Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible alternative to GLM while still providing better interpretability than machine learning techniques such as neural networks. In BGNLM, the methods of Bayesian Variable Selection and Model Averaging are applied in an extended GLM setting. Models are fitted to data using MCMC within a genetic framework in an algorithm called GMJMCMC. In this thesis, we present a new implementation of the algorithm as a package in the programming language R. We also present a novel algorithm called S-IRLS-SGD for estimating the MLE of a GLM by subsampling the data. Finally, we present some theory combining the novel algorithm with GMJMCMC/MJMCMC/MCMC and a number of experiments demonstrating the performance of the contributed algorithm.

This thesis serves to investigate the varying effects of public health-care expenditure and private health-care expenditure on economic growth in developed and developing countries. I have contributed to the literature by using an expansive geographical dataset, lagged variables to address endogeneity, and model averaging techniques. I do so by first addressing the issue of model uncertainty, which is inherent in growth studies, by using Bayesian Model Averaging as the method of analysis in the thesis. Examination of 126 countries (32 developed and 94 developing) in the period 2000-2018 reveals that there is no variation in the impact of public health expenditure on economic growth between developed and developing countries. Contrary to public health expenditure, private health expenditure has a varying impact on both developed and developing countries. My analysis also reveals that the results hold when lagged variables are used in the model. Public health expenditure has unanimously a negative effect on economic growth in both developed and developing countries. Private health expenditure, on the other hand, has a positive impact on economic growth in developed and developing countries. Furthermore, I found that the results are robust to different model specifications. JEL Classification I15, O11,...

The current literature on international migration is diverse, and there is an ongoing debate as to the size and magnitude of the development-migration nexus, and no consensus about this effect has been reached. In this thesis, I explore quantitatively the effect of GDP (as a measure of development) on migration using a meta-analysis approach by synthesizing the empirical findings on this effect, adjusting for the biases, and controlling for the design of the studies. To examine the phenomenon in a systematic way, I collected 179 regression coefficients from 40 different articles, where the results suggest a weak presence of publication selection. Nevertheless, when correcting for publication bias, the effect of development on migration is rather small. Additionally, to explain the inherent model uncertainty, the Bayesian model averaging (BMA) was conducted. The results suggest that studies controlling for the variables of direct foreign investment and age results in a larger effect of development on migration and that the presence of country- level differences boosts migration inflows, particularly in OECD countries.

Using an early warning system (EWS) methodology, this thesis analyses the predictability of stock market crises from the perspective of behavioural fnance. Specifcally, in our EWS based on the multinomial logit model, we consider in- vestor sentiment as one of the potential crisis indicators. Identifcation of the relevant crisis indicators is based on Bayesian model averaging. The empir- ical results reveal that price-earnings ratio, short-term interest rate, current account, credit growth, as well as investor sentiment proxies are the most rele- vant indicators for anticipating stock market crises within a one-year horizon. Our thesis hence provides evidence that investor sentiment proxies should be a part of the routinely considered variables in the EWS literature. In general, the predictive power of our EWS model as evaluated by both in-sample and out-of-sample performance is promising. JEL Classifcation G01, G02, G17, G41 Keywords Stock market crises, Early warning system, In- vestor sentiment, Crisis prediction, Bayesian model averaging Title Predicting stock market crises using investor sentiment indicators

