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	<title>Computational Legal Studies™ &#187; Judicial Peer Effects</title>
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		<title>Hustle and Flow: A Social Network Analysis of the American Federal Judiciary [Repost from 3/25]</title>
		<link>http://computationallegalstudies.com/2009/11/05/hustle-and-flow-a-social-network-analysis-of-the-american-federal-judiciary-repost-from-325/</link>
		<comments>http://computationallegalstudies.com/2009/11/05/hustle-and-flow-a-social-network-analysis-of-the-american-federal-judiciary-repost-from-325/#comments</comments>
		<pubDate>Thu, 05 Nov 2009 04:30:50 +0000</pubDate>
		<dc:creator>Daniel Martin Katz</dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Judicial Decision Making]]></category>
		<category><![CDATA[Judicial Peer Effects]]></category>
		<category><![CDATA[Law as a Complex System]]></category>
		<category><![CDATA[network analysis]]></category>
		<category><![CDATA[Public Law]]></category>
		<category><![CDATA[supreme court]]></category>

		<guid isPermaLink="false">http://computationallegalstudies.com/?p=2475</guid>
		<description><![CDATA[Together with Derek Stafford from the University of Michigan Department of Political Science, Hustle and Flow: A Social Network Analysis of the American Federal Judiciary represents our initial foray into Computational Legal Studies. The full paper contains a number of interesting visualizations &#8230; <a href="http://computationallegalstudies.com/2009/11/05/hustle-and-flow-a-social-network-analysis-of-the-american-federal-judiciary-repost-from-325/">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
			<content:encoded><![CDATA[<p style="text-align: center; "><a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1103573"><img class="size-full wp-image-2476 aligncenter" title="Zoom on Network" src="http://ec2-107-21-222-181.compute-1.amazonaws.com/wp-content/uploads/2009/11/Picture-4.jpg" alt="Zoom on Network" width="681" height="365" /></a></p>
<p style="text-align: justify; ">Together with Derek Stafford from the University of Michigan Department of Political Science, <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1103573"><strong>Hustle and Flow: A Social Network Analysis of the American Federal Judiciary</strong></a> represents our initial foray into Computational Legal Studies. The full paper contains a number of interesting visualizations where we draw various federal judges together on the basis of their shared law clerks (1995-2004). The screen print above is a zoom very center of the center of the network.  <strong><span style="color: #ffff00;">Yellow Nodes</span></strong> represent Supreme Court Justices, <strong><span style="color: #00ff00;">Green Nodes</span></strong><span style="color: #00ff00;"> </span>represent Circuit Court Justices, <strong><span style="color: #0000ff;">Blue Nodes</span></strong> represent District Court Justices.</p>
<p style="text-align: justify; ">There exist many high quality formal models of judicial decision making including those considering decisions rendered by judges in judicial hierarchy, whistle blowing, etc. One component which might meaningfully contribute to the extent literature is the rigorous consideration of the social and professional relationships between jurists and the impacts (if any) these relationships impose upon outcomes. Indeed, from a modeling standpoint, we believe the &#8220;judicial game&#8221; is a game on a graph&#8211;one where an individual strategic jurist must take stock of time evolving social topology upon which he or she is operating. Even among judges of equal institutional rank, we observe jurists with widely variant levels social authority (specifically social authority follows a <a href="http://en.wikipedia.org/wiki/Power_law"><strong>power law distribution</strong></a>).</p>
<p style="text-align: justify; ">So what does all of this mean? Take whistle blowing &#8212; the power law distribution implies that if the average judge has a whistle, the &#8220;super-judges&#8221; we identify within the paper could be said to have an air horn. With the goal of enriching positive political theory / formal modeling of the courts, we believe the development of a <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1277282"><strong>positive theory of judicial social structure</strong></a><strong> </strong>can enrich our understanding of the dynamics of prestige and influence. In addition, we believe, at least in part, &#8220;<a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1277282"><strong>judicial peer effects</strong></a>&#8221; can help legal doctrine socially spread across the network. In that vein, here is a view of our operationalization of the social landscape &#8230; a wide shot of the broader network visualized using the <a href="http://en.wikipedia.org/wiki/Force-based_algorithms"><strong>Kamada-Kawai</strong></a> visualization algorithm:</p>
<p style="text-align: center; "><a style="text-decoration: none;" href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1103573"><img class="size-full wp-image-2479 aligncenter" src="http://ec2-107-21-222-181.compute-1.amazonaws.com/wp-content/uploads/2009/11/Picture-3.jpg" alt="" width="440" height="198" /></a></p>
