generated content from crowds have great significance in the stock market. We examine and compare different effects of analyst attitude and crowd sentiment on stock prices in this article with data from CSMAR. By estimating a multivariate linear regression model, we find that although the wisdom of both experts and crowds has impact on stock prices, the latter's impact on stock prices prevails. We also adopt LightGBM, a novel machine learning model, to predict stock trends based on empirical results. Portfolio returns of different models also suggest that crowd wisdom is more valuable for creating investment strategy than expert wisdom. And it is necessary to take the wisdom of both experts and crowds into consideration when making investment decision.

A GMCDM approach with linguistic Z-numbers based on TOPSIS and Choquet integral considering risk preference

Group multi-criteria decision-making (GMCDM) is an important part of decision theory, which is aimed to assess alternatives according to multiple criteria by collecting the wisdom of experts. However, in the process of evaluating, because of the limitation of human knowledge and the complexity of problems, an efficient GMCDM approach under uncertain environment still need to be further explored. Thus, in this paper, a novel GMCDM approach with linguistic Z-numbers based on TOPSIS and Choquet integral is proposed. Firstly, since linguistic Z-numbers performs better in coping with uncertain information, it is used to express the evaluation information. Secondly, TOPSIS, one of the most useful and systematic multi-criteria decision-making (MCDM) method, is adopted as the framework of the proposed approach. Thirdly, frequently it exists interaction between criteria, so Choquet integral is introduced to capture this kind of influence. What’s more, viewing that decision makers (DMs) show different preferences for uncertainty, the risk preference is regarded as a vital parameter when calculating the score of linguistic Z-numbers. An application in supplier selection is illustrated to demonstrate the effectiveness of the proposed approach. Finally, a further comparison and discussion of the proposed GMCDM method is given.

Towards the Further Professionalization of the Preparation of Jamaican TVET Leaders

The genesis of this study began with the understanding that there are many Technical Vocational Education and Training (TVET) public, government, and private schools and institutes in Jamaica at the secondary and tertiary education levels. At the time of this research, there was no specifically designed TVET principal/director preparation program or specialized qualifying credential. Such preparation programs and professional credentialing help to legitimize the profession and produce the leadership required to offer and implement quality TVET programs that contribute to creating a highquality workforce. This research study was conducted in Jamaica to produce expert recommended content and administrative models that may guide the development of a specialized TVET campus principal or director training program. Employing the expert wisdom approach to research, this study utilized a descriptive qualitative research design to gather information regarding the required qualifications and credentials, program content, and nature of the planning, implementation, and administrative body.

A new approach for selecting best development face ventilation mode based on G1-coefficient of variation method

The current popular methods for decision making and project optimisation in mine ventilation contain a number of deficiencies as they are solely based on either subjective knowledge or objective information. This paper presents a new approach to rank the alternatives by G1-coefficient of variation method. The focus of this approach is the use of the combination weighing, which is able to compensate for the deficiencies in the method of evaluation index single weighing. In the case study, an appropriate evaluation index system was established to determine the evaluation value of each ventilation mode. Then the proposed approach was used to select the best development face ventilation mode. The result shows that the proposed approach is able to rank the alternative development face ventilation mode reasonably, the combination weighing method had the advantages of both subjective and objective weighing methods in that it took into consideration of both the experience and wisdom of experts, and the new changes in objective conditions. This approach provides a more reasonable and reliable procedure to analyse and evaluate different ventilation modes.

An Analytic Hierarchical Approach to Building Airline Safety Management Systems

The airline literature has attracted considerable attention of scholars and significant modelling and empirical reports have been contributed. However, despite being significantly agitated in the recent past in view of unprecedented airline failures, the Nigerian airline industry is least studied. The safety management system (SMS) meant to be a constant measuring tool has been downplayed in research concerning this country’s airline system. To correct this anomaly, this study takes a frontline empirical initiative to prioritize the safety management system components and elements which hitherto has been very challenging and currently being approached by intuition. In this paper, the collective wisdom of experts is integrated into a framework of the analytical hierarchy process (AHP) wherein experienced professionals’ ideas are obtained through questionnaire administration and interviews. The AHP methodology is a classical, merit-driven and validated approach in decision making and applied in this instance to the Nigerian aviation industry. The results indicated an effective prioritization of SMS elements and further validate the efficacy of the AHP model developed originally by Thomas Saaty.

Wikipedia and the Wisdom of Crowds: A Student Project

Students examined the question of whether the 'wisdom of experts' or 'the wisdom of crowds' is more reliable and useful in a writing course by engaging in a parallel Wikipedia project. Each student either created a new entry or made significant changes to an existing Wikipedia entry, tracked changes to their contributions, and then wrote a paper and gave a presentation reflecting on what they learned; simultaneously, the class as a whole collaborated on a Wikipedia entry about a local landmarks controversy. Background readings familiarizing students with Wikipedia's procedures, as well as critical and philosophical interpretations of Wikipedia's significance, provided students perspective on Wikipedia's utility. While the instructor expected students to enter with an uncritical understanding of Wikipedia's reliability, and then to see Wikipedia's fundamental untrustworthiness, student work demonstrated that they entered the class skeptical about Wikipedia and that their projects showed them that Wikipedia was mostly reliable and useful.

Perspectives on Supervision in Human Services: Gazing Through Critizal and Feminist Lenses

Supervision is a cornerstone activity in the development of practice skills for human service professionals. Traditionally, supervision is conducted in ways that emphasize the expertise of the supervisor and the need to impart expert wisdom to the supervisee. In many ways, the supervisory relationship often mirrors traditional teacher/student roles contextualized in dominant/subordinate ways of being together. Oversight and monitoring become central activities in such traditional supervisory approaches, which may limit possibilities for growth and development. Critical and feminist perspectives offer other ways of conceptualizing supervision, namely through careful consideration of how power operates and may be used in the supervisory relationship. Intentional reciprocity may be discovered if dominant/subordinate power differentials are re-visioned. This article suggests ways that supervision and supervisory relationships may be (re)considered beyond traditional approaches.

Knowledge Management Revisited

A number of social, economic, technological, and scientific trends have led to the emergence of communities of practice centered on the notion of the knowledge-based organization. However, the scientific foundation (knowledge elicitation methodology) and the commercial growth of knowledge management (KM) have largely developed in parallel. So, the creation of human-centered systems faces lingering challenges. In the KM process, company management establishes a program whereby experts who possess valuable knowledge collaborate with a knowledge engineer. Working together, they elicit the expert's wisdom for inclusion in the organization's knowledge base.

Improving surface‐analysis methods for characterization of advanced materials by development of standards, reference data, and interlaboratory comparisons

AbstractThis paper summarizes the results of two surveys examining current needs for improved analyses of surfaces. Surfaces and interfaces are increasingly important to science and technologies associated with nanoparticles, nano‐structured materials and other complex materials including those associated with information systems and medical or biological applications. Adequate characterization of advanced materials frequently requires application of more than one analysis method along with the need to analyze data in increasingly sophisticated and sometimes interrelated ways. It is useful for both new and experienced analysts to have ready access to best practices for obtaining accurate and useful information from a variety of different analysis tools. The International Organization for Standardization (ISO) Committee TC 201 on surface chemical analysis and the ASTM Committee E‐42 on surface analysis are working to address these needs by assembling guides and standards reflecting the collective experience and wisdom of experts in this community. Published in 2007 by John Wiley & Sons, Ltd