Dementia is a clinical syndrome caused by neurodegeneration or cerebrovascular injury. Patients with dementia suffer from deterioration in memory, thinking, behavior and the ability to perform everyday activities. Since there are no cures or disease-modifying therapies for dementia, there is much interest in identifying modifiable risk factors that may help prevent or slow the progression of cognitive decline. Medications are a common focus of this type of research. Importantly, according to a report from the Centers for Disease Control and Prevention (CDC), 19.1% of the population aged 60 and over report taking antidepressants during 2011-2014, and this number tends to increase. However, antidepressant use among the elderly may be concerning because of the potentially harmful effects on cognition. To assess the impacts of antidepressants on the risk of dementia, we conducted three consecutive projects. In the first project, a retrospective cohort study using Marginal Structural Cox Proportional Hazards regression model with Inverse Probability Weighting (IPW) was conducted to evaluate the average causal effects of different classes of antidepressant on the risk of dementia. Potential causal effects of selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), atypical anti-depressants (AAs) and tri-cyclic antidepressants (TCAs) on the risk of dementia were observed at the 0.05 significance level. Multiple sensitivity analyses supported these findings. Unmeasured confounding is a threat to the validity of causal inference methods. In evaluating the effects of antidepressants, it is important to consider how common comorbidities of depression, such as sleep disorders, may affect both the exposure to anti-depressants and the onset of cognitive impairment. In this dissertation, sleep apnea and rapid-eye-movement behavior disorder (RBD) were unmeasured and thus uncontrolled confounders for the association between antidepressant use and the risk of dementia. In the second project, a bias factor formula for two binary unmeasured confounders was derived in order to account for these variables. Monte Carlo analysis was implemented to estimate the distribution of the bias factor for each class of antidepressant. The effects of antidepressants on the risk of dementia adjusted for both measured and unmeasured confounders were estimated. Sleep apnea and RBD attenuated the effect estimates for SSRI, SNRI and AA on the risk of dementia. In the third project, to account for potential time-varying confounding and observed time-varying treatment, a multi-state Markov chain with three transient states (normal cognition, mild cognitive impairment (MCI), and impaired but not MCI) and two absorbing states (dementia and death) was performed to estimate the probabilities of moving between finite and mutually exclusive cognitive state. This analysis also allowed participants to recover from mild impairments (i.e., mild cognitive impairment, impaired but not MCI) to normal cognition, and accounted for the competing risk of death prior to dementia. These findings supported the results of the main analysis in the first project.

When trying to make sense of uncertain situations, we might rely on summary information from a description, or information gathered from our personal experience. There are two approaches that both attempt to explain how we make risky decisions using descriptive or experiential information—the cognitive-based explanation from the description-experience gap, and the emotion-based explanation from the somatic marker hypothesis (SMH). This dissertation brings together these two approaches to better understand how we make risky decisions. Four options were presented, with options differing in terms of advantageousness and riskiness. How easy or difficult it was to consciously comprehend the reward structure, or cognitive penetrability, was manipulated by displaying single outcomes or multiple, diverse outcomes per trial. Within the description or experience task, participants were randomly assigned to the more or less penetrable version of an all gain or all loss set of options. How often the riskier or advantageous options were chosen served as a measure of risky or advantageous decision making. Regardless of penetrability, risk preferences were generally but not completely as predicted by the SMH. Instead, the primary effect of cognitive penetrability was on advantageous decision making. Furthermore, description was found to be more cognitively penetrable than experience. Overall, the results suggest that clarification is needed regarding how somatic markers are formed in the loss versus gain domain, and future research should consider the difference in penetrability between description and experience when trying to explain preferences between the two decisions.