<p style="text-align: justify; "><strong>Here is the current abstract for the paper: </strong>Scholars have long asserted that social structure is an important feature of a variety of societal institutions. As part of a larger effort to develop a fully integrated model of judicial decision making, we argue that social structure-operationalized as the professional and social connections between judicial actors-partially directs outcomes in the hierarchical federal judiciary. Since different social structures impose dissimilar consequences upon outputs, the precursor to evaluating the doctrinal consequences that a given social structure imposes is a descriptive effort to characterize its properties. Given the difficulty associated with obtaining appropriate data for federal judges, it is necessary to rely upon a proxy measure to paint a picture of the social landscape. In the aggregate, we believe the flow of law clerks reflects a reasonable proxy for social and professional linkages between jurists. Having collected available information for <span style="color: #000000;"><span style="text-decoration: underline;"><span style="color: #ff0000;">all federal judicial law clerks employed by an Article III judge during the “natural” Rehnquist Court (1995-2004)</span></span></span>, we use these roughly <span style="text-decoration: underline;"><span style="color: #ff0000;">19,000 clerk events</span></span> to craft a series of network based visualizations.   Using network analysis, our visualizations and subsequent analytics provide insight into the path of peer effects in the federal judiciary. For example, we find the distribution of “degrees” is highly skewed implying the social structure is dictated by a small number of socially prominent actors. Using a variety of centrality measures, we identify these socially prominent jurists. Next, we draw from the extant complexity literature and offer a possible generative process responsible for producing such inequality in social authority. While the complete adjudication of a generative process is beyond the scope of this article, our results contribute to a growing literature documenting the highly-skewed distribution of authority across the common law and its constitutive institutions.</p>
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		<slash:comments>3</slash:comments>
		</item>
		<item>
		<title>Power Laws, Preferential Attachment and Positive Legal Theory [Part 2] [Repost]</title>
		<link>http://computationallegalstudies.com/2009/08/12/power-laws-preferential-attachment-and-positive-legal-theory-part-2/</link>
		<comments>http://computationallegalstudies.com/2009/08/12/power-laws-preferential-attachment-and-positive-legal-theory-part-2/#comments</comments>
		<pubDate>Wed, 12 Aug 2009 22:49:56 +0000</pubDate>
		<dc:creator>Daniel Martin Katz</dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[computational legal studies]]></category>
		<category><![CDATA[evolution of law]]></category>
		<category><![CDATA[judicial citation network]]></category>
		<category><![CDATA[Judicial Peer Effects]]></category>
		<category><![CDATA[Law as a Complex System]]></category>

		<guid isPermaLink="false">http://computationallegalstudies.com/?p=1587</guid>
		<description><![CDATA[As was stated in Part 1 of this thread, it is by no means a given that the statistical artifact displayed above would appear. Namely, such large scale patterns need not assume this flavor as many social and physical systems &#8230; <a href="http://computationallegalstudies.com/2009/08/12/power-laws-preferential-attachment-and-positive-legal-theory-part-2/">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
			<content:encoded><![CDATA[<p style="text-align: center; "><a href="http://en.wikipedia.org/wiki/Power_law"><img class="aligncenter size-full wp-image-1588" title="Law as a Complex System?" src="http://ec2-107-21-222-181.compute-1.amazonaws.com/wp-content/uploads/2009/07/Picture-1.png" alt="Law as a Complex System?" width="680" height="512" /></a></p>
<p style="text-align: justify; ">As was stated in <a href="http://computationallegalstudies.com/2009/07/15/power-laws-preferential-attachment-and-positive-legal-theory-part-1/"><strong>Part 1</strong></a> of this thread, it is by no means a given that the statistical artifact displayed above would appear. Namely, such large scale patterns need not assume this flavor as many social and physical systems feature substantially different properties.</p>
<p style="text-align: justify; ">For purpose of generating an empirically grounded theory of American Common Law development &#8230; explaining these artifacts would seem to critical. Fortunately, with respect to the above pattern, there exist a definable set of generative processes plausibly responsible for producing what is displayed. While certainly not the only generative process responsible for a <a href="http://www.hpl.hp.com/research/idl/papers/ranking/ranking.html"><strong>power law</strong></a>, the <a href="http://en.wikipedia.org/wiki/Preferential_attachment"><strong>preferential attachment</strong></a> model, first outlined in the physics literature by <a href="http://arxiv.org/abs/cond-mat/9910332"><strong>Barabási &amp; Albert</strong></a>, is among the likely candidates.</p>
<p style="text-align: justify; ">Confronting much of the extant literature, query as to whether a <a href="http://en.wikipedia.org/wiki/Closed-form_expression"><strong>closed form</strong></a> equilibria based analytical apparatus (<a href="http://en.wikipedia.org/wiki/Punctuated_equilibrium"><strong>punctuated</strong></a><strong> </strong>or otherwise) is up to the task of describing the relevant dynamics? If anything, the distributions displayed above provide first-order evidence of a system which is likely to feature dynamics of a non-linear flavor. Indeed, while significant work still remains, the weight of available evidence indicates <a href="http://en.wikipedia.org/wiki/Complex_adaptive_system"><strong>Law is a Complex Adaptive System</strong></a>.<strong> </strong>As such, we believe it would be appropriate to leverage the methods typically reserved for the study of complexity.  For purposes of generating positive legal theory, we believe agent based models, dynamic network analysis and other methods of computational social science offer great potential. We encourage scholars to consider learning more about these approaches.</p>
