Formal Model for Explicit Aware Knowledge

2021 ◽  
pp. 85-117
Author(s):  
Claudia Fernández-Fernández
Keyword(s):  
Formal Model

DEVELOPMENT OF A METHOD FOR DESIGNING INFORMATION SYSTEMS FOR ENERGY COMPANIES

2020 ◽  
Vol 5 (333) ◽  
pp. 53-57
Author(s):  
T.B. Aldongar ◽  
◽  
F.U. Malikova ◽  
G.B. Issayeva ◽  
B.R. Absatarova ◽  
...  
Keyword(s):  
Information Systems ◽  
Business Processes ◽  
Design Research ◽  
Graphical Model ◽  
Formal Model ◽  
Workflow Management ◽  
Research Process ◽  
Information Models ◽  
The Creation ◽  
Concept Of Information

The creation of information models requires the use of known methods and the development of new methods of formalizing the pre-design research process. The modeling process consists of four stages: data collection on the object of management - pre-project research; creation of a graphical model of business processes taking place in the enterprise; development of a formal model of business processes; business research by optimizing the formal model. To support the creation of workflow management services and systems, the complex offers methodologies, standards and specialized software that make up the developer's tools. This can be ensured only by modern automated methods based on information systems. It is important that the information collected is structured to meet the needs of potential users and stored in a form that allows the use of modern access technologies. Before discussing the effectiveness of FIM, it should be noted that the basic concept of information itself is still not the same. In a pragmatic way, it is a set of messages in the form of an important document for the system. Information can be evaluated not only by volume, but also by various parameters, the most important of which are: timeliness, relevance, value, aging, accuracy, etc. in addition, the information may be clear, probable and accurate. The methods of its reception and processing are different in each case.

An Information-theoretical Model for Breast Cancer Detection

2008 ◽  
Vol 47 (04) ◽  
pp. 322-327 ◽  
Author(s):  
D. Blokh ◽  
N. Zurgil ◽  
I. Stambler ◽  
E. Afrimzon ◽  
Y. Shafran ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Hamming Distance ◽  
Formal Model ◽  
Methodological Approach ◽  
Breast Cancer Detection ◽  
Receptor Expression ◽  
Diagnostic Model ◽  
Model Parameters ◽  
Two Stages

Summary Objectives: Formal diagnostic modeling is an important line of modern biological and medical research. The construction of a formal diagnostic model consists of two stages: first, the estimation of correlation between model parameters and the disease under consideration; and second, the construction of a diagnostic decision rule using these correlation estimates. A serious drawback of current diagnostic models is the absence of a unified mathematical methodological approach to implementing these two stages. The absence of aunified approach makesthe theoretical/biomedical substantiation of diagnostic rules difficult and reduces the efficacyofactual diagnostic model application. Methods: The present study constructs a formal model for breast cancer detection. The diagnostic model is based on information theory. Normalized mutual information is chosen as the measure of relevance between parameters and the patterns studied. The “nearest neighbor” rule is utilized for diagnosis, while the distance between elements is the weighted Hamming distance. The model concomitantly employs cellular fluorescence polarization as the quantitative input parameter and cell receptor expression as qualitative parameters. Results: Twenty-four healthy individuals and 34 patients (not including the subjects analyzed for the model construction) were tested by the model. Twenty-three healthy subjects and 34 patients were correctly diagnosed. Conclusions: The proposed diagnostic model is an open one,i.e.it can accommodate new additional parameters, which may increase its effectiveness.

Maximizing Utility vis-à-vis Developing Countries

2018 ◽  
Author(s):  
Sarah Blodgett Bermeo
Keyword(s):  
Developing Countries ◽  
Developing Country ◽  
Formal Model ◽  
Policy Goals ◽  
The Government ◽  
Development Outcomes

This chapter develops a formal model of targeted development. It starts from the assumption that governments in industrialized states seek to maximize their own utility in interactions with developing countries. Development concerns compete with other policy goals for scarce government resources. The level of development resources an industrialized country government targets to a particular developing country depends on the weight the government places on development in that country as well as the efficiency of the country in turning resources into development outcomes that the industrialized state values. One of the key insights of the model is that, as governments work to maximize the utility gained per dollar (or euro, yen, etc.) spent, development motives will influence policy in multiple issue areas. The chapter also draws out implications of the theory for each of the issue areas examined in the empirical chapters.

scholarly journals Systemic Risk and Insurance Regulation †

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 74 ◽  
Author(s):  
Fabiana Gómez ◽  
Jorge Ponce
Keyword(s):  
Systemic Risk ◽  
Formal Model ◽  
Public Support ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Insurance Companies ◽  
Prudential Regulation ◽  
High Exposure ◽  
Insurance Firms ◽  
Low Exposure

This paper provides a rationale for the macro-prudential regulation of insurance companies, where capital requirements increase in their contribution to systemic risk. In the absence of systemic risk, the formal model in this paper predicts that optimal regulation may be implemented by capital regulation (similar to that observed in practice, e.g., Solvency II ) and by actuarially fair technical reserve. However, these instruments are not sufficient when insurance companies are exposed to systemic risk: prudential regulation should also add a systemic component to capital requirements that is non-decreasing in the firm’s exposure to systemic risk. Implementing the optimal policy implies separating insurance firms into two categories according to their exposure to systemic risk: those with relatively low exposure should be eligible for bailouts, while those with high exposure should not benefit from public support if a systemic event occurs.

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