Questa tesi è composta da due saggi indipendenti. Il saggio presentato nel Capitolo 1 studia la conformità fiscale all'interno di un modello basato su agenti. Il modello è progettato tenendo conto di una serie di regole fiscali in vigore in Italia e calibrato con micro-dati forniti dall'autorità fiscale italiana. I risultati delle simulazioni mostrano che, considerando livelli di deterrenza realistici, agenti strettamente razionali generano un livello (medio) di non conformità fiscale sostanzialmente superiore a quello suggerito dai dati empirici. Quando includiamo nel processo decisionale dell’agente il calcolo e l’aggiornamento della probabilità soggettiva di subire un controllo, così come l’attitudine alla conformità sociale e gli effetti di rete, il modello fornisce risultati maggiormente in linea con l'evidenza empirica. Il saggio presentato nel Capitolo 2 impiega diverse tecniche di apprendimento automatico, con l'obiettivo di identificare quei contribuenti che hanno maggiore probabilità di aumentare l’importo della loro dichiarazione dei redditi dopo essere stati controllati dall'autorità fiscale. Tra i metodi impiegati, la foresta casuale ha garantito la maggiore accuratezza predittiva. Per valutare l'utilità pratica del nostro approccio, calcoliamo l'aumento del reddito netto riportato dai contribuenti identificati dal modello random forest. Troviamo che, in media, questo aumento è significativo rispetto alla media di tutti i contribuenti ispezionati. Riteniamo, dunque, che il nostro approccio possa rivelarsi uno strumento utile al fine di individuare e selezionare quei contribuenti che hanno una maggiore probabilità di dichiarare un reddito più alto in seguito ad un controllo, consentendo, quindi, una migliore allocazione delle - tipicamente scarse - risorse finanziarie a disposizione dell’autorità fiscale nell'ambito della sua attività ordinaria di controllo. / The essay presented in Chapter 1 studies tax compliance within an agent-based framework. The model is designed according to a set of normative taxing rules for the Italian case and calibrated with micro-data provided by the Italian tax authority. Simulation results show that, under realistic deterrence levels, strict rational agents generate a (average) level of tax noncompliance substantially higher than that suggested by the empirical data. When subjective audit probability computing and updating as well as social conformity attitude and network effects are included in the decision process, the model provides results more in line with the empirical evidence. The essay presented in Chapter 2 employs several machine learning techniques, with the aim to identify those taxpayers who are more likely to increase their net income declarations after being audited by the tax authority. Among the employed methods, random forest guaranteed higher predictive accuracy. In order to assess the practical utility of our approach, we compute the reported net income increase by taxpayers identified through the random forest model. We find that, on average, this increase is significant compared to the average of all the inspected taxpayers. We believe that our approach could prove a useful tool in order to identify and select those taxpayers who are more likely to increase the income reporting after an audit, therefore allowing for a better allocation of the – typically scarce – financial resources available to the tax authority for its ordinary auditing activities.

Huge amount of medical data has been generated in practical experiments which makes data analysis a challenging problem. This requires novel techniques to be developed. The improvements in computational power suggest to use computerbased modeling approaches to process a large set of data. One of the important systems in the human body to be investigated is the immune system. The previous studies of medical scientists and ongoing experiments at Karolinska Institute provide information about the human immune system. This information includes attributes of human immune system’s blood cells and the interactions between these cells. This interactions are provided as ‘if-then’ logical rules. Each rule verifies a condition on the attribute of one cell and it may initiate interaction processes to modify the attributes of other cells. A specific temporal value is associated to each process to quantify the speed of that process in the body (i.e., slow, medium, fast). We propose an agent-based model (ABM) to study human immune system cells and their interactions. The ABM is selected to overcome the complexity of large amount of data and find emergent properties and behavior patterns of the cells. Immune system cells are modeled as autonomous agents which have interactions with each other. Different values of a cell attributes define possible states of the cell and the collection of states of all cells constructs the state of the whole agent-based model. In order to consider the state transitions of the cells, we used a finite state machine (FSM). The first state is constructed from the input initial values for the cells and considering the logical time of 1. In each step, the program goes one time unit further and computes next state by applying the changes based on the cells’ interactions rules. This evolution of states in time is similar to game of life (GOL) automaton. The final model based on three modeling approaches of ABM, FSM and GOL are used to test medical hypothesis related to human immune system. This model provides a useful framework for medical scientists to do experiments on the cells’ attributes and their interaction rules. Considering a set of cells and their interactions, the proposed framework shows emergent properties and behavior patterns of the human immune system.

