Submitted by Milton Saito (milton.saito@gvmail.br) on 2013-09-11T03:21:58Z No. of bitstreams: 1 dissertacao-MiltonSaito.pdf: 8406128 bytes, checksum: 2c7b006b9cca9d097d0ee1116e898ef2 (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2013-09-11T13:27:29Z (GMT) No. of bitstreams: 1 dissertacao-MiltonSaito.pdf: 8406128 bytes, checksum: 2c7b006b9cca9d097d0ee1116e898ef2 (MD5) / Made available in DSpace on 2013-09-11T13:28:23Z (GMT). No. of bitstreams: 1 dissertacao-MiltonSaito.pdf: 8406128 bytes, checksum: 2c7b006b9cca9d097d0ee1116e898ef2 (MD5) Previous issue date: 2013-08-14 / Essa dissertação tem como objetivo a modelagem, implementação e a simulação de um mercado de leilão duplo artificial utilizando a abordagem multiagente. Mercado de leilão duplo permite que ofertas de compra e de venda possam ser feitas a qualquer momento e de forma contínua. As bolsas de Nova Iorque (NYSE) e de Chicago (CME) são exemplos deste tipo de leilão. Mercados artificiais são modelos que têm o objetivo de capturar as propriedades dos mercados reais para reproduzir e analisar a dinâmica do mercado através de experimentos computacionais. Assim como no mercado real, o modelo propõe que os agentes interagem entre si assincronamente em sessões de negociações contínuos. Estas últimas características do modelo são viabilizadas através do uso de técnicas e arcabouços tecnológicos que são atualmente utilizados nos mercados reais. Neste trabalho, são investigados os comportamentos do mercado artificial para diferentes grupos de agentes e parâmetros. Ao longo dos experimentos foram constatados que o volume de negociação e a volatilidade dos preços, por exemplo, são diretamente proporcionais ao orçamento dos agentes. Também foram identificados alguns fatos estilizados nas séries de preços geradas a partir do mercado artificial. / The purpose of this work is to model, implement and simulate a double auction artificial market using a multi-agent approach. Double auction markets allow offers to buy and sell to be made at any time and in a continuous fashion. The New York Stock Exchange (NYSE) and the Chicago Mercantile Exchange (CME) are examples of exchanges that adopt this type of auction. Artificial markets are models that aim to capture the properties of real markets to reproduce and analyze the market dynamics through computational experiments. As in real markets, the model proposes that agents interact asynchronously in continuous trading sessions. These features are made possible by using modern techniques and technological frameworks currently used in real markets. In this work, we investigated the behavior of a artificial market using different groups of agents and parameters. Throughout the experiments it was identified that, for example, trading volume and price volatility are directly proportional to the agents' budget. Also, some stylized facts were identified on the price series generated by the artificial market

Comment penser la relation entre les cultures scientifiques contemporaines et l’usage grandissant de l’ordinateur dans la production des savoirs ? Cette thèse se propose de donner une réponse à telle question à partir de l’analyse historique et sociologique d’un domaine scientifique fondé par le Santa Fe Institute (SFI) dans les années 1980 aux États-Unis : les « sciences des systèmes complexes » (SSC). Rendues célèbres par des publications grand-public, les SSC se répandent au cours des années 1990 et 2000 en Europe et dans d’autres pays du monde. Ce travail propose une histoire de la fondation de ce domaine en se concentrant sur le SFI et sur le Réseau National des Systèmes Complexes français. Avec un regard sociologique ancré dans les Science & Technology Studies et dans le courant pragmatiste, elle pose ensuite des questions sur le statut socio-épistémique de ce domaine, sur les modalités de l’administration de la preuve dans des savoirs fondés sur la simulation numérique et enfin sur les engagements épistémiques tenus par les spécialistes des systèmes complexes. Le matériau empirique – composé d’environ 200 entretiens, plusieurs milliers de pages d’archives et quelques visites de laboratoire – nous amène non seulement à mieux connaître ce champ de recherche – dont le langage est très répandu aujourd’hui, mais peu étudié par les historiens et les sociologues ; il nous porte aussi à questionner trois opinions courantes dans la littérature humaniste à propos des sciences numériques. À savoir : 1) l’ordinateur produit des connaissances de plus en plus interdisciplinaires, 2) il donne vie à des savoirs de type nouveau qui nécessitent une toute autre épistémologie pour être pensés et 3) il fait inévitablement advenir des visions du monde néolibérales. Or, cette thèse déconstruit ces trois formes de déterminisme technologique concernant les effets de l’ordinateur sur les pratiques scientifiques, en montrant d’abord que, dans les sciences computationnelles, les rapports interdisciplinaires ne se font pas sans effort ni pacifiquement ou sur pied d’égalité ; ensuite que les chercheurs et les chercheuses des SSC mobilisent des