Many statistical methods for truncated data rely on the assumption that the failure and truncation time are independent, which can be unrealistic in applications. The study cohorts obtained from bone marrow transplant (BMT) registry data are commonly recognized as truncated samples, the time-to-failure is truncated by the transplant time. There are clinical evidences that a longer transplant waiting time is a worse prognosis of survivorship. Therefore, it is reasonable to assume the dependence between transplant and failure time. To better analyze BMT registry data, we utilize a Cox analysis in which the transplant time is both a truncation variable and a predictor of the time-to-failure. An inverse-probability-weighted (IPW) estimator is proposed to estimate the distribution of transplant time. Usefulness of the IPW approach is demonstrated through a simulation study and a real application.

This thesis consists of four papers that are related to commonly used propensity score-based estimators for average causal effects. The first paper starts with the observation that researchers often have access to data containing lots of covariates that are correlated. We therefore study the effect of correlation on the asymptotic variance of an inverse probability weighting and a matching estimator. Under the assumptions of normally distributed covariates, constant causal effect, and potential outcomes and a logit that are linear in the parameters we show that the correlation influences the asymptotic efficiency of the estimators differently, both with regard to direction and magnitude. Further, the strength of the confounding towards the outcome and the treatment plays an important role. The second paper extends the first paper in that the estimators are studied under the more realistic setting of using the estimated propensity score. We also relax several assumptions made in the first paper, and include the doubly robust estimator. Again, the results show that the correlation may increase or decrease the variances of the estimators, but we also observe that several aspects influence how correlation affects the variance of the estimators, such as the choice of estimator, the strength of the confounding towards the outcome and the treatment, and whether constant or non-constant causal effect is present. The third paper concerns estimation of the asymptotic variance of a propensity score matching estimator. Simulations show that large gains can be made for the mean squared error by properly selecting smoothing parameters of the variance estimator and that a residual-based local linear estimator may be a more efficient estimator for the asymptotic variance. The specification of the variance estimator is shown to be crucial when evaluating the effect of right heart catheterisation, i.e. we show either a negative effect on survival or no significant effect depending on the choice of smoothing parameters.   In the fourth paper, we provide an analytic expression for the covariance matrix of logistic regression with normally distributed regressors. This paper is related to the other papers in that logistic regression is commonly used to estimate the propensity score.

In Chapter 1 of the dissertation, the role of social networks is analyzed as an important determinant in the search behavior of the unemployed. Based on the hypothesis that the unemployed generate information on vacancies through their social network, search theory predicts that individuals with large social networks should experience an increased productivity of informal search, and reduce their search in formal channels. Due to the higher productivity of search, unemployed with a larger network are also expected to have a higher reservation wage than unemployed with a small network. The model-theoretic predictions are tested and confirmed empirically. It is found that the search behavior of unemployed is significantly affected by the presence of social contacts, with larger networks implying a stronger substitution away from formal search channels towards informal channels. The substitution is particularly pronounced for passive formal search methods, i.e., search methods that generate rather non-specific types of job offer information at low relative cost. We also find small but significant positive effects of an increase of the network size on the reservation wage. These results have important implications on the analysis of the job search monitoring or counseling measures that are usually targeted at formal search only. Chapter 2 of the dissertation addresses the labor market effects of vacancy information during the early stages of unemployment. The outcomes considered