Methods for Approximations of Quantitative Measures in Self-Organizing Systems

Holzer, Richard; Meer, Hermann de

doi:10.1007/978-3-642-19167-1_1

Cited by 17 publications

(10 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[176] defines self-organization as an increase in statistical complexity, which in turn is defined as the amount of information required to minimally specify the state of the systemʼs causal architecture. As an alternative to entropy, the use of the mean value of random variables has also been proposed [92].…”

Section: Usagementioning

confidence: 99%

Self-Organization and Artificial Life

et al. 2020

View full text Add to dashboard Cite

Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely “soft” (mathematical/computational modeling), “hard” (physical robots), and “wet” (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

show abstract

Section: Usagementioning

confidence: 99%

Self-Organization and Artificial Life

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Examples for measurement techniques include the following: Shalizi presented an approach that is based on the efficiency of predictions and makes use of approaches known from the domain of information theory [31], a closely related approach has been presented by Prokopenko et al in [32]: In general, both aim at assessing which level (i.e., from micro to macro) is more efficient for predicting the next states. Similarly, Holzer and De Meer compare the information at the system level (i.e., the sum of all edges in a network-based representation) with the information of a lower level (i.e., a single edge in a network-based representation) [33]. As a result, emergence values are high for those systems with many components depending on each other and low for those systems that mostly comprise independent components.…”

Section: State Of the Artmentioning

confidence: 99%

To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation

Tomforde

Goller

2020

Computers

View full text Add to dashboard Cite

Self-adaptation and self-organization (SASO) have been introduced to the management of technical systems as an attempt to improve robustness and administrability. In particular, both mechanisms adapt the system's structure and behavior in response to dynamics of the environment and internal or external disturbances. By now, adaptivity has been considered to be fully desirable. This position paper argues that too much adaptation conflicts with goals such as stability and user acceptance. Consequently, a kind of situation-dependent degree of adaptation is desired, which defines the amount and severity of tolerated adaptations in certain situations. As a first step into this direction, this position paper presents a quantification approach for measuring the current adaptation behavior based on generative, probabilistic models. The behavior of this method is analyzed in terms of three application scenarios: urban traffic control, the swidden farming model, and data communication protocols. Furthermore, we define a research roadmap in terms of six challenges for an overall measurement framework for SASO systems.

show abstract

“…For example, Shalizi (2001) defines self-organization as an increase in statistical complexity, which in turn is defined as the amount of information required to minimally specify the state of the system's causal architecture. As an alternative to entropy, the use of the mean value of random variables has also been proposed (Holzer and De Meer, 2011).…”

Section: Usagementioning

confidence: 99%

Self-Organization and Artificial Life: A Review

Gershenson

Trianni

Werfel

et al. 2018

The 2018 Conference on Artificial Life

View full text Add to dashboard Cite

Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of selforganization within ALife studies, point to perspectives for future research, and list open questions.

show abstract

Methods for Approximations of Quantitative Measures in Self-Organizing Systems

Cited by 17 publications

References 16 publications

Self-Organization and Artificial Life

Self-Organization and Artificial Life

To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation

Self-Organization and Artificial Life: A Review

Contact Info

Product

Resources

About