abstract: This dissertation uses a comparative approach to investigate long-term human- environment interrelationships in times of climate change. It uses Geographical Information Systems and ecological models to reconstruct the Magdalenian (~20,000- 14,000 calibrated years ago) environments of the coastal mountainous zone of Cantabria (Northwest Spain) and the interior valleys of the Dordogne (Southwest France) to contextualize the social networks that could have formed during a time of high climate and resource variability. It simulates the formation of such networks in an agent-based model, which documents the processes underlying the formation of archaeological assemblages, and evaluates the potential impacts of climate-topography interactions on cultural transmission. This research then reconstructs the Magdalenian social networks visible through a multivariate statistical analysis of stylistic similarities among portable art objects. As these networks cannot be analyzed directly to infer social behavior, their characteristics are compared to the results of the agent-based model, which provide characteristics estimates of the Magdalenian latent social networks that most likely produced the empirical archaeological assemblage studied. This research contributes several new results, most of which point to the advantages of using an inter-disciplinary approach to the study of the archaeological record. It demonstrates the benefits of using an agent-based model to parse social data from long- term palimpsests. It shows that geographical and environmental contexts affect the structure of social networks, which in turn affects the transmission of ideas and goods that flow through it. This shows the presence of human-environment interactions that not only affected our ancestors’ reaction to resource insecurities, but also led them to innovate and improve the productivity of their own environment. However, it also suggests that such alterations may have reduced the populations’ resilience to strong climatic changes, and that the region with diverse resources provided a more stable and resilient environment than the region transformed to satisfy the immediate needs of its population. / Dissertation/Thesis / Appendix_D_Sites_Dates / Appendix_E_Flowchart_Biome_Reconstruction / Appendix_H_Flowchart_ABM / Appendix_I_Flowchart_Social_Network / Appendix_J_Portable_Art_Objects / Appendix_J_Art_Characteristics / Appendix_L_Poster_Summary / Appendix_A_Prehistoric_Fauna / Appendix_B_Modern_PFT_Distribution / Appendix_C_Prehistoric_PFT_Distribution / Doctoral Dissertation Anthropology 2017

Este trabalho adota a técnica de simulação baseada em agentes para analisar o processo de precificação de energia comercializada no ambiente de contratação regulada (leilões) do mercado elétrico do Brasil. Nesse contexto, são simulados leilões de energia nova - produto fonte eólica. O simulador dos leilões de energia do Brasil foi construído através de programação realizada em matlab. Nesse programa, é possível comparar a escolha de lances de vendedores participantes nos pleitos. Os agentes (vendedores) participantes dos leilões aprendem com o decorrer dos leilões simulados. A aprendizagem é determinada através da utilização de variação do algoritmo Q-learning. Os resultados claramente demonstram que as técnicas de aprendizagem consideradas têm resultados mais favoráveis do que escolhas aleatórias (sem aprendizagem). Considerando outro ponto de vista, é possível verificar diferença de média de preços nos leilões entre os perfis de geradores públicos e privados. Além disso, é possível afirmar que o preço da energia se altera dada alteração na participação relativa de vendedores públicos ou privados nos pleitos. / This thesis adopts an agent based simulation in order to analyses the pricing process of energy that is negotiate by auctions in Brazil. In this work, wind energy power auctions are simulate. The model was developed in Matlab platform, and so, it was possible to compare the bidding process of the players in those auctions. The players learn during the auctions, and the process of learning is defined by a variation of the Q-learning algorithm. The results of the research show that when Q-learning is considered by generators there are more benefits than it is not. From another point of view, it is possible to say that there is difference between the prices of public and private players (enterprises that sell Wind energy). Besides it is possible to say that when the number of public and private players in an auction change the energy price.

