Nuevos Métodos basados en Soft Computing para Calibración de Modelos Basados en Agentes: Aplicaciones en Marketing y Ciencias Políticas
Metadatos
Mostrar el registro completo del ítemAutor
Moya Señas, IgnacioEditorial
Universidad de Granada
Departamento
Universidad de Granada. Programa de Doctorado en Tecnologías de la Información y de la ComunicaciónMateria
Agent-Based Modeling Model calibration Evolutionary multiobjective optimization Soft computing Marketing Political science
Fecha
2021Fecha lectura
2021-10-21Referencia bibliográfica
Moya Señas, Ignacio. Nuevos Métodos basados en Soft Computing para Calibración de Modelos Basados en Agentes: Aplicaciones en Marketing y Ciencias Políticas. Granada: Universidad de Granada, 2021. [https://digibug.ugr.es/handle/10481/71116]
Patrocinador
Tesis Univ. Granada.; Spanish Ministerio de Economía y Competitividad under the EXASOCO project (ref. PGC2018-101216-B-I00), including European Regional Development Funds (ERDF); Spanish Ministerio de Economía y Competitividad under the NEWSOCO project (ref. TIN2015-67661-P), including European Regional Development Funds (ERDF); AIMAR (A-TIC-284-UGR18); SIMARK (P18-TP-4475)Resumen
Esta tesis doctoral trata el problema de calibrar y validar modelos basados en agentes (ABMs). En concreto, se propone mejorar las técnicas de calibración existentes utilizando metaheurísticas novedosas y proponiendo un framework integral para la calibración de modelos con múltiples indicadores de rendimiento (KPIs). Los avances propuestos se aplicaron a modelos para escenarios de ciencias políticas y de marketing.
Entre las principales contribuciones podemos destacar el diseño de un motor de simulación de ABMs para escenarios de ciencias políticas y marketing, con el que se compuso un framework de calibración para ABMs. También destacamos el diseño y validación de distintos algoritmos de calibración para uno o más KPIs basados en metaheurísticas novedosas. Finalmente, destacamos también la propuesta de un framework integral para calibración y validación que combina un algoritmo EMO con una técnica de visualización avanzada basada en grafos. Model simulation is an established approach for analyzing complex systems but these computational
models need to be carefully calibrated and validated before they can be useful. This task can be
challenging in many cases as the lack of information prevents the users for precisely estimating the
parameters of the model and the number of parameters to estimate can be too high. The agentbased
model methodology is a well-known model simulation approach that relies in the behaviour of
artificial agents, which are autonomous entities that act following simple rules and interacting with
other agents and their environment. Due to the mentioned issues, the calibration of agent-based
models is usually carried out using automated calibration methods, since they can estimate those
parameters which cannot be set because of the lack of information. However, the use of automatic
calibration does not release the user from carefully reviewing and validating the resulting parameter
set, as a good fitting between the models' output and the calibration data is not a guarantee of a
valid configuration.
This doctoral dissertation addresses these issues in several ways. First, it proposes multiple
agent-based models for defining political scenarios and marketing strategies that serve as the
foundation of the dissertation, since they are calibrated and validated with the techniques and
methods proposed by this thesis. In addition, these agent-based models are useful for obtaining
insights relevant to their application domain, which is two-fold. On the one hand, two models
tackle the Spanish national elections on the 14th of March of 2004, that were severely in
uenced
by the terrorist attacks that happened three days before. On the other hand, we introduce a
benchmark containing several instances of a model for defining marketing strategies that considers
the awareness of the brands and their word-of-mouth volume.
This doctoral dissertation reviews and compares several relevant metaheuristics to design
the best performing method for agent-based model automatic calibration. The use of a well
performing optimization method is important for automatic calibration as its success depends
on the method's ability for exploring the complex and ill-defined parameter search space. Thus,
this dissertation conducts an exhaustive experimentation comparing well-established and recent
evolutionary algorithms and including their hybridization with local search procedures. The
computational study analyzes the calibration accuracy of the metaheuristics using an integer
coding scheme over the developed benchmark of instances of the agent-based model for marketing,
which considers an increasing number of decision variables. This study reported the outstanding
performance of the memetic coral reefs optimization algorithm after performing multiple statistical
tests to the results.
The choice of the best performing metaheuristic is specially relevant for models calibrated
over more than a single output, as its calibration requires handling different criteria jointly. This
fact increases the problem complexity and can be achieved by using automated calibration and
evolutionary multiobjective optimization methods as they can find a set of representative Pareto
solutions under these restrictions and in a single run. However, selecting the best algorithm for
performing automated calibration can be overwhelming. Therefore, this dissertation proposes an exhaustive analysis of the performance of several evolutionary multiobjective optimization
algorithms when calibrating different instances from the agent-based model for marketing. This
analysis evaluates the performance of the compared algorithms by using multiobjective performance
indicators and attainment surfaces, including a statistical test for studying the significance of the
indicator values, and benchmarking their performance with respect to a classical mathematical
optimization method. The results of this experimentation re
ect that those algorithms based
on decomposition perform significantly better than the remaining methods in most instances.
Moreover, we also identify how different properties of the problem instances (i.e., the shape of
the feasible search space region, the shape of the Pareto front, and the increased dimensionality)
erode the behavior of the algorithms to different degrees.
Finally, this dissertation introduces a multicriteria integral framework to assist the modeler
in the calibration and validation processes of agent-based models that combines evolutionary
multiobjective optimization with network-based visualization. Up to our knowledge is the first
integral approach to model calibration in the specialized literature. This approach combines the
outstanding performance of evolutionary multiobjective optimization algorithms with an advanced
visualization method to better understand the decision space and the set of solutions from the
obtained Pareto set approximation. This proposal is tested by using two instances of the agentbased
model for marketing. The final analysis of the calibrated solutions shows how the proposed
framework eases the analysis of Pareto sets with high cardinality and helps with the identification
of
exible solutions (i.e., those having close values in the design space), thus helping the designer
in the agent-based model validation.