are the speed of exit from unemployment, the effects on the quality of employment and the short-and medium-term effects on active labor market program (ALMP) participation. It is found that vacancy information significantly increases the speed of entry into employment; at the same time the probability to participate in ALMP is significantly reduced. Whereas the long-term reduction in the ALMP arises in consequence of the earlier exit from unemployment, we also observe a short-run decrease for some labor market groups which suggest that caseworker use high and low intensity activation measures interchangeably which is clearly questionable from an efficiency point of view. For unemployed who find a job through vacancy information we observe a small negative effect on the weekly number of hours worked. In Chapter 3, the long-term effects of participation in ALMP are assessed for unemployed youth under 25 years of age. Complementary to the analysis in Chapter 2, the effects of participation in time- and cost-intensive measures of active labor market policies are examined. In particular we study the effects of job creation schemes, wage subsidies, short-and long-term training measures and measures to promote the participation in vocational training. The outcome variables of interest are the probability to be in regular employment, and participation in further education during the 60 months following program entry. The analysis shows that all programs, except job creation schemes have positive and long-term effects on the employment probability of youth. In the short-run only short-term training measures generate positive effects, as long-term training programs and wage subsidies exhibit significant locking-in'' effects. Measures to promote vocational training are found to increase the probability of attending education and training significantly, whereas all other programs have either no or a negative effect on training participation. Effect heterogeneity with respect to the pre-treatment level education shows that young people with higher pre-treatment educational levels benefit more from participation most programs. However, for longer-term wage subsidies we also find strong positive effects for young people with low initial education levels. The relative benefit of training measures is higher in West than in East Germany. In the evaluation studies of Chapters 2 and 3 semi-parametric balancing methods of Propensity Score Matching (PSM) and Inverse Probability Weighting (IPW) are used to eliminate the effects of counfounding factors that influence both the treatment participation as well as the outcome variable of interest, and to establish a causal relation between program participation and outcome differences. While PSM and IPW are intuitive and methodologically attractive as they do not require parametric assumptions, the practical implementation may become quite challenging due to their sensitivity to various data features. Given the importance of these methods in the evaluation literature, and the vast number of recent methodological contributions in this field, Chapter 4 aims to reduce the knowledge gap between the methodological and applied literature by summarizing new findings of the empirical and statistical literature and practical guidelines for future applied research. In contrast to previous publications this study does not only focus on the estimation of causal effects, but stresses that the balancing challenge can and should be discussed independent of question of causal identification of treatment effects on most empirical applications. Following a brief outline of the practical implementation steps required for PSM and IPW, these steps are presented in detail chronologically, outlining practical advice for each step. Subsequently, the topics of effect estimation, inference, sensitivity analysis and the combination with parametric estimation methods are discussed. Finally, new extensions of the methodology and avenues for future research are presented. / In Kapitel 1 der Dissertation wird die Rolle von sozialen Netzwerken als Determinante im Suchverhalten von Arbeitslosen analysiert. Basierend auf der Hypothese, dass Arbeitslose durch ihr soziales Netzwerk Informationen über Stellenangebote generieren, sollten Personen mit großen sozialen Netzwerken eine erhöhte Produktivität ihrer informellen Suche erfahren, und ihre Suche in formellen Kanälen reduzieren. Durch die höhere Produktivität der Suche sollte für diese Personen zudem der Reservationslohn steigen. Die modelltheoretischen Vorhersagen werden empirisch getestet, wobei die Netzwerkinformationen durch die Anzahl guter Freunde, sowie Kontakthäufigkeit zu