<?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>1665-5346</journal-id>
<journal-title><![CDATA[Revista mexicana de economía y finanzas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. econ. finanz]]></abbrev-journal-title>
<issn>1665-5346</issn>
<publisher>
<publisher-name><![CDATA[Instituto Mexicano de Ejecutivos de Finanzas A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1665-53462019000200245</article-id>
<article-id pub-id-type="doi">10.21919/remef.v14i2.382</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Negociaciones de máxima probabilidad para juegos cooperativos con fines comerciales]]></article-title>
<article-title xml:lang="en"><![CDATA[Maximum Probability Negotiations in Cooperative Games with Commercial Purposes]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Manuell Cid]]></surname>
<given-names><![CDATA[Gerardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Montiel]]></surname>
<given-names><![CDATA[Luis V.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico Autónomo de México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>14</volume>
<numero>2</numero>
<fpage>245</fpage>
<lpage>259</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-53462019000200245&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-53462019000200245&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-53462019000200245&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El objetivo de este trabajo presenta un método para encontrar la estrategia de pagos que maximiza la probabilidad de cerrar una negociación en escenarios cooperativos de utilidad transferible con fines comerciales. Este procedimiento utiliza una herramienta de simulación llamada JDSIM para muestrear el Core del juego y generar funciones empíricas de probabilidad acumuladas de los pagos de cada actor con el fin de establecer la negociación que maximiza el producto de estas funciones. Este método se compara con otros dos ya establecidos en la literatura, llamados Shapley Value (SV) y el centroide del Core, a través de treinta escenarios aleatorios y un caso práctico en los que se observa que este siempre ofrece soluciónes únicas, implementables, y proporciona una justificación racional, a diferencia de los ya mencionados. Hay que considerar que este trabajo únicamente pretende resolver negociaciones en las que la distribución de los pagos permite a todos los actores unirse en una sola coalición, sin embargo, el paradigma presentado se puede adaptar a subcoaliciones de menor tamaño para extender su alcance. Esta solución propone una mejor alternativa a las que se encuentran en la literatura y representa un avance importante en áreas de negociación y teoría de juegos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This work presents a method to find the payment strategy that maximizes the probability of closing a negotiation in cooperative scenarios of transferable utility for commercial purposes. This procedure uses a simulation tool called JDSIM to sample the core of the game and generate cumulative empirical probability functions of the payments of each actor in order to establish the negotiation that maximizes the product of these functions. This method is compared with other two methods already established in the literature, called Shapley Value (SV) and the centroid of the Core, through thirty random scenarios and a practical case in which it is observed that this always offers unique and implementable solutions, and provides a rational justification unlike those already mentioned. It should be borne in mind that this work is only intended to resolve negotiations in which the distribution of payments allows all actors to unite in a single coalition; however, the paradigm presented can be adapted to smaller sub-coalitions to extend its scope. This solution proposes a better alternative to those found in the literature and represents an important advance in areas of negotiation and game theory.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Negociación]]></kwd>
<kwd lng="es"><![CDATA[Shapley Value]]></kwd>
<kwd lng="es"><![CDATA[Core]]></kwd>
<kwd lng="es"><![CDATA[JDSIM]]></kwd>
<kwd lng="es"><![CDATA[Simulación de Juegos]]></kwd>
<kwd lng="es"><![CDATA[C44]]></kwd>
<kwd lng="es"><![CDATA[C71]]></kwd>
<kwd lng="en"><![CDATA[Negotiation]]></kwd>
<kwd lng="en"><![CDATA[Shapley Value]]></kwd>
<kwd lng="en"><![CDATA[Core]]></kwd>
<kwd lng="en"><![CDATA[JDSIM]]></kwd>
<kwd lng="en"><![CDATA[Game simulation]]></kwd>
<kwd lng="en"><![CDATA[C44]]></kwd>
<kwd lng="en"><![CDATA[C71]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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