Common pool resource management systems are complex to manage due to the absence of a clear understanding of the effects of users’ behavioral characteristics. Non-cooperative decision making based on individual rationality (as opposed to group rationality) and a tendency to free ride due to lack of trust and information about other users’ behavior creates externalities and can lead to tragedy of the commons without intervention by a regulator. Nevertheless, even regulatory institutions often fail to sustain natural common pool resources in the absence of clear understanding of the responses of multiple heterogeneous decision makers to different regulation schemes. While modeling can help with our understanding of complex coupled human-natural systems, past research has not been able to realistically simulate these systems for two major limitations: 1) lack of computational capacity and proper mathematical models for solving distributed systems with self-optimizing agents; and 2) lack of enough information about users’ characteristics in common pool resource systems due to absence of reliable monitoring information. Recently, different studies have tried to address the first limitation by developing agent-based models, which can be appropriately handled with today’s computational capacity. While these models are more realistic than the social planner’s models which have been traditionally used in the field, they normally rely on different heuristics for characterizing users’ behavior and incorporating heterogeneity. This work is a step-forward in addressing the second limitation, suggesting an efficient method for collecting information on diverse behavioral characteristics of real agents for incorporation in distributed agent-based models. Gaming in interactive virtual environments is suggested as a reliable method for understanding different variables that promote sustainable resource use through observation of decision making and iii behavior of the resource system beneficiaries under various institutional frameworks and policies. A review of educational or "serious" games for environmental management was undertaken to determine an appropriate game for collecting information on real-agents and also to investigate the state of environmental management games and their potential as an educational tool. A web-based groundwater sharing simulation game—Irrigania—was selected to analyze the behavior of real agents under different common pool resource management institutions. Participants included graduate and undergraduate students from the University of Central Florida and Lund University. Information was collected on participants’ resource use, behavior and mindset under different institutional settings through observation and discussion with participants. Preliminary use of water resources gaming suggests communication, cooperation, information disclosure, trust, credibility and social learning between beneficiaries as factors promoting a shift towards sustainable resource use. Additionally, Irrigania was determined to be an effective tool for complementing traditional lecture-based teaching of complex concepts related to sustainable natural resource management. The different behavioral groups identified in the study can be used for improved simulation of multi-agent groundwater management systems.

Decision-making by one or more individuals to select a course of action is predicated on the values and preferences to identify, choose options, and finally select the option that is evaluated to be the “best option.” Decision theory provides the means to model and analyze both the processes and options available to the decision-makers. This dissertation assembled in three phases: 1) An effort to collect and review existing literature relating to the concept of expanding decision analysis options to provide a model of decision-making made with time-dependent factors along with uncertainty and risk. Further, adding the concept of a decision to update time-dependent decision data in a Bayesian fashion aids in modeling decision-making thought processes. This review included a total of 395 research artifacts. 2) Development of a technical approach using the information gathered in the literature review to guide planning for a decision-making simulation of individuals and organizations. The approach emphasizes creating a decision-making simulation framework with capabilities to model time-dependent factors, information processing and communication, and fuzzy-stochastic data. 3) Use of the technical approach to develop a simulation framework to simulate complex decision-making and work packages at multiple levels in an organization using time-dependent factors, information processing and communication, and fuzzy-stochastic data. Using a Discrete Event Simulation (DES) and Agent-Based Models (ABM) to simulate people and their interactions, this framework was then be used to simulate decision-making and work processes within an organization. Ultimately, the Agile Enterprise Simulation (AES) capability was created and demonstrated.