formes d’administration de la preuve déjà mises au point dans d’autres disciplines ; et enfin que les engagements épistémiques des scientifiques peuvent prendre une forme proche de la vision (néo)libérale, mais aussi des formes qui s’en éloignent ou qui s’y opposent. / How to think the relationship between contemporary scientific cultures and the rising usage of computer in the production of knowledge ? This thesis offers to give an answer to such a question, by analyzing historically and sociologically a scientific domain founded by the Santa Fe Institute (SFI) in the 1980s in the United States : the « complex systems sciences » (CSS). Become well-known thanks to popular books and articles, CSS have spread in Europe and in other countries of the world in the course of the 1990s and the 2000s. This work proposes a history of the foundation of this domain, by focussing on the SFI and on the French Complex Systems National Network. With a sociological take rooted into Science & Technology Studies and into pragmatism, it then asks some questions about the socio-epistemic status of such a domain, about the modalities of production of evidence as they are employed in the context of digital simulation and, finally, about the epistemic engagements hold by complexity specialists. Empirical material – composed by circa 200 interviews, several thousands archival pages and a small number of laboratory visits – allows us not only to improve knowledge about this field – whose language is very common today, but little studied by historians and sociologists ; it also brings us to question three current opinions in the human and social sciences literature regarding digital sciences. That is : 1) that the computer produces more and more interdisciplinary knowledge, 2) that it gives birth to a new type of knowledge which needs an entirely new epistemology to be well understood and 3) that it inevitably brings about neoliberal visions of the world. Now, this thesis deconstructs these three forms of technological determinism concerning the effects of computer on scientific practices, by showing firstly that, in digital sciences, the interdisciplinary collaborations are not made without any effort and in a symetrical and pacific way ; secondly, that CSS’ researchers mobilize a kind of evidence production techniques which are well known in other disciplines ; and, thirdly, that scientists’ epistemic engagements can take (neo)liberal forms, but also other forms that depart from neoliberalism or that stand against it.

Lors de l’infection par un pathogène intracellulaire, l’organisme déclenche une réponse immunitaire spécifique dont les acteurs principaux sont les lymphocytes T-CD8. Ces cellules sont responsables de l’éradication de ce type d’infections et de la constitution du répertoire immunitaire de l’individu. Les processus qui composent la réponse immunitaire se répartissent sur plusieurs échelles physiques inter-connectées (échelle intracellulaire, échelle d’une cellule, échelle de la population de cellules). La réponse immunitaire est donc un processus complexe, pour lequel il est difficile d’observer ou de mesurer les liens entre les différents phénomènes mis en jeu. Nous proposons trois modèles mathématiques multi-échelles de la réponse immunitaire, construits avec des formalismes différents mais liés par une même idée : faire dépendre le comportement des cellules TCD8 de leur contenu intracellulaire. Pour chaque modèle, nous présentons, si possible, sa construction à partir des hypothèses biologiques sélectionnées, son étude mathématique et la capacité du modèle à reproduire la réponse immunitaire au travers de simulations numériques. Les modèles que nous proposons reproduisent qualitativement et quantitativement la réponse immunitaire T-CD8 et constituent ainsi de bons outils préliminaires pour la compréhension de ce phénomène biologique. / Upon infection by an intracellular pathogen, the organism triggers a specific immune response,mainly driven by the CD8 T cells. These cells are responsible for the eradication of this type of infections and the constitution of the immune repertoire of the individual. The immune response is constituted by many processes which act over several interconnected physical scales (intracellular scale, single cell scale, cell population scale). This biological phenomenon is therefore a complex process, for which it is difficult to observe or measure the links between the different processes involved. We propose three multiscale mathematical models of the CD8 immune response, built with different formalisms but related by the same idea : to make the behavior of the CD8 T cells depend on their intracellular content. For each model, we present, if possible, its construction process based on selected biological hypothesis, its mathematical study and its ability to reproduce the immune response using numerical simulations. The models we propose succesfully reproduce qualitatively and quantitatively the CD8 immune response and thus constitute useful tools to further investigate this biological phenomenon.

