Cooperación y competencia bajo dureza ambiental: evidencia de evolución mediante simulaciones basadas en agentes

dc.contributor.advisorGutiérrez Domínguez, German Antonio
dc.contributor.authorRavelo Rivera, Pedro Luis
dc.contributor.orcidRavelo Rivera, Pedro Luis [0000000291021776]
dc.contributor.researchgateRavelo Rivera, Pedro Luis [Pedro-Ravelo-R]
dc.contributor.researchgroupAprendizaje y Comportamiento Animal
dc.date.accessioned2025-09-17T15:34:28Z
dc.date.available2025-09-17T15:34:28Z
dc.date.issued2025-09-09
dc.description.abstractLa cooperación constituye uno de los desafíos más significativos en la teoría evolutiva debido a la aparente paradoja que representa: los comportamientos en los que un individuo asume costos en beneficio de otros parecen oponerse directamente al proceso mismo de la selección natural. Aunque diversos estudios han señalado una asociación entre la cooperación y las condiciones de dureza ambiental, los mecanismos específicos que explican esta relación aún no se han dilucidado completamente. Aclarar este proceso explicativo puede contribuir a resolver el problema de cómo la cooperación llega a consolidarse como rasgo evolutivo estable. Por ello, esta investigación evalúa si la cooperación puede evolucionar y mantenerse como estrategia adaptativa en contextos ambientales adversos, e identifica el principio adaptativo que media dicha relación. Mediante simulaciones basadas en agentes (ABM, por sus siglas en inglés), enmarcadas en la teoría de juegos evolutivos (EGT, por sus siglas en inglés) — que estudia la interacción estratégica entre individuos— y la teoría de historia de vida (LHT, por sus siglas en inglés)— que analiza cómo los organismos equilibran su energía entre crecer, sobrevivir y reproducirse—, se plantea la hipótesis de que la dureza ambiental, entendida como un aumento en el costo de vida, favorece la cooperación a través de una compensación (trade-off) supervivencia- reproducción. Los resultados de las simulaciones indican que, a medida que aumenta el costo de vida, los cooperadores prevalecen, mostrando perfiles específicos de historia de vida con mayor longevidad y menor tasa reproductiva neta. Este patrón evidencia un mecanismo evolutivo claro: la cooperación resulta siendo una estrategia adaptativa en escenarios críticos para la persistencia individual, como escasez de alimentos o recursos vitales, condiciones ambientales extremas o impredecibles. Finalmente, estos hallazgos se analizan a la luz de corrientes históricas como la tradición rusa de la ayuda mutua del siglo XIX, que cuestionó la lucha intraespecífica como motor principal de la evolución y resaltó el papel crucial de la cooperación en contextos ambientales adversos. Así, la investigación integra perspectivas teóricas, históricas y empíricas para ofrecer una comprensión más amplia y profunda del fenómeno de la cooperación. (Texto tomado de la fuente)spa
dc.description.abstractCooperation remains a central puzzle in evolutionary theory because behaviors that impose costs on actors while benefiting others appear to contradict the logic of natural selection. Although several studies have reported associations between cooperation and environmentally harsh conditions, the specific processes linking the two are still not fully resolved. Clarifying these processes can help explain how cooperation becomes an evolutionarily stable trait. Here I assess whether cooperation can evolve and persist as an adaptive strategy under harsh ecological conditions and identify the adaptive principle that mediates this relationship. Using an agent-based modeling (ABM) approach framed by evolutionary game theory (EGT)—which examines strategic interactions among individuals—and life-history theory (LHT)—which analyzes how organisms allocate energy to growth, survival, and reproduction—I hypothesize that environmental harshness, operationalized as a higher cost of living, favors cooperation through a survival–reproduction trade-off. Simulation results show that as the cost of living increases, cooperators come