Die vorliegende Dissertation untersucht, wie Menschen Urteile und Entscheidungen in moralischen Situationen unter Unsicherheit treffen. In theoretischer Hinsicht wird Verhalten in moralischen Situationen aus der Perspektive begrenzter und ökologischer Rationalität analysiert, die das Zusammenspiel zwischen Kognition und der Struktur der Umwelt betont. Empirisch ist das Ziel, moralische Urteile und Verhalten unter epistemischen Bedingungen zu untersuchen, denen Menschen in der realen Welt begegnen. Das erste Projekt diskutiert aus der Perspektive ökologischer Rationalität wie das Zusammenspiel von Heuristiken und Umwelt hilft, moralisches Verhalten zu verstehen, das inkonsistent erscheint, solange es durch Charaktereigenschaften erklärt wird. Aus dieser Perspektive ist es entscheidend, soziale Umwelten zu untersuchen, da Urteile und Verhalten in moralischen Situationen oft nicht durch speziell moralische Regeln sondern durch moralisch neutrale, soziale Heuristiken entstehen können, die den Zusammenhalt sozialer Gruppen fördern. Das zweite Projekt untersucht empirisch Entscheidungen in sozialen Dilemmata. Die Kernfrage ist, wie Kