Modeling of Self-Organizing Systems: An Overview

This paper gives a systematic overview on modeling formalisms suitable for modeling self-organizing systems. We distinguish between micro-level modeling and macro-level modeling. On the micro level, the behavior of each entity and the interaction between different object must be described by the model. Macrolevel modeling abstracts from the individual entities and only looks at the behavior of the system variables of interest. The differentiations between discrete and continuous time and between discrete and continuous state space lead to different descriptions of the model

Designing Comprehensible Self-Organising Systems

textabstractSelf-organising systems are a popular engineering concept for designing decentralised autonomic computing systems. They are able to find solutions in complex and versatile problem domains, but as they capture more complexity in their own design, they are becoming less and less comprehensible to their users (be they humans or intelligent agents). We describe a design challenge that relates to usability theory in general and in particular resembles an observation made by Phoebe Senger, who noted that software agents tend to become incomprehensible in their behaviour as they grow more complex. In the manifestation of self-organising systems, the problem is more urgent (since we find ourselves using them more and more) and harder to solve at the same time (since these systems are not centrally controlled). We describe the problem domain and propose three system properties that could be used as quality indicators in this regard: Stability, Learnability and Engageability. We demonstrate their usage in a simple model of dynamic pricing markets (e.g. the electricity domain) and evaluate them in different ways

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies

Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review.Comment: 57 pages, 17 figures, 1 tabl

Grammar-Based Set-Theoretic Formalization of Emergence in Complex Systems

Master'sMASTER OF SCIENC

Self-Organized Specialization and Controlled Emergence in Organic Computing Systems

In this chapter we studied a first approach to generate suitable rule sets for solving classification problems on systems of autonomous, memory constrained components. It was shown that a multi agent system that uses interacting Pittsburgh-style classifier systems can evolve appropiate rule sets. The system evolves specialists for parts of the classification problem and cooperation between them. In this way the components overcome their restricted memory size and are able to solve the entire problem. It was shown that the communication topology between the components strongly influences the average number of components that a request has to pass until it is classified. It was also shown that the introduction of communication costs into the fitness function leads to a more even distribution of knowledge between the components and reduces the communication overhead without influencing the classification performance very much. If the system is used to generate rule sets to solve classification tasks on real hardware systems, communication cost in the training phase can thus lead to a better knowledge distribution and small communication cost. That is, in this way the system will be more robust against the loss of single components and longer reliable in case of limited energy resources