A complexidade da cooperação climática internacional

Although there are several collective efforts to address the problem of climate change, the main initiatives, such as the Kyoto Protocol and the Paris Agreement, have not shown satisfactory results so far. The difficulty in engaging states into effective coordinated cooperative practices can be explained as a consequence of neoclassical rationality, given that the characterization of states as rationality-endowed entities bound them to situations like the Prisoners' Dilemma (PD) game and its related collective action dilemmas. There are models that provide ways to circumvent PD and foster cooperation among selfish rational agents, such as the application of strategies based on reciprocity (Tit-for-Tat) in iterated games. However, these approaches do not avoid the short-sighted neoclassical rationality that lies at the root of the problem. Thus, in order to develop more productive approaches to the development of global climate change policies, I present a characterization of the international political system as a complex adaptive system (CAS) and argue that this perspective, along with models based on evolutionary games rather than iterated games, provide a more promising approach.Embora existam vários esforços coletivos para enfrentar o problema das mudanças climáticas, as principais iniciativas, como o Protocolo de Quioto e o Acordo de Paris, não têm apresentado resultados satisfatórios até o momento. A dificuldade em envolver os Estados em práticas cooperativas coordenadas efetivas pode ser explicada como consequência da racionalidade neoclássica, uma vez que a caracterização dos Estados como entidades dotadas de racionalidade os vincul a situações como o jogo do Dilema do Prisioneiro (DP), bem como os dilemas da ação coletiva relacionados a esse jogo. Existem modelos que fornecem maneiras de contornar o PD e promover a cooperação entre agentes racionais egoístas, como por exemplo a aplicação de estratégias baseadas na reciprocidade (Tit-for-Tat) em jogos iterados. No entanto, essas abordagens não evitam a racionalidade neoclássica de curto prazo, que está na raiz do problema. Assim, para desenvolver abordagens mais produtivas para o desenvolvimento de políticas globais para lidar com a mudança climática, apresento uma caracterização do sistema político internacional como um sistema adaptativo complexo (CAS) e argumento que essa perspectiva, acompanhada de modelos baseados em jogos evolutivos em vez de jogos iterados, fornece uma abordagem mais promissora

State Cooperation on Regulatory Policies for Transboundary Environmental Issues

This research analyzes three contributing factors, perception, knowledge, and affordability, in order to estimate the likelihood of state cooperation on effective regulatory policies for transboundary environmental problems. The correlative hypothesis in this research postulates that states are more likely to support environmental regulatory policies when the issue is perceived by policymakers as serious, substantiated by a high level of knowledge, and affordable for the state. Regulatory policies for transboundary environmental issues require policymakers to act in foresight, employ precautionary measures, and cooperate. Cooperation implies that states will coordinate their policies and eschew their dominant strategy of independent decision making. However, this research contends that states decide to cooperate because they perceive the strategic interaction to be beneficial. Thus, the theory of cooperation in this research is consistent with realist assumptions of rational egoism

Le rôle épistémique de certaines simulations informatiques fondamentales en théorie de l'évolution

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

The Adaptive Dynamics of Altruism in Spatially Heterogeneous Populations

We study the spatial adaptive dynamics of a continuous trait that measures individual investment in altruism. Our study is based on an ecological model of a spatially heterogeneous population from which we derive an appropriate measure of fitness. The analysis of this fitness measure uncovers three different selective processes controlling the evolution of altruism: the direct physiological cost, the indirect genetic benefits of cooperative interactions, and the indirect genetic costs of competition for space. In contrast with earliest suggestions, we find that the cost of competing for space with relatives exerts a negligible selective pressure against altruism. Our study yields a classification of adaptive patterns of altruism according to how the costs of altruism vary with an individuals investment in altruism (we distinguish between decelerating, linear, and accelerating dependence). The invasion of altruism occurs readily in species with accelerating costs, but large mutations are critical for altruism to evolve in selfish species with decelerating costs. Strict selfishness is maintained by natural selection only under very restricted conditions. In species with rapidly accelerating costs, adaptation leads to an evolutionarily stable rate of investment in altruism that decreases smoothly with the level of mobility. A rather different adaptive pattern emerges in species with slowly accelerating costs: high altruism evolves at low mobility, whereas a quasi-selfish state is promoted in more mobile species. The high adaptive level of altruism can be predicted solely from habitat connectedness and physiological parameters that characterize the pattern of cost. We also show that environmental changes that cause increased mobility in those highly altruistic species can beget selection-driven self-extinction, which may contribute to the rarity of social species

An exploration and validation of computer modeling of evolution, natural selection, and evolutionary biology with cellular automata for secondary students.

The Evolutionary Tool Kit, a new software package, is the prototype of a concept simulator providing an environment for students to create microworlds of populations of artificial organisms. Its function is to model processes, concepts and arguments in natural selection and evolutionary biology, using either Mendelian asexual or sexual reproduction, or counterfactual systems such as \u27paint pot\u27 or blending inheritance. In this environment students can explore a conceptual What if? in evolutionary biology, test misconceptions and deepen understanding of inheritance and changes in populations. Populations can be defined either with typological, or with populational thinking, to inquire into the role and necessity of variation in natural selection. The approach is generative not tutorial. The interface is highly graphic with twenty traits set as icons that are moved onto the \u27phenotypes\u27. Activities include investigations of evolutionary theory of aging, reproductive advantage, sexual selection and mimicry. Design of the activities incorporates Howard Gardner\u27s Theory of Multiple Intelligences. Draft of a teacher and student manual are included

Distributed multi-agent based traffic management system

Ph.DDOCTOR OF PHILOSOPH

Approaches to multi-agent learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaves 165-171).Systems involving multiple autonomous entities are becoming more and more prominent. Sensor networks, teams of robotic vehicles, and software agents are just a few examples. In order to design these systems, we need methods that allow our agents to autonomously learn and adapt to the changing environments they find themselves in. This thesis explores ideas from game theory, online prediction, and reinforcement learning, tying them together to work on problems in multi-agent learning. We begin with the most basic framework for studying multi-agent learning: repeated matrix games. We quickly realize that there is no such thing as an opponent-independent, globally optimal learning algorithm. Some form of opponent assumptions must be necessary when designing multi-agent learning algorithms. We first show that we can exploit opponents that satisfy certain assumptions, and in a later chapter, we show how we can avoid being exploited ourselves. From this beginning, we branch out to study more complex sequential decision making problems in multi-agent systems, or stochastic games. We study environments in which there are large numbers of agents, and where environmental state may only be partially observable.(cont.) In fully cooperative situations, where all the agents receive a single global reward signal for training, we devise a filtering method that allows each individual agent to learn using a personal training signal recovered from this global reward. For non-cooperative situations, we introduce the concept of hedged learning, a combination of regret-minimizing algorithms with learning techniques, which allows a more flexible and robust approach for behaving in competitive situations. We show various performance bounds that can be guaranteed with our hedged learning algorithm, thus preventing our agent from being exploited by its adversary. Finally, we apply some of these methods to problems involving routing and node movement in a mobilized ad-hoc networking domain.by Yu-Han Chang.Ph.D