<bibliography>
  <abstract>
    <formalpara>
      <title>Swarm in the literature</title>
      <para>The most recent version of this document can be found at the <ulink url="http://www.swarm.org/pubs.html">SDG site</ulink>.  Last modified on: $Date: 2002/01/17 10:02:31 $</para>
    </formalpara></abstract>

  <bibliodiv>
    <title>Books</title>
    <biblioentry id="luna:perrone">
      <abbrev>Luna:Perrone:2001</abbrev>
      <citetitle pubwork="book">Agent-Based Methods in Economics and
      Finance: Simulations in Swarm</citetitle>
      <authorgroup>
        <editor>
          <firstname>Francesco</firstname>
          <surname>Luna</surname>
        </editor>
        <editor>
          <firstname>Alessandro</firstname>
          <surname>Perrone</surname>
        </editor>
      </authorgroup>
      <isbn>0-7923-7419-3</isbn>
      <publisher><publishername>Kluwer Academic
          Publishers</publishername></publisher> 
      <pubdate>October 2001</pubdate>
      <biblioset relation="series">
        <title>Advances in Computational Economics</title>
        <seriesvolnums>volume 17</seriesvolnums>
        <authorgroup>
          <editor>
            <firstname>Hans</firstname>
            <surname>Amman</surname>
          </editor>
          <editor>
            <firstname>Anna</firstname>
            <surname>Nagurney</surname>
          </editor>
        </authorgroup>
      </biblioset>

      <abstract>
        <para><emphasis>Extract of <ulink
            url="http://www.wkap.nl/prod/b/0-7923-7419-3">book</ulink>
            description from the publishers:</emphasis></para>

	<para> This volume on financial and economic simulations in
	Swarm marks the continued progress by a group of researchers
	to incorporate agent-based computer models as an important
	tool within their discipline.</para>

	<para>Swarm promotes agent-based computer models as a tool
	for the study of complex systems. A common language is leading
	to the growth of user communities in specific areas of
	application. Furthermore, by providing an organizing framework
	to guide the development of more problem-specific structures,
	and by dealing with a whole range of issues that affect their
	fundamental correctness and their ability to be developed and
	reused, Swarm has sought to make the use of agent-based models
	a legitimate tool of scientific investigation that also meets
	the practical needs of investigators within a community....</para>

<!--
	<para>Swarm's principal foundation is an object-oriented
	representation of active agents interacting among themselves
	and with their environment. To this base layer it adds its own
	structures to drive, record and portrait the events that occur
	across this world. The specific contents of any world,
	however, are up to the experimenter to provide, either by
	building them from scratch or by tapping previous
	contributions.</para>

	<para>This book is notable in assembling a rich array of such
	contributions, which are significant in their own right, but
	which can also be mined to extract the reusable elements in
	their respective areas of finance and economics. It also
	presents three interesting software additions with tutorials
	in the form of simple financial and economic applications. A
	Swarm meta-language closer to a `natural language', the use of
	internet-augmented Swarm for experimental economics, and a
	Swarm visual builder will meet the challenges launched by
	other agent-based modelling competitors.</para>

	<para>The Swarm community at large can benefit greatly from
	the lead that the growing field of computational economics is
	taking to address its own needs, as represented by this book.</para>
-->
      </abstract>
    </biblioentry>

    <biblioentry id="luna">
    <abbrev>Luna:Stefansson:2000</abbrev>
      <citetitle pubwork="book">Economic Simulations in Swarm: Agent-Based Modelling and Object Oriented Programming</citetitle>
      <authorgroup>
        <editor>
          <firstname>Francesco</firstname>
          <surname>Luna</surname>
        </editor>
        <editor>
          <firstname>Benedikt</firstname>
          <surname>Stefannson</surname>
        </editor>
      </authorgroup>
      <isbn>0-7923-8665-5</isbn>
      <publisher><publishername>Kluwer Academic
          Publishers</publishername></publisher> 
      <pubdate>2000</pubdate>
      <biblioset relation="series">
        <title>Advances in Computational Economics</title>
        <seriesvolnums>volume 14</seriesvolnums>
        <authorgroup>
          <editor>
            <firstname>Hans</firstname>
            <surname>Amman</surname>
          </editor>
          <editor>
            <firstname>Anna</firstname>
            <surname>Nagurney</surname>
          </editor>
        </authorgroup>
      </biblioset>

      <abstract>
        <para><emphasis>Book description from the
            publishers:</emphasis></para>
        
        <para>Computer simulations of economic systems are slowly
          gaining ground within the economic profession. However, such
          a process is hindered by a lack of communication among
          researchers who do not share a common language. For its
          object-oriented structure and its versatility, Swarm has the
          necessary characteristics to become a credible universal
          language of agent-based simulations. Economic Simulations in
          Swarm collects a series of original articles in such domains
          as macro and micro economics, industrial organization,
          monetary theory, and finance, all linked by a common
          denominator: the use of the Swarm simulation
          platform. </para>

        <para>Swarm, a standard set of program libraries, allows
          users to construct simulations where a collection of
          heterogeneous independent agents or elements interact through
          discrete events. This volume offers the first extensive
          tutorial to the use of these software libraries developed at
          the Santa Fe Institute as part of the ongoing research into
          complexity.</para>
        
        <para>The editors conceived the idea of this book while
          visiting the Santa Fe Institute as members of the `Working
          Group on Adaptive and Computable Economics'. Francesco Luna is
          a specialist in Computable Economics, and Benedikt Stefansson
          is an active contributor to the Swarm community.</para>

        <para>An outline of the book and source code for models
          discussed in the book are available <ulink url="ftp://ftp.swarm.org/pub/swarm/src/users-contrib/anarchy/lunabook/index.html">here</ulink>.</para>
      </abstract>
    </biblioentry>
  </bibliodiv>

  <bibliodiv>
    <title>Papers</title>
    <biblioentry>
    <abbrev>Satterfield:1999</abbrev>
    <authorgroup>
      <author>
        <firstname> T.  </firstname>
        <surname> Satterfield  </surname>
      </author>
      <author>
        <firstname> M.  </firstname>
          <surname> Murphy  </surname>
      </author>
    </authorgroup>
    <citetitle pubwork="article"> A computational model of creole genesis </citetitle>
    <confgroup>
      <conftitle>Linguistic Society of America Meeting</conftitle>
      <confdates>January 1999</confdates> 
      <address
               format="linespecific"><city>Los Angeles</city></address>
    </confgroup>
  </biblioentry>
  
<biblioentry>
<abbrev>Dagaeff:1997</abbrev>
 <authorgroup>
  <author>
   <firstname> T.  </firstname>
   <surname> Dagaeff  </surname>
  </author>
  <author>
   <firstname> F.  </firstname>
   <surname> Chantemargue  </surname>
  </author>
  <author>
   <firstname> B.  </firstname>
   <surname> Hirsbrunner  </surname>
  </author>
      </authorgroup>
      <confgroup>
        <conftitle>Proceedings of the Second European Conference on Cognitive
          Science (ECCS'97)</conftitle>
        <address format="linespecific">
          <city>Manchester</city>
          <country>U.K.</country>
        </address>
        <confdates>April 9-11</confdates>
      </confgroup>
      <pagenums> 91-96 </pagenums>
      <citetitle pubwork="article"><ulink url="http://www.santafe.edu/projects/swarm/users/ebcmas.ps">Emergence-based Cooperation in a Multi-Agent System</ulink> </citetitle>
      <pubdate> 1997 </pubdate>

    </biblioentry>
    <biblioentry>
      <abbrev>Terna:1998b</abbrev>
      <authorgroup>
        <author>
          <firstname> P.  </firstname>
          <surname> Terna  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article">ABCDE: Agent Based Chaotic Dynamic Emergence</citetitle> 

      <biblioset relation="series">
        <title>Lecture Notes in Artificial Intelligence</title>
        <seriesvolnums>1534</seriesvolnums>
        <confgroup>
        <conftitle>Multi-Agent Systems and Agent-Based Simulation, First
International Workshop, MABS'98</conftitle>
        </confgroup>
        <publisher>
          <publishername> Springer </publishername>
          <address format="linespecific"><city>Berlin</city></address>
        </publisher>
        <pubdate>1998</pubdate>
      </biblioset>
      