früheren Kollegen approximiert wird. Die Ergebnisse zeigen, dass das Suchverhalten der Arbeitslosen durch das Vorhandensein sozialer Kontakte signifikant beeinflusst wird. Insbesondere sinkt mit der Netzwerkgröße formelle Arbeitssuche - die Substitution ist besonders ausgeprägt für passive formelle Suchmethoden, d.h. Informationsquellen die eher unspezifische Arten von Jobangeboten bei niedrigen relativen Kosten erzeugen. Im Einklang mit den Vorhersagen des theoretischen Modells finden sich auch deutlich positive Auswirkungen einer Erhöhung der Netzwerkgröße auf den Reservationslohn. Kapitel 2 befasst sich mit den Arbeitsmarkteffekten von Vermittlungsangeboten (VI) in der frühzeitigen Aktivierungsphase von Arbeitslosen. Die Nutzung von VI könnte dabei eine „doppelte Dividende“ versprechen. Zum einen reduziert die frühe Aktivierung die Dauer der Arbeitslosigkeit, und somit auch die Notwendigkeit späterer Teilnahme in Arbeitsmarktprogrammen (ALMP). Zum anderen ist die Aktivierung durch Information mit geringeren locking-in‘‘ Effekten verbunden als die Teilnahme in ALMP. Ziel der Analyse ist es, die Effekte von frühen VI auf die Eingliederungsgeschwindigkeit, sowie die Teilnahmewahrscheinlichkeit in ALMP zu messen. Zudem werden mögliche Effekte auf die Qualität der Beschäftigung untersucht. Die Ergebnisse zeigen, dass VI die Beschäftigungswahrscheinlichkeit signifikant erhöhen, und dass gleichzeitig die Wahrscheinlichkeit in ALMP teilzunehmen signifikant reduziert wird. Für die meisten betrachteten Subgruppen ergibt sich die langfristige Reduktion der ALMP Teilnahme als Konsequenz der schnelleren Eingliederung. Für einzelne Arbeitsmarktgruppen ergibt sich zudem eine frühe und temporare Reduktion, was darauf hinweist, dass Maßnahmen mit hohen und geringen „locking-in“ Effekten aus Sicht der Sachbearbeiter austauschbar sind, was aus Effizienzgesichtspunkten fragwürdig ist. Es wird ein geringer negativer Effekt auf die wöchentliche Stundenanzahl in der ersten abhängigen Beschäftigung nach Arbeitslosigkeit beobachtet. In Kapitel 3 werden die Langzeiteffekte von ALMP für arbeitslose Jugendliche unter 25 Jahren ermittelt. Die untersuchten ALMP sind ABM-Maßnahmen, Lohnsubventionen, kurz-und langfristige Maßnahmen der beruflichen Bildung sowie Maßnahmen zur Förderung der Teilnahme an Berufsausbildung. Ab Eintritt in die Maßnahme werden Teilnehmer und Nicht-Teilnehmer für einen Zeitraum von sechs Jahren beobachtet. Als Zielvariable wird die Wahrscheinlichkeit regulärer Beschäftigung, sowie die Teilnahme in Ausbildung untersucht. Die Ergebnisse zeigen, dass alle Programme, bis auf ABM, positive und langfristige Effekte auf die Beschäftigungswahrscheinlichkeit von Jugendlichen haben. Kurzfristig finden wir jedoch nur für kurze Trainingsmaßnahmen positive Effekte, da lange Trainingsmaßnahmen und Lohnzuschüsse mit signifikanten locking-in‘‘ Effekten verbunden sind. Maßnahmen zur Förderung der Berufsausbildung erhöhen die Wahrscheinlichkeit der Teilnahme an einer Ausbildung, während alle anderen Programme keinen oder einen negativen Effekt auf die Ausbildungsteilnahme haben. Jugendliche mit höherem Ausbildungsniveau profitieren stärker von der Programmteilnahme. Jedoch zeigen sich für längerfristige Lohnsubventionen ebenfalls starke positive Effekte für Jugendliche mit geringer Vorbildung. Der relative Nutzen von Trainingsmaßnahmen ist höher in West- als in Ostdeutschland. In den Evaluationsstudien der Kapitel 2 und 3 werden die semi-parametrischen Gewichtungsverfahren Propensity Score Matching (PSM) und Inverse Probability Weighting (IPW) verwendet, um den Einfluss verzerrender Faktoren, die sowohl die Maßnahmenteilnahme als auch die Zielvariablen beeinflussen zu beseitigen, und kausale Effekte der Programmteilahme zu ermitteln. Während PSM and IPW intuitiv und methodisch sehr attraktiv sind, stellt die Implementierung der Methoden in der Praxis jedoch oft eine große Herausforderung dar. Das Ziel von Kapitel 4 ist es daher, praktische Hinweise zur Implementierung dieser Methoden zu geben. Zu diesem Zweck werden neue Erkenntnisse der empirischen und statistischen Literatur zusammengefasst und praxisbezogene Richtlinien für die angewandte Forschung abgeleitet. Basierend auf einer theoretischen Motivation und einer Skizzierung der praktischen Implementierungsschritte von PSM und IPW werden diese Schritte chronologisch dargestellt, wobei auch auf praxisrelevante Erkenntnisse aus der methodischen Forschung eingegangen wird. Im Anschluss werden die Themen Effektschätzung, Inferenz, Sensitivitätsanalyse und die Kombination von IPW und PSM mit anderen statistischen Methoden diskutiert. Abschließend werden neue Erweiterungen der Methodik aufgeführt.

