The dissertation studies the value of both investment banks' services on the whole and fairness opinions specifically, which the banks provide to the acquiring firms. In the first chapter, I examine how investment banks and acquiring firms' governance quality interact to affect shareholders' wealth in corporate mergers and acquisitions. Although the wealth impact of investment banks in mergers and acquisitions is widely studied in the literature, existing studies do not consider the interaction between governance quality and investment banks. I examine how investment banks and governance quality of acquiring firms interact to affect the wealth of acquiring firms' shareholders. I find that acquiring firms with poor governance are more likely to use investment banks in the deal. This association holds even after controlling for deal feature and other characteristics. I find that the use of investment banks per se does not result in a wealth reduction for the acquiring firms' shareholders. However, when the acquiring firm has poor governance, the use of investment bank is associated with extra value loss for the shareholders. The finding suggests that investment banks may help managerial empire building at the expense of shareholders under some circumstances. The study indicates that when studying investment bank's impact it is important to consider the quality of the hiring firms' governance. In the second chapter, I investigate the wealth implications of fairness opinions that the board of an acquiring firm purchases in corporate mergers from investment banks. Using the propensity score matching method to address the self-selection issue, I find that firms undertaking opinioned mergers under-perform firms with non-opinioned matching mergers in short windows around the announcement date. In the long run, the firms with opinioned merger do not perform better than firms with non-opinioned mergers. The acquiring firms perform poorly relative to their performance before the mergers, irrespective of whether their mergers are opinioned. Over a 12-month window after the mergers, the acquiring firms involved in both opinioned and non-opinioned mergers under-perform matching firms that do not make mergers. These findings are consistent with the hypothesis that the board buys a fairness opinion for its self-protection instead of maximization of shareholder wealth. The implication of this finding is that when investors evaluate mergers, they should focus primarily on deal characteristics, not fairness opinion.

Optical networking, employing wavelength division multiplexing (WDM), is seen as the technology of the future for the Internet. This dissertation investigates several important problems affecting optical circuit switching (OCS) and optical burst switching (OBS) networks. Novel algorithms and new approaches to improve the performance of these networks through effective routing and channel scheduling are presented. Extensive simulations and analytical modeling have both been used to evaluate the effectiveness of the proposed algorithms in achieving lower blocking probability, better fairness as well as faster switching. The simulation tests were performed over a variety of optical network topologies including the ring and mesh topologies, the U.S. Long-Haul topology, the Abilene high-speed optical network used in Internet 2, the Toronto Metropolitan topology and the European Optical topology. Optical routing protocols previously published in the literature have largely ignored the noise and timing jitter accumulation caused by cascading several wavelength conversions along the lightpath of the data burst. This dissertation has identified and evaluated a new constraint, called the wavelength conversion cascading constraint. According to this constraint, the deployment of wavelength converters in future optical networks will be constrained by a bound on the number of wavelength conversions that a signal can go through when it is switched all-optically from the source to the destination. Extensive simulation results have conclusively demonstrated that the presence of this constraint causes significant performance deterioration in existing routing and wavelength assignment (RWA) algorithms. Higher blocking probability and/or worse fairness have been observed for existing RWA algorithms when the cascading constraint is not ignored. To counteract the negative side effect of the cascading constraint, two constraint-aware routing algorithms are proposed for OCS networks: the desirable greedy algorithm and the weighted adaptive algorithm. The two algorithms perform source routing using link connectivity and the global state information of each wavelength. Extensive comparative simulation results have illustrated that by limiting the negative cascading impact to the minimum extent practicable, the proposed approaches can dramatically decrease the blocking probability for a variety of optical network topologies. The dissertation has developed a suite of three fairness-improving adaptive routing algorithms in OBS networks. The adaptive routing schemes consider the transient link congestion at the moment when bursts arrive and use this information to reduce the overall burst loss probability. The proposed schemes also resolve the intrinsic unfairness defect of existing popular signaling protocols. The extensive simulation results have shown that the proposed schemes generally outperform the popular shortest path routing algorithm and the improvement could be substantial. A two-dimensional Markov chain analytical model has also been developed and used to analyze the burst loss probabilities for symmetrical ring networks. The accuracy of the model has been validated by simulation. Effective proactive routing and preemptive channel scheduling have also been proposed to address the conversion cascading constraint in OBS environments. The proactive routing adapts the fairness-improving adaptive routing mentioned earlier to the environment of cascaded wavelength conversions. On the other hand, the preemptive channel scheduling approach uses a dynamic priority for each burst based on the constraint threshold and the current number of performed wavelength conversions. Empirical results have proved that when the cascading constraint is present, both approaches would not only decrease the burst loss rates greatly, but also improve the transmission fairness among bursts with different hop counts to a large extent.