      <abstract><para> This paper concerns agent based experiments in
      the field of negotiation and exchange simulation. A computer
      simulation environment is built, showing the emergence of
      chaotic price sequences in a simple model of interacting
      consumers and vendors, both equipped with minimal rules. em
      Swarm the framework of the model
      (www.santafe.edu/projects/swarm), a simulation tool with a
      strong object oriented structure, also very useful to separate
      in a clear way the model level from the level of the
      observer. Swarm is fully programmable in Objective C, with many
      powerful libraries, aimed at modeling the objects and the
      schedules of our experiments, with lists and arrays where
      necessary. Finally we introduce a tool (Cross Target method:
      CT), useful in building artificial laboratories, for experiments
      with learning, self-developed consistency and interaction of
      agents in artificial worlds, in order to observe the emergence
      of complexity without a priori behavioral rules: The perspective
      of our work is that of developing CT within the Swarm framework
      to replicate the ABCDE experiment in this light-rules or
      no-rules context. </para></abstract>
      
</biblioentry>

<biblioentry>
<abbrev>Burkhart:1995</abbrev>
      
      <authorgroup>
        <author>
          <firstname> Roger  </firstname>
          <surname> Burkhart  </surname>
  </author>
      </authorgroup>
      <confgroup>
        <conftitle>Object-Oriented Programming Systems, Languages, and
          Applications (OOPSLA) '95 Adaptable and Adaptive Software Workshop</conftitle>
        <confdates>8 October 1995</confdates>
      </confgroup>
      <citetitle pubwork="article"><ulink
                                          url="http://www.santafe.edu/~rmb/oopsla95.html"> Create-phase
          Protocols for Object Customization</ulink> </citetitle>
    </biblioentry>
    
    <biblioentry>
<abbrev>Parunak:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> H. V. D.  </firstname>
          <surname> Parunak  </surname>
        </author>
        <author>
          <firstname> R.  </firstname>
          <surname> Savit  </surname>
        </author>
        <author>
          <firstname> R. L.  </firstname>
          <surname> Riolo  </surname>
        </author>
      </authorgroup>
      <biblioset relation="series">
        <title>LNAI series</title>
        <seriesvolnums>1534</seriesvolnums>
        <authorgroup>
          <editor>
            <surname>Sichman</surname>
          </editor>
          <editor>
            <surname>Conte</surname>
          </editor>
          <editor>
            <surname>Gilbert</surname>
          </editor>
        </authorgroup>
        <confgroup>
          <conftitle>Multi-agent systems and Agent-based Simulation (MABS'98)</conftitle>
          <confdates>1998</confdates>
        </confgroup>
        <publisher><publishername>Springer-Verlag</publishername>
        </publisher>
      </biblioset>
      <citetitle pubwork="article"> Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide </citetitle>
      <pubdate> 1998 </pubdate>
    </biblioentry>
    
    <biblioentry>
<abbrev>Booth:1997</abbrev>
      <authorgroup>
        <author>
          <firstname> Ginger  </firstname>
          <surname> Booth  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"> <ulink
        url="http://peaplant.biology.yale.edu:8001/papers/swarmgecko/rewrite.html">Gecko:
        A Continuous 2D World for Ecological Modeling</ulink>
        </citetitle> <citetitle pubwork="journal">Artificial
        Life</citetitle> 

      <volumenum>3</volumenum> <issuenum>3</issuenum>
      <pagenums>147--163</pagenums> <pubdate>Summer 1997</pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Terna:1998a</abbrev>
      <authorgroup>
        <author>
          <firstname> P.  </firstname>
          <surname> Terna  </surname>
        </author>
      </authorgroup>
      <abstract><para> Social scientists are not computer scientists,
          but their skills in the field have to become better and
          better to cope with the growing field of social simulation
          and agent based modelling techniques. A way to reduce the
          weight of software development is to employ generalised
          agent development tools, accepting both the boundaries
          necessarily existing in the various packages and the subtle
          and dangerous differences existing in the concept of agent
          in computer science, artificial intelligence and social
          sciences. The choice of tools based on the object oriented
          paradigm that offer libraries of functions and graphic
          widgets is a good compromise. A product with this kind of
          capability is Swarm, developed at the Santa Fe Institute and
          freely available, under the terms of the GNU license. A
          small example of a model developed in Swarm is introduced,
          in order to show directly the possibilities arising from the
          use of these techniques, both as software libraries and
          methodological guidelines. With simple agents - interacting
          in a Swarm context to solve both memory and time simulation
          problems - we observe the emergence of chaotic sequences of
          transaction prices. </para></abstract> 

      <citetitle pubwork="article"><ulink
        url="http://www.soc.surrey.ac.uk/JASSS/1/2/4.html">Simulation
        Tools for Social Scientists: Building Agent Based Models with
        Swarm</ulink> </citetitle>
      <citetitle>Journal of Artificial Societies and
        Social Simulation</citetitle> 
      <volumenum>1</volumenum><issuenum>2</issuenum><pubdate>March 1998</pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>McMullin:1997a</abbrev>
      <authorgroup>
        <author>
          <firstname> Barry  </firstname>
          <surname> McMullin  </surname>
        </author>
      </authorgroup>

      <abstract><para> This report presents a detailed review and
      re-presentation of the algorithm for (computational) realisation
      of autopoiesis, originally presented by Varela et
      al. (1974). The review is from the perspective of one seeking to
      re-implement this algorithm. It arises from an on-going project
      to develop such a re-implementation using the Swarm simulation
      system.[1] The motivation for such re-implementation is firstly
      to critically re-examine the phenomenology generated by this
      model chemistry, and secondly to use it as a basis for exploring
      more complex systems. The algorithm is first reviewed and
      annotated for internal consistency and clarity; it is then
      separately reviewed for consistency with the experimental
      results which originally accompanied it. A number of significant
      discrepancies are discussed. By kind permission of Francisco
      Varela, this report also includes, as an Appendix, some
      previously unpublished documentation and source code (in FORTRAN
      IV) associated with the original model. By critical
      consideration of the experimental results in conjunction with
      this code, an important--perhaps crucial--interaction, not
      included in any previous description of the model, has been
      re-discovered. This interaction is presented and discussed
      briefly.  </para></abstract>
      <pubsnumber>Santa Fe Institute Working Paper 97-01-001</pubsnumber>
      <citetitle pubwork="article"> <ulink
                                           url='http://www.santafe.edu/sfi/publications/Working-Papers/97-01-001/'>Computational
        Autopoiesis: The Original Algorithm</ulink> </citetitle>

      <pubdate>January 1997</pubdate>
      
    </biblioentry>
    
    <biblioentry>
<abbrev>Chantemargue:1998b</abbrev>
      <authorgroup>
        <author>
          <firstname> F.  </firstname>
          <surname> Chantemargue  </surname>
        </author>
        <author>
          <firstname> O.  </firstname>
          <surname> Krone  </surname>
        </author>
        <author>
          <firstname> M.  </firstname>
          <surname> Schumacher  </surname>
        </author>
        <author>
          <firstname> T.  </firstname>
          <surname> Dagaeff  </surname>
        </author>
        <author>
          <firstname> B.  </firstname>
          <surname> Hirsbrunner  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"> Autonomous Agents: from Concepts to Implementation </citetitle>
      <confgroup>
        <conftitle>Proceedings of the Fourteenth European Meeting on
          Cybernetics and Systems Research (EMCSR'98)</conftitle>
        <confdates>April 14-17 1998</confdates> <address
                                                         format="linespecific"><city>Vienna</city><country>Austria</country></address>
      </confgroup>
      
      <pagenums> 731--736 </pagenums>
      <volumenum> 2 </volumenum>      
      <pubdate> 1998 </pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Savage:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> Melissa  </firstname>
          <surname> Savage  </surname>
        </author>
        <author>
          <firstname> Manor  </firstname>
   <surname> Askenazi  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"> <ulink
        url="http://www.santafe.edu/sfi/publications/Abstracts/98-06-056abs.html">Arborscapes:
          A Swarm-Based Multi-agent Ecological Disturbance Model</ulink>
      </citetitle>
      
      <pubsnumber>Submitted, available as SFI Working Paper 98-06-056</pubsnumber>
      
      <citetitle pubwork="journal">Geographical and Environmental Modelling</citetitle>
      <pubdate> 1998 </pubdate> 
      
