Difference between revisions of "Randomness, Structure and Causality"
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{{Randomness, Structure and Causality}}  
== Randomness, Structure, and Causality:  
<br>  
Measures of complexity from theory to applications ==  
Line 10:  Line 14:  
    
Workshop summary  '''Workshop summary''' [[File:Rsc.pdf]]  
<br>  
<br>  
In 1989, SFI hosted a  
workshop<i>Complexity, Entropy, and the Physics of Information<i>on  
fundamental definitions of complexity. This  
workshop and the proceedings that resulted \cite{Zure89a}  
stimulated a  
great deal of thinking about how to define complexity. In many wayssome  
direct, many indirectthe foundational theme colored much of SFI's research  
planning and, more generally, the evolution of complex system science since  
then. Complex systems science has considerably matured as a field in the intervening  
decades and we believe it is now time to revisit fundamental aspects of the field in a  
workshop format at SFI. Partly, this is to take stock; but it is  
also to ask what innovations are needed for the coming decades, as  
complex systems continues to extend its influence in the sciences,  
engineering, and humanities.  
<p>  
The goal of the workshop is to bring together workers from a  
variety of fields to discuss structural and dynamical measures of complexity appropriate for their  
field and the commonality between these measures. Some of the questions that  
we will address in the workshop are:  
<ul>  
<li>  
Are there fundamental measures of complexity that can be applied across  
disciplines or are measures of complexity necessarily tied to particular  
domains?  
<li>  
How is a system's causal organization, reflected in models of its  
dynamics, related to its complexity?  
<li>  
Are there universal mechanisms at work that lead to increases in  
complexity or does complexity arise for qualitatively different reasons in  
different settings?  
<li>  
Can we reach agreement on general properties that all measures of  
complexity must have?  
<li>  
How would the scientific community benefit from a consensus on the  
properties that measures of complexity should possess?  
<\ul>  
It's a fourday workshop with about 20 or so participants.  
We will have a stimulating and highly interdisciplinary group with  
representation from physics, biology, computer science, social science, and  
mathematics. An important goal is to understand the successes and  
difficulties in deploying complexity measures in practice. And so,  
participants come from both theory and experiment, with a  
particular emphasis on those who can constructively bridge the two.  
Since the 1989 SFI workshop, a number of distinct strands have developed in  
the effort to measure complexity. Several of the welldeveloped strands are  
based on  
<ul>  
<li>Predictive information and excess entropy,  
<li>Statistical complexity and causal structure,  
<li>  
Logical depth and computational complexity, and  
<li>  
Effective complexity.  
</ul>  
While these measures are broadly based on information theory or the theory of  
computation, the full set of connections and contrasts between them is not  
well developed. Some have sought to clarify the relationship among these  
measures and so another goal of  
the workshop is to foster this kind of comparative work by bringing together  
researchers developing various measures.  
<p>  
A second motivation for the workshop is to bring together workers interested in  
foundational questionswho are mainly from the physics, mathematics, and  
computer science communitieswith complex systems scientists in experimental,  
datadriven fields who have developed quantitative measures of complexity,  
organization, and emergence that are useful in their fields. The range of  
datadriven fields using complexity measures is impressively broad: ranging  
from molecular excitation dynamics and spectroscopic  
observations of the conformational dynamics of single molecules  
through modeling subgrid structure in turbulent fluid flows  
and new visualization methods for emergent flow patterns  
to monitoring market efficiency and the organization of animal  
social structure. The intention is to find relations between the  
practically motivated measures and the more general and fundamentally motivated  
measures. Can the practically motivated measures be improved by an  
appreciation of fundamental principles? Can  
fundamental definitions be sharpened by consideration of how they interact with  
realworld data?  
<p>  
Overall, the workshop's intention is to reignite the efforts that began with  
<i>Complexity, Entropy, and the Physics of Information</i> workshop. A new level  
of rigor, in concepts and in analysis, is now apparent in how statistical  
mechanics, nonlinear dynamics, information theory, and computation theory can be applied to complex  
systems. The meteoric rise of both computer power and machine learning has led  
to new algorithms that address many of the original computational difficulties in  
managing data from complex systems and in estimating various complexity  
measures. Given progress on all these fronts, the time is ripe to develop a  
much closer connection between fundamental theory and applications in many  
areas of complex systems science. 
