A meso-level empirical validation approach for agent-based computational economic models drawing on micro-data: a use case with a mobility mode-choice model

The complex nature of agent-based modeling may reveal more descriptive accuracy than analytical tractability. That leads to an additional layer of methodological issues regarding empirical validation, which is an ongoing challenge. This paper offers a replicable method to empirically validate agent-based models, a specific indicator of “goodness-of-validation” and its statistical distribution, leading to a statistical test in some way comparable to the p value. The method involves an unsupervised machine learning algorithm hinging on cluster analysis. It clusters the ex-post behavior of real and artificial individuals to create meso-level behavioral patterns. By comparing the balanced composition of real and artificial agents among clusters, it produces a validation score in [0, 1] which can be judged thanks to its statistical distribution. In synthesis, it is argued that an agent-based model can be initialized at the micro-level, calibrated at the macro-level, and validated at the meso-level with the same data set. As a case study, we build and use a mobility mode-choice model by configuring an agent-based simulation platform called BedDeM. We cluster the choice behavior of real and artificial individuals with the same ex-ante given characteristics. We analyze these clusters’ similarity to understand whether the model-generated data contain observationally equivalent behavioral patterns as the real data. The model is validated with a specific score of 0.27, which is better than about 95% of all possible scores that the indicator can produce. By drawing lessons from this example, we provide advice for researchers to validate their models if they have access to micro-data

Metodologia para clusterização de clientes e recomendação de produtos

Orientadora: Prof.ª Dra. Mariana KleinaCoorientador: Prof. Dr. Marcos Augusto Mendes MarquesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa : Curitiba, 25/05/2021Inclui referências: p. 133-138Resumo: O agrupamento de clientes auxilia o marketing estratégico, permitindo traçar estratégias diferenciadas a grupos específicos de clientes, com objetivo de criar relacionamento. A identificação do perfil de cada grupo associada a algoritmos de recomendação de produtos, auxilia os clientes a encontrarem os itens mais indicados às necessidades específicas. Esta facilidade pode auxiliar muito empresas que possuem uma extensa gama de produtos, pois, a tarefa exaustiva da busca de produtos por parte dos clientes pode ocasionar que ele compre da concorrência que pode conseguir fazer uma recomendação assertiva. Este trabalho se baseou nesta necessidade para demonstrar a aplicação de um método que combinou o algoritmo de clusterização e o de regras de associação, mostrando cada etapa da aplicação em uma base mista, que possuem tanto variáveis quantitativas quanto qualitativa. Os resultados mostraram que a medida de distância de Gower, utilizada para verificar a semelhança entre os clientes, gerou clusters com estrutura mais forte, de acordo com Coeficiente de Silhueta e o Índice de Davies Bouldin, se comparada a Jaccard. Para possibilitar o agrupamento empregou-se o K-Medoid, por ser mais flexível a utilização de diferentes medidas, o que propiciou a comparação e gerou onze clusters com perfis diferentes de clientes em um estudo de caso no setor de serviços. Para a recomendação de produtos foi avaliado o desempenho dos algoritmos Apriori, Filtragem Colaborativa Baseada em Clientes e Filtragem Colaborativa Baseada no Item, este último apresentou êxito nos dez primeiros clusters, analisando-se as Taxas de Recall e Precisão, e Curva ROC. Porém no cluster onze o Apriori apresentou melhores resultados. Após a identificação dos algoritmos de recomendação, visando otimizar as métricas de eficiência, foi ajustado o número de vizinhos mais próximos do algoritmo de Filtragem colaborativa e os parâmetros de suporte e confiança do Apriori, o que garantiu melhor desempenho de ambos. Palavras-chave: Clusterização. Recomendação de Produtos. K-Medoid. Filtragem Colaborativa. Apriori.Abstract: The grouping of clients assists strategic marketing, allowing the design of differentiated strategies to specific groups of clients, with the objective of creating relationships. The identification of the profile of each group associated with product recommendation algorithms, helps customers to find the most suitable items for their specific needs. This facility can help a lot of companies that have an extensive range of products, because the exhaustive task of searching for products on the part of customers can cause them to buy from the competition that may be able to make an assertive recommendation. This work was based on this need to demonstrate the application of a method that combined the clustering algorithm and that of association rules, showing each step of the application on a mixed basis, which have both quantitative and qualitative variables. The results showed that the Gower distance measure, used to verify the similarity between the clients, generated clusters with a stronger structure, according to the Silhouette Coefficient and the Davies Bouldin Index, when compared to Jaccard. To make the grouping possible, K-Medoid was used, as it is more flexible to use different measures, which enabled the comparison and generated eleven clusters with different customer profiles in a case study in the service sector. For the recommendation of products, the performance of the Apriori algorithms, Collaborative Client-Based Filtering and Item-Based Collaborative Filtering was evaluated, the latter was successful in the first ten clusters, analyzing the Recall and Precision Rates, and ROC Curve. However, in cluster eleven, Apriori presented better results. After identifying the recommendation algorithms, in order to optimize the efficiency metrics, the number of neighbors closest to the collaborative filtering algorithm and the support and trust parameters of Apriori were adjusted, which guaranteed better performances by both. Keywords: Clustering. Product Recommendation. K-Medoid. Collaborative Filtering. Apriori

How to optimize Gower distance weights for the k-Medoids clustering algorithm to obtain mobility profiles of the Swiss population

