Cette thèse est organisée en trois chapitres. Chaque article analyse les déviations systématiques des décideurs par rapport aux prédictions économiques classiques dans certaines expériences bien connues. Les agents s’écartent de la voie optimale et explorent ou exploitent de manière excessive dans le problème du bandit manchot, ils exigent des taux d’intérêt bien plus élevés par rapport aux taux du marché financier afin de reporter leurs dépenses lorsqu’ils prennent des décisions de choix intertemporel, et ils ne se contentent pas de recevoir des petites sommes d’argent, même si, objectivement, ils devraient accepter cette offre, dans des expériences de négociation comme le jeu de l’ultimatum. Ces soi-disant «irrégularités» sont documentées dans les trois essais de thèse. Le essaies représentent une première étape afin de formuler des stratégies adaptées au profile psychologique de chaque individu, nécessaires pour surmonter les biais de décision. / This dissertation is organized in three chapters. Each chapter analyzes decision makers’ systematic deviations from economic predictions in well-known experiments. People deviate from the optimal path and excessively explore or exploit in n-armed bandit games, demand interest rates well above financial market averages in order to defer consumption in intertemporal choice settings, and do not settle for receiving small amounts of money, even though they would be better off objectively, in bargaining games such as the ultimatum game. Such “irregularities” are documented in the three dissertation essays. The essays are intended as a first step to formulate individual specific, customized decision aids, useful to overcome such decision biases.

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>

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<div>This thesis consists of three chapters. In the first chapter, I investigate the effects of complexity in various voting systems on individual behavior in small group electoral competitions. Using a laboratory experiment, I observe individual behavior within one of three voting systems -- plurality, instant runoff voting (IRV), and score then automatic runoff (STAR). I then estimate subjects' behavior in three different models of bounded rationality. The estimated models are a model of Level-K thinking (Nagel, 1995), the Cognitive Hierarchy (CH) model (Camerer, et al. 2004), and a Quantal Response Equilibrium (QRE) (McKelvey and Palfrey 1995). I consistently find that more complex voting systems induce lower levels of strategic thinking. This implies that policy makers desiring more sincere voting behavior could potentially achieve this through voting systems with more complex strategy sets. Of the tested behavioral models, Level-K consistently fits observed data the best, implying subjects make decisions that combine of steps of thinking with random, utility maximizing, errors.</div><div><br></div><div>In the second chapter, I investigate the relationship between the mechanisms used to select leaders and both measures of group performance and leaders' ethical behavior. Using a laboratory experiment, we measure group performance in a group minimum effort task with a leader selected using one of three mechanisms: random, a competition task, and voting. After the group task, leaders must complete a task that asks them to behave honestly or dishonestly in questions related to the groups performance. We find that leaders have a large impact on group performance when compared to those groups without leaders. Evidence for which selection mechanism performs best in terms of group performance seems mixed. On measures of honesty, the strongest evidence seems to indicate that honesty is most positively impacted through a voting selection mechanism, which differences in ethical behavior between the random and competition selection treatments are negligible.</div><div><br></div><div><br></div><div>In the third chapter, I provide an investigation into the factors and conditions that drive "free riding" behavior in dynamic innovation contests. Starting from a dynamic innovation contest model from Halac, et al. (2017), I construct a two period dynamic innovation contest game. From there, I provide a theoretical background and derivation of mixed strategies that can be interpreted as an agent's degree to which they engage in free riding behavior, namely through allowing their opponent to exert effort in order to uncover information about an uncertain state of the world. I show certain conditions must be fulfilled in order to induce free riding in equilibrium, and also analytically show the impact of changing contest prize structures on the degree of free riding. I end this paper with an experimental design to test these various theoretical conclusions in a laboratory setting while also considering the behavioral observations recorded in studies investigating similar contest models and provide a plan to analyze the data collected by this laboratory experiment.</div><div><br></div><div>All data collected for this study consists of individual human subject data collected from laboratory experiments. Project procedures have been conducted in accordance with Purdue's internal review board approval and known consent from all participants was obtained.</div>

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<p>Diss. Stockholm : Handelshögskolan, 2008 Sammanfattning jämte 5 uppsatser</p>