Made available in DSpace on 2016-03-15T19:26:50Z (GMT). No. of bitstreams: 1 Paulo Roberto Domingues de Faria.pdf: 1135125 bytes, checksum: 6edb1659db23a9f945a0d8a4edd49911 (MD5) Previous issue date: 2010-02-02 / Fundo Mackenzie de Pesquisa / Market efficiency and rationality of economic agents are among the main subjects for debate in the area of Finance. With the development of the field of study of Behavioral Finance some serious works have been developed to improve the financial models with the incorporation of social and psychological elements to the financial theory. This paper presents some of the assumptions of the modern theory of finance and the main ideas of behavioral finance. It also presents empirical research involving the simulation of heterogeneous agents in an artificial stock market. The objective is to evaluate if the interaction of these agents can impact the behavior of asset prices in a different way from that expected by the traditional finance theories. / Entre os principais assuntos em discussão na área de Finanças estão as questões relacionadas à eficiência de mercado e à racionalidade dos agentes econômicos, que se apresentam como premissa para os principais modelos de finanças. Nos últimos anos surgiram trabalhos com o objetivo de aperfeiçoar esses modelos e suas premissas, a partir da incorporação de elementos sociais e psicológicos à teoria financeira. Esses trabalhos deram origem ao campo de estudos de Finanças Comportamentais. Esta dissertação apresenta algumas das premissas da teoria moderna de finanças e os principais pressupostos das finanças comportamentais. Além disso, apresenta pesquisa empírica envolvendo a simulação do comportamento de agentes heterogêneos em um mercado acionário artificial, utilizando o método de simulação baseada em agentes. Os resultados visam avaliar se a interação dos agentes pode impactar o comportamento de preços dos ativos de forma diferente da prevista pelas teorias de finanças.

In this thesis we present model of the human work process on projects using multi-agent model. Within the project management plan is carried out comparisons with the fact not only in the context of subsequent checks, but also in the course of the project. One of the most item is cost of human resources. To increase efficiency and control over the actual cost to introducing a range of organizations link the accounting system to a system of reporting work. Such a system registry of the work is not only complex, but also demanding in terms of managing the time gap between the creation of estimates and their own work. In general, there may be several variants of complications such as delay work on the project because of inaccurate estimates of job performance and therefore insufficient funds in the accounts sections and stages of the project. The aim of this work is to find the characteristics of such projects for which this system work. In the first part we are addressed basic theoretical assumptions for modeling work in the field of project management and multi-agent modeling. Next part relates to the creation of multi-agent model, including detailed characteristics and verification. At the end of this research are described a several experiments with the model and analysis results.

abstract: This dissertation consists of three essays, each examining distinct aspects about public organization adaptation to extreme events using evidence from public transit agencies under the influence of extreme weather in the United States (U.S.). The first essay focuses on predicting organizational adaptive behavior. Building on extant theories on adaptation and organizational learning, it develops a theoretical framework to uncover the pathways through which extreme events impact public organizations and identify the key learning mechanisms involved in adaptation. Using a structural equation model on data from a 2016 national survey, the study highlights the critical role of risk perception to translate signals from the external environment to organizational adaptive behavior. The second essay expands on the first one to incorporate the organizational environment and model the adaptive system. Combining an agent-based model and qualitative interviews with key decision makers, the study investigates how adaptation occurs over time in multiplex contexts consisting of the natural hazards, organizations, institutions and social networks. The study ends with a series of refined propositions about the mechanisms involved in public organization adaptation. Specifically, the analysis suggests that risk perception needs to be examined relative to risk tolerance to determine organizational motivation to adapt, and underscore the criticality of coupling between the motivation and opportunities to enable adaptation. The results further show that the coupling can be enhanced through lowering organizational risk perception decay or synchronizing opportunities with extreme event occurrences to promote adaptation. The third essay shifts the gaze from adaptation mechanisms to organizational outcomes. It uses a stochastic frontier analysis to quantify the impacts of extreme events on public organization performance and, importantly, the role of organizational adaptive capacity in moderating the impacts. The findings confirm that extreme events negatively affect organizational performance and that organizations with higher adaptive capacity are more able to mitigate those effects, thereby lending support to research efforts in the first two essays dedicated to identifying preconditions and mechanisms involved in the adaptation process. Taken together, this dissertation comprehensively advances understanding about public organization adaptation to extreme events. / Dissertation/Thesis / Doctoral Dissertation Public Administration and Policy 2020