Reaching agreements in conflicts is an important developmental challenge. Here, German 5-year- olds (N = 284, 49% female, mostly White, mixed socioeconomic backgrounds; data collection: June 2016–November 2017) faced repeated face-to- face bargaining problems in which they chose between fair and unfair reward divisions. Across three studies, children mostly settled on fair divisions. However, dominant children tended to benefit more from bargaining outcomes (in Study 1 and 2 but not Study 3) and children mostly failed to use leverage to enforce fairness. Communication analyses revealed that children giving orders to their partner had a bargaining advantage and that children provided and responded to fairness reasons. These findings indicate that fairness concerns and dominance are both key factors that shape young children's bargaining decisions

Children have an early-emerging expectation that resources should be divided fairly amongst agents, yet their behavior does not begin to align with these expectations until later in development. This dissociation between knowledge and behavior raises important questions about the mechanisms that encourage children to behave how they know they should behave. Here I tested whether explicitly invoking fairness norms encourages costly fair decisions in 4- to 9-year-old-children. I examine children’s responses to unequal resource allocations in the Inequity Game by varying the direction of inequity (advantageous versus disadvantageous inequity) and normative information (to be fair or to act autonomously). The results show children are more likely to reject advantageous allocation in the Fairness norm condition than in the Autonomous norm condition, but I did not see this difference when children are presented with disadvantageous allocations. This study showcases children’s costly fairness norm enforcement as a flexible process, one that can be brought in and out of alignment with their knowledge of fairness by shining a spotlight on how one ought to behave.

Recommender systems are information filtering tools that discover potential matchings between users and items and benefit both parties. This benefit can be considered a social resource that should be equitably allocated across users and items, especially in critical domains such as education and employment. Biases and unfairness in recommendations raise both ethical and legal concerns. In this dissertation, we investigate the concept of unfairness in the context of recommender systems. In particular, we study appropriate unfairness evaluation metrics, examine the relation between bias in recommender models and inequality in the underlying population, as well as propose effective unfairness mitigation approaches. We start with exploring the implication of fairness in recommendation and formulating unfairness evaluation metrics. We focus on the task of rating prediction. We identify the insufficiency of demographic parity for scenarios where the target variable is justifiably dependent on demographic features. Then we propose an alternative set of unfairness metrics that measured based on how much the average predicted ratings deviate from average true ratings. We also reduce these unfairness in matrix factorization (MF) models by explicitly adding them as penalty terms to learning objectives. Next, we target a form of unfairness in matrix factorization models observed as disparate model performance across user groups. We identify four types of biases in the training data that contribute to higher subpopulation error. Then we propose personalized regularization learning (PRL), which learns personalized regularization parameters that directly address the data biases. PRL poses the hyperparameter search problem as a secondary learning task. It enables back-propagation to learn the personalized regularization parameters by leveraging the closed-form solutions of alternating least squares (ALS) to solve MF. Furthermore, the learned parameters are interpretable and provide insights into how fairness is improved. Third, we conduct theoretical analysis on the long-term dynamics of inequality in the underlying population, in terms of the fitting between users and items. We view the task of recommendation as solving a set of classification problems through threshold policies. We mathematically formulate the transition dynamics of user-item fit in one step of recommendation. Then we prove that a system with the formulated dynamics always has at least one equilibrium, and we provide sufficient conditions for the equilibrium to be unique. We also show that, depending on the item category relationships and the recommendation policies, recommendations in one item category can reshape the user-item fit in another item category. To summarize, in this research, we examine different fairness criteria in rating prediction and recommendation, study the dynamic of interactions between recommender systems and users, and propose mitigation methods to promote fairness and equality. / Doctor of Philosophy / Recommender systems are information filtering tools that discover potential matching between users and items. However, a recommender system, if not properly built, may not treat users and items equitably, which raises ethical and legal concerns. In this research, we explore the implication of fairness in the context of recommender systems, study the relation between unfairness in recommender output and inequality in the underlying population, and propose effective unfairness mitigation approaches. We start with finding unfairness metrics appropriate for recommender systems. We focus on the task of rating prediction, which is a crucial step in recommender systems. We propose a set of unfairness metrics measured as the disparity in how much predictions deviate from the ground truth ratings. We also offer a mitigation method to reduce these forms of unfairness in matrix factorization models Next, we look deeper into the factors that contribute to error-based unfairness in matrix factorization models and identify four types of biases that contribute to higher subpopulation error. Then we propose personalized regularization learning (PRL), which is a mitigation strategy that learns personalized regularization parameters to directly addresses data biases. The learned per-user regularization parameters are interpretable and provide insight into how fairness is improved. Third, we conduct a theoretical study on the long-term dynamics of the inequality in the fitting (e.g., interest, qualification, etc.) between users and items. We first mathematically formulate the transition dynamics of user-item fit in one step of recommendation. Then we discuss the existence and uniqueness of system equilibrium as the one-step dynamics repeat. We also show that depending on the relation between item categories and the recommendation policies (unconstrained or fair), recommendations in one item category can reshape the user-item fit in another item category. In summary, we examine different fairness criteria in rating prediction and recommendation, study the dynamics of interactions between recommender systems and users, and propose mitigation methods to promote fairness and equality.