      <abstract><para> This paper presents an agent-based,
          object-oriented ecological model of forest dynamics designed
          to examine the role of disturbance on diversity. Arborscapes
          is based on Swarm, an agent-based software platform that
          offers advantages for ecological modeling, including a suite
          of standardized libraries of objects, schedules, and probes,
          and architectural features such as inheritance, message
          passing, encapsulation, and hierarchical
          structure. Object-oriented models are more transparent,
          portable and more easily modified than process oriented
          models, and therefore promise to facilitate collaboration on
          computational experiments. The initial application of
          Arborscapes was the analysis of disturbance dynamics, but
          the model was designed to be modified for a variety of
          applications in the simulation of vegetation community
          dynamics. </para></abstract>
    </biblioentry>

    <biblioentry>
<abbrev>Johnson:1998a</abbrev>
      <authorgroup>
        <author>
          <firstname> Paul  </firstname>
          <surname> Johnson  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"> Adaptive Agents versus Rational Actors: Social Science Implications </citetitle>
      <confgroup>
        <conftitle>Annual Meeting of the American Political Science
        Association</conftitle>
        <confdates>3-6 September 1998</confdates>
        <address format="linespecific">
          <otheraddr>Marriott Copley Place and Sheraton Boston Hotel
            and Tower</otheraddr><city>Boston</city>
        </address>
      </confgroup>
    </biblioentry>


    <biblioentry>
<abbrev>Kohler:1999</abbrev>
      <authorgroup>
        <author>
          <firstname> T. A.  </firstname>
          <surname> Kohler  </surname>
        </author>
        <author>
          <firstname> J.  </firstname>
          <surname> Kresl  </surname>
        </author>
        <author>
          <firstname> C. Van  </firstname>
          <surname> West  </surname>
        </author>
        <author>
          <firstname> Eric  </firstname>
          <surname> Carr  </surname>
        </author>
        <author>
          <firstname> Richard  </firstname>
          <surname> Wilshusen  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="chapter">Be There Then: A Modeling Approach
        to Settlement Determinants and Spatial Efficiency among late
        Ancestral Pueblo Populations of the Mesa Verde Region,
        U.S. Southwest</citetitle>
      
      <biblioset relation="book">
        <citetitle pubwork="book">Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes</citetitle>
        <authorgroup>
          <editor>
            <firstname>Timothy</firstname>
            <surname>Kohler</surname>
          </editor>
          <editor>
            <firstname>George</firstname>
            <surname>Gumerman</surname>
          </editor>
        </authorgroup>
        <publisher><publishername> Santa Fe Institute and Oxford University Press </publishername></publisher>
        <pubdate> 1999 </pubdate>
      </biblioset>

    </biblioentry>

    
    <biblioentry>
<abbrev>Bruhn:1997</abbrev>
      <authorgroup>
        <author>
          <firstname> Peter  </firstname>
          <surname> Bruhn  </surname>
          <affiliation>
            <orgname>University of Illinois, Urbana-Champaign and Beckman Institute of Advanced Science and Technology</orgname>
          </affiliation>
        </author>
      </authorgroup>

      <pubsnumber>Master's Thesis</pubsnumber>

      <citetitle pubwork="manuscript"> Enterprise Simulation using Multi-Agent System Modeling and the Swarm Toolkit </citetitle>

      <pubdate>October 1997</pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Strader:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> Troy J.  </firstname>
          <surname> Strader  </surname>
        </author>
        <author>
          <firstname> Fu-Ren  </firstname>
          <surname> Lin  </surname>
        </author>
        <author>
          <firstname> Michael J.  </firstname>
          <surname> Shaw  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"><ulink
      url="http://www.soc.surrey.ac.uk/JASSS/1/2/5.html">Simulation of
      Order Fulfillment in Divergent Assembly Supply Chains</ulink>
      </citetitle>
      
      <citetitle pubwork="journal">Journal of Artificial Societies and Social Simulation</citetitle>
      <volumenum> 1 </volumenum>
      <issuenum>2</issuenum>
      <pubdate>March 1998</pubdate>

      <abstract><para> Management of supply chains is a difficult task
          involving coordination and decision-making across
          organizational boundaries. Computational modeling using
          multi-agent simulation is a tool that can provide decision
          support for supply chain managers. We identify the
          components of a supply chain model and implement it in the
          Swarm multi-agent simulation platform. The model is used to
          study the impact of information sharing on order fulfillment
          in divergent assembly supply chains (commonly associated
          with the computer and electronics industries). We find that
          efficient information sharing enables inventory costs to be
          reduced while maintaining acceptable order fulfillment cycle
          times. This is true because information, which provides the
          basis for enhanced coordination and reduced uncertainty, can
          substitute for inventory.  </para></abstract>
      
    </biblioentry>

    <biblioentry>
<abbrev>Chantemargue:1998a</abbrev>
      <authorgroup>
        <author>
          <firstname> F.  </firstname>
          <surname> Chantemargue  </surname>
        </author>
        <author>
          <firstname> T.  </firstname>
          <surname> Dagaeff  </surname>
        </author>
        <author>
          <firstname> M.  </firstname>
          <surname> Schumacher  </surname>
        </author>
        <author>
          <firstname> B.  </firstname>
          <surname> Hirsbrunner  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"> Autonomous Agents and Cooperation:
      Application to Collective Robotics </citetitle>
      
      <affiliation>
        <orgname>University of Fribourg</orgname>
        <orgdiv>Computer Science Department</orgdiv>
        <address format="linespecific"><city>Fribourg</city>
          <country>Switzerland</country></address>
      </affiliation>
      <pubsnumber>Internal Note 98-03</pubsnumber>
      <pubdate>February 1998</pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Armstrong:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> Aaron A.  </firstname>
          <surname> Armstrong  </surname>
        </author>
        <author>
          <firstname> Edmund H.  </firstname>
          <surname> Durfee  </surname>
        </author>
      </authorgroup>
      
      <citetitle pubwork="article"> Mixing and Memory: Emergent Cooperation in an Information Marketplace </citetitle>

      <confgroup>
        <conftitle>Proceedings of the Third International Conference
          on Multiagent Systems</conftitle>
        <confdates>July 1998</confdates>
      </confgroup>
      <publisher><publishername> IEEE Computer Society Press </publishername></publisher>
    </biblioentry>

    <biblioentry>
<abbrev>Strader:1999</abbrev>
      <authorgroup>
        <author>
          <firstname> Troy J.  </firstname>
          <surname> Strader  </surname>
        </author>
        <author>
          <firstname> Fu-Ren  </firstname>
          <surname> Lin  </surname>
        </author>
        <author>
          <firstname> Michael J.  </firstname>
          <surname> Shaw  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"> The Impact of Information Sharing on Order Fulfillment in Divergent Differentiation Supply Chain </citetitle>

      <citetitle pubwork="journal">Journal of Global Information Management</citetitle>
      <volumenum> 7 </volumenum>
      <issuenum>1</issuenum>
      <pubdate>January - March 1999</pubdate>
    </biblioentry>


    <biblioentry>
<abbrev>Burkhart:1997</abbrev>
      <authorgroup>
        <author>
          <firstname> Roger  </firstname>
          <surname> Burkhart  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"><ulink url="http://www.santafe.edu/~rmb/oopsla97.ps">Schedules of Activity in the Swarm Simulation System</ulink></citetitle>
      <confgroup>
        <conftitle>OOSPLA '97 Workshop on OO Behavioral Semantics</conftitle>
        <confdates>1997</confdates>
      </confgroup>
    </biblioentry>

    <biblioentry>
<abbrev>Cohen:1999</abbrev>
      <authorgroup>
        <author>
          <firstname> Michael D.  </firstname>
          <surname> Cohen  </surname>
        </author>
        <author>
          <firstname> Rick L.  </firstname>
          <surname> Riolo  </surname>
        </author>
        <author>
          <firstname> Robert  </firstname>
          <surname> Axelrod  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"><ulink url="http://www.pscs.umich.edu/RESEARCH/pscs-tr.html">The Emergence of Social Organization in the Prisoners' Dilemma: How Context-Preservation and other Factors Promote Cooperation</ulink></citetitle>

