We discuss several aspects of creation of adequate mathematical models in other sciences. In particular, many difficulties stem from great complexity of the source systems and the presence of a variety of uncertain factors. We illustrate the effect of uncertainty on the known consumer demand model. We conclude that not every uncertainty can be represented by a random variable, and that these concepts are not equivalent. We discuss also the role of different information concepts in mathematical models. We give additional illustrative examples of models of quite complex systems
We study decision problems in which consequences of the various alternative actions depend on states...
market modelling, uncertainty. This paper focuses on two problems of modelling used for objective in...
For obvious reasons, models for decision-making under severe uncertainty are austere. Simply put, th...
Basic types and sources of uncertainty in mathematical modeling are brieefly mentioned. The notions ...
This book provides a thorough introduction to the challenge of applying mathematics in real-world sc...
In this article, we introduce the concept of model uncertainty.We review the frequentist and Bayesia...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
The better the model, the more features of the problem it explains. However, showing that the model ...
Mathematical models can be classified in a number of ways, e.g., static and dynamic; deterministic a...
Abstract. When searching for nature rules we encounter a fundamental difficulty: for technical reaso...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
How do we know how much we know? Quantifying uncertainty associated with our modelling work is the o...
The problem of model uncertainty versus model inaccuracy is examined in the light of the concept of ...
summary:The goal of this contribution is to introduce some approaches to uncertainty modeling in a w...
Explanations of human behavior are most often presented in a verbal form as theories. Psychologists ...
We study decision problems in which consequences of the various alternative actions depend on states...
market modelling, uncertainty. This paper focuses on two problems of modelling used for objective in...
For obvious reasons, models for decision-making under severe uncertainty are austere. Simply put, th...
Basic types and sources of uncertainty in mathematical modeling are brieefly mentioned. The notions ...
This book provides a thorough introduction to the challenge of applying mathematics in real-world sc...
In this article, we introduce the concept of model uncertainty.We review the frequentist and Bayesia...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
The better the model, the more features of the problem it explains. However, showing that the model ...
Mathematical models can be classified in a number of ways, e.g., static and dynamic; deterministic a...
Abstract. When searching for nature rules we encounter a fundamental difficulty: for technical reaso...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
How do we know how much we know? Quantifying uncertainty associated with our modelling work is the o...
The problem of model uncertainty versus model inaccuracy is examined in the light of the concept of ...
summary:The goal of this contribution is to introduce some approaches to uncertainty modeling in a w...
Explanations of human behavior are most often presented in a verbal form as theories. Psychologists ...
We study decision problems in which consequences of the various alternative actions depend on states...
market modelling, uncertainty. This paper focuses on two problems of modelling used for objective in...
For obvious reasons, models for decision-making under severe uncertainty are austere. Simply put, th...
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