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第25026号 / 地環博第248号 / 京都大学大学院地球環境学舎環境マネジメント専攻 / (主査)教授 宇佐美 誠, 准教授 TRENCHER Gregory, 教授 竹内 憲司 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DGAM

This study investigated the extent to which gender moderates, and perceptions of fairness mediate, the link between marital power and overall marital satisfaction, as well as cortisol stress trajectories in response to marital distress. Study 1 examined a sample of 213 opposite sex newlywed couples from western Massachusetts, and focused on marital satisfaction as the dependent variable. Findings from the structural equation analysis suggested that perceptions of relationship fairness concerning the division of labor completely mediated the association between marital power and marital satisfaction for wives, but not for husbands. These results also implied an association between wives' perceptions of fairness and husbands' marital satisfaction. Study 2 looked at a subsample (N = 158 couples) of newlywed couples and investigated the effect of experiencing marital power on cortisol stress reactivity and recovery in response to a marital conflict discussion. Findings from the structural equation model suggested a significant association between marital power and stress reactivity & recovery for all participants, with low power wives exhibiting a failure to recover back to baseline levels of stress post-conflict. Methodological and measurement issues pertaining to the study of marital power are discussed, as well as potential implications of this work on future studies related to marital well-being.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24257号 / 情博第801号 / 新制||情||135(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 下平 英寿 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Besides entailing various benefits, AI technologies have also led to increased ethical concerns. Due to the growing attention, a large number of frameworks discussing responsible AI development have been released since 2016. This work aims at analyzing some of these proposals to answer the question (1) “Which approaches can be found to ensure responsible AI development?” For this, the theory section of this paper is looking at various approaches, including (inter-)governmental regulations, research organizations and private companies.  Further, expert interviews have been conducted to answer the second research question (2) “How can a unified solution be reached to ensure responsible AI development?” The results of the study have identified the governments as the main driver of this process. Overall, a detailed plan is necessary that brings together the public and private sector as well as research organizations. The paper also points out the importance of education in regard to making AI explainable and comprehensive for everyone. / Utöver de fördelar som AI-teknologier har bidragit med, så har även etiska dilemman och problem uppstått. På grund av ökat fokus, har ett stort antal förslag till system och regelverk som diskuterar ansvarstagande AI-utveckling publicerats sedan 2016. Denna rapport kommer analysera ett urval av dessa förslag med avsikt att besvara frågan (1) “Vilka tillvägagångssätt kan försäkra oss om en ansvarsfull AI-utveckling?” För att utforska denna fråga kommer denna rapport analysera olika metoder och tillvägagångssätt, på bland annat mellanstatliga- och statliga regelverk, forskningsgrupper samt privata företag. Dessutom har expertintervjuer genomförts för att besvara den andra problemformuleringen (2) “Hur kan vi nå en övergripande, gemensam, lösning för att försäkra oss om ansvarsfull AI-utveckling?” Denna rapport redogör för att statliga organisationer och myndigheter är den främsta drivkraften för att detta ska ske. Vidare krävs en detaljerad plan som knyter ihop forskningsgrupper med den offentliga- och privata sektorn. Slutligen anser rapporten även att det är av stor vikt för vidare utbildning när det kommer till att göra AI förklarbart och tydligt för alla.

The objectives of this thesis are: (1) to investigate the antecedents and consequences of fairness of performance evaluation processes (procedural fairness) in the context of performance measurement, evaluation, and reward systems; and (2) to investigate the behavioural effects of reliance on multiple performance measures (RMPM) in evaluating subordinates¿ performance. In relation to the first objective, it examines whether managers¿ perceptions of procedural fairness are influenced by the form (financial or nonfinancial) of performance measures used to evaluate performance, and by goal-related variables such as participation in setting performance targets, the goal-attainment-reward link, and the specificity of goals to be achieved by managers. With regard to the consequences of procedural fairness, it examines the effects of procedural fairness on job satisfaction, performance, organisational commitment, and goal commitment, and also examines whether any such associations are direct or indirect. In relation to the second objective, it examines whether RMPM affects managerial performance or whether the effect is contingent on goal difficulty and goal specificity. To address these objectives, this thesis draws on organisational justice theory and goal theory and employs both quantitative and qualitative approaches. Quantitative data are collected using a questionnaire survey sent to managers in four organisations and qualitative data are gathered by means of interviews and focus group discussions within the organisations. The results indicate that procedural fairness is affected by participation in setting performance targets, the goal-attainment-reward link, and the specificity of goals to be achieved by managers, but not by the type of performance measure used to evaluate performance. With regard to the consequences of procedural fairness, the results indicate that: (1) the effects of procedural fairness on job satisfaction and performance are indirect and fully mediated by distributive fairness, trust, and organisational commitment; (2) the effect of procedural fairness on organisational commitment is partially mediated by distributive fairness and trust; and (3) the effect of procedural fairness on goal commitment is partially mediated by trust. Finally, the results indicate that the effect of RMPM on performance is contingent on goal specificity, but not on goal difficulty.