      <pubsnumber>PSCS Working Paper 99-01-002</pubsnumber>

      <pubdate>January 1999</pubdate>
    </biblioentry>
    
    <biblioentry>
<abbrev>Kohler:1996b</abbrev>
      <authorgroup>
        <author>
          <firstname> T. A.  </firstname>
          <surname> Kohler  </surname>
        </author>
        <author>
          <firstname> Carla R. Van  </firstname>
          <surname> West  </surname>
        </author>
        <author>
          <firstname> Eric P.  </firstname>
          <surname> Carr  </surname>
        </author>
        <author>
          <firstname> Christopher G.  </firstname>
          <surname> Langton  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"><ulink url="http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/kohler_tim/kohler.html">Agent-Based Modeling of Prehistoric Settlement Systems in the Northern American Southwest</ulink></citetitle>
      
      <confgroup>
        <conftitle>Proceedings of Third International Conference Integrating GIS and Environmental Modeling, Santa Fe, New Mexico</conftitle>
        <confdates>1996</confdates>
      </confgroup>
      <publisher>
        <publishername>National Center for Geographic Information and Analysis</publishername>
        <address format="linespecific"><city>Santa Barbara</city></address>
      </publisher>
    </biblioentry>
    
    <biblioentry>
<abbrev>Krothapalli:1997</abbrev>
      <authorgroup>
        <author>
          <firstname> N.K.C.  </firstname>
          <surname> Krothapalli  </surname>
        </author>
        <author>
          <firstname> A. V.  </firstname>
          <surname> Deshmukh  </surname>
        </author>
      </authorgroup>
      
      <citetitle pubwork="article"> Effects of negotiation mechanisms on performance of agent based manufacturing Systems </citetitle>

      <confgroup>
        <conftitle>Proceedings of the Seventh International Conference
          on Flexible Automation and Intelligent
          Manufacturing</conftitle>
        <confdates>1997</confdates>
      </confgroup>

      <pagenums> 704-717 </pagenums> <abstract><para> This paper
      proposes new inter-agent negotiation mechanisms for improving
      the performance of agent based or decentralized manufacturing
      systems. The focus of this paper is on demonstrating efficiency
      of different negotiation and collaboration schemes between
      agents of the same class (parts, machines, etc) and inter-class
      negotiations using currency metrics. The cooperation and
      negotiation protocols are modeled using the em Swarm multi-agent
      simulation platform. We demonstrate the robustness of the
      proposed schemes, and compare them with hierarchical scheduling
      systems. </para></abstract>
    </biblioentry>
    
    <biblioentry>
<abbrev>Jares:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> Tim  </firstname>
          <surname> Jares  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="manuscript"> The Survival and Consequences of Noise Traders in Financial Markets: A Numerical Modeling Approach </citetitle>
      <pubsnumber>PhD Thesis</pubsnumber>
      <affiliation>
        <orgname>University of Nebraska</orgname>
      </affiliation>
      <pubdate>August 1998</pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Fulkerson:1997</abbrev>
      <authorgroup>
        <author>
          <firstname> B.  </firstname>
          <surname> Fulkerson  </surname>
        </author>
        <author>
          <firstname> G.  </firstname>
          <surname> Staffend  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"> Decentralized Control in the Customer Focused Enterprise </citetitle>

      <citetitle pubwork="journal">Annals of Operations Research</citetitle>

      <pagenums> 325--333 </pagenums>
      <volumenum> 77 </volumenum>
      <pubdate> 1997 </pubdate>

      <abstract><para> This paper discusses analytical methods to
      enable the order fulfillment process (OFF) of a customer-focused
      enterprise. We present three examples of agent-based systems
      that illustrate the benefits of decentralized control in
      discrete part manufacturing. The Swarm simulation platform is
      introduced as a novel means to investigate supply chain
      management strategies.  </para></abstract>

    </biblioentry>

    <biblioentry>
<abbrev>Savit:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> Robert  </firstname>
          <surname> Savit  </surname>
        </author>
        <author>
          <firstname> Radu  </firstname>
          <surname> Manuca  </surname>
        </author>
        <author>
          <firstname> Rick  </firstname>
          <surname> Riolo  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article">
        <ulink
               url="http://www.pscs.umich.edu/RESEARCH/pscs-tr.html">The
          Dynamics of Minority competition</ulink> </citetitle> 
      
      <citetitle
                 pubwork="journal">Physical Review Letters</citetitle> 
      <pubdate>1998</pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Krothapalli:1998b</abbrev>
      <authorgroup>
        <author>
          <firstname> N.K.C.  </firstname>
          <surname> Krothapalli  </surname>
        </author>
        <author>
          <firstname> A.V.  </firstname>
          <surname> Deshmukh  </surname>
        </author>
      </authorgroup>
      
      <citetitle pubwork="article"> Design of negotiation protocols for multi--agent
      manufacturing systems </citetitle>

      <citetitle pubwork="journal">International Journal of Production
        Research</citetitle>
      <pubdate>1998 (in press)</pubdate>

      <abstract><para> This paper proposes new inter-agent and
          intra-agent negotiation mechanisms for improving the
          performance of multi-agent or decentralized manufacturing
          systems. The overall performance of this system depends on
          the effective interactions between agents. This research
          presents methods which would permit cooperation and
          multi--stage interactions among agents. Agents may
          collaborate or preempt other agents based on the available
          currency and task criticality. The objective of this
          research is to demonstrate the efficiency of different
          negotiation and collaboration schemes between agents of the
          same class (parts, machines, etc), and inter-class
          negotiations using currency metrics and preemption. The
          proposed negotiation schemes are implemented using the Swarm
          simulation platform. </para></abstract>
    </biblioentry>
    
    <biblioentry>
<abbrev>Marshall:1999</abbrev>
      <authorgroup>
        <author>
          <firstname> J.A.R.  </firstname>
          <surname> Marshall  </surname>
        </author>
        <author>
          <firstname> J.E.  </firstname>
          <surname> Rowe  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article">The Evolution of Cooperation Through Kin Selection </citetitle>
      <citetitle pubwork="journal">International Journal of Systems Science</citetitle>
      <pubdate>1999 (submitted)</pubdate>
    </biblioentry>


    <biblioentry>
<abbrev>Minar:1996</abbrev>
      <authorgroup>
        <author>
          <firstname> Nelson  </firstname>
          <surname> Minar  </surname>
        </author>
        <author>
          <firstname> Rogert  </firstname>
          <surname> Burkhart  </surname>
        </author>
        <author>
          <firstname> Chris  </firstname>
          <surname> Langton  </surname>
        </author>
        <author>
          <firstname> Manor  </firstname>
          <surname> Askenazi  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"><ulink url="http://www.swarm.org/archive/overview.ps">The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations</ulink> </citetitle>

      <pubsnumber>SFI Working Paper 96-06-042</pubsnumber>
      <pubdate> 1996 </pubdate>

      <abstract><para> Swarm is a multi-agent software platform for
          the simulation of complex adaptive systems. In the Swarm
          system the basic unit of simulation is the swarm, a
          collection of agents executing a schedule of actions. Swarm
          supports hierarchical modeling approaches whereby agents can
          be composed of swarms of other agents in nested
          structures. Swarm provides object oriented libraries of
          reusable components for building models and analyzing,
          displaying, and controlling experiments on those
          models. Swarm is currently available as a beta version in
          full, free source code form. It requires the GNU C Compiler,
          Unix, and X Windows. More information about Swarm can be
          obtained from our web pages <ulink
          url="http://www.swarm.org"></ulink> </para></abstract>
      
    </biblioentry>
    
    <biblioentry>
<abbrev>Carnahan:1997</abbrev>
      <authorgroup>
        <author>
          <firstname> John  </firstname>
          <surname> Carnahan  </surname>
        </author>
        <author>
          <firstname> Song-gang  </firstname>
          <surname> Li  </surname>
        </author>
        <author>
          <firstname> Carlo  </firstname>
          <surname> Costantini  </surname>
        </author>
        <author>
          <firstname> Yeya T.  </firstname>
          <surname> Toure  </surname>
        </author>
        <author>
          <firstname> Charles E.  </firstname>
          <surname> Taylor  </surname>
        </author>
      </authorgroup>
      <publisher><publishername> MIT Press </publishername></publisher>