Revision as of 20:36, 16 December 2010
Workshop Navigation 
== Randomness, Structure, and Causality:
Measures of complexity from theory to applications ==
Organizers
Jim Crutchfield (UC Davis)
Jon Machta (UMass Amherst)
Workshop summary File:Rsc.pdf
In 1989, SFI hosted a
workshopComplexity, Entropy, and the Physics of Informationon
fundamental definitions of complexity. This
workshop and the proceedings that resulted \cite{Zure89a}
stimulated a
great deal of thinking about how to define complexity. In many wayssome direct, many indirectthe foundational theme colored much of SFI's research planning and, more generally, the evolution of complex system science since then. Complex systems science has considerably matured as a field in the intervening decades and we believe it is now time to revisit fundamental aspects of the field in a workshop format at SFI. Partly, this is to take stock; but it is also to ask what innovations are needed for the coming decades, as complex systems continues to extend its influence in the sciences, engineering, and humanities.
The goal of the workshop is to bring together workers from a variety of fields to discuss structural and dynamical measures of complexity appropriate for their field and the commonality between these measures. Some of the questions that we will address in the workshop are:
 Are there fundamental measures of complexity that can be applied across disciplines or are measures of complexity necessarily tied to particular domains?
 How is a system's causal organization, reflected in models of its dynamics, related to its complexity?
 Are there universal mechanisms at work that lead to increases in complexity or does complexity arise for qualitatively different reasons in different settings?
 Can we reach agreement on general properties that all measures of complexity must have?

How would the scientific community benefit from a consensus on the
properties that measures of complexity should possess?
<\ul>
It's a fourday workshop with about 20 or so participants.
We will have a stimulating and highly interdisciplinary group with
representation from physics, biology, computer science, social science, and
mathematics. An important goal is to understand the successes and
difficulties in deploying complexity measures in practice. And so,
participants come from both theory and experiment, with a
particular emphasis on those who can constructively bridge the two.
Since the 1989 SFI workshop, a number of distinct strands have developed in
the effort to measure complexity. Several of the welldeveloped strands are
based on
 Predictive information and excess entropy,
 Statistical complexity and causal structure,
 Logical depth and computational complexity, and
 Effective complexity.
While these measures are broadly based on information theory or the theory of computation, the full set of connections and contrasts between them is not well developed. Some have sought to clarify the relationship among these measures and so another goal of the workshop is to foster this kind of comparative work by bringing together researchers developing various measures.
A second motivation for the workshop is to bring together workers interested in foundational questionswho are mainly from the physics, mathematics, and computer science communitieswith complex systems scientists in experimental, datadriven fields who have developed quantitative measures of complexity, organization, and emergence that are useful in their fields. The range of datadriven fields using complexity measures is impressively broad: ranging from molecular excitation dynamics and spectroscopic observations of the conformational dynamics of single molecules through modeling subgrid structure in turbulent fluid flows and new visualization methods for emergent flow patterns to monitoring market efficiency and the organization of animal social structure. The intention is to find relations between the practically motivated measures and the more general and fundamentally motivated measures. Can the practically motivated measures be improved by an appreciation of fundamental principles? Can fundamental definitions be sharpened by consideration of how they interact with realworld data?
Overall, the workshop's intention is to reignite the efforts that began with Complexity, Entropy, and the Physics of Information workshop. A new level of rigor, in concepts and in analysis, is now apparent in how statistical mechanics, nonlinear dynamics, information theory, and computation theory can be applied to complex systems. The meteoric rise of both computer power and machine learning has led to new algorithms that address many of the original computational difficulties in managing data from complex systems and in estimating various complexity measures. Given progress on all these fronts, the time is ripe to develop a much closer connection between fundamental theory and applications in many areas of complex systems science.