Information and Self-Organization of Behavior

@article{Polani2013InformationAS,
  title={Information and Self-Organization of Behavior},
  author={Daniel Polani and Mikhail Prokopenko and Larry S. Yaeger},
  journal={Adv. Complex Syst.},
  year={2013},
  volume={16}
}
The goal of Guided Self-Organization (GSO) is to leverage the strengths of selforganization while still being able to direct the outcome of the self-organizing process. GSO typically has the following features: (i) an increase in organization (structure and/or functionality) over some time; (ii) the local interactions are not explicitly guided by any external agent; and (iii) task-independent objectives are combined with task-dependent constraints. Over the last few years a mathematical… 

Information Characteristics, Processes, and Mechanisms of Self-Organization Evolution

In order to explain the self-organization’s characteristics, processes, and mechanisms of system evolution at a more comprehensive level, the complexity research program must pay enough attention to and give due status to the information factors and information science creed.

Self-Organization and Artificial Life

The fundamental aspects of self-organization are discussed and the main usages within three primary ALife domains are listed, namely “soft” (mathematical/computational modeling), “hard’ (physical robots), and “wet”/chemical/biological systems) ALife.

Entropy Methods in Guided Self-Organisation

This special issue covers abroad diversity of GSO approaches which can be classified in three categories: information theory, intelligent agents, and collective behavior.

Self-Organization and Artificial Life: A Review

Some fundamental aspects of self-organization are articulate, its usage is outlined, and its applications to ALife within its soft, hard, and wet domains are reviewed.

A O ] 1 4 M ar 2 01 9 Self-Organization and Artificial Life

The fundamental aspects of self-organization are discussed and the main usages within three primary ALife domains are listed, namely “soft” (mathematical/computational modeling), “hard’ (physical robots), and “wet“ (chemical/biological systems) ALife.

On the Cross-Disciplinary Nature of Guided Self-Organisation

Self-organisation is difficult to engineer on demand: the intricate fabric of interactions within a self-organising system cannot follow a simple-minded blueprint and resists crude interventions.

Interactional Effects Between Individual Heterogeneity and Collective Behavior in Complex Organizational Systems

A straightforward and comprehensive model named dynamic object interaction model is proposed to address complex relations between individuals features, organizational structure and collective behavior based on the idea of “near decomposability” to promote understanding for effective organizational redesign and managerial decision.

Metrics of Emergence, Self-Organization, and Complexity for EWOM Research

It is argued possible pragmatic ways to understanding the valuable information present in word-of-mouth data found on electronic commerce platforms are argued.

Requisite variety, autopoiesis, and self-organization

Guided self-organization has been shown to produce systems which can adapt to the requisite variety of their environment, offering more efficient solutions for problems that change in time than those obtained with traditional techniques.

In Anticipation of Black Swans

Problematic situations are recurrent in organisational systems. We are all, individually or collectively, managing them and need strategies for this purpose. Indeed, for as long as we maintain

References

SHOWING 1-10 OF 43 REFERENCES

Guided self-organization: perception–action loops of embodied systems

In general, self-organization is defined as the transition of asystem into an organized form in the absence of external or centralized control, and an increase in organization over some time is manifested by such an optimal network (paths).

An information-theoretic primer on complexity, self-organization, and emergence

A set of concepts, together with their possible information-theoretic interpretations, which can be used to facilitate the Complex Systems Science discourse are proposed and it is hoped that the suggested information- theoretic baseline may promote consistent communications among practitioners, and provide new insights into the field.

Causal architecture, complexity and self-organization in time series and cellular automata

This work develops computational mechanics for four increasingly sophisticated types of process—memoryless transducers, time series, transducers with memory, and cellular automata, and proves the optimality and uniqueness of the e-machine's representation of the causal architecture, and gives reliable algorithms for pattern discovery.

Guiding the self-organization of random Boolean networks

This article reviews eight different methods for guiding RBNs toward the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases.

Assortativeness and information in scale-free networks

We analyze Shannon information of scale-free networks in terms of their assortativeness, and identify classes of networks according to the dependency of the joint remaining degree distribution on the

Quantifying and Tracing Information Cascades in Swarms

This work proposes a novel, information-theoretic, characterisation of cascades within the spatiotemporal dynamics of swarms, explicitly measuring the extent of collective communications and observed that maximal information transfer tends to follow the stage with maximal collective memory.

Information Dynamics in Small-World Boolean Networks

An ensemble investigation of the computational capabilities of small-world networks as compared to ordered and random topologies finds that the ordered phase of the dynamics and topologies with low randomness are dominated by information storage, while the chaotic phase is dominated byInformation storage and information transfer.

Maps of random walks on complex networks reveal community structure

An information theoretic approach is introduced that reveals community structure in weighted and directed networks of large-scale biological and social systems and reveals a directional pattern of citation from the applied fields to the basic sciences.

Managing organizational complexity : philosophy, theory and application

Series Introduction, Michael Lissack and Kurt Richardson. Volume Introduction, Kurt Richardson. PART ONE: PHILOSOPHY. Section Introduction, Kurt Richardson. Knowing complex systems, Paul Cilliers.

Self-organized acquisition of situated behaviors

  • R. Der
  • Materials Science
    Theory in Biosciences
  • 2001
Based on a quantitative measure of behavioral situatedness a learning dynamics is introduced which enables the controller to sustain the situatedness of the agent.