Risk analysis in complex systems: intelligent systems in finance

2009 ◽  
Vol 16 (1-2) ◽  
pp. 1-3 ◽  
Author(s):  
Antoaneta Serguieva ◽  
Guglielmo Maria Caporale ◽  
Edward Tsang ◽  
Ronald Yager
Keyword(s):  
Risk Analysis ◽  
Complex Systems ◽  
Intelligent Systems

Food waste and dynamic complex systems: The case of Peru and a solution model proposal Part 1: Peruvian Anchoveta (Engraulis ringens)

2020 ◽  
Author(s):  
Carlos A Almenara
Keyword(s):  
United Nations ◽  
Complex Systems ◽  
Revenue Management ◽  
Food Waste ◽  
Intelligent Systems ◽  
Solution Model ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Engraulis Ringens ◽  
The United Nations

[THE MANUSCRIPT IS A DRAFT] According to the Food and Agriculture Organization of the United Nations (FAO, 2020), food waste and losses comprises nearly 1.3 billion tonnes every year, which equates to around US$ 990 billion worldwide. Ironically, over 820 million people do not have enough food to eat (FAO, 2020). This gap production-consumption puts in evidence the need to reformulate certain practices such as the controversial monocropping (i.e., growing a single crop on the same land on a yearly basis), as well as to improve others such as revenue management through intelligent systems. In this first part of a series of articles, the focus is on the Peruvian anchoveta fish (Engraulis ringens).

Modularity in Artificial Neural Networks

2011 ◽  
pp. 1095-1101
Author(s):  
Ricardo Téllez ◽  
Cecilio Angulo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Complex Systems ◽  
Intelligent Systems ◽  
Point Of View ◽  
Main Concern ◽  
Complex Structures ◽  
Computational Point ◽  
Points Of View

The concept of modularity is a main concern for the generation of artificially intelligent systems. Modularity is an ubiquitous organization principle found everywhere in natural and artificial complex systems (Callebaut, 2005). Evidences from biological and philosophical points of view (Caelli and Wen, 1999) (Fodor, 1983), indicate that modularity is a requisite for complex intelligent behaviour. Besides, from an engineering point of view, modularity seems to be the only way for the construction of complex structures. Hence, whether complex neural programs for complex agents are desired, modularity is required. This article introduces the concepts of modularity and module from a computational point of view, and how they apply to the generation of neural programs based on modules. Two levels, strategic and tactical, at which modularity can be implemented, are identified. How they work and how they can be combined for the generation of a completely modular controller for a neural network based agent is presented.

Introduction

2020 ◽  
pp. 1-18
Author(s):  
J. Ladyman ◽  
K. Wiesner
Keyword(s):  
Complex Systems ◽  
Systems Theory ◽  
Intelligent Systems ◽  
Complexity Science ◽  
Scientific Discipline ◽  
The Status ◽  
History Of ◽  
Definition Of ◽  
Conceptual Foundations

This introductory chapter provides an overview and a brief history of complexity science, which is the study of complex systems. All living systems and all intelligent systems are complex systems. Complexity science is relatively new but already indispensable. Many of the most important problems in engineering, medicine, and public policy are now addressed with the ideas and methods of complexity science. However, there is no agreement about the definition of 'complexity' or 'complex system', nor even about whether a definition is possible or needed. The conceptual foundations of complexity science are disputed, and there are many and diverging views among scientists about what complexity and complex systems are. Even the status of complexity as a discipline can be questioned given that it potentially covers almost everything. The origins of complexity science lie in cybernetics and systems theory, both of which began in the 1950s. Complexity science is related to dynamical systems theory, which matured in the 1970s, and to the study of cellular automata, which were invented at the end of the 1940s. By then computer science had become established as a new scientific discipline.

Uncertainty treatment in risk analysis of complex systems: The cases of STAMP and FRAM

2016 ◽  
Vol 156 ◽  
pp. 203-209 ◽  
Author(s):  
Torbjørn Bjerga ◽  
Terje Aven ◽  
Enrico Zio
Keyword(s):  
Risk Analysis ◽  
Complex Systems
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