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Lewis's counterfactual analysis of causation starts with the claim that c causes e if ~ C > ~ E, where c and e are events, C and E are the propositions that c and e respectively occur, ~ is negation and > is the counterfactual conditional. The purpose of my project is to provide a counterfactual analysis of causation which departs signigicantly from Lewis's starting point, and thus can hope to solve several stubborn problems for that approach. Whereas Lewis starts with a sufficiency claim, my analysis claims that a certain counterfactual is necessary for causation. I say that, if c causes e, then ~ E > ~ C - I call the latter the Reverse Counterfactual. This will often, perhaps always, be a backtracking counterfactual, so two chapters are devoted to defending a conception of counterfactuals which allows backtracking. Thus prepared, I argue that the Reverse Counterfactual is true of causes, but not of mere conditions for an effect. This provides a neat analysis of the principles governing causal selection, which is extended in a discussion of causal transitivity. Standard counterfactual accounts suffer counterexamples from preemption, but I argue that the Reverse Counterfactual has resources to deal neatly with those too. Finally I argue that the Reverse counterfactual, as a necessary condition oncausation, is the most we can hope for: in principle, there can be no counterfactual sufficient condition for causation.

The momentum puzzle, i.e., stocks that have performed better in the past tend to perform better in the future, has been a constant challenge to classic finance theory. Prior research has failed to provide valid risk-based explanations because winner portfolios do not exhibit higher risk characteristics. Without a convincing risk explanation, the persistence of momentum profit is a violation of the efficient market hypothesis. Today, the momentum puzzle remains one of the very few major anomalies that cannot be explained by Fama-French factor models. I find prior empirical efforts to measure momentum profits and its sources are contaminated by the state of the market during both formation and holding periods. By looking into different market states, classified by both traditional and non-traditional bull and bear market definition, I find the key to at least partially solve the momentum mystery. Momentum stocks are riskier when formed in bull market, and momentum profit is much higher in continuation of market than reverses of market condition, lending empirical support to a risk-based explanation. My definition of market states is essentially based on the risk premium of major risk factors. When market risk is considered a risk factor, if realized market risk premium is positive, it is a bull market; when size is considered a proxy for risk factor, if SMB (small minus big risk premium) is positive, it is a bull market; when valuation (book-to-market) ratio is a proxy for risk factor, if HML (High-minus-Low risk premium) is positive, it is a bull market. This paper also explores simulations using models based on the positive relationship between risk and return. The simulation result confirms that at least part of the momentum profit can be explained by risk.

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Master of Arts / Department of Economics / Peri da Silva / Economists use various models to explain why it is that firms are capable of pricing above marginal cost. In this paper, we will examine two of them: the Cournot and Bertrand duopoly models. Economists generally accept both models as good explanations of the phenomenon, but the two models contradict each other in various important ways. The puzzle is that two inconsistent explanations are both regarded as good explanations for the same phenomenon. This becomes especially worrisome when the two models are offering divergent policy recommendations. This report presents that puzzle by laying out how the two models contradict each other in a myriad of ways and then offers five possible solutions to that puzzle from various economists, philosophers of science, and philosophers of economics.

Nesta dissertação, investigarei a configuração do quadro partidário brasileiro pós-revolução de 30 e busco levantar explicações sobre o porquê deste rearranjo partidário. Estas explicações foram levantadas com base em dois elementos explicativos encontrados na literatura, que corresponde: a explicação institucionalista e a contingencial e em cima destas será elaborada a resposta sobre o problema em tela. Este trabalho ao dar atenção a este tema traz luz a um problema ainda não investigado pela ciência política brasileira e abre um leque para a emergência de uma nova agenda de pesquisa na Ciência Política brasileira. / In this dissertation I will investigate the Brazilian party framework configuration in the period after the revolution of 1930 and I aim to seek for explanations about this party rearrangement. These explanations were raised in connection with two explanatory elements found in the literature: the institutionalism and contingency explanations, over these two theories the answer to the research problem will be given. This work pretends to give attention to a very important problem that wasnt investigated by Brazilian political science and consequently this will open a new research agenda inside Brazilian political science.

Nesta dissertação, investigarei a configuração do quadro partidário brasileiro pós-revolução de 30 e busco levantar explicações sobre o porquê deste rearranjo partidário. Estas explicações foram levantadas com base em dois elementos explicativos encontrados na literatura, que corresponde: a explicação institucionalista e a contingencial e em cima destas será elaborada a resposta sobre o problema em tela. Este trabalho ao dar atenção a este tema traz luz a um problema ainda não investigado pela ciência política brasileira e abre um leque para a emergência de uma nova agenda de pesquisa na Ciência Política brasileira. / In this dissertation I will investigate the Brazilian party framework configuration in the period after the revolution of 1930 and I aim to seek for explanations about this party rearrangement. These explanations were raised in connection with two explanatory elements found in the literature: the institutionalism and contingency explanations, over these two theories the answer to the research problem will be given. This work pretends to give attention to a very important problem that wasnt investigated by Brazilian political science and consequently this will open a new research agenda inside Brazilian political science.

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The present study combined subgoal learning and self-explanation frameworks to improve problem solving performance. Subgoal learning has been used to promote retention and transfer in procedural domains, such as programming. The primary method for learning subgoals, however, has been through passive learning methods, and passive learning methods are typically less effective than constructive learning methods. To promote constructive methods of learning subgoals, a subgoal learning framework was used to guide self-explanation. Self-explanation is an effective method for engaging learners to make sense of new information based on prior knowledge and logical reasoning. Self-explanation is typically more effective when learners receive some guidance, especially if they are novices, because it helps them to focus their attention on relevant information. In the present study, only some of the constructive learning methods produced better problem solving performance than passive learning methods. Learners performed best when they learned constructively and either received hints about the subgoals of the procedure or received feedback on the self-explanations that they constructed, but not when they received both hints and feedback. When students received both types of guidance, they did not perform better than those who learned subgoals through passive learning methods. These findings suggest that constructive learning of subgoals can further improve the benefits of learning subgoals, but there is an optimal level of guidance for students engaging in constructive learning. Providing too much guidance can be as detrimental as providing too little. This nuance is important for educators who engage their students in constructive learning and self-explanation to recognize and promote the best results.