Many scientific disciplines rely on the construction and use of models: biomedical sciences are no exception. This PhD thesis addresses several aspects of the practice of scientific modeling. First, I discuss the nature of modeling as such, proposing a novel, complementary account of scientific modeling which I term the experimentation-driven modeling account and which drives the construction of mechanistic models in many fields of biological and biomedical research, such as cancer immunology. Second, I scrutinize an objection to the mechanistic account of explanation according to which the account fails to accommodate the common practice of idealizing difference-making factors. I argue that this objection ultimately fails because it is riddled with a number of conceptual inconsistencies. Third, I analyze the roles of similarity judgments in some fields of cancer research which employ a variety of mouse models to learn about the disease mechanisms, arguing that by appreciating the epistemic complexities it is possible to shed new light on more general philosophical debates regarding scientific representation. Fourth, mechanisms can also be studied using more theoretical apparatus in the form of simulations. I investigate an example of an agent-based model used to model the outbreak of SARS-CoV-2...

Im ersten Teil analysiere ich die Selbstorganisation zur Kritikalität (hier ein Phasenübergang von Ordnung zu Unordnung) und untersuche, ob Evolution ein möglicher Organisationsmechanismus ist. Die Kernfrage ist, ob sich ein simulierter kohäsiver Schwarm, der versucht, einem Raubtier auszuweichen, durch Evolution selbst zum kritischen Punkt entwickelt, um das Ausweichen zu optimieren? Es stellt sich heraus, dass (i) die Gruppe den Jäger am besten am kritischen Punkt vermeidet, aber (ii) nicht durch einer verstärkten Reaktion, sondern durch strukturelle Veränderungen, (iii) das Gruppenoptimum ist evolutionär unstabiler aufgrund einer maximalen räumlichen Selbstsortierung der Individuen. Im zweiten Teil modelliere ich experimentell beobachtete Unterschiede im kollektiven Verhalten von Fischgruppen, die über mehrere Generationen verschiedenen Arten von größenabhängiger Selektion ausgesetzt waren. Diese Größenselektion soll Freizeitfischerei (kleine Fische werden freigelassen, große werden konsumiert) und die kommerzielle Fischerei mit großen Netzbreiten (kleine/junge Individuen können entkommen) nachahmen. Die zeigt sich, dass das Fangen großer Fische den Zusammenhalt und die Risikobereitschaft der Individuen reduziert. Beide Befunde lassen sich mechanistisch durch einen Aufmerksamkeits-Kompromiss zwischen Sozial- und Umweltinformationen erklären. Im letzten Teil der Arbeit quantifiziere ich die kollektive Informationsverarbeitung im Feld. Das Studiensystem ist eine an sulfidische Wasserbedingungen angepasste Fischart mit einem kollektiven Fluchtverhalten vor Vögeln (wiederholte kollektive Fluchttauchgängen). Die Fische sind etwa 2 Zentimeter groß, aber die kollektive Welle breitet sich über Meter in dichten Schwärmen an der Oberfläche aus. Es zeigt sich, dass die Wellengeschwindigkeit schwach mit der Polarisation zunimmt, bei einer optimalen Dichte am schnellsten ist und von ihrer Richtung relativ zur Schwarmorientierung abhängt. / In the first part, I focus on the self-organization to criticality (here an order-disorder phase transition) and investigate if evolution is a possible self-tuning mechanism. Does a simulated cohesive swarm that tries to avoid a pursuing predator self-tunes itself by evolution to the critical point to optimize avoidance? It turns out that (i) the best group avoidance is at criticality but (ii) not due to an enhanced response but because of structural changes (fundamentally linked to criticality), (iii) the group optimum is not an evolutionary stable state, in fact (iv) it is an evolutionary accelerator due to a maximal spatial self-sorting of individuals causing spatial selection. In the second part, I model experimentally observed differences in collective behavior of fish groups subject to multiple generation of different types of size-dependent selection. The real world analog to this experimental evolution is recreational fishery (small fish are released, large are consumed) and commercial fishing with large net widths (small/young individuals can escape). The results suggest that large harvesting reduces cohesion and risk taking of individuals. I show that both findings can be mechanistically explained based on an attention trade-off between social and environmental information. Furthermore, I numerically analyze how differently size-harvested groups perform in a natural predator and fishing scenario. In the last part of the thesis, I quantify the collective information processing in the field. The study system is a fish species adapted to sulfidic water conditions with a collective escape behavior from aerial predators which manifests in repeated collective escape dives. These fish measure about 2 centimeters, but the collective wave spreads across meters in dense shoals at the surface. I find that wave speed increases weakly with polarization, is fastest at an optimal density and depends on its direction relative to shoal orientation.