Introdução: Dados de custo necessários na Análise de Custo-Efetividade (CEA) são, muitas vezes, obtidos de estudos longitudinais primários. Neste contexto, é comum a presença de censura caracterizada por não se ter os dados de custo a partir de certo momento, devido ao fato de que indivíduos saem do estudo sem esse estar finalizado. A ideia da Ponderação pela Probabilidade Inversa (IPW – do inglês, Inverse Probability Weighting) vem sendo bastante estudada na literatura relacionada a esse problema, mas é desconhecida a disponibilidade de ferramentas computacionais para esse contexto. Objetivo: Construir ferramentas computacionais em software Excel e R, para estimação de custos pelo método IPW conforme proposto por Bang e Tsiatis (2000), com o objetivo de lidar com o problema da censura em dados de custos. Métodos: Através da criação de planilhas eletrônicas em software Excel e programação em software R, e utilizando-se bancos de dados hipotéticos com situações diversas, busca-se propiciar ao pesquisador maior entendimento do uso desse estimador bem como a interpretação dos seus resultados. Resultados: As ferramentas desenvolvidas, ao proporcionarem a aplicação do método IPW de modo intuitivo, se mostraram como facilitadoras para a estimação de custos na presença de censura, possibilitando calcular a ICER a partir de dados de custo. Conclusão: As ferramentas desenvolvidas permitem ao pesquisador, além de uma compreensão prática do método, a sua aplicabilidade em maior escala, podendo ser considerada como alternativa satisfatória às dificuldades postas pelo problema da censura na CEA. / Introduction: Cost data needed in Cost-Effectiveness Analysis (CEA) are often obtained from longitudinal primary studies. In this context, it is common the presence of censoring characterized by not having cost data after a certain point, due to the fact that individuals leave the study without this being finalized. The idea of Inverse Probability Weighting (IPW) has been extensively studied in the literature related to this problem, but is unknown the availability of computational tools for this context. Objective: To develop computational tools in software Excel and software R, to estimate costs by IPW method, as proposed by Bang and Tsiatis (2000), in order to deal with the problem of censorship in cost data. Methods: By creating spreadsheets in Excel software and programming in R software, and using hypothetical database with different situations, we seek to provide to the researcher most understanding of the use of IPW estimator and the interpretation of its results. Results: The developed tools, affording the application of IPW method in an intuitive way, showed themselves as facilitators for the cost estimation in the presence of censorship, allowing to calculate the ICER from more accurate cost data. Conclusion: The developed tools allow the researcher, besides a practical understanding of the method, its applicability on a larger scale, and may be considered a satisfactory alternative to the difficulties posed by the problem of censorship in CEA.

Introdução: Dados de custo necessários na Análise de Custo-Efetividade (CEA) são, muitas vezes, obtidos de estudos longitudinais primários. Neste contexto, é comum a presença de censura caracterizada por não se ter os dados de custo a partir de certo momento, devido ao fato de que indivíduos saem do estudo sem esse estar finalizado. A ideia da Ponderação pela Probabilidade Inversa (IPW – do inglês, Inverse Probability Weighting) vem sendo bastante estudada na literatura relacionada a esse problema, mas é desconhecida a disponibilidade de ferramentas computacionais para esse contexto. Objetivo: Construir ferramentas computacionais em software Excel e R, para estimação de custos pelo método IPW conforme proposto por Bang e Tsiatis (2000), com o objetivo de lidar com o problema da censura em dados de custos. Métodos: Através da criação de planilhas eletrônicas em software Excel e programação em software R, e utilizando-se bancos de dados hipotéticos com situações diversas, busca-se propiciar ao pesquisador maior entendimento do uso desse estimador bem como a interpretação dos seus resultados. Resultados: As ferramentas desenvolvidas, ao proporcionarem a aplicação do método IPW de modo intuitivo, se mostraram como facilitadoras para a estimação de custos na presença de censura, possibilitando calcular a ICER a partir de dados de custo. Conclusão: As ferramentas desenvolvidas permitem ao pesquisador, além de uma compreensão prática do método, a sua aplicabilidade em maior escala, podendo ser considerada como alternativa