No description available.

i Abstract In this work, we describe a novel methodology to analyse the price elasticity of demand. This method combines an artificial neural network that serves as the model of the behaviour of the customers and a subsequent simulation based on this model. We present the validation of our approach using a real-world dataset obtained from an e-commerce retailer and demonstrate its advantages, notably the ability to estimate the elasticity in distinct price points and the inclusion of the complete pricing situations (not only product's own price). JEL Classification C45, C44, C15, D12 Keywords price elasticity of demand, artificial neural net- work, agent-based model Title A simulation based analysis of price elasticity of demand Author's e-mail Supervisor's e-mail

<p>In many economic settings agents behave strategically. Understanding and, sometimes regulating, that behavior is often crucial to enhance the efficiency with which scarce resources are allocated. A peculiar feature of economics is that cooperation among agents sometimes boosts efficiency, and sometimes hinders it. Social dilemmas, highly ubiquitous in economics, are situations in which cooperation boosts efficiency. Highly concentrated markets where a few firms operate, are situations in which cooperation (also known as collusion) among firms hinders efficiency. In such markets competition, rather than cooperation, boosts efficiency. In this dissertation, I study how uncertainty affects cooperation in social dilemmas, and how the presence of cooperative firms affects competition in concentrated markets.</p> <p><br></p> <p>Both of the settings I study in this dissertation (social dilemmas with noisy payoffs and duopsony with endogenous location and pricing strategy) face a similar challenge. Their complexity compromises the tractability of conventional equilibrium concepts. In other words, Nash equilibria do not exist, or there is a multiplicity of equilibria. This, in turn, precludes comparative static analyses characterizing the effect of exogenous market forces (uncertainty and firm ownership structure) on market and welfare outcomes.</p> <p><br></p> <p>I address this key challenge through a combination of genetic algorithms and laboratory experiments. A genetic algorithm consists of a selection process that identifies strategies that perform better than others, on average. Therefore, surviving strategies constitute, in a sense, average best responses. More than one strategy may survive. This happens when none of the surviving strategies is weakly dominated by the other surviving strategies. An equilibrium is a combination of surviving strategies. In this context, a comparative static analysis consists of the change in equilibrium (combination of surviving strategies) due to a change in exogenous forces. These comparative static analyses generate testable hypotheses. In Essays 1 and 2, I implement laboratory experiments to test these hypotheses.</p> <p><br></p> <p>In Essay 1, I compare infinitely repeated social dilemmas with deterministic and noisy payoffs. I test whether noise in payoffs (where noisy payoffs are generated by a random shock and are uncorrelated amongst agents), which introduces imperfect monitoring, affects cooperation. Experimental evidence shows that imperfect monitoring reduces cooperation because it hinders agents’ ability to threaten defectors with a reciprocal defection. Therefore, noise reduces efficiency by unraveling cooperation in social dilemmas. In Essay 2, I study whether correlation among agents’ noisy payoffs strengthens monitoring and restores cooperation. Experimental evidence shows that stronger (though still imperfect) monitoring due to correlation helps cooperation if and only if agents are prone to cooperate in the initial rounds of the repeated game. Therefore, correlation among shocks affecting agents’ payoffs may or may not increase efficiency depending on the type of players participating in the social dilemma.</p> <p><br></p> <p>Finally, in Essay 3, I use a genetic algorithm to generate comparative statics characterizing the effect of a cooperative firm on market equilibrium and efficiency in a spatial duoposony. A Nash equilibrium in this setting does not exist when location, price, and the degree of spatial price discrimination are all endogenous in the seminal Hotelling’s model. I use a genetic algorithm to identify a stable equilibrium in this setting. I find that a cooperative firm increases efficiency. But, counterintuitively, it does so when the cooperative does not directly compete with the privately owned firm. This is because the cooperative maximizes market share when its procurement region does not overlap with the privately owned firm’s procurement region.</p> <p><br></p>

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.