      <citetitle pubwork="article"> Computer Simulation of Dispersal by em Anopheles Gambiae s.l. in West Africa </citetitle>
      <confgroup>
        <conftitle>Artificial Life V: Proceedings of the Fifth
          International Workshop on the Synthesis and Simulation of
          Living Systems</conftitle>
        <confdates>1997</confdates>
      </confgroup>
      <pagenums> 387--394 </pagenums>      

      <biblioset relation="series">
        <title>Complex Adaptive Systems</title>
        <publisher>
          <publishername>MIT Press</publishername>
          <address format="linespecific"><city>Boston</city></address>
        </publisher>
      </biblioset>
    </biblioentry>

    <biblioentry>
<abbrev>Burkhart:1994</abbrev>
      <authorgroup>
        <author>
          <firstname> Roger  </firstname>
          <surname> Burkhart  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"><ulink
      url="http://www.santafe.edu/~rmb/oopsla94.html">The Swarm
      Multi-Agent Simulation System</ulink></citetitle>
      <confgroup>
        <conftitle>(OOPSLA) '94 Workshop on "The Object Engine"</conftitle>
        <confdates>7 September 1994</confdates>
      </confgroup>
    </biblioentry>

    <biblioentry>
<abbrev>Manuca:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> Radu  </firstname>
          <surname> Manuca  </surname>
        </author>
        <author>
          <firstname> Yi  </firstname>
          <surname> Li  </surname>
        </author>
        <author>
          <firstname> Rick  </firstname>
          <surname> Riolo  </surname>
        </author>
        <author>
          <firstname> Robert  </firstname>
          <surname> Savit  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"><ulink
                                          url="http://www.pscs.umich.edu/RESEARCH/pscs-tr.html">The
          Structure of Adaptive Competition in Minority Games</ulink>
      </citetitle> 
      <pubsnumber>PSCS Working Paper98-11-001</pubsnumber> 
      <pubdate>1998</pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Pepper:1999</abbrev>
      <authorgroup>
        <author>
          <firstname> J.W.  </firstname>
          <surname> Pepper  </surname>
        </author>
        <author>
          <firstname> B.  </firstname>
          <surname> Smuts  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="chapter"><ulink
        url="http://www.pscs.umich.edu/RESEARCH/pscs-tr.html">The
        evolution of cooperation in an ecological context: an
        agent-based model</ulink></citetitle>

      <biblioset relation="book">
        <citetitle pubwork="book">Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes</citetitle>
        <authorgroup>
          <editor>
            <firstname>Timothy</firstname>
            <surname>Kohler</surname>
          </editor>
          <editor>
            <firstname>George</firstname>
            <surname>Gumerman</surname>
          </editor>
        </authorgroup>
        <publisher>
          <publishername>Santa Fe Institute and Oxford University Press </publishername></publisher>
        <pubdate> 1999 </pubdate>
      </biblioset>
    </biblioentry>

    <biblioentry>
<abbrev>Hinsch:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> M.  </firstname>
          <surname> Hinsch  </surname>
        </author>
        <author>
          <firstname> J. J.  </firstname>
          <surname> Merelo  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"><ulink url="ftp://kal-el.ugr.es/pub/evIPD/IPD.ps.gz">Coevolving Iterated Prisonner's dilemma strategies in different environments</ulink> </citetitle>
      <affiliation>
        <orgname>Univ. Granada, Spain</orgname>
        <orgdiv>GeNeura Team, Dept. ATC,</orgdiv>
      </affiliation>
      <pubsnumber>g-98-1</pubsnumber>
      <pubdate> 1998 </pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Downing:1999</abbrev>
      <authorgroup>
        <author>
          <firstname> Keith  </firstname>
          <surname> Downing  </surname>
        </author>
        <author>
          <firstname> Peter  </firstname>
          <surname> Zvirinsky  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"><ulink
      url="http://alife.tuke.sk/projekty/mag_html/guild/guild-intro.html">The
      Simulated Evolution of Biochemical Guilds: Reconciling Gaia
      Theory and Natural Selection</ulink> </citetitle>

      <citetitle pubwork="journal">Artificial Life</citetitle>
      <volumenum>5</volumenum>
      <issuenum>4</issuenum>
      <pubdate>2000</pubdate>

      <abstract><para> Gaia theory, which states that organisms both
          affect and regulate their environment, poses an interesting
          problem to Neo-Darwinian evolutionary biologists and
          provides an exciting set of phenomena for artificial-life
          investigation. The key challenge is to explain the emergence
          of biotic communities that are capable, via their implicit
          coordination, of regulating large-scale biogeochemical
          factors such as the temperature and chemical composition of
          the biosphere, but to assume no evolutionary mechanisms
          beyond contemporary natural selection. Along with providing
          an introduction to Gaia theory, this paper presents
          simulations of Gaian emergence based on an artificial-life
          model involving genetic algorithms and guilds of simple
          metabolizing agents. In these simulations, resource
          competition leads to guild diversity; the ensemble of guilds
          then manifests life-sustaining nutrient recycling and exerts
          distributed control over environmental nutrient
          ratios. These results illustrate that standard
          individual-based natural selection is sufficient to explain
          Gaian self-organization, and they help clarify the
          relationships between two key metrics of Gaian activity:
          recycling and regulation. </para></abstract>
      
    </biblioentry>
    
    <biblioentry>
<abbrev>Kohler:1996a</abbrev>
      <authorgroup>
        <author>
          <firstname> T.A.  </firstname>
          <surname> Kohler  </surname>
        </author>
        <author>
          <firstname> Eric  </firstname>
          <surname> Carr  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"><ulink
        url="http://www.archaeology.usyd.edu.au/resources/documents/kohler/index.htm">
        Swarm-based Modeling of Prehistoric Settlement Systems in
          Southwestern North America</ulink>
      </citetitle>
      
      <confgroup>
        <conftitle>Proceedings of Colloquium II, UISPP, XIIIth Congress</conftitle>
        <confdates>September 1996</confdates>
      </confgroup>
      <biblioset relation="series">
        <title>Sydney University Archaeological Methods</title>
        <seriesvolnums>5</seriesvolnums>
        <authorgroup>
          <editor>
            <firstname>I.</firstname>
            <surname>Johnson</surname>
          </editor>
          <editor>
            <firstname>M.</firstname>
            <surname>North</surname>
          </editor>
        </authorgroup>
        <publisher>
          <publishername> School of Archaeology, University of Sydney, Australia </publishername>
        </publisher>
      </biblioset>
    </biblioentry>

    <biblioentry>
<abbrev>Stefansson:1997</abbrev>
      <authorgroup>
        <author>
          <firstname> B.  </firstname>
          <surname> Stefansson  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"> Swarm: An Object Oriented Simulation Platform Applied to Markets and Organizations </citetitle>

      <confgroup>
        <conftitle>Evolutionary Programming VI</conftitle>
        <confdates>1997</confdates>
      </confgroup>

      <biblioset relation="series">
        <title>Lecture Notes in Computer Science</title>
        <seriesvolnums>1213</seriesvolnums>
        <authorgroup>
          <editor>
            <firstname>P.</firstname>
            <surname>Angeline</surname>
          </editor>
          <editor>
            <firstname>R.</firstname>
            <surname>Reynolds</surname>
          </editor>
          <editor>
            <firstname>J.</firstname>
            <surname>McDonnel</surname>
          </editor>
          <editor>
            <firstname>R.</firstname>
            <surname>Eberhart</surname>
          </editor>
        </authorgroup>
        <publisher><publishername> Springer-Verlag </publishername>
          <address format="linespecific"><city>New York</city></address>
        </publisher>
      </biblioset>
    </biblioentry>

    <biblioentry>
<abbrev>Johnson:1998b</abbrev>
      <authorgroup>
        <author>
          <firstname> Paul  </firstname>
          <surname> Johnson  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"> An Agent Based Model of the
      Exchange Theory of Interest Groups </citetitle>

      <confgroup>
        <conftitle>Annual Meeting of the American Political Science
        Association</conftitle>
        <confdates>3-6 September 1998</confdates>
        <address format="linespecific"><otheraddr>Marriott Copley Place and Sheraton Boston Hotel and Tower</otheraddr><city>Boston</city></address>
      </confgroup>
    </biblioentry>