satisfatória às dificuldades postas pelo problema da censura na CEA. / Introduction: Cost data needed in Cost-Effectiveness Analysis (CEA) are often obtained from longitudinal primary studies. In this context, it is common the presence of censoring characterized by not having cost data after a certain point, due to the fact that individuals leave the study without this being finalized. The idea of Inverse Probability Weighting (IPW) has been extensively studied in the literature related to this problem, but is unknown the availability of computational tools for this context. Objective: To develop computational tools in software Excel and software R, to estimate costs by IPW method, as proposed by Bang and Tsiatis (2000), in order to deal with the problem of censorship in cost data. Methods: By creating spreadsheets in Excel software and programming in R software, and using hypothetical database with different situations, we seek to provide to the researcher most understanding of the use of IPW estimator and the interpretation of its results. Results: The developed tools, affording the application of IPW method in an intuitive way, showed themselves as facilitators for the cost estimation in the presence of censorship, allowing to calculate the ICER from more accurate cost data. Conclusion: The developed tools allow the researcher, besides a practical understanding of the method, its applicability on a larger scale, and may be considered a satisfactory alternative to the difficulties posed by the problem of censorship in CEA.

Introdução: Dados de custo necessários na Análise de Custo-Efetividade (CEA) são, muitas vezes, obtidos de estudos longitudinais primários. Neste contexto, é comum a presença de censura caracterizada por não se ter os dados de custo a partir de certo momento, devido ao fato de que indivíduos saem do estudo sem esse estar finalizado. A ideia da Ponderação pela Probabilidade Inversa (IPW – do inglês, Inverse Probability Weighting) vem sendo bastante estudada na literatura relacionada a esse problema, mas é desconhecida a disponibilidade de ferramentas computacionais para esse contexto. Objetivo: Construir ferramentas computacionais em software Excel e R, para estimação de custos pelo método IPW conforme proposto por Bang e Tsiatis (2000), com o objetivo de lidar com o problema da censura em dados de custos. Métodos: Através da criação de planilhas eletrônicas em software Excel e programação em software R, e utilizando-se bancos de dados hipotéticos com situações diversas, busca-se propiciar ao pesquisador maior entendimento do uso desse estimador bem como a interpretação dos seus resultados. Resultados: As ferramentas desenvolvidas, ao proporcionarem a aplicação do método IPW de modo intuitivo, se mostraram como facilitadoras para a estimação de custos na presença de censura, possibilitando calcular a ICER a partir de dados de custo. Conclusão: As ferramentas desenvolvidas permitem ao pesquisador, além de uma compreensão prática do método, a sua aplicabilidade em maior escala, podendo ser considerada como alternativa satisfatória às dificuldades postas pelo problema da censura na CEA. / Introduction: Cost data needed in Cost-Effectiveness Analysis (CEA) are often obtained from longitudinal primary studies. In this context, it is common the presence of censoring characterized by not having cost data after a certain point, due to the fact that individuals leave the study without this being finalized. The idea of Inverse Probability Weighting (IPW) has been extensively studied in the literature related to this problem, but is unknown the availability of computational tools for this context. Objective: To develop computational tools in software Excel and software R, to estimate costs by IPW method, as proposed by Bang and Tsiatis (2000), in order to deal with the problem of censorship in cost data. Methods: By creating spreadsheets in Excel software and programming in R software, and using hypothetical database with different situations, we seek to provide to the researcher most understanding of the use of IPW estimator and the interpretation of its results. Results: The developed tools, affording the application of IPW method in an intuitive way, showed themselves as facilitators for the cost estimation in the presence of censorship, allowing to calculate the ICER from more accurate cost data. Conclusion: The developed tools allow the researcher, besides a practical understanding of the method, its applicability on a larger scale, and may be considered a satisfactory alternative to the difficulties posed by the problem of censorship in CEA.

