Measuring Complexity using Information DOI Creative Commons
Klaus Jaffé

Published: July 26, 2024

Measuring complexity in multidimensional systems with high degrees of freedom and a variety types information, remains an important challenge. The system is related to the number components, type interactions among them, degree redundancy, system. Examples show that different disciplines science converge measures for low dimensional problems. For systems, such as coded strings symbols (text, computer code, DNA, RNA, proteins, music), Shannon’s Information Entropy (expected amount information event drawn from given distribution) Kolmogorov‘s Algorithmic Complexity (the length shortest algorithm produces object output), are used quantitative measurements complexity. more dimensions (ecosystems, brains, social groupings), network provides better tools purpose. highly complex none former methods useful. Here, can be ranging subatomic ecological, social, mental AI. Useful Φ (Information thermodynamic free energy) quantified by measuring Free Energy and/or useful Work it produces. measured Total I system, includes Φ, useless or Noise N, Redundant R. one these variables allows quantifying classifying two windows overlooking same fundamental phenomenon, broadening out explore deep structural dynamics nature at all levels complexity, including natural artificial intelligence.

Language: Английский

Measuring Complexity using Information DOI Creative Commons
Klaus Jaffé

Published: June 13, 2024

Measuring complexity in multidimensional systems with high degrees of freedom and a variety types information, remains an important challenge. Complexity system is related to the number components, type interactions among them, degree redundancy, system. Examples show that different disciplines science converge measures for low dimensional problems. For systems, such as coded strings symbols (text, computer code, DNA, RNA, proteins, music), Shannon’s Information Entropy (expected amount _information_ event drawn from given distribution) Kolmogorov‘s Algorithmic (the length shortest algorithm produces object output), are used quantitative measurements complexity. more dimensions (ecosystems, brains, social groupings), network provides better tools purpose. complex highly none former methods useful. Useful Φ, proposed by Infodynamics, can be It quantified measuring thermodynamic Free Energy F and/or useful Work it produces. measured Total I, then defined information system, includes useless or Noise N, Redundant R. one these variables allows quantifying classifying

Language: Английский

Citations

0
Measuring Complexity using Information DOI Creative Commons
Klaus Jaffé

Published: June 24, 2024

Measuring complexity in multidimensional systems with high degrees of freedom and a variety types information, remains an important challenge. Complexity system is related to the number components, type interactions among them, degree redundancy, system. Examples show that different disciplines science converge measures for low dimensional problems. For systems, such as coded strings symbols (text, computer code, DNA, RNA, proteins, music), Shannon’s Information Entropy (expected amount _information_ event drawn from given distribution) Kolmogorov‘s Algorithmic (the length shortest algorithm produces object output), are used quantitative measurements complexity. more dimensions (ecosystems, brains, social groupings), network provides better tools purpose. complex highly none former methods useful. Useful Φ (Information thermodynamic free energy) can be quantified by measuring Free Energy F and/or useful Work it produces. Here I propose measure Total I, defined information system, including Φ, useless or Noise N, Redundant R. one these variables allows quantifying classifying two windows overlooking same fundamental phenomenon broadening out quantify both.

Language: Английский

Citations

0
Measuring Complexity using Information DOI
Klaus Jaffé

Published: July 3, 2024

Measuring complexity in multidimensional systems with high degrees of freedom and a variety types information, remains an important challenge. The system is related to the number components, type interactions among them, degree redundancy, system. Examples show that different disciplines science converge measures for low dimensional problems. For systems, such as coded strings symbols (text, computer code, DNA, RNA, proteins, music), Shannon’s Information Entropy (expected amount information event drawn from given distribution) Kolmogorov‘s Algorithmic Complexity (the length shortest algorithm produces object output), are used quantitative measurements complexity. more dimensions (ecosystems, brains, social groupings), network provides better tools purpose. highly complex none former methods useful. Here, can be ranging subatomic ecological, social, mental AI. Useful Φ (Information thermodynamic free energy) quantified by measuring Free Energy and/or useful Work it produces. measured Total I system, includes Φ, useless or Noise N, Redundant R. one these variables allows quantifying classifying two windows overlooking same fundamental phenomenon, broadening out explore deep structural dynamics nature at all levels complexity, including natural artificial intelligence.

Language: Английский

Citations

0
Measuring Complexity using Information DOI
Klaus Jaffé

Published: July 16, 2024

Measuring complexity in multidimensional systems with high degrees of freedom and a variety types information, remains an important challenge. The system is related to the number components, type interactions among them, degree redundancy, system. Examples show that different disciplines science converge measures for low dimensional problems. For systems, such as coded strings symbols (text, computer code, DNA, RNA, proteins, music), Shannon’s Information Entropy (expected amount information event drawn from given distribution) Kolmogorov‘s Algorithmic Complexity (the length shortest algorithm produces object output), are used quantitative measurements complexity. more dimensions (ecosystems, brains, social groupings), network provides better tools purpose. highly complex none former methods useful. Here, can be ranging subatomic ecological, social, mental AI. Useful Φ (Information thermodynamic free energy) quantified by measuring Free Energy and/or useful Work it produces. measured Total I system, includes Φ, useless or Noise N, Redundant R. one these variables allows quantifying classifying two windows overlooking same fundamental phenomenon, broadening out explore deep structural dynamics nature at all levels complexity, including natural artificial intelligence.

Language: Английский

Citations

0
Measuring Complexity using Information DOI
Klaus Jaffé

Published: July 22, 2024

Measuring complexity in multidimensional systems with high degrees of freedom and a variety types information, remains an important challenge. The system is related to the number components, type interactions among them, degree redundancy, system. Examples show that different disciplines science converge measures for low dimensional problems. For systems, such as coded strings symbols (text, computer code, DNA, RNA, proteins, music), Shannon’s Information Entropy (expected amount information event drawn from given distribution) Kolmogorov‘s Algorithmic Complexity (the length shortest algorithm produces object output), are used quantitative measurements complexity. more dimensions (ecosystems, brains, social groupings), network provides better tools purpose. highly complex none former methods useful. Here, can be ranging subatomic ecological, social, mental AI. Useful Φ (Information thermodynamic free energy) quantified by measuring Free Energy and/or useful Work it produces. measured Total I system, includes Φ, useless or Noise N, Redundant R. one these variables allows quantifying classifying two windows overlooking same fundamental phenomenon, broadening out explore deep structural dynamics nature at all levels complexity, including natural artificial intelligence.

Language: Английский

Citations

0
Measuring Complexity using Information DOI Creative Commons
Klaus Jaffé

Published: July 26, 2024

Measuring complexity in multidimensional systems with high degrees of freedom and a variety types information, remains an important challenge. The system is related to the number components, type interactions among them, degree redundancy, system. Examples show that different disciplines science converge measures for low dimensional problems. For systems, such as coded strings symbols (text, computer code, DNA, RNA, proteins, music), Shannon’s Information Entropy (expected amount information event drawn from given distribution) Kolmogorov‘s Algorithmic Complexity (the length shortest algorithm produces object output), are used quantitative measurements complexity. more dimensions (ecosystems, brains, social groupings), network provides better tools purpose. highly complex none former methods useful. Here, can be ranging subatomic ecological, social, mental AI. Useful Φ (Information thermodynamic free energy) quantified by measuring Free Energy and/or useful Work it produces. measured Total I system, includes Φ, useless or Noise N, Redundant R. one these variables allows quantifying classifying two windows overlooking same fundamental phenomenon, broadening out explore deep structural dynamics nature at all levels complexity, including natural artificial intelligence.

Language: Английский

Citations

0