    <biblioentry>
<abbrev>McMullin:Varela:1997</abbrev>
      <authorgroup>
        <author>
          <firstname> Barry  </firstname>
          <surname> McMullin  </surname>
        </author>
        <author>
          <firstname> Francisco J.  </firstname>
          <surname> Varela  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"><ulink url="http://www.eeng.dcu.ie/~alife/bmcm-ecal97">Rediscovering Computational Autopoiesis</ulink> </citetitle>

      <confgroup>
        <conftitle>Proceedings of the Fourth European Conference on
        Artificial Life</conftitle> 
        <confdates>July 1997</confdates>
        <confsponsor>School of Cognitive and Computing Sciences,
        University of Sussex</confsponsor>
      </confgroup>

      <biblioset relation="series">
        <title>Complex Adaptive Systems</title>
        <authorgroup>
          <editor>
            <firstname>Phil</firstname>
            <surname>Husbands</surname>
          </editor>
          <editor>
            <firstname>Inman</firstname>
            <surname>Harvey</surname>
          </editor>
        </authorgroup>
        <publisher><publishername> MIT Press </publishername></publisher>
      </biblioset>

      <abstract><para> This paper summarises some initial empirical
          results from a new computer model (artificial chemistry)
          which exhibits spontaneous emergence and persistence of
          autopoietic organisation. The model is based on a system
          originally presented by Varela, Maturana and Uribe. In
          carrying out this re-implementation it was found that an
          additional interaction (chain-based bond inhibition), not
          documented in the original description by Varela et al., is
          critical to the realisation of the autopoietic
          phenomena. This required interaction was re-discovered only
          following careful examination of (unpublished) source code
          for an early version of the original model. The purpose of
          the paper is thus twofold: firstly to identify and discuss
          this previously undocumented, but essential, interaction;
          and secondly to argue, on the basis of this particular case,
          for the importance of exploiting the emerging technologies
          which support publication of completely detailed software
          models (in addition, of course, to conventional publication
          of summary experimental results).  </para></abstract>
      
    </biblioentry>
  
  <biblioentry>
    <abbrev>Railsback:1999a</abbrev>
      <authorgroup>
      <author>
        <firstname> S. F.  </firstname>
        <surname> Railsback  </surname>
      </author>
      <author>
        <firstname> R. H.  </firstname>
        <surname> Lamberson  </surname>
      </author>
        <author>
        <firstname> B. C.  </firstname>
        <surname> Harvey  </surname>
      </author>
      <author>
        <firstname> W. E.  </firstname>
        <surname> Duffy  </surname>
      </author>
    </authorgroup>
    <citetitle pubwork="article">Movement rules for individual-based
      models of stream fish</citetitle>
    <citetitle pubwork="journal">Ecological Modelling</citetitle>
    <issuenum>2-3</issuenum>
    <volumenum>123</volumenum>
    <pagenums>73-89</pagenums>
    <pubdate>1999</pubdate> 
    
    <abstract><para> Spatially explicit individual-based models use
        movement rules to determine when an animal departs its current
        location and to determine its movement destination; these
        rules are therefore critical to accurate simulations. Movement
        rules typically define some measure of how an individual's
        expected fitness varies among locations, under the assumption
        that animals make movement decisions at least in part to
        increase their fitness. Recent research shows that many fish
        move quickly in response to changes in physical and biological
        conditions, so movement rules should allow fish to rapidly
        select the best location that is available and accessible and
        not impose randomness or time lags on movement. The theory
        that a fish's fitness is maximized by minimizing the ratio of
        mortality risk to food intake is not applicable to typical
        individual-based model movement decisions and can cause
        serious errors in common situations. Instead, we developed
        fitness measures from unified foraging theory that are
        theoretically and computationally compatible with
        individual-based fish models. One such fitness measure causes
        a fish to select habitat that maximizes its expected survival
        over a specified time horizon, considering both starvation and
        non-starvation risks. This fitness measure is dependent on the
        fish's current state, making fish with low energy reserves
        more willing to accept risks in exchange for higher intake. A
        second measure represents expected reproductive maturity by
        multiplying expected survival by a factor representing how
        close to the size of first reproduction the fish grows within
        the time horizon.  </para></abstract>
  </biblioentry>

  <biblioentry>
    <abbrev>Railback:1999b</abbrev>
    <authorgroup>
      <author>
        <firstname>S. F.</firstname>
        <surname>Railsback</surname>
      </author>
      <author>
        <firstname>R. H.</firstname>
        <surname>Lamberson</surname>
      </author>
      <author>
        <firstname>S.</firstname>
        <surname>Jackson</surname>
      </author>
    </authorgroup>
    <citetitle pubwork="article">Individual-based Models: Progress
      Toward Viability for Fisheries Management</citetitle>
    <confgroup>
      <conftitle>Spatial Processes and Management of Fish Populations,
        Proceedings of the 17th Lowell Wakefield Symposium</conftitle>
      <address><city>Anchorage</city><state>Alaska</state></address>
    </confgroup>
    <pubdate>1999 (submitted)</pubdate>
    <abstract>
      <para>Individual-based models have many features desirable for
        fisheries management: they are the only class of models that
        can easily represent many natural complexities, spatial
        processes, and cumulative effects; and their individual
        orientation makes them easy to understand and parameterize
        with laboratory and field observations. However, these models
        are currently viewed as impractical because of complexity and
        cost, and they lack scientific and regulatory credibility. We
        have a program to overcome these obstacles by developing new
        theoretical approaches and software. Theoretical development
        has focused on improving and testing the rules that govern
        fish movement, because movement is the primary way fish
        respond to spatial processes. We let each fish select habitat
        that maximizes a simple definition of fitness that is based on
        the expected probability of surviving starvation and other
        risks over a future time horizon. We also developed software
        with graphical interfaces allowing the behavior of individual
        fish to be observed and tested, a prerequisite for
        establishing the credibility of individual-based models.
      </para>
    </abstract>
  </biblioentry>

  <biblioentry>
    <abbrev>Railsback:1999c</abbrev> 
    <corpauthor> Lang, Railsback &amp; Assoc.</corpauthor> 

    <citetitle pubwork="article">Tools for Individual-based Stream
      Fish Models: Improving the Cost-Effectiveness and Credibility of
      Individual-based Approaches for Instream Flow
      Assessment</citetitle> 
    <contractsponsor>EPRI, Electric Power
      Research Institute, Palo Alto, CA</contractsponsor>
    <pubsnumber>EPRI TR-114006</pubsnumber>
    <pubdate>1999</pubdate>
  </biblioentry>

  <biblioentry>
    <abbrev>Railsback:2000a</abbrev>
    <authorgroup>
      <author>
        <firstname>S. F.</firstname>
        <surname>Railsback</surname>
      </author>
      <author>
        <firstname>B. C.</firstname>
        <surname>Harvey</surname>
      </author>
    </authorgroup>
    <citetitle>Individual-based Model Formulation for Cutthroat Trout,
      Little Jones Creek, California</citetitle> 
    
    <contractsponsor>U.S. Forest Service, Redwood Sciences
      Laboratory, Arcata, CA.</contractsponsor>
    <pubdate>(in preparation)</pubdate>
  </biblioentry>

  <biblioentry>
    <abbrev>Railsback:2000b</abbrev>
    <authorgroup>
      <author>
        <firstname>S. F.</firstname>
        <surname>Railsback</surname>
      </author>
      <author>
        <firstname>B. C.</firstname>
        <surname>Harvey</surname>
      </author>
    </authorgroup>
    <citetitle pubwork="article">Comparison of salmonid habitat
      selection objectives in an individual-based model</citetitle>

    <citetitle pubwork="journal">Ecology</citetitle>
    <pubdate>January 2000 (in preparation)</pubdate>
  </biblioentry>

  <biblioentry>
    <abbrev>Krone:1998b</abbrev>
      <authorgroup>
        <author>
          <firstname> O.  </firstname>
          <surname> Krone  </surname>
        </author>
        <author>
          <firstname> F.  </firstname>
          <surname> Chantemargue  </surname>
        </author>
        <author>
          <firstname> T.  </firstname>
          <surname> Dagaeff  </surname>
        </author>
        <author>
          <firstname> M.  </firstname>
          <surname> Schumacher  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"> Coordinating Autonomous Entities with STL </citetitle>