This piece of research aims to obtain mobility profiles of the Swiss population. To that end, a survey of the Swiss Statistical Office (FSO) called Mobility and Transport Micro-census (MTMC) is utilized. Along with a qualitative method clustering, the respondents in the survey are clustered based on their mobility characteristics to obtain their profiles. The clustering, in particular acquiring medoids (centrotypes or exemplars), helps us then to generate a synthetic population of Switzerland. To gain medoids of each cluster, the k-Medoids clustering algorithm is utilized which partitions instances based on their positions in a latent space (symmetric distance matrix). Distances that shape this space can be generated by various metrics e.g. Euclidean, Gower, Manhattan. Since in this study features are mixed-type (e.g. numeric, categorical, etc.), the Gower distance metric is preferred. In this study, the default weights of the Gower distance are optimized to obtain a higher Average Silhouette Width (ASW) value of the clustering results. ASW can be used to measure the quality of clustering results in which high value leads to higher intra-cluster homogeneity and inter-cluster dissimilarity. So, maximizing the ASW value improves the quality of the clusters which is the goal of the optimization. At the end, this process helps us to obtain more accurate mobility profiles of the Swiss population

How to Optimize Gower Distance Weights for the k-Medoids Clustering Algorithm to Obtain Mobility Profiles of the Swiss Population

This piece of research aims to obtain mobility profiles of the Swiss population. To that end, a survey of the Swiss Statistical Office (FSO) called Mobility and Transport Micro-census (MTMC) is utilized. Along with a qualitative method clustering, the respondents in the survey are clustered based on their mobility characteristics to obtain their profiles. The clustering, in particular acquiring medoids (centrotypes or exemplars), helps us then to generate a synthetic population of Switzerland. To gain medoids of each cluster, the k-Medoids clustering algorithm is utilized which partitions instances based on their positions in a latent space (symmetric distance matrix). Distances that shape this space can be generated by various metrics e.g. Euclidean, Gower, Manhattan. Since in this study features are mixed-type (e.g. numeric, categorical, etc.), the Gower distance metric is preferred. In this study, the default weights of the Gower distance are optimized to obtain a higher Average Silhouette Width (ASW) value of the clustering results. ASW can be used to measure the quality of clustering results in which high value leads to higher intra-cluster homogeneity and inter-cluster dissimilarity. So, maximizing the ASW value improves the quality of the clusters which is the goal of the optimization. At the end, this process helps us to obtain more accurate mobility profiles of the Swiss population

A Behavioural Decision-Making Framework For Agent-Based Models

In the last decades, computer simulation has become one of the mainstream modelling techniques in many scientific fields. Social simulation with Agent-based Modelling (ABM) allows users to capture higher-level system properties that emerge from the interactions of lower-level subsystems. ABM is itself an area of application of Distributed Artificial Intelligence and Multiagent Systems (MAS). Despite that, researchers using ABM for social science studies do not fully benefit from the development in the field of MAS. It is mainly because the MAS architectures and frameworks are built upon cognitive and computer science foundations and principles, creating a gap in concepts and methodology between the two fields. Building agent frameworks based on behaviour theory is a promising direction to minimise this gap. It can provide a standard practice in interdisciplinary teams and facilitate better usage of MAS technological advancement in social research. From our survey, Triandis' Theory of Interpersonal Behaviour (TIB) was chosen due to its broad set of determinants and inclusion of an additive value function to calculate utility values of different outcomes. As TIB's determinants can be organised in a tree-like structure, we utilise layered architectures to formalise the agent's components. The additive function of TIB is then used to combine the utilities of different level determinants. The framework is then applied to create models for different case studies from various domains to test its ability to explain the importance of multiple behavioural aspects and environmental properties. The first case study simulates the mobility demand for Swiss households. We propose an experimental method to test and investigate the impact of core determinants in the TIB on the usage of different transportation modes. The second case study presents a novel solution to simulate trust and reputation by applying subjective logic as a metric to measure an agent's belief about the consequence(s) of action, which can be updated through feedback. The third case study investigates the possibility of simulating bounded rationality effects in an agent's decision-making scheme by limiting its capability of perceiving information. In the final study, a model is created to simulate migrants' choice of activities in centres by applying our framework in conjunction with Maslow's hierarchy of needs. The experiment can then be used to test the impact of different combinations of core determinants on the migrants' activities. Overall, the design of different components in our framework enables adaptations for various contexts, including transportation modal choice, buying a vehicle or daily activities. Most of the work can be done by changing the first-level determinants in the TIB's model based on the phenomena simulated and the available data. Several environmental properties can also be considered by extending the core components or employing other theoretical assumptions and concepts from the social study. The framework can then serve the purpose of theoretical exposition and allow the users to assess the causal link between the TIB's determinants and behaviour output. This thesis also highlights the importance of data collection and experimental design to capture better and understand different aspects of human decision-making

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

Adopting Circular Economy Current Practices and Future Perspectives

The development of a closed-loop cycle is a necessary condition so as to develop a circular economy model as an alternative to the linear model, in order to maintain the value of products and materials for as long as possible. For this motive, the definition of the value must be demonstrated for both the environment and the economy. The presence of these analyses should be associated with the social dimension and the human component. A strong cooperation between social and technical profiles is a new challenge for all researchers. End of life of products attract a lot of attention, and the final output could be the production of technologies suitable for managing this waste