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Diese Arbeit befasst sich mit der Frage, wie ein Transfer einer Aufgabe zwischen Mensch und Maschine gestaltet werden kann, um die Qualität und Sicherheit während des gesamten Übergabeprozesses zu gewährleisten. Dazu wird eine Beschreibung der Mensch-Maschine-Interaktion mit Hilfe von zeitvarianten Differentialspielen vorgeschlagen, welche einen modellbasierten Reglerentwurf ermöglicht. Durch haptische Interaktion am Stellglied wird der Mensch optimal unterstützt und sicher an die Aufgabe heranführt. Darüber hinaus wurde in dieser Arbeit eine Methodik entwickelt, um die aktuelle Bereitschaft des Menschen zur Laufzeit zu schätzen und den Transitionsprozess entsprechend anzupassen. Der Anwendung der Methoden auf der Übernahme der Fahraufgabe nach einer hochautomatisierten Fahrt resultiert im Gegensatz zum Stand der Technik in sicheren und kontrollierten Fahrmänövern.

This dissertation examines how expectations are formed and how they interact with economic activities. Beliefs about economic outcomes vary with timing and accuracy of information, which have important implications for macroeconomic dynamics. The importance of expectations has long been emphasized in rational expectations (RE) models (see e.g. Lucas 1972, 1976; Kydland and Prescott 1982), and diffusion of information has been modeled in many ways (see e.g. Beaudry and Portier 2004, 2006; Mankiw and Reis 2002; Woodford 2003; Sims 2003). My work builds on this literature and aims to improve the understanding of information structure, formation of beliefs, and decision-making, and how they contribute to macro business cycles. In the first chapter, I point out how identification of full information rational expectations (FIRE) models suffers from Manski's (1993) reflection problem. I extend the standard rational expectations (RE) model to allow for a more general information structure and introduce a new framework to identify the generalized model with forecaster data. Identification is no longer subject to the reflection problem when two changes are made to the information structure: the addition of news shocks and imperfect information. News shocks provide additional variation in expectations about the future. Imperfect information provides changes in beliefs about past states, through which the feedback between expectations and decisions goes only in one direction. Expectations data are consistent with both. An application to Greenbook forecasts illustrates the importance of both news shocks and learning about the past. When I apply this framework to a Blanchard and Quah (1989) decomposition, I reach qualitatively new results. For example, expansionary supply shocks decrease unemployment. Supply shocks are also particularly subject to both news and information rigidities, so relaxing the information structure is key to correctly identifying these shocks. In the second chapter, I discover how both good and bad news shocks coincide with higher uncertainty on impact. This new stylized fact is robust to different empirical models of the news shocks literature and different proxies for U.S. macro uncertainty. The new stylized fact has implications in three fields. First, bad news shocks produce the dynamics discovered in the uncertainty literature: spikes in uncertainty are followed by drops in output. I show that there is indeed some overlap between bad news and uncertainty shocks, as the effect of an uncertainty shock gets weaker when controlling for bad news shocks. Second, I show that the close relationship between news shocks and uncertainty seems to be also responsible for the close relationship between quarterly stock returns and stock market volatility - a proxy for uncertainty. This contributes to the finance literature that works on this relationship. Third, introducing a non-linear empirical model, I find additional asymmetries in the responses to news shocks due to the asymmetric response of uncertainty. This contributes directly to the news shocks literature. An important conclusion of chapters one and two is that economic shocks vary with availability of information. The third chapter deals with such heterogeneity. I relax the assumption that economic shocks of the same type are homogeneous, respectively, always have the same effect. Instead, I argue that economists identify a shock that consists of a variety of heterogeneous components. For example, a technology shock is the sum of all disaggregate technology shocks, from innovations in marketing up to inventions in the manufacturing process, which all have different effects on the economy. I discuss how standard identification methods can identify the shocks of interest despite this heterogeneity. I find that the weights on the shock components depend on the identification strategy so that different identification strategies produce different effects. This could explain why different macro papers often identify different responses to the same shock, in the same country, and over the same time period.