Computational Experimentation

2011 ◽  
pp. 51-57 ◽  
Author(s):  
Mark E. Nissen ◽  
Raymond E. Levitt
Keyword(s):  
Knowledge Management ◽  
Social Science ◽  
Modern Science ◽  
Field Research ◽  
Knowledge Development ◽  
Trial And Error ◽  
Computational Experimentation ◽  
New Knowledge ◽  
Systematic Development

Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management.

Computational Experimentation

2011 ◽  
pp. 412-420
Author(s):  
Mark E. Nissen ◽  
Raymond E. Levitt
Keyword(s):  
Knowledge Management ◽  
Social Science ◽  
Modern Science ◽  
Field Research ◽  
Knowledge Development ◽  
Trial And Error ◽  
Computational Experimentation ◽  
New Knowledge ◽  
Systematic Development

Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management.

Knowledge Management Research through Computational Experimentation

2010 ◽  
pp. 728-737
Author(s):  
Mark E. Nissen ◽  
Raymond E. Levitt
Keyword(s):  
Knowledge Management ◽  
Modern Science ◽  
Internal Validity ◽  
Computational Experiments ◽  
Research Approach ◽  
Field Methods ◽  
Complementary Method ◽  
Computational Experimentation ◽  
New Knowledge ◽  
Computational Modeling And Simulation

Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management. This article combines and builds upon recent results to describe a research approach that bridges the chasm between laboratory and field methods in KM: computational experimentation. As implied by the name, computational experiments are conducted via computer simulation. But such experiments can go beyond most simulations (e.g., incorporating experimental controls, benefiting from external model validation). And they can offer simultaneously benefits of laboratory methods (e.g., internal validity, lack of confounding) and fieldwork (e.g., external validity, generalizability). Further, computational experiments can be conducted at a fraction of the cost and time associated with either laboratory experiments or field studies. And they provide a window to view the kinds of meta-knowledge that are important for understanding knowledge management. Thus, computational experimentation offers potential to mitigate many limitations of both laboratory and field methods and to enhance KM research. We discuss computational modeling and simulation as a complementary method to bridge the chasm between laboratory and field methods—not as a replacement for either of these methods.

scholarly journals THE INFLUENCE OF KNOWLEDGE MANAGEMENT ON THE DEVELOPMENT OF INNOVATION IN THE ENTERPRISES IN THE REPUBLIC OF NORTH MACEDONIA-SELECTED RESULTS

2020 ◽  
Author(s):  
Marija Pendevska ◽  
Keyword(s):  
Knowledge Management ◽  
New Product Development ◽  
Technology Development ◽  
Rapid Change ◽  
Theoretical Framework ◽  
Business Environment ◽  
Knowledge Development ◽  
Study Results ◽  
New Knowledge ◽  
Empirical Framework

Business community faces rapid change due to the technology development. Its influence on business environment causes change in the knowledge base and its possibilities on achieving new solution as innovation thus gaining new knowledge. Enterprises are managing these continuous changes using the knowledge of its unique set of enterprise’s knowledge infrastructure, employee’s knowledge skills and business environment. This implies that fast knowledge development from technology development and innovation makes high pressure on the enterprises and on its employees as well. The manner how this is used and utilized within enterprise becomes dominant challenge for every enterprise and its respective management globally. Many researches in the past years have shown that innovations as commercialisation of new knowledge development and knowledge management practices can assist facing those challenges remarkably. Creating the balance between them is unique for every enterprise, for every respective management. This research paper consists of the following parts: introduction, selected theoretical and empirical framework and conclusion. The theoretical framework gives selected overview of the relevant researches in the field of knowledge management and innovation and their respective interrelation in new knowledge creation and commercialising of this new knowledge as innovation. The empirical framework describes the research design and gives the selected results obtained through the research of selected enterprises based on Questionnaire that covers key parameters previously discussed in the theoretical framework. Research focus is measuring the existence, the exchange, the creation of knowledge within enterprises and its usage in terms of new product development and/or improved products of the respective enterprises. Finally, in the conclusion, the study results are elaborated and their contribution to the existing body of knowledge and industry practices is discussed.

