Entropy Methods in Guided Self-Organization

Dear Colleague,

The goal of Guided Self-Organization (GSO) is to leverage the strengths of self-organization 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; (iii) task-independent objectives are combined with task-dependent constraints.

A number of attempts have been made to formalize aspects of GSO within information theory, thermodynamics and dynamical systems. However, the lack of a broadly applicable mathematical framework across multiple scales and contexts leaves GSO methodology incomplete. Devising such a framework and identifying common principles of guidance are the main themes of the GSO workshops.

Of particular interest are well-founded, but general methods for characterizing GSO systems in a principled way, with the view of ultimately allowing them to be guided toward pre-specified goals. In general, various entropy methods drawing from, and overlapping with, information theory, thermodynamics, nonlinear dynamics and graph theory are relevant, while quantifying complexity and its sources is a common theme.

The key areas of interest will include entropy methods for:

• information-driven self-organization
• quantifying complexity
• self-organizing complex networks
• adaptive behaviour
• distributed computation
• machine learning
• computational neuroscience
• swarm intelligence
• cooperative and modular robotics

Dr. Mikhail Prokopenko
Dr. Carlos Gershenson
Guest Editors

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