Whitebark pine is an important keystone and foundation species in western North American mountain ranges, and facilitates tree island development in Rocky Mountain treelines. The manifestation of white pine blister rust in the cold and dry treelines of the Rockies, and the subsequent infection and mortality of whitebark pines raises questions as to how these extreme environments harbor the invasive disease, and what the consequences may be for treeline dynamics. This dissertation research comprises three studies that investigate abiotic factors influential for blister rust infection in treeline whitebark pines, how disease coupled with changing climate may affect whitebark pine treeline dynamics, and the connection between treeline spatial patterns and disease. The first study examined the spatial variation of blister rust infection in two whitebark pine treeline communities, and potential topographic correlates. Using geospatial and field approaches to generate high resolution terrain models of treeline landscapes, microtopography associated with solar radiation and moisture were found most influential to blister rust infection in treeline whitebark pines. Using field-based observations from sampled treeline communities, the second study developed an agent-based model to examine the effects of disease and climate on treeline pattern and process. Treeline dynamics were simulated under five hypothetical scenarios to assess changes in tree spatial patterns and populations. Blister rust-induced loss of whitebark pines resulted in a decline of facilitative processes, and an overall negative treeline response to disease—despite the beneficial effects of climate amelioration. The objective of the third study was to explore whether spatial patterns of tree proximity, size, and growth infer disease patterns. Comparing spatial patterns of tree characteristics between diseased and undiseased treeline communities, I found that trees growing near trees with larger stem diameters, and larger tree islands, tended to have more blister rust cankers, and displayed clustered spatial patterns. Undiseased treeline patterns revealed near neighbors smaller in stem diameter and tree island size, and were randomly dispersed. Blister rust diseased whitebark pines reveal spatial autocorrelation, despite the complex blister rust disease life cycle. Overall, findings from this dissertation reveal the implications of invasive disease on sensitive treeline ecotones dependent on a keystone species. / Ph. D.

Agent-based models are computer models made up of agents that can exist in a finite number of states. The state of the system at any given time is determined by rules governing agents' interaction. The rules may be deterministic or stochastic. Optimization is the process of finding a solution that optimizes some value that is determined by simulating the model. Optimal control of an agent-based model is the process of determining a sequence of control inputs to the model that steer the system to a desired state in the most efficient way. In large and complex models, the number of possible control inputs is too large to be enumerated by computers; hence methods must be developed for use with these models in order to find solutions without searching the entire solution space. Heuristic algorithms have been applied to such models with some success. Such algorithms are discussed; case studies of examples from biology are presented. The lack of a standard format for agent-based models is a major issue facing the study of agent-based models; presentation as polynomial dynamical systems is presented as a viable option. Algorithms are adapted and presented for use in this framework. / Master of Science

En préservation-transplantation rénale, l’ischémie-reperfusion (IR) induit inflammation, fibrose, dysfonction et perte du greffon. Les événements d’IR sont de mieux en mieux identifiés, mais leurs complexité limite prédiction et thérapeutique. A partir d’une analyse bibliographique détaillée, nous proposons un modèle par agents de la réponse rénale aux lésions d’IR au niveau cellulaire/tissulaire, réalisé avec l'outil de modélisation NetLogo.Dans un premier temps, nous développons un modèle dynamique de l’oxygénation du cortex rénal, avec apport et diffusion de l’oxygène (O2), et consommation dépendant de la filtration. Nous adaptons ce modèle à l’état stationnaire pour l’O2, puis nous couplons les niveaux de PO2 à l'état énergétique (ATP) des cellules épithéliales et endothéliales (avec voies aérobie et anaérobie). Le statut de viabilité cellulaire est lié au niveau d'ATP, aboutissant à une représentation semi-phénoménologique de la réparation/survie vs apoptose/nécrose. Enfin, nous explorons le destin cellulaire et tissulaire lors d’IR simulées, avec l’ajout progressif d’éléments clefs de l’inflammation (invasion tissulaire par leucocytes, signaux lésionnels, phagocytose) et de la fibrose (fibroblastes, collagène). L’évolution du modèle vers la résolution de l’inflammation/régénération du tissu ou vers la fibrose tissulaire est observée selon les conditions imposées (durée/intensité, ischémie vs hypoxémie).Cette construction constitue le premier modèle des effets de l’oxygénation sur la dynamique cellulaire-tissulaire de l’inflammation-fibrose rénale en réponse à l’IR. A terme, elle permettra d'aborder clinique et thérapeutique de la conservation-transplantation rénale. / In renal preservation-transplantation, ischemia-reperfusion (IR) causes graft inflammation and fibrosis, dysfunction and loss. Events involved in IR injury grow identified, but their intricacy hampers prediction and therapeutics. Based on a detailed bibliographical analysis, we propose an Agent-Based Model of renal response to IR injury at cell and tissue levels, created with the modeling tool NetLogo.First, we develop and validate a dynamic model of the oxygenation of the renal cortex, featuring blood perfusion, oxygen diffusion, and oxygen consumption (driven by sodium filtered load and transport). We then adapt this model to oxygen steady-state, and PO2 level is coupled to energetic status (ATP) in epithelial and endothelial cells (aerobic and anaerobic pathways). Cell viability is coupled to ATP level, leading to a semi-phenomenological representation of repair/survival versus apoptosis/necrosis. Finally, we explore (and verify) cell and tissue fate during simulated IR sequences, with the gradual addition of key elements of inflammation (leukocytes infiltration, injury signals, phagocytosis) and fibrosis (fibroblasts, collagen). Model evolution toward the resolution of inflammation/tissue regeneration or toward tissue fibrosis is observed along imposed conditions (duration/intensity, ischemia vs hypoxemia). Results are compared to experiments from our laboratory.This construction is the first model of the effects of oxygenation on cell-tissue dynamics during renal inflammation-fibrosis response to IR. Ultimately, it will allow to address clinical and therapeutic aspects of renal transplantation and conservation.