      <citetitle pubwork="journal">The Applied Computing Review</citetitle>
      <bibliomisc>Special issue on Coordination Models Languages and Applications</bibliomisc>
      
      <pubdate>1998 (to appear)</pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Kreft:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> Jan-Ulrich  </firstname>
          <surname> Kreft  </surname>
        </author>
        <author>
          <firstname> Ginger  </firstname>
          <surname> Booth  </surname>
        </author>
        <author>
          <firstname> Julian W. T.  </firstname>
          <surname> Wimpenny  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"><ulink
      url="http://www.eeb.yale.edu/ginger/bacillus/paper.html">BacSim,
      a simulator for individual-based modelling of bacterial colony
      growth</ulink></citetitle>

      <citetitle pubwork="journal">Microbiology</citetitle>
      <pagenums>3275 - 3287</pagenums>
      <volumenum> 144 </volumenum>
      <pubdate> 1998 </pubdate>
    </biblioentry>

    <biblioentry>
<abbrev>Schretzenmayr:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> Martina  </firstname>
          <surname> Schretzenmayr  </surname>
          <affiliation>
            <orgname>Eidgenoessische Technische Hochschule (Swiss
              Federal Institute of Technology)</orgname> <address
                                                                format="linespecific"><city>Zurich</city><country>Switzerland</country>
              <email>schretzenmayr@orl.arch.ethz.ch</email>
            </address>
          </affiliation>
        </author>
      </authorgroup>
      <citetitle pubwork="manuscript"> Strategien zur Umnutzung von
        grossflaechigen innerstaedtischen Industrie- und
        Gewerbebrachen (Strategies for the redevelopment of
        large-scale inner city industrial sites) </citetitle>

      <pubsnumber>PhD Thesis, ETH No. 12473, in German</pubsnumber>
      <pubdate> 1998 </pubdate>

      <abstract><para> This work examines the redevelopment of
          large-scale inner city brownfield sites using case studies
          of five German and two Swiss sites. Special emphasis is
          placed on the suitability of different planning strategies
          for redeveloping brownfield sites. It could be shown, that
          the control mechanism of stage-wise planning, which is
          normally used for the development of greenfield sites,
          becomes ineffective for brownfield sites because every
          parcel of an industrial or commercial site is available for
          immediate redevelopment Despite the inadequacies of
          stage-wise planning, this approach was the most commonly
          observed in the case studies. The examination of the case
          study sites has revealed that the redevelopment of
          industrial sites tends to be concentrated around the edges
          of the sites and also around parcels which are made
          especially attractive by their accessibility to
          transportation (e.g. near subway stations). Furthermore,
          parcels near so-called &quot;facilities with surplus
          importance&quot; are preferred. &quot;Facilities with
          surplus importance&quot; refer to those facilities which can
          serve as seeds for the attachment of facilities with similar
          or associated uses (multiplier effect). Examples include
          technology transfer facilities or recreational and cultural
          facilities which might encourage the development of nearby
          hotels and restaurants. A hypothesis formulated at the
          beginning of this work proved to be appropriate for the
          redevelopment of brownfield sites. This strategy aims to
          establish &quot;facilities of surplus importance&quot; at
          suitable locations in the interior of the site. These points
          will then serve as &quot;condensation nuclei&quot;. The
          &quot;condensation nuclei&quot; should be placed at
          intersections of public and private transportation links in
          order to exploit available access routes. Public
          transportation, especially rail transport, should have high
          priority as an investment for the future of the site. The
          redevelopment process will be initiated at the
          &quot;condensation nuclei&quot; and will grow from these to
          cover the whole site. Placing such &quot;condensation
          nuclei&quot; adjacent to traffic intersections guarantees
          access to new buildings and facilities and ensures that new
          modes of public transportation will be fully utilized - even
          in the very beginning of the redevelopment process. The
          proposed strategy has been tested using a multi-agent
          simulation of complex systems (Swarm) and could be
          verified. Finally, after analyzingq the planning process of
          the surveyed case studies, this work formulates
          recommendations for the execution of redevelopment projects.
        </para></abstract> 
    </biblioentry>

    <biblioentry>
<abbrev>Villa:1998</abbrev>
      <authorgroup>
        <author>
          <firstname> F.  </firstname>
          <surname> Villa  </surname>
        </author>
        <author>
          <firstname> R.  </firstname>
          <surname> Costanza  </surname>
        </author>
      </authorgroup>

      <citetitle pubwork="article"><ulink
      url="http://kabir.cbl.umces.edu/~villa/sni/paper">Design of
      multi-paradigm integrating modeling tools for ecological
      research </ulink></citetitle>

      <citetitle pubwork="journal">Journal of Environmental Modelling and Software</citetitle>
      <bibliomisc>multi-paradigm ecological modelling, remote simulation control, simulation interface design, model coordination, Swarm</bibliomisc>
      <pubdate>1998 (submitted)</pubdate>
    </biblioentry>
    
    <biblioentry>
<abbrev>McMullin:1997b</abbrev>
      <authorgroup>
        <author>
          <firstname> Barry  </firstname>
          <surname> McMullin  </surname>
        </author>
      </authorgroup>
    <citetitle pubwork="article"><ulink
      url="http://www.santafe.edu/sfi/publications/Working-Papers/97-01-002">SCL:
      An Artificial Chemistry in Swarm</ulink></citetitle>
      <pubsnumber>SFI Working Paper 97-01-002</pubsnumber>
      <pubdate>January 1997</pubdate>
      
      <abstract><para> This report describes the SCL (v0.04)
          system. This is an implementation of an artificial
          chemistry, using the Swarm simulation system. This chemistry
          is qualitatively based on the system first described in
          Varela et al. (1974). This involves three distinct chemical
          species: Substrate, Catalyst and Link, hence SCL. It was
          designed with a view to generating simple phenomena of
          autopoietic organisation. Varela et al. included a detailed
          algorithmic account of their original model; however, as
          documented in (McMullin 1997), there are a number of
          problems with interpreting and/or re-implementing that
          algorithm. Arising from this, that original algorithm was
          essentially set aside in designing SCL; instead, SCL seeks
          to capture only the general, qualitative, reaction schemes
          described by Varela et al. SCL was developed for two
          separate purposes. Firstly, it provides a platform to
          critically re-evaluate the phenomena--particularly
          autopoietic phenomena--that can be realised with this
          general kind of reaction scheme. That phenomenological
          investigation will be described in a separate report. The
          second objective was to gain experience of the Swarm
          simulation system and evaluate its suitability for this kind
          of project. This report is concerned solely with this second
          objective: i.e. with documenting the implementation, and
          with evaluating the use of Swarm.  </para></abstract>
    </biblioentry>
    
    <biblioentry>
<abbrev>Krone:1998a</abbrev>
      <authorgroup>
        <author>
          <firstname> O.  </firstname>
          <surname> Krone  </surname>
        </author>
        <author>
          <firstname> F.  </firstname>
          <surname> Chantemargue  </surname>
        </author>
        <author>
          <firstname> T.  </firstname>
          <surname> Dagaeff  </surname>
        </author>
        <author>
          <firstname> M.  </firstname>
          <surname> Schumacher  </surname>
        </author>
        <author>
          <firstname> B.  </firstname>
          <surname> Hirsbrunner  </surname>
        </author>
      </authorgroup>
      <pagenums> 149-158 </pagenums>

      <citetitle pubwork="article"> Coordinating Autonomous Entities </citetitle>
      <confgroup>
        <conftitle>Proceedings of the ACM Symposium on Applied
          Computing (SAC'98). Special Track on Coordination, Languages
          and Applications</conftitle>
        <confdates>February 27 - March 1 1998</confdates>

        <address
                 format="linespecific"><city>Atlanta</city><state>Georgia</state><country>USA</country></address>
        
      </confgroup>
    </biblioentry>

    <biblioentry>
<abbrev>Savit:1997</abbrev>
      <authorgroup>
        <author>
          <firstname> Robert  </firstname>
          <surname> Savit  </surname>
        </author>
        <author>
          <firstname> Radu  </firstname>
          <surname> Manuca  </surname>
        </author>
        <author>
          <firstname> Rick  </firstname>
          <surname> Riolo  </surname>
        </author>
      </authorgroup>
      <citetitle pubwork="article"><ulink
                                          url="http://xxx.lanl.gov/abs/adap-org/9712006">Adaptive
      Competition, Market Efficiency, Phase Transitions and
          Spin-Glasses</ulink> </citetitle>
      