Geocartographic thinking in modern science

2020 ◽  
Vol 961 (7) ◽  
pp. 27-36
Author(s):  
A.K. Cherkashin
Keyword(s):  
Differential Geometry ◽  
Fiber Bundle ◽  
Modern Science ◽  
The State ◽  
Theory Of Knowledge ◽  
Knowledge Map ◽  
Knowledge Based ◽  
Geographical Environment ◽  
New Knowledge ◽  
Thematic Layer

The purpose of the study is to show how the features of geocartographic way of thinking are manifested in the meta-theory of knowledge based on mathematical formalisms. General cartographic concepts and regularities are considered in the view of metatheoretic analysis using cognitive procedures of fiber bundle from differential geometry. On levels of metainformation generalization, the geocartographic metatheoretic approach to the study of reality is higher than the system-theoretical one. It regulates the type of equations, models, and methods of each intertheory expressed in its own system terms. There is a balance between the state of any system and its geographical environment; therefore the observed phenomena are only explained theoretically in a metatheoretic projection on the corresponding system-thematic layer of the knowledge map. Metatheoretic research enables passing from the systematization of already known patterns to the formation of new knowledge through the scientific stratification of reality. General methods of metatheoretic analysis are mathematically distinguished

Conceptualising a New Knowledge Management Logic Model for Global Health: A Case-Study Approach

2015 ◽  
Vol 14 (02) ◽  
pp. 1550015 ◽  
Author(s):  
Saori Ohkubo ◽  
Sarah V. Harlan ◽  
Naheed Ahmed ◽  
Ruwaida M. Salem
Keyword(s):  
Knowledge Management ◽  
Global Health ◽  
Case Studies ◽  
Health Professionals ◽  
Health Workers ◽  
Logic Model ◽  
Unique Contribution ◽  
Program Outcomes ◽  
New Knowledge ◽  
Health Field

Over the past few decades, knowledge management (KM) has become well-established in many fields, particularly in business. Several KM models have been at the forefront of promoting KM in businesses and organisations. However, the applicability of these traditional KM models to the global health field is limited by their focus on KM processes and activities with few linkages to intended outcomes. This paper presents the new Knowledge Management for Global Health (KM4GH) Logic Model, a practical tool that helps global health professionals plan ways in which resources and specific KM activities can work together to achieve desired health program outcomes. We test the validity of this model through three case studies of global and field-level health initiatives: an SMS-based mobile phone network among community health workers (CHWs) and their supervisors in Malawi, a global electronic Toolkits platform that provides health professionals access to health information resources, and a netbook-based eHealth pilot among CHWs and their clients in Bangladesh. The case studies demonstrate the flexibility of the KM4GH Logic Model in designing various KM activities while defining a common set of metrics to measure their outcomes, providing global health organisations with a tool to select the most appropriate KM activities to meet specific knowledge needs of an audience. The three levels of outcomes depicted in the model, which are grounded in behavioural theory, show the progression in the behaviour change process, or in this case, the knowledge use process, from raising awareness of and using the new knowledge to contributing to better health systems and behaviours of the public, and ultimately to improving the health status of communities and individuals. The KM4GH Logic Model makes a unique contribution to the global health field by helping health professionals plan KM activities with the end goal in mind.

scholarly journals Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rama K. Vasudevan ◽  
Maxim Ziatdinov ◽  
Lukas Vlcek ◽  
Sergei V. Kalinin
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Bayesian Methods ◽  
Deep Neural Networks ◽  
Applied Research ◽  
Modern Science ◽  
Generative Models ◽  
Knowledge Development ◽  
Physical Constraints

AbstractDeep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

scholarly journals Knowledge management at Ukrainian industrial enterprises in the context of innovative development

2020 ◽  
Vol 12 (3) ◽  
pp. 43-56
Author(s):  
Sergii Illiashenko ◽  
Yuliia Shypulina ◽  
Nataliia Illiashenko ◽  
Olena Gryshchenko ◽  
Anna Derykolenko
Keyword(s):  
Knowledge Management ◽  
Swot Analysis ◽  
Innovative Development ◽  
Competitive Advantages ◽  
Review System ◽  
Industrial Enterprises ◽  
Sources Of Knowledge ◽  
New Knowledge ◽  
The Eu ◽  
Number Of Firms