In public policies, agents are part of an emergent and complex context, reason for which their actions should not be examined in isolation. The state of an agent is influenced by the state of others, in an environment where feedback is continuous and full of interactions. These characteristics result in a system where the total is more unpredictable and dazzling than the mere sum of its parts. As a result, there are a growing number of studies that use typical methods of complex systems to analyze public policies in various areas, such as healthcare, education, crime prevention, energy resources and others. Moreover, such distinct approach allows for more accessible investigations of public policy models, including policies that were not evaluated ex ante from the traditional lenses. This research had two main objectives: to verify how complex systems apply to the context of public policies theoretically and to present a practical application of a model, which was built based upon a case study. Since there is not a clear comprehension on how complex systems could benefit policy makers, this study presents, in its first part, a systematic literature review including some existing applications and the benefits of complexity science in the policy arena. On the whole, it can be asserted that there is a strong consensus that complex systems can be highly beneficial for policy makers and, consequently, for the overall population. Researchers perceive different benefits, such as the opportunity of testing policies a priori, the possibility of comparing different policies for the same topic, and the contemplation of new ideas and insights for better policy formulation. Although there are several simulations and models proposed for public policies in several areas, it lacks an empirical demonstration that effectively proves the benefits of applying complex systems in public policies, i.e., apparently, there are obstacles that prevent such models from having effects in the real world. In this way, the second part of the research presents an agent-based model that can be applied empirically in a government agency: a regulatory body. Such model allows policy makers to compare different enforcement strategies and anticipate side effects that would be difficult to predict without the use of simulations. In this sense, the objective of the second part of this research was to build an agent-based model of a public policy and for which a practical implementation could be carried out. Therefore, a public policy from a professional regulatory board in the healthcare area was chosen, for which two different strategies were tested, with the objective of comparing their efficiency and effectiveness. Such strategies were modeled and simulated with the use of Netlogo software with different scenarios. Results indicate that agent based models can serve as predictive tools for comparing and improving inspection strategies, and also as source of insights for anticipating unintended consequences that would hardly be noticed ex ante without the use of simulation tools / Em políticas públicas, as ações dos agentes envolvidos não podem ser analisadas de forma isolada. O estado de um agente é influenciado pelo estado dos demais, num ambiente em que o feedback é contínuo e repleto de interações. Essas características resultam num sistema onde o total é mais imprevisível e deslumbrante do que a mera soma de suas partes. Com isso, há um crescente número de estudos que utilizam métodos típicos de sistemas complexos para analisar políticas públicas de diversas áreas, como saúde pública, educação, segurança, recursos energéticos e outros. Além disso, essa forma diferente de abordagem permite que alguns modelos de políticas públicas sejam investigados com mais facilidade, incluindo políticas que sequer eram analisadas pelo prisma tradicional. Esta pesquisa teve dois objetivos principais: verificar como os sistemas complexos se aplicam às políticas públicas no campo teórico e apresentar uma aplicação prática de modelagem dentro do contexto de um estudo de caso. Como ainda não há um entendimento sistematizado sobre como sistemas complexos podem ser úteis em políticas públicas, este estudo apresenta, em sua primeira parte, uma revisão sistemática de literatura para uma melhor compreensão de como essas aplicações ocorrem e de quais benefícios essa ciência, de fato, pode trazer. Em decorrência desse estudo, pode-se afirmar que há consenso, na literatura, de que a teoria da complexidade é benéfica para formuladores de políticas e, consequentemente, para a população em geral. Tais benefícios são vistos de diversas formas pelos pesquisadores, como, por exemplo, a possibilidade de se testar políticas a priori, a possibilidade de se comparar diversos tipos de políticas para um mesmo problema e a obtenção de novas perspectivas e ideias para formulação de políticas. Apesar de haver diversas simulações e modelos propostos para políticas públicas em diversas áreas, não foi constatada uma demonstração empírica que comprove efetivamente o benefício de se aplicar sistemas complexos em políticas