LNAI 2801 Contextual Random Boolean Networks 1st Edition by Carlos Gershenson, Jan Broekaert, Diederik Aerts ISBN 9783540200574 354020057X

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Product details:
ISBN 10: 354020057X
ISBN 13: 9783540200574
Author: Carlos Gershenson, Jan Broekaert, Diederik Aert

We propose the use of Deterministic Generalized Asynchronous Random Boolean Networks [1] as models of contextual deterministic discrete dynamical systems. We show that changes in the context have drastic effects on the global properties of the same networks, namely the average number of attractors and the average percentage of states in attractors. We introduce the situation where we lack knowledge on the context as a more realistic model for contextual dynamical systems. We notice that this makes the network non-deterministic in a specific way, namely introducing a non-Kolmogorovian quantum-like structure for the modelling of the network [2]. In this case, for example, a state of the network has the potentiality (probability) of collapsing into different attractors, depending on the specific form of lack of knowledge on the context.

LNAI 2801 Contextual Random Boolean Networks 1st Edition Table of contents:

Chapter 1: Introduction to Random Boolean Networks

• Basic Concepts of Boolean Networks
• Historical Development of Random Boolean Networks
• Applications of Random Boolean Networks in Complex Systems
• Why Study Contextual Random Boolean Networks?

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Chapter 2: Foundations of Boolean Networks

• Defining Boolean Networks and Their Structure
• Boolean Logic and State Transitions in Networks
• The Role of Nodes and Connections in Boolean Networks
• Deterministic vs. Stochastic Boolean Networks

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Chapter 3: Contextual Influence in Boolean Networks

• What Is Context in Boolean Networks?
• The Role of Context in Network Behavior
• How Contextual Information Affects Node States
• Mechanisms for Incorporating Context into Boolean Networks

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Chapter 4: Mathematical Modeling of Contextual Random Boolean Networks

• Defining Contextual Random Boolean Networks (CRBNs)
• Formalizing Contextual Influence in Network Models
• Mathematical Techniques for Analyzing CRBNs
• Stability and Dynamics of CRBNs

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Chapter 5: Analysis of the Behavior of Contextual Random Boolean Networks

• Characterizing the Dynamics of CRBNs
• Identifying Stable and Periodic States in CRBNs
• Transition States and Critical Transitions in CRBNs
• Methods for Analyzing Network Complexity and Behavior

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Chapter 6: Computational Methods for Simulating CRBNs

• Simulation Algorithms for Contextual Random Boolean Networks
• Tools for Modeling and Experimenting with CRBNs
• Case Studies Using Computational Approaches to CRBNs
• Implementing CRBNs in Software: Challenges and Solutions

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Chapter 7: Applications of Contextual Random Boolean Networks

• CRBNs in Biological Systems and Genetic Networks
• CRBNs for Modeling Social Systems and Behavior
• Application of CRBNs in Artificial Life and Complex Systems
• CRBNs in Machine Learning and Neural Networks

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Chapter 8: Understanding Emergent Behavior in CRBNs

• Emergence in Complex Systems: Definitions and Principles
• How Contextual Factors Lead to Emergent Properties in CRBNs
• Analyzing Emergent Patterns and Structures
• Exploring Criticality and Self-Organization in CRBNs

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Chapter 9: Experimentation and Case Studies with CRBNs

• Empirical Studies of Contextual Random Boolean Networks
• Real-World Case Studies of CRBN Applications
• Comparative Studies: CRBNs vs. Traditional Random Boolean Networks
• Results and Insights from Experimental Applications of CRBNs

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Chapter 10: Future Directions and Challenges

• Open Problems in the Study of Contextual Random Boolean Networks
• Future Applications in Biological, Social, and Technological Systems
• Interdisciplinary Approaches to Furthering the Understanding of CRBNs
• Prospects for CRBNs in Artificial Intelligence and Evolutionary Systems

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Conclusion

• Summary of Key Findings
• The Impact of Contextual Factors on Network Dynamics
• Final Thoughts on the Significance of CRBNs in Complex Systems Research

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