This thesis investigates the choices under risk in the framework of non-expected utility theories. One of the key contributions of this thesis is providing an approach that allows for a complete characterisation of Cumulative Prospect Theory (CPT) preferences without prior knowledge of the reference point. The location of the reference point that separates gains from losses is derived endogenously, thus, without any additional assumptions on the decision maker’s risk behaviour. This is different to the convention used in the literature, according to which, the reference point is preselected. The problem arising from imposing the location of the reference point is that the underlying preference conditions might not be alligned with the predictions made by the model. Consequently, it is difficult to verify such a model or to test it empirically. The present contribution offers a set of normatively and descriptively appealing preference conditions, which enable the elicitation of the reference point from the decision maker’s behaviour. Since these conditions are derived using objective probabilities, they can also be applied to settings such as health or insurance, where the continuity of the utility function is not required. As a result, the obtained representation theorem is not only the most general foundation for CPT currently available, but it also provides further support for the use of CPT as a modelling tool in decision theory and fi…nance. Another contribution that this thesis can be credited with is an application of rank-dependent utility theory (RDU) to the problem of insurance demand in the monopoly market affected by adverse selection. The present approach extends the classical model of Stiglitz (1977) by accounting for an additional component of heterogeneity among consumers, the heterogeneity in risk perception. Speci…fically, consumers employ distinctive probability weighting functions to assess the likelihood of risky events. This aspect of consumers’' behaviour highlights the importance that the probabilistic risk attitudes within the RDU framework, such as optimism and pessimism, have for the choice of insurance contract. The analysis yields a separating equilibrium, with full insurance for a sufficiently pessimistic decision maker. An important implication of this result is that any low-risk individual who sufficiently overestimates his probability of loss will induce the uninformed insurer to o¤er him full coverage, thereby, affecting the high-risk type adversely. This outcome is consistent with the recent empirical puzzle regarding the correlation between ex-post risk and insurance coverage, according to which, agents with low exposure to risk receive a larger amount of compensation. By providing an explanation of this pattern of individual behaviour, the current work demonstrates that theory and practice of insurance demand can be reconciled to a greater extent. The paper also provides a behavioural rationale for policy intervention in the market with RDU agents, where the initial distortions in contracts due to unobservable risks are aggravated by the non-linear weighting of probability of a risky event.

This thesis contains three chapters, each of which covers a different topic in experimental economics.<br><br>The first chapter investigates power and power analysis in economics experiments. Power is the probability of detecting an effect when a true effect exists, which is an important but under-considered concept in empirical research. Power analysis is the process of selecting the number of observations in order to avoid issues with low power. However, it is often not clear ex-ante what the required parameters for a power analysis, like the effect size and standard deviation, should be. <br>This chapter considers the use of Quantal Choice/Response (QR) simulations for ex-ante power analysis, as it maps related data-sets into predictions for novel environments. <br>The QR can also guide optimal design decisions, both ex-ante as well as ex-post for conceptual replication studies. This chapter demonstrates QR simulations on a wide variety of applications related to power analysis and experimental design.<br><br>The second chapter considers a question of interest to computer scientists, information technology and security professionals. How do people distribute defenses over a directed network attack graph, where they must defend a critical node? Decision-makers are often subject to behavioral biases that cause them to make sub-optimal defense decisions. Non-linear probability weighting<br>is one bias that may lead to sub-optimal decision-making in this environment. An experimental test provides support for this conjecture, and also other empirically important biases such as naive diversification and preferences over the spatial timing of the revelation of an overall successful defense. <br><br>The third chapter analyzes how individuals resolve an exploration versus exploitation trade-off in a laboratory experiment. The experiment implements the single-agent exponential bandit model. The experiment finds that subjects respond in the predicted direction to changes in the prior belief, safe action, and discount factor. However, subjects also typically explore less than predicted. A structural model that incorporates risk preferences, base rate neglect/conservatism, and non-linear probability weighting explains the empirical findings well. <br>