      <pubsnumber>LANL Eprint archives paper adap-org/9712006</pubsnumber>
      <pubdate> 1998 </pubdate>
    </biblioentry>

    <biblioentry>
    <abbrev>Krothapalli:1998a</abbrev>
      <authorgroup>
        <author>
          <firstname> N.K.C.  </firstname>
          <surname> Krothapalli  </surname>
        </author>
        <author>
          <firstname> A. V.  </firstname>
          <surname> Deshmukh  </surname>
        </author>
    </authorgroup>
    
      <citetitle pubwork="article"> Self-regulating negotiating
        schemes for robust agent--based manufacturing systems
        </citetitle>
      <confgroup>
        <conftitle>Proceedings of the Seventh Industrial Engineering
        Research Conference</conftitle>
        <confdates>1998</confdates>
      </confgroup>
      
      <abstract><para> Agent-based manufacturing systems offer several
          advantages over hierarchically organized systems. However,
          performance of agent-based systems has been a major concern
          which has hampered widespread acceptance of these
          systems. Several researchers have noted that the performance
          of agent-based systems is highly sensitive to the bidding or
          negotiation protocols used. In this paper, we study the
          robustness of system performance measures with respect to
          the negotiation protocols used by the individual agents. We
          propose self-regulating negotiation schemes which prevent
          the agents from ``price'' or ``utility'' cascades. The core
          of the self-regulatory mechanism lies in a dynamic utility
          curve, which is based on the current state and the past
          history of the agent.  </para></abstract>
    </biblioentry>
    <biblioentry>
      <abbrev>Pepper:2000</abbrev>
      <author>
	<firstname>John</firstname>
	<surname>Pepper</surname>
      </author>
      <citetitle pubwork="article"><ulink url="http://homepages.feis.herts.ac.uk/~nehaniv/al7ev/pepper.ps">The evolution of modularity in genome architecture</ulink></citetitle>
      <confgroup>
	<conftitle><ulink url="http://www.cs.herts.ac.uk/~nehaniv/al7ev/cnts.html">Evolvability workshop</ulink> at <ulink url="http://alife7.alife.org">Artificial Life VII</ulink></conftitle>
	<confdates>1-2 August 2000</confdates>
	<address format="linespecific">
          <otheraddr>Reed College</otheraddr>
          <city>Portland</city> <state>Oregeon</state>
       </address>
      </confgroup>
    </biblioentry>
    <biblioentry>
      <abbrev>Savage:2000</abbrev>
      <authorgroup>
	<author>
	  <firstname>Melissa</firstname>
	  <surname>Savage</surname>
	</author>
	<author>
	  <firstname>Bruce</firstname>
	  <surname>Sawhill</surname>
	</author>
	<author>
	  <firstname>Manor</firstname>
	  <surname>Askenazi</surname>
	</author>
      </authorgroup>
      <citetitle pubwork="article">Community Dynamics: What Happens
      When We Rerun the Tape?</citetitle>
      <citetitle pubwork="journal">Journal of Theoretical Biology</citetitle>
      <pagenums>515-526</pagenums>
      <volumenum>205</volumenum>
      <issuenum>4</issuenum>
      <pubdate>2000</pubdate>
    </biblioentry>

    <biblioentry>
      <abbrev>Terna:2000c</abbrev>
      <authorgroup>
	<author>
	  <firstname>P.</firstname>
	  <surname>Terna</surname>
	</author>
      </authorgroup>
      <citetitle pubwork="article">Economic Experiments with Swarm: a Neural Network Approach to the Self-Development of Consistency in Agents' Behavior</citetitle>
      <biblioset relation="book">
        <citetitle pubwork="book">Economic Simulations in Swarm: Agent-Based Modelling and Object Oriented Programming</citetitle>
        <authorgroup>
          <editor>
            <firstname>F.</firstname>
            <surname>Luna</surname>
          </editor>
          <editor>
            <firstname>B.</firstname>
            <surname>Stefansson</surname>
          </editor>
        </authorgroup>
        <publisher>
          <publishername>Dordrecht and London, Kluwer Academic</publishername></publisher>
        <pubdate>2000</pubdate>
      </biblioset>
      <abstract><para>We underline the usefulness of agent based models in the social science perspective, also focusing on the main computational problems due to the structure of our models: to simplify the task we introduce a generalized Environment-Rules-Agents scheme. Finally, within Swarm, we introduce a neural network tool (Cross Target method), useful in building artificial laboratories, for experiments with learning, self-developed consistency and interaction of agents in artificial worlds, in order to observe the emergence of complexity without a priori behavioral rules.</para></abstract>
    </biblioentry>
    <biblioentry>
      <abbrev>Terna:2000d</abbrev>
      <authorgroup>
	<author>
	  <firstname>P.</firstname>
	  <surname>Terna</surname>
	</author>
      </authorgroup>
      <citetitle pubwork="article">The "mind or no mind" dilemma in agents behaving in a market</citetitle>
      <biblioset relation="book">
        <citetitle pubwork="book">Applications of Simulation to Social Sciences</citetitle>
        <authorgroup>
          <editor>
            <firstname>G.</firstname>
            <surname>Ballot</surname>
          </editor>
          <editor>
            <firstname>G.</firstname>
            <surname>Weisbuch</surname>
          </editor>
        </authorgroup>
        <publisher>
          <publishername>Paris, Hermes Science Publications</publishername></publisher>
        <pubdate>2000</pubdate>
      </biblioset>
      <abstract><para>In computer simulation models based upon agents, what is the degree of sophistication that we have to put into the agents? Should we provide them or not with a "mind"? The answer ranges from Axelrod's simplicity principle to the use of full BDI (Beliefs, Intentions, Desires) cognitive agents. To discuss the subject we introduce here three models: one with "no-mind" agents that operate in an unstructured market, the second with "minded" agents assuring some stability to an emerging unstructured market and, finally, the third with no mind agents, that show a sophisticated outcome in a structured market. No generalised results come from this presentation, but many useful doubts.</para></abstract>
     </biblioentry>
     <biblioentry id="polhill2001">
     <abbrev>Polhill:2001</abbrev>
     <citetitle>Imitative versus nonimitative strategies in a land-use simulation</citetitle>
     <authorgroup>
     <author>
     <firstname>J. G.</firstname>
     <surname>Polhill</surname>
     </author>
     <author>
     <firstname>N. M.</firstname>
     <surname>Gotts</surname>
     </author>
     <author>
     <firstname>A. N. R.</firstname>
     <surname>Law</surname>
     </author>
     </authorgroup>
     <pubdate>2001</pubdate>
     <citetitle pubwork="journal">Cybernetics and Systems</citetitle>
     <volumenum>32</volumenum>
     <issuenum>1-2</issuenum>
     <pagenums>285-307</pagenums>
     <abstract>
<para>
This article describes results from a simulation model of rural land use,
focusing on how the relative advantages of imitative and nonimitative
approaches to land use selection change under different circumstances. It
is shown that the success of "imitation" depends in quite complex ways
on the type of imitation used, the strategies of other agents with which
the imitator is interacting, and aspects of the heterogeneity of the
environment.
</para>
</abstract>
     </biblioentry>

  <biblioentry id="Coyle:2001">
  <abbrev>Coyle:2001</abbrev>
  <citetitle><ulink url="http://www.cs.tcd.ie/Lorcan.Coyle/FYP/DESwarm.pdf">Demonstrating Darwinian Evolution Using Swarm</ulink></citetitle>
  <authorgroup>
  <author>
    <firstname>Lorcan</firstname>
    <surname>Coyle</surname>
  </author>
  </authorgroup>
  <pubdate>May 2001</pubdate>
  <abstract>
  <para>This report details the design and implementation of an artificial world that demonstrates Darwinian evolution.  This is done using an agent based modellingtool called Swarm.  This world is populated with hundreds of agents that compete with one another to survive.  Their behavioural attributes are coded into their genes.  The initial population consists of individuals with randomly coded genes.  It is hoped that by exerting Darwinian evolution on this, a population of agents, optimally suited for survival in the world will emerge.</para>
  </abstract>
  </biblioentry>

  </bibliodiv>
</bibliography>

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