to prevail, displaying distinctive life-history profiles characterized by greater longevity and a lower net reproductive rate. This pattern reveals a clear evolutionary mechanism: cooperation functions as an adaptive strategy when individual persistence is strongly constrained—for example, under scarcity of food or vital resources, or under extreme or unpredictable environmental conditions. Finally, I interpret these findings in light of historical perspectives such as the nineteenth-century Russian tradition of mutual aid, which questioned intra-specific struggle as the primary driver of evolution and emphasized the role of cooperation under adverse environments. In doing so, the study integrates theoretical, historical, and modeling perspectives to offer a broader and deeper account of the evolution of cooperation.eng
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Psicología
dc.description.methodsLa presente investigación adoptó una metodología cuantitativa y computacional, empleando simulaciones basadas en agentes (ABM) sobre una población espacial para estudiar la dinámica de cooperación y competencia. Este enfoque permitió evaluar la evolución de la cooperación bajo diferentes condiciones de dureza ambiental, enmarcado en el debate clásico de la teoría evolutiva que contrasta la tradición británica (centrada en la competencia individual) con la tradición rusa (que enfatiza la ayuda mutua como respuesta a la adversidad) El diseño metodológico se sustentó en los principios de la Teoría de Juegos Evolutivos (EGT), específicamente el Dilema del Prisionero iterado y espacial, y la Teoría de Historia de Vida (LHT) para analizar los trade-offs entre supervivencia y reproducción. El modelo fue implementado en la plataforma NetLogo. Consistió en una población de agentes (Cooperadores y Desertores) y agentes-recurso en una cuadrícula toroidal bidimensional (mundo 2D), donde cada celda podía albergar un agente o un recurso. Los agentes interactuaban con sus 8 vecinos más cercanos (Vecindad de Moore), poseían atributos de energía, edad, etapa del ciclo de vida y una estrategia social. En el modelo se implementó un ciclo de vida estructurado (etapas pre-reproductiva, reproductiva y post-reproductiva) y se incluyeron fuentes de energía que no requerían interacción social, ofreciendo una vía de supervivencia alternativa relevante para los defertores. La dureza ambiental se manipuló como la variable independiente ("Costo de Vida" o CV), que representaba el consumo de energía basal por agente por unidad de tiempo (tick). El CV se aplicó como un gradiente continuo de 24 niveles (0.1–2.5), realizándose 30 réplicas por cada nivel. Se utilizó un protocolo de incremento gradual para valores altos de CV con el fin de evitar extinciones y estabilizar la dinámica. Las variables dependientes o de salida medidas fueron: la proporción de cooperadores en la población, la tasa neta de reproducción (R0) y la esperanza de vida (e0) para cada fenotipo. La ejecución de los experimentos se realizó con BehaviourSpace de NetLogo. El flujo de análisis de datos empleó métodos no paramétricos (bootstrap y permutación) para estimar pendientes, diferencias de medias e interacciones, evitando suposiciones de normalidad u homocedasticidad. El procesamiento de los datos se llevó a cabo en Python (pandas/numpy/statsmodels), utilizando cuadernos de Jupyter/Colab. Finalmente, el estudio cuenta con prerregistro en OSF (DOI: 10.17605/OSF.IO/H5YMB), donde se documentan el diseño, las variables y el plan analítico.
dc.description.notesTesis recomendada para meritoria por el jurado. Documento distribuido bajo CC BY 4.0. El modelo NetLogo modificado (derivado de Smaldino, 2017) se distribuye bajo CC BY-NC-SA 3.0, en concordancia con la licencia del original Para consultas o materiales complementarios: Pedro Ravelo — ORCID: 0000-0002-9102-1776, plravelor@unal.edu.co, plravelor@gmail.com Prerregistro OSF: https://doi.org/10.17605/OSF.IO/H5YMB El archivo .nlogo se aloja en OSF y no se redistribuye aquí; véase el enlace del prerregistro.