públicas, ou seja, aparentemente há obstáculos que impedem esses modelos terem efeitos nas políticas de facto. Dessa maneira, o objetivo da segunda parte da pesquisa foi o de construir um modelo baseado em agentes relacionado a uma política pública e cuja implementação prática fosse factível. Assim, foi selecionada uma política relacionada a um órgão público de fiscalização do exercício profissional (conselho de classe), especificamente na área da saúde, para a qual foram traçadas duas estratégias diferentes, com o objetivo de compará-las em termos de eficácia e de efetividade. Essas estratégias foram modeladas e simuladas em software específico de modelos baseados em agentes para análise dos resultados considerando diversos cenários possíveis. Os resultados indicam que os modelos baseados em agentes podem auxiliar o formulador de políticas a comparar diferentes estratégias de fiscalização e antecipar efeitos colaterais que dificilmente seriam constatados ex ante sem a utilização de simulações

No contexto dos sistemas complexos, o presente trabalho investiga 3 estratégias de precificação:(1)desnatação,(2) penetração e (3)aprendizado, na difusão de carros elétricos em diferentes contextos.Por meio da modelagem baseada em agentes com 100.000 entidades autônomas, o primeiro modelo testa três situações relacionados à demanda energética:(1)desabastecimento,(2)estabilidade e (3)crescimento moderado da demanda.A forte escassez de energia estimulou a rápida migração dos agentes aos carros elétricos. As três estratégias de precificação exibiram resultados similares em termos de faturamento e % na participação do mercado, no entanto a estratégia de penetração foi capaz de capturar uma parcela maior do mercado em menor tempo.No segundo modelo,3 diferentes comportamentos sociais são aplicadas aos agentes: (1)conservador,(2)racional e (3) social,em diferentes proporções afim de avaliar a influência da composição social na dinâmica difusora.No que concerne ao faturamento e % na participação do mercado, o segundo modelo detectou diferenças estatisticamente significativas para cada estratégia de precificação.Em sociedades proeminentemente conservadoras, as três estratégias não apresentaram indícios de diferença significativa no tocante relação ao faturamento,% na participação final do mercado e taxa de adoção média.Sociedades compostas majoritariamente por agentes racionais, apresentaram a mais rápida convergência aos carros elétricos,sendo este, o veículo mais caro.Isto se deve à percepção positiva do custo/benefício ao longo prazo.O maior faturamento é proveniente das sociedades compostas preponderantemente por agentes com atitudes sociais dado à compra e troca mais recorrente entre diferentes veículos no decorrer das interações.A estratégia de desnatação demonstrou maior versatilidade ao exibir performance superior com maior regularidade no que tange em faturamento em todas as composições sociais testadas.A estratégia de penetração exibiu índices maiores em taxa de adoção e faturamento em redes compostas integralmente por agentes com comportamentos sociais iguais,mas não foi possível detectar este padrão em redes parciais. Por fim, a estratégia de aprendizado apresentou o menor faturamento em todos os cenários, no entanto, sua taxa de adoção similar à estratégia de penetração, pode ser a estratégia de precificação mais crível e eficiente para empresas iniciantes / In the context of complex systems,the following research investigated 3 pricing strategies:(1)skimming,(2)penetration and (3)learning, at electric car diffusion in several different scenarios. Through the agent-based modelling with 100.000 autonomous entities, the first model tested 3 situations related to energy demand:(1)severe shortage,(2)stability and (3)moderate growing of demand. The strong shortage of energy forced an fast-paced migration of agents towards the electric cars. The 3 strategies showed up similar results in terms revenues and market share, however the penetration strategy was able to capture a large part of the market faster than others. At the second model, 3 different social behaviors were implemented in each agent:(1)conservative,(2)rational and (3)social,in different proportions in order to assess social composition and its influence in the innovation diffusion process. Revenue and market share as concerned,the second model detected significant statistic difference for each pricing strategy. For societies predominantly conservative, all strategies did not show significant differences evidences regarding to revenue,market share and average adoption rate. Societies comprised mostly of rational agents presented the quickest convergence to electric cars, which it is the most expensive car. This is because a positive perception of benefits over cost in the long term.The largest revenue were derived from societies primarily composed of agents with social behaviors due to recurrent purchase and exchange between different vehicles over the interactions. Skimming strategy demonstrated greater versatility by displaying superior performance more regularly in terms of revenue in every social composition simulated. Penetration strategy exhibited highest rates of adoption and revenue in social networks composed entirely of agents with same social behavior, but it was not possible to detect such pattern at partial social networks. Finally, the learning strategy reported the lowest revenues at every scenario, none the less, its rate of adoption was equivalent to penetrations strategy rates, this strategy could be the most feasible and efficient to startups and small companies

