A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

THE PROPOSITION VALUE OF CORPORATE RATINGS - A RELIABILITY TESTING OF CORPORATE RATINGS BY APPLYING ROC AND CAP TECHNIQUES

We analyze the Altman model, a Logit model as well as the KMV model in order to evaluate their performance. Therefore, we use a random sample of 132 US firms. We create a yearly and a quarterly sample set to construct a portfolio of defaulting and a counter portfolio of non-defaulting companies. As we stay close to the recommendations of the Basel Capital Accord framework in order to evaluate the models, we use Receiver Operating Characteristic (ROC) and Cumulative Accuracy Profile (CAP) techniques. We find that the Logit model outperforms the Altman as well as the KMV model. Furthermore, we find that the Altman model outperforms the KMV model, which is nearly as accurate as a random model.Altman Model, Cumulative Accuracy Profile (CAP), Distance to Default, Logit Model, Moody’s KMV, Receiver Operating Characteristic (ROC), Z-score.

Impact of California's Transitional Kindergarten Program, 2013-14

Transitional kindergarten (TK)—the first year of a two-year kindergarten program for California children who turn 5 between September 2 and December 2—is intended to better prepare young five-year-olds for kindergarten and ensure a strong start to their educational career. To determine whether this goal is being achieved, American Institutes for Research (AIR) is conducting an evaluation of the impact of TK in California. The goal of this study is to measure the success of the program by determining the impact of TK on students' readiness for kindergarten in several areas. Using a rigorous regression discontinuity (RD) research design,1 we compared language, literacy, mathematics, executive function, and social-emotional skills at kindergarten entry for students who attended TK and for students who did not attend TK. Overall, we found that TK had a positive impact on students' kindergarten readiness in several domains, controlling for students' age differences. These effects are over and above the experiences children in the comparison group had the year before kindergarten, which for more than 80 percent was some type of preschool program

Bouncing back from COVID-19: A Western Australian community perspective

Introduction: This study explored the behavioral profiles of residing Western Australians during a COVID-19 lockdown period and transitions in behavior post-lockdown. Methods: A total of 313 participants (76% female, age: M = 50.1, SD = 15.7 years) completed behavioral and mental health questionnaire items ~2 months after a 3-month COVID-19 lockdown in October 2020, using a retrospective recall to assess their experience during the lockdown period. Latent transition analysis (LTA) was used to identify behavioral profiles and transitions. Indicators were identified by assessing during–post-lockdown group differences (Kruskal–Wallis, chi-square tests) and profiles described using qualitative open-ended questions. Results: Significant indicators included changes in physical activity, leisure screen time, alcohol intake, psychological distress, and loneliness, but not fast food consumption. The significant indicators were used to form LTA models. The five latent class model showed the best model fit (Log-likelihood = −1301.66, AIC = 426.12, BIC = 609.68). Approximately one in four participants reported a change in their behavior profiles after the lockdown ceased. Key differences between the profiles were age, household income, education, resilience, sense of control, existing mental health issues, and social relations. Washing hands and social distancing were the most recalled and effective health campaigns across the classes, with health campaigns encompassing physical activity/alcohol consumption, or domestic violence having the least attention. Discussion: Overall, while most participants recovered relatively well after the lockdown period, LTA did identify subgroups such as those who were inactive and lonely experienced more difficulties than other groups, and engagement with public health campaigns differed. The results provide important insights for future public health campaigns on how these campaigns might be diversified to effectively target more people and particular groups to maximize engagement for maintaining people\u27s mental health with additional focus on physical activity, alcohol consumption, and domestic violence

Does school accountability pressure improve school quality?

This dissertation evaluates the impact of accountability pressure on a context where there is school choice. The Chilean context provides a unique opportunity for this purpose since it constitutes a system with school choice since the 1980s and since 2008 there is a policy that introduces a means-tested voucher that introduces incentives to schools to improve their performance. Under this policy, schools are classified based on students’ test scores and other school factors (such as teacher evaluations, approval rates, retention rates), and are linked to punishments and rewards. I assess the impact of accountability pressure on outcomes that have consequences attached (high-stakes outcomes), and other outcomes that may reflect the quality of the school but do not have direct consequences attached (low-stakes outcomes). I use a fuzzy regression discontinuity to estimate the effect of receiving different school classifications on high-stakes test scores, low-stakes test scores, school behavioral responses, and student and teacher body composition. Estimates of the effects on 4th and 8th grade math, language and science are never significantly different from zero. There is also no evidence of parental or teacher response

Color-grapheme synesthesia: A study of population prevalence

Given the current state of the literature and weaknesses of many previous prevalence studies, the primary purpose of this research study was to gather base-rate data of color-grapheme synesthesia in a general population sample. Over a period of seven months, 502 participants enrolled in the study on Mechanical Turk and completed the online Synesthesia Battery. The primary data collected was the participant’s score on the synesthesia test, whereby a score of a one or below is deemed by the battery to be indicative of someone with a color-grapheme synesthetic ability. Of the 502 participants, eight percent (0.082) of the population sampled had scores below one, the cutoff suggestive of synesthesia on the Synesthesia Battery. This is a much higher percentage of the population than previously reported by previous studies. Exploratory analyses of demographic variables revealed some significant findings for handedness and education, such that left-handed people may have a greater representation among synesthetes than right-handed people and participants meeting the one score cutoff suggestive of synesthesia were more likely to have a graduate education