dc.description.researchareaPsicología Básica y Experimental
dc.description.researchareaAprendizaje y evolucion del comportamiento
dc.description.technicalinfoObjeto técnico: Modelo de simulaciones basadas en agentes (ABM) para estudiar cooperación bajo dureza ambiental (parámetro “Costo de Vida”, CV). Entradas/Parámetros: población espacial, juego Dilema del Prisionero, CV (rango definido en la tesis), número de ticks y réplicas; salidas principales: tasa neta de reproducción (𝑅0) y esperanza de vida (𝑒0).spa
dc.description.technicalinfoDisponibilidad de materiales: Ver archivo Netlogo y archivos modelo de los datos recabados en el preregistro OSF: https://osf.io/h5ymb. Información adicional disponible previa solicitud al autor.spa
dc.description.technicalinfoLicencia del software de Netlogo usado en la tesis: CC BY-NC-SA 3.0 (Creative Commons Atribución–NoComercial–CompartirIgual 3.0). El archivo .nlogo es una modificación del modelo de Paul Smaldino (2017) y se distribuye bajo los mismos términos; se conserva la atribución y el aviso de licencia original. Créditos: Copyright © 2017 Paul Smaldino; modificaciones © 2025 Pedro Ravelo.spa
dc.description.technicalinfoPrerregistro OSF: https://doi.org/10.17605/OSF.IO/H5YMB Plan preregistrado (OSF): hipótesis, variables de resultado, reglas de detención, parámetros del ABM y análisis previstos. Repositorio OSF: https://osf.io/h5ymbspa
dc.format.mimetypeapplication/pdf
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/88865
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
dc.publisher.facultyFacultad de Ciencias Humanas
dc.publisher.placeBogotá, Colombia
dc.publisher.programBogotá - Ciencias Humanas - Maestría en Psicología
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dc.rights.licenseReconocimiento 4.0 Internacional
dc.subject.bneCooperación (Psicología)
dc.subject.ddc150 - Psicología::156 - Psicología comparada
dc.subject.ddc510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
dc.subject.ddc570 - Biología::576 - Genética y evolución
dc.subject.ddc570 - Biología::577 - Ecología
dc.subject.lccCooperativenesseng
dc.subject.lembAltruismospa
dc.subject.lembAltruismeng
dc.subject.lembPsicología evolutivaspa
dc.subject.lembDevelopmental psychologyeng
dc.subject.lembConducta animal -- Modelos matemáticosspa
dc.subject.lembAnimal behavior -- Mathematical modelseng
dc.subject.lembTeoría de los juegosspa
dc.subject.lembGame theoryeng
dc.subject.lembAdaptación (Biología) -- Aspectos psicológicosspa
dc.subject.lembAdaptation (Biology) -- Psychological aspectseng
dc.subject.proposalEvolución de la cooperaciónspa
dc.subject.proposalAltruismospa
dc.subject.proposalDureza ambientalspa
dc.subject.proposalTrade-offs de historia de vidaspa
dc.subject.proposalTeoría de juegos evolutivos (EGT)spa
dc.subject.proposalModelos basados en agentes (ABM)spa
dc.subject.proposalComponentes de fitnessspa
dc.subject.proposalAyuda mutuaspa
dc.subject.proposalTeoría de historia de vidaspa
dc.subject.proposalEvolution of cooperationeng
dc.subject.proposalAltruismeng
dc.subject.proposalEnvironmental harshnesseng
dc.subject.proposalLife-history trade-offseng
dc.subject.proposalEvolutionary game theory (EGT)eng
dc.subject.proposalAgent-based models (ABM)eng
dc.subject.proposalFitness componentseng
dc.subject.proposalMutual Aideng
dc.subject.proposalLife-history theoryeng
dc.titleCooperación y competencia bajo dureza ambiental: evidencia de evolución mediante simulaciones basadas en agentesspa
dc.title.translatedCooperation and competition under harsh environmental conditions: Evidence of evolution through agent-based simulationseng
dc.typeTrabajo de grado - Maestría
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dcterms.audience.professionaldevelopmentInvestigadores
dcterms.audience.professionaldevelopmentEstudiantes
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dcterms.audience.professionaldevelopmentMedios de comunicación
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