AbstractThe research aimed to identify promising areas and outline problems associated with the transition of Ukrainian industrial enterprises towards advanced innovative development based on information and knowledge and to formulate recommendations for improving the knowledge management and commercialisation at these enterprises. The study used several methods for analysis, including a literature review; system, structural and statistical analyses; SWOT analysis; the inference method; and interpretation. The research efforts resulted in systemised major sources of knowledge in an enterprise and types of their utilisation. The performed analysis found the key ways to obtain and commercialise knowledge used by Ukrainian industrial enterprises. The results were compared with data of the EU countries. The analysis produced strengths and weaknesses of the existing knowledge management system used in Ukrainian enterprises. Strengths: growth in the number of enterprises producing new knowledge and implementing marketing and organisational innovations; intensified patent activity; and a rational structure of innovation-active enterprises by their size. Weaknesses: the new knowledge structure does not meet the needs of enterprises; an insignificant and unstable share of innovation-active enterprises in the total number of firms; and insignificant sales volumes of patents. The research revealed that Ukrainian enterprises had the potential ability to produce and commercialise new knowledge effectively and to use it as the basis to form, strengthen and implement relative competitive advantages, which would contribute to the innovative growth of the Ukrainian economy as a whole. Recommendations were designed for the formation of prerequisites necessary to improve the efficiency of knowledge management in the context of conditions required for the innovative development of domestic enterprises. The obtained results can be used as an information base for evaluating the system of knowledge production and commercialisation at Ukrainian enterprises to enhance the management and identify promising areas for innovative development.

scholarly journals GESTÃO DO CONHECIMENTO E GESTÃO DA INOVAÇÃO: UM ESTUDO BIBLIOMÉTRICO NA WEB OF SCIENCE (1945-2015)

2016 ◽  
pp. 113
Author(s):  
Paulo Fernando Marschner ◽  
Lucas Veiga Ávila ◽  
Analisa Tiburski Sommer
Keyword(s):  
Knowledge Management ◽  
Social Science ◽  
Computer Science ◽  
Operations Research ◽  
Information Science ◽  
Web Of Science ◽  
Innovation Management ◽  
Library Science ◽  
Research Management ◽  
Business Economics

Este estudo tem como objetivo analisar as características das publicações sobre Knowledge management (Gestão do conhecimento) e Innovation management (Gestão da inovação) na base de dados Web of Science, no período de 1945 a 2015. O trabalho descritivo e quantitativo, de natureza bibliométrica, busca levantar as características da produção acadêmica. Como principal resultado das 372 publicações analisadas constatou-se que os anos com maior publicação foram os de 2008 e 2015, em especial nas seguintes áreas temáticas: Business economics (Economia Empresarial), Operations research management science (Gestão de Operações), Engineering (Engenharias), Computer science (Ciência da Computação), Information science library science (Ciência da informação/biblioteconomia), Social science (Ciências Sociais). Os documentos são 66,6% proceedings paper, e o principal titulo é o International journal of technology management. Os países com maior número de produção são a China e os Estados Unidos, e o principal idioma é a língua inglesa.

scholarly journals Artificial Intelligence in Public Health Dentistry

2021 ◽  
Vol 5 (9) ◽  
pp. RV1-RV5
Author(s):  
Sahrish Tariq ◽  
Nidhi Gupta ◽  
Preety Gupta ◽  
Aditi Sharma
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Practice ◽  
Correct Diagnosis ◽  
Scientific Research ◽  
Modern Science ◽  
Appropriate Technology ◽  
Human Element ◽  
New Knowledge

The educational needs must drive the development of the appropriate technology”. They should not be viewed as toys for enthusiasts. Nevertheless, the human element must never be dismissed. Scientific research will continue to offer exciting technologies and effective treatments. For the profession and the patients, it serves to benefit fully from modern science, new knowledge and technologies must be incorporated into the mainstream of dental education. The technologies of modern science have astonished and intrigued our imagination. Correct diagnosis is the key to a successful clinical practice. In this regard, adequately trained neural networks can be a boon to diagnosticians, especially in conditions having multifactorial etiology.

Sign in / Sign up

Export Citation Format

Share Document