Using Information Markets to Improve Public Decision Making

Information markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as a presidential election. The prices in these markets provide useful information about a particular issue, such as a president's reelection probability. The purpose of this paper is to suggest how the use of information markets can improve the quality of public policy. Our central contribution is to propose an efficient way to implement well-informed policy decisions. We do this by linking and building upon the literatures on information markets and mechanism design. Our claim is that the prices in information markets can inform the mechanism design process, thereby making previously infeasible mechanisms feasible for the policy maker. Specifically, information markets make pay-for-performance contracts viable in the policy domain. Although we focus on public sector decision making, the analysis is sufficiently general to apply to a wide range of problems in private sector and not-for-profit decision making. The framework can be applied to any situation in which a decision maker has the resources, but not the necessary information and ability, to achieve his specified objective. First, we show how it is generally possible to design contracts based on different contingencies whose prices will convey useful information on the costs and benefits of a number of policy choices, ranging from regulation to public works projects. Second, we describe one way of providing incentives for self-interested agents to implement policies that maximize net social benefits. Third, we show how information markets can be used to provide a stronger foundation for implementing a variety of government oversight mechanisms, such as a regulatory budget. We also show how legislators can use traditional budgetary controls in conjunction with information markets to exercise more effective oversight. Finally, we identify and analyze the strengths and limitations of using information markets to help improve policy. To make the analysis concrete, we examine how the "Copenhagen Consensus" which makes recommendations on spending $50 billion wisely, could have benefited from applying information markets. We argue that there is a large scope for expanding the use of information markets. These markets could promote greater transparency in governmental decision making, provide more accurate estimates of the efficiency and distributional impacts of different policies, provide a better understanding of uncertainties, help with sensitivity analysis, offer a low-cost way of assessing new policy proposals, finance government projects and regulations with positive net benefits, allow those affected by specific policies the opportunity to hedge risk, and aid in the design of policies. Furthermore, information markets can help assess the value of additional research on the decision to undertake a project. At the same time, we suggest that there are important limits to the application of information markets. We also suggest how government could play an important role in the expansion of information markets and researchers could help in the development and assessment of these markets.

THE INFLUENCE OF PHYSICAL HEALTH, EMOTIONAL HEALTH, AND SOCIOECONOMIC FACTORS ON THE MUSCULOSKELETAL PAIN EXPERIENCE IN PATIENTS ATTENDING A PRO BONO PHYSICAL THERAPY CLINIC

Non-communicable, chronic diseases are highly prevalent in the United States, reducing the quality of life for those affected and contributing to the majority of the nation’s healthcare expenditure. These conditions include, among others, cardiovascular disease, diabetes, and musculoskeletal disease. Musculoskeletal disease is particularly of interest for the field of physical therapy as the vast majority of patients seeking care in the outpatient setting present with musculoskeletal pain complaints, resulting in limitations in function, participation, and quality of life for the patient. The factors influencing health outcomes are diverse and include a person’s physical environment, social and economic factors, access to quality clinical care, and health behaviors. Thus, managing chronic disease requires intervention at the level of the patient, provider, healthcare organization, community, and the local, state, and federal governments. Implementing multilevel intervention and advocacy can reduce the impact of chronic disease and allow people to more meaningfully engage in their lives. The purpose of this dissertation was to first describe a population attending a pro bono physical therapy clinic for musculoskeletal pain complaints in the southeastern United States in regards to measures of physical health, emotional health, socioeconomic status, and pain presentation. These measures were then assessed to discover their usefulness in identifying chronic disease as well as their ability to identify clinically-important patient subgroups that may require a more tailored treatment approach. By understanding the patient population more completely, future directions for addressing patient needs through clinical intervention, clinical programming, and advocacy endeavors can be implemented to produce more positive health outcomes. Theoretical foundation for the management of chronic disease was informed by the Innovative Care for Chronic Conditions framework (World Health Organization, 2002). The County Health Ratings Model (University of Wisconsin Population Health Institute, 2019) and the Tool for Health & Resilience in Vulnerable Environments (Prevention Institute, 2004) were used as guides in determining the important factors influencing health outcomes and routes of intervention to improve health equity. Models of the pathophysiology of metabolic syndrome (Eckel et al, 2005), a precursor to cardiovascular disease and diabetes, and their impact on musculoskeletal disease (Collins et al, 2018) were also considered to identify clinical measures in the physical therapy setting that can better inform the clinician of the patient’s condition. A clinically-based, standardized intake process was created and implemented at a pro bono physical therapy clinic to capture measures of physical health, emotional health, health behaviors, and social and economic variables. The measures chosen fall within the scope of physical therapy practice and were selected to bolster the treating clinician’s clinical decision making to provide patient-centered care. A retrospective chart review was performed over a two-year period (December 2017 to December 2019) to collect these data from the initial patient evaluation. Descriptive statistics were used to define the population attending the clinic and their potential healthcare needs. Regression analysis was then performed to determine which measures best inform the clinician regarding metabolic disease status in this population and whether those at risk of metabolic disease presented differently from those without. Finally, a latent class analysis was performed to identify unique patient subgroups within those presenting to the clinic and the distinguishing features of these subgroups