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<head><title>Santa Fe Institute Workshop Summary Description//</title>
 
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  <div class="maketitle">
 
  <h2 class="titleHead"><a
 
id="x1-1doc"></a>
 
Santa Fe Institute Workshop<br />
 
Summary Description
 
  </h2>
 
  <div class="RRAP">
 
<span
 
class="cmr-10">(</span><span class="date" ><span
 
class="cmr-10">Dated: December 13, 2010</span></span><span
 
class="cmr-10">)</span>
 
  </div>
 
<div class="abstractheading"></div>
 
        <div class="abstract">
 
      <!--l. 50--><p class="noindent" ><span
 
class="cmbx-12">Title</span><span
 
class="cmr-12">:</span><br
 
class="newline" /><span
 
class="cmr-12">&#x00A0;      Randomness, Structure, and Causality:</span><br
 
class="newline" /><span
 
class="cmr-12">&#x00A0;</span><span
 
class="cmr-12">&#x00A0;</span><span
 
class="cmr-12">&#x00A0;          Measures of complexity from theory to applications</span><br
 
class="newline" />
 
      <span
 
class="cmbx-12">Dates</span><span
 
class="cmr-12">: 9-13 January 2011</span><br
 
class="newline" /> <span
 
class="cmbx-12">Location</span><span
 
class="cmr-12">: Santa Fe Institute, Santa Fe, New Mexico</span><br
 
class="newline" /> <span
 
class="cmbx-12">Organizers</span><span
 
class="cmr-12">:</span><br
 
class="newline" /><span
 
class="cmr-12">&#x00A0;      Jim Crutchfield (SFI and UC Davis, chaos@ucdavis.edu)</span><br
 
class="newline" /><span
 
class="cmr-12">&#x00A0;      Jon Machta (SFI and University of Massachusetts, machta@physics.umass.edu)</span>
 
        </div>
 
  <div class="frontpagefootnotes">
 
  </div>
 
  </div>
 
<a
 
id="likesection.1"></a>
 
  <h3 class="likesectionHead"><a
 
id="x1-1000"></a>Description</h3>
 
<!--l. 75--><p class="noindent" >In 1989, SFI hosted a workshop&#8212;<span
 
class="cmti-10x-x-109">Complexity, Entropy, and the Physics of Information</span>&#8212;on fundamental
 
                                                                                       
 
                                                                                       
 
definitions of complexity. This workshop and the proceedings that resulted [<a
 
href="#XZure89a">1</a>] stimulated a great deal of
 
thinking about how to define complexity. In many ways&#8212;some direct, many indirect&#8212;the foundational
 
theme colored much of SFI&#8217;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.
 
<!--l. 90--><p class="indent" >  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:
 
      <ol  class="enumerate1" >
 
      <li
 
  class="enumerate" id="x1-1002x1">Are there fundamental measures of complexity that can be applied across disciplines or are
 
      measures of complexity necessarily tied to particular domains?
 
      </li>
 
      <li
 
  class="enumerate" id="x1-1004x2">How is a system&#8217;s causal organization, reflected in models of its dynamics, related to its
 
      complexity?
 
      </li>
 
      <li
 
  class="enumerate" id="x1-1006x3">Are  there  universal  mechanisms  at  work  that  lead  to  increases  in  complexity  or  does
 
      complexity arise for qualitatively different reasons in different settings?
 
      </li>
 
      <li
 
  class="enumerate" id="x1-1008x4">Can we reach agreement on general properties that all measures of complexity must have?
 
      </li>
 
      <li
 
  class="enumerate" id="x1-1010x5">How would the scientific community benefit from a consensus on the properties that measures
 
      of complexity should possess?</li></ol>
 
<!--l. 112--><p class="indent" >  It&#8217;s a four-day 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.
 
<!--l. 120--><p class="indent" >  Since the 1989 SFI workshop, a number of distinct strands have developed in the effort to measure
 
complexity. Several of the well-developed strands are based on
 
                                                                                       
 
                                                                                       
 
      <ul class="itemize1">
 
      <li class="itemize">Predictive information and excess entropy&#x00A0;[<a
 
href="#XJunc79">2</a>&#8211;<a
 
href="#XCrut01a">7</a>],
 
      </li>
 
      <li class="itemize">Statistical complexity and causal structure&#x00A0;[<a
 
href="#XCrut88a">8</a>&#8211;<a
 
href="#XShal98a">10</a>],
 
      </li>
 
      <li class="itemize">Logical depth and computational complexity&#x00A0;[<a
 
href="#XBenn90">11</a>&#8211;<a
 
href="#XMach06a">15</a>], and
 
      </li>
 
      <li class="itemize">Effective complexity&#x00A0;[<a
 
href="#XGeLl96">16</a>,&#x00A0;<a
 
href="#Xay-2008">17</a>].</li></ul>
 
<!--l. 134--><p class="noindent" >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 [<a
 
href="#XCrut01a">7</a>,&#x00A0;<a
 
href="#Xay-2008">17</a>&#8211;<a
 
href="#XFeld98b">20</a>] and so another goal of the workshop is
 
to foster this kind of comparative work by bringing together researchers developing various
 
measures.
 
<!--l. 141--><p class="indent" >  A second motivation for the workshop is to bring together workers interested in foundational
 
questions&#8212;who are mainly from the physics, mathematics, and computer science communities&#8212;with
 
complex systems scientists in experimental, data-driven fields who have developed quantitative
 
measures of complexity, organization, and emergence that are useful in their fields. The range of
 
data-driven fields using complexity measures is impressively broad: ranging from molecular
 
excitation dynamics [<a
 
href="#XNeru08a">21</a>] and spectroscopic observations of the conformational dynamics of
 
single molecules [<a
 
href="#XLi08a">22</a>] through modeling subgrid structure in turbulent fluid flows [<a
 
href="#XPalm00a">23</a>] and new
 
visualization methods for emergent flow patterns [<a
 
href="#XJani07a">24</a>] to monitoring market efficiency [<a
 
href="#XYang08a">25</a>] and the
 
organization of animal social structure [<a
 
href="#XAy07a">26</a>]. 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 real-world
 
data?
 
<!--l. 159--><p class="indent" >  Overall, the workshop&#8217;s intention is to re-ignite the efforts that began with <span
 
class="cmti-10x-x-109">Complexity, Entropy,</span>
 
<span
 
class="cmti-10x-x-109">and the Physics of Information </span>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.
 
<!--l. 1--><p class="indent" >
 
                                                                                        <a
 
id="likesection.2"></a><a
 
id="Q1-1-1"></a>
 
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Revision as of 23:11, 13 December 2010