No contexto dos sistemas complexos, o presente trabalho investiga 3 estratégias de precificação:(1)desnatação,(2) penetração e (3)aprendizado, na difusão de carros elétricos em diferentes contextos.Por meio da modelagem baseada em agentes com 100.000 entidades autônomas, o primeiro modelo testa três situações relacionados à demanda energética:(1)desabastecimento,(2)estabilidade e (3)crescimento moderado da demanda.A forte escassez de energia estimulou a rápida migração dos agentes aos carros elétricos. As três estratégias de precificação exibiram resultados similares em termos de faturamento e % na participação do mercado, no entanto a estratégia de penetração foi capaz de capturar uma parcela maior do mercado em menor tempo.No segundo modelo,3 diferentes comportamentos sociais são aplicadas aos agentes: (1)conservador,(2)racional e (3) social,em diferentes proporções afim de avaliar a influência da composição social na dinâmica difusora.No que concerne ao faturamento e % na participação do mercado, o segundo modelo detectou diferenças estatisticamente significativas para cada estratégia de precificação.Em sociedades proeminentemente conservadoras, as três estratégias não apresentaram indícios de diferença significativa no tocante relação ao faturamento,% na participação final do mercado e taxa de adoção média.Sociedades compostas majoritariamente por agentes racionais, apresentaram a mais rápida convergência aos carros elétricos,sendo este, o veículo mais caro.Isto se deve à percepção positiva do custo/benefício ao longo prazo.O maior faturamento é proveniente das sociedades compostas preponderantemente por agentes com atitudes sociais dado à compra e troca mais recorrente entre diferentes veículos no decorrer das interações.A estratégia de desnatação demonstrou maior versatilidade ao exibir performance superior com maior regularidade no que tange em faturamento em todas as composições sociais testadas.A estratégia de penetração exibiu índices maiores em taxa de adoção e faturamento em redes compostas integralmente por agentes com comportamentos sociais iguais,mas não foi possível detectar este padrão em redes parciais. Por fim, a estratégia de aprendizado apresentou o menor faturamento em todos os cenários, no entanto, sua taxa de adoção similar à estratégia de penetração, pode ser a estratégia de precificação mais crível e eficiente para empresas iniciantes / In the context of complex systems,the following research investigated 3 pricing strategies:(1)skimming,(2)penetration and (3)learning, at electric car diffusion in several different scenarios. Through the agent-based modelling with 100.000 autonomous entities, the first model tested 3 situations related to energy demand:(1)severe shortage,(2)stability and (3)moderate growing of demand. The strong shortage of energy forced an fast-paced migration of agents towards the electric cars. The 3 strategies showed up similar results in terms revenues and market share, however the penetration strategy was able to capture a large part of the market faster than others. At the second model, 3 different social behaviors were implemented in each agent:(1)conservative,(2)rational and (3)social,in different proportions in order to assess social composition and its influence in the innovation diffusion process. Revenue and market share as concerned,the second model detected significant statistic difference for each pricing strategy. For societies predominantly conservative, all strategies did not show significant differences evidences regarding to revenue,market share and average adoption rate. Societies comprised mostly of rational agents presented the quickest convergence to electric cars, which it is the most expensive car. This is because a positive perception of benefits over cost in the long term.The largest revenue were derived from societies primarily composed of agents with social behaviors due to recurrent purchase and exchange between different vehicles over the interactions. Skimming strategy demonstrated greater versatility by displaying superior performance more regularly in terms of revenue in every social composition simulated. Penetration strategy exhibited highest rates of adoption and revenue in social networks composed entirely of agents with same social behavior, but it was not possible to detect such pattern at partial social networks. Finally, the learning strategy reported the lowest revenues at every scenario, none the less, its rate of adoption was equivalent to penetrations strategy rates, this strategy could be the most feasible and efficient to startups and small companies