]]></content:encoded>
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		</item>
		<item>
		<title>Hustle &amp; Flow: A Network Analysis of the American Federal Judiciary</title>
		<link>http://computationallegalstudies.com/2009/03/25/hustle-flow-a-network-analysis-of-the-american-federal-judiciary/</link>
		<comments>http://computationallegalstudies.com/2009/03/25/hustle-flow-a-network-analysis-of-the-american-federal-judiciary/#comments</comments>
		<pubDate>Wed, 25 Mar 2009 22:28:20 +0000</pubDate>
		<dc:creator>Daniel Martin Katz</dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[clerk tournament]]></category>
		<category><![CDATA[Judicial Decision Making]]></category>
		<category><![CDATA[Judicial Peer Effects]]></category>
		<category><![CDATA[Law as a Complex System]]></category>
		<category><![CDATA[Law Clerks]]></category>
		<category><![CDATA[network analysis]]></category>
		<category><![CDATA[Public Law]]></category>
		<category><![CDATA[Sociology of Law]]></category>

		<guid isPermaLink="false">http://computationallegalstudies.com/?p=79</guid>
		<description><![CDATA[This paper written by CLS Blog Co-Founder Daniel Katz and Derek Stafford from the University of Michigan Department of Political Science representes an initial foray into Computational Legal Studies by the graduate students here at the University of Michigan Center &#8230; <a href="http://computationallegalstudies.com/2009/03/25/hustle-flow-a-network-analysis-of-the-american-federal-judiciary/">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
			<content:encoded><![CDATA[<p><img class="aligncenter size-full wp-image-80" title="picture-3" src="http://ec2-107-21-222-181.compute-1.amazonaws.com/wp-content/uploads/2009/03/picture-3.png" alt="picture-3" width="742" height="328" /></p>
<p><a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1103573"><strong>This paper</strong></a><strong> </strong>written by CLS Blog Co-Founder<strong> </strong><a href="http://www.sitemaker.umich.edu/dankatz/home"><strong>Daniel Katz</strong></a><strong> </strong>and Derek Stafford from the University of Michigan Department of Political Science representes an initial foray into Computational Legal Studies by the graduate students here at the<strong> </strong><a href="http://www.cscs.umich.edu/"><strong>University of Michigan Center for the Study of Complex Systems</strong></a>.  The full paper contains a number of interesting visualizations where we draw various federal judges together on the basis of their shared law clerks (1995-2004).  The screen print above is a zoom very center of the center of the network.  <span style="color: #ffff00;"><span style="color: #ffff00;"><strong><span style="color: #ecfb03;">Yellow Nodes</span></strong></span> </span>represent Supreme Court Justices, <span style="color: #008000;"><strong>Green Nodes</strong></span> represent Circuit Court Justices, <span style="color: #0000ff;"><strong>Blue Nodes</strong></span> represent Circuit Court Justices.  Here is a wide shot of the broader network visualized using the <a href="http://en.wikipedia.org/wiki/Force-based_algorithms"><strong>Kamada-Kawai</strong></a> visualization algorithm:    </p>
<p><img class="aligncenter size-medium wp-image-81" title="test" src="http://ec2-107-21-222-181.compute-1.amazonaws.com/wp-content/uploads/2009/03/test-300x178.png" alt="test" width="300" height="178" /></p>
<p><strong>Here is the abstract:      <span style="font-weight: normal;">Scholars have long asserted that social structure is an important feature of a variety of societal institutions. As part of a larger effort to develop a fully integrated model of judicial decision making, we argue that social structure-operationalized as the professional and social connections between judicial actors-partially directs outcomes in the hierarchical federal judiciary. Since different social structures impose dissimilar consequences upon outputs, the precursor to evaluating the doctrinal consequences that a given social structure imposes is a descriptive effort to characterize its properties. Given the difficulty associated with obtaining appropriate data for federal <span class="searchword">judges</span>, it is necessary to rely upon a proxy measure to paint a picture of the social landscape. In the aggregate, we believe the <span class="searchword">flow</span> of law clerks reflects a reasonable proxy for social and professional linkages between jurists. Having collected available information for all federal judicial law clerks employed by an Article III judge during the &#8220;natural&#8221; Rehnquist Court (1995-2004), we use these roughly 19,000 clerk events to craft a series of network based visualizations.   Using network analysis, our visualizations and subsequent analytics provide insight into the path of peer effects in the federal judiciary. For example, we find the distribution of &#8220;degrees&#8221; is highly skewed implying the social structure is dictated by a small number of socially prominent actors. Using a variety of centrality measures, we identify these socially prominent jurists. Next, we draw from the extant complexity literature and offer a possible generative process responsible for producing such inequality in social authority. While the complete adjudication of a generative process is beyond the scope of this article, our results contribute to a growing literature documenting the highly-skewed distribution of authority across the common law and its constitutive institutions. </span></strong></p>
]]></content:encoded>
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