Artificial intelligence (AI) agents will need to interact with both other AI agents and humans. One way to enable effective interaction is to create models of associates to help to predict the modeled agents' actions, plans, and intentions. If AI agents are able to predict what other agents in their environment will be doing in the future and can understand the intentions of these other agents, the AI agents can use these predictions in their planning, decision-making and assessing their own potential. Prior work [13, 14] introduced the S# algorithm, which is designed as a robust algorithm for many two-player repeated games (RGs) to enable cooperation among players. Because S# generates actions, has (internal) experts that seek to accomplish an internal intent, and associates plans with each expert, it is a useful algorithm for exploring intent, plan, and action in RGs. This thesis presents a graphical Bayesian model for predicting actions, plans, and intents of an S# agent. The same model is also used to predict human action. The actions, plans and intentions associated with each S# expert are (a) identified from the literature and (b) grouped by expert type. The Bayesian model then uses its transition probabilities to predict the action and expert type from observing human or S# play. Two techniques were explored for translating probability distributions into specific predictions: Maximum A Posteriori (MAP) and Aggregation approach. The Bayesian model was evaluated for three RGs (Prisoners Dilemma, Chicken and Alternator) as follows. Prediction accuracy of the model was compared to predictions from machine learning models (J48, Multi layer perceptron and Random Forest) as well as from the fixed strategies presented in [20]. Prediction accuracy was obtained by comparing the model's predictions against the actual player's actions. Accuracy for plan and intent prediction was measured by comparing predictions to the actual plans and intents followed by the S# agent. Since the plans and the intents of human players were not recorded in the dataset, this thesis does not measure the accuracy of the Bayesian model against actual human plans and intents. Results show that the Bayesian model effectively models the actions, plans, and intents of the S# algorithm across the various games. Additionally, the Bayesian model outperforms other methods for predicting human actions. When the games do not allow players to communicate using so-called cheaptalk, the MAP-based predictions are significantly better than Aggregation-based predictions. There is no significant difference in the performance of MAP-based and Aggregation-based predictions for modeling human behavior when cheaptalk is allowed, except in the game of Chicken.

京都大学 / 新制・課程博士 / 博士(人間健康科学) / 甲第24539号 / 人健博第110号 / 新制||人健||8(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 木下 彩栄, 教授 中尾 恵, 教授 中山 健夫 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

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Music and emotion are two realms traditionally considered to be unique to human intelligence. This dissertation focuses on furthering artificial intelligence research, specifically in the area of computational creativity, by investigating methods of composing music that elicits desired emotional and physiological responses. It includes the following: an algorithm for generating original musical selections that effectively elicit targeted emotional and physiological responses; a description of some of the musical features that contribute to the conveyance of a given emotion or the elicitation of a given physiological response; and an account of how this algorithm can be used effectively in two different situations, the generation of soundtracks for fairy tales and the generation of melodic accompaniments for lyrics. This dissertation also presents research on more general machine learning topics. These include a method of combining output from base classifiers in an ensemble that improves accuracy over a number of different baseline strategies and a description of some of the problems inherent in the Bayesian model averaging strategy and a novel algorithm for improving it.

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This dissertation consists of three essays. The first essay estimates a demand function for compressed natural gas as a fuel substitute to diesel fuel for firms with hybrid fleets. The data is from the Energy Information Administration, for the years 1989 to 2009, for 47 states. Results show that an increase of $0.10 in the price of diesel fuel will increase compressed natural gas demand by 5.59%. The second essay focuses on regional trade agreements (RTAs). A number of studies have found that RTAs significantly increase members' trade flows. While recent studies have begun to explore the reasons for this, none have examined whether the RTA trade effect varies systematically with the number and type of policy areas covered by the agreement. While the empirical trade literature has shed considerable light on the trade-creating ability of RTAs (Grant and Lambert, 2008), much less is known about why these agreements are so successful. In this study, we draw on a new database from the World Trade Organization of trade policy areas covered by RTAs to examine whether the degree of trade liberalization is an important determinant of the RTA trade effect. An augmented, theoretically consistent gravity equation is developed to explore the effects of RTAs on trade, conditional on the policy areas they include. In particular, we investigate two policy areas that are particularly important for agricultural trade, sanitary and phytosanitary measures (SPS) and technical barriers to trade (TBT). The results suggest that harmonization of non-tariff measures inside RTAs matters: Agreements that liberalize these policies increase members' agricultural trade by an additional 62 percent compared to agreements that do not. We conclude that studying the components of RTAs -- in particular, the policy areas covered by these agreements -- is important when analyzing the determinants of RTA trade effects. The third essay uses Bayesian Model Averaging (BMA) to study the effect of membership in the General Agreement on Tariffs and Trade (GATT), the predecessor to the World Trade Organization (WTO), and the WTO on trade flows. Existing GATT/WTO literature is not univocal as to whether membership in the GATT/WTO increases trade flows. In this study, Bayesian model averaging (BMA) is used in the presence of theoretical uncertainty to address whether membership in the GATT/WTO plays a role in the gravity model. Several datasets are examined: a dataset from a previous study; and two datasets compiled for this study, world trade and agricultural trade. Results show, for all three sets of data, that membership in the GATT/WTO does belong in the gravity equation and increases trade flows. / Ph. D.