<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0120-4645</journal-id>
<journal-title><![CDATA[Cuadernos de Administración (Universidad del Valle)]]></journal-title>
<abbrev-journal-title><![CDATA[cuad.adm.]]></abbrev-journal-title>
<issn>0120-4645</issn>
<publisher>
<publisher-name><![CDATA[Universidad del Valle]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-46452017000100007</article-id>
<article-id pub-id-type="doi">10.25100/cdea.v33i57.4535</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Learning in the Regional Innovation Systems: An Agent Based Model]]></article-title>
<article-title xml:lang="es"><![CDATA[Aprendizaje en los sistemas regionales de innovación: Un modelo basado en agentes]]></article-title>
<article-title xml:lang="fr"><![CDATA[L&#8217;apprentissage dans les systèmes régionaux d&#8217;innovation: Un modèle à base d&#8217;agents]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quintero Ramírez]]></surname>
<given-names><![CDATA[Santiago]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruiz Castañeda]]></surname>
<given-names><![CDATA[Walter Lugo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Robledo Velásquez]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Pontificia Bolivariana School of Engineering Industrial Engineering Faculty]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Pontificia Bolivariana Center for Industrial Development ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Colombia Faculty of Mines Department of Organizational Engineering]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2017</year>
</pub-date>
<volume>33</volume>
<numero>57</numero>
<fpage>7</fpage>
<lpage>20</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-46452017000100007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-46452017000100007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-46452017000100007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Learning in regional innovation systems is a complex phenomenon. Therefore, its analysis is being increasingly approached through computer-simulated strategies. The agent-based model in particular has demonstrated to be a useful approximation to overcome the limitations of other methodological strategies since it allows a more trustworthy representation of the agent&#8217;s capabilities, their reasoning limitations, of the mechanisms used for decision making, their interaction, and their success formulas to take advantage of market opportunities. Nevertheless, the development of these models represents serious conceptual and methodological challenges. This article proposes a model that represents the agents of a regional innovation system as vectors of capabilities and the learning process as the accumulation of their innovation capabilities. The proximity among agents and the influence of public policies favors the result of the interaction induced by the market. Methodologically, the development of the model starts with a conceptual proposal validated through contrast against. specialized literature. After that, a model verified by computer was elaborated and its behavior was validated. Finally, a simulation of scenarios was performed to prove its potential application. The resulting model contributes to the understanding the learning dynamics and the emerging patterns of the agent&#8217;s specialization and their influence on the system&#8217;s performance. Finally, the simulation exercises demonstrate the model&#8217;s potential to guide policy decisions that seek to improve the performance regional innovation systems.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El aprendizaje en los sistemas regionales de innovación es un fenómeno complejo, cuyo análisis se está abordando cada vez más mediante estrategias de simulación computacional. En particular, la modelación basada en Agentes ha demostrado ser una aproximación útil para superar las limitaciones de otras estrategias metodológicas pues posibilita una representación más fiel de las capacidades de los agentes, sus limitaciones racionales, los mecanismos de decisión que explican sus decisiones, su interacción y las fórmulas de éxito para aprovechar las oportunidades del mercado. No obstante, el desarrollo de estos modelos presenta serios desafíos conceptuales y metodológicos. Este trabajo propone un modelo que representa los agentes de un sistema regional de innovación, como vectores de capacidades y el aprendizaje como la acumulación de sus capacidades de innovación. El resultado de la interacción inducida por el mercado, se favorece por la cercanía entre agentes e influenciada por las políticas públicas. Metodológicamente, el desarrollo del modelo inicia con una propuesta conceptual y validada mediante contrastación con la literatura especializada. Seguido, se elaboró un modelo que se verificó computacionalmente y se validó en su comportamiento. Para finalizar, se realizó un ejercicio de simulación de escenarios para comprobar su potencial aplicación. El modelo resultante, contribuye a comprender las dinámicas de aprendizaje y los patrones emergentes de especialización de los agentes y su influencia en el desempeño del sistema. Finalmente, los ejercicios de simulación demuestran el potencial del modelo para orientar decisiones de política que busquen mejorar el desempeño de los sistemas regionales de innovación.]]></p></abstract>
<abstract abstract-type="short" xml:lang="fr"><p><![CDATA[Résumé L&#8217;apprentissage dans les systèmes régionaux d&#8217;innovation est un phénomène complexe, dont l&#8217;analyse est abordée de plus en plus à travers de stratégies de simulation informatique. En particulier, la Modélisation à base d&#8217;agents a démontrée être une approche utile pour surmonter les limites d&#8217;autres stratégies méthodologiques. Elle rend possible une représentation des capacités d&#8217; agents plus fidèles, leurs limites rationnelles, des mécanismes de prise de décisions qui expliquent leurs décisions, leur interaction et les formules de succès afin de profiter des opportunités du marché. Cependant, le développement de ces modèles pose des défis conceptuels et méthodologiques graves. Cet article propose un modèle qui représente les agents d&#8217;un système régional d&#8217;innovation, comme les vecteurs des capacités et d&#8217;apprentissage en tant que l&#8217;agglomérat de leurs capacités d&#8217;innovation. Le résultat de l&#8217;interaction induite par le marché est favorisé par la proximité entre les agents et influencé par les politiques publiques. Au niveau de la méthodologie, le développement du modèle commence avec une proposition conceptuelle validée par constatation avec la littérature spécialisée. Ensuite, nous avons élaboré un modèle qui a été vérifié et validé dans son comportement par informatique. Pour finir, nous avons fait un exercice de simulation de scénarios pour vérifier son application potentielle. Le modèle qui en résulte aide à comprendre la dynamique de l&#8217;apprentissage et les nouvelles tendances de spécialisation des agents ainsi que leur influence sur la performance du système. Finalement, les exercices de simulation montrent le potentiel du modèle pour orienter les décisions politiques visant à améliorer l&#8217;accomplissement des systèmes régionaux d&#8217;innovation.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Learning]]></kwd>
<kwd lng="en"><![CDATA[Interactions]]></kwd>
<kwd lng="en"><![CDATA[Capabilities]]></kwd>
<kwd lng="en"><![CDATA[Agents based models]]></kwd>
<kwd lng="en"><![CDATA[Regional innovation systems]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje]]></kwd>
<kwd lng="es"><![CDATA[Interacciones]]></kwd>
<kwd lng="es"><![CDATA[Capacidades]]></kwd>
<kwd lng="es"><![CDATA[Modelos basados en agentes]]></kwd>
<kwd lng="es"><![CDATA[Sistemas regionales de innovación]]></kwd>
<kwd lng="fr"><![CDATA[Apprentissage, iInteractions, Capacités, modèles à base d&#8217;agents]]></kwd>
<kwd lng="fr"><![CDATA[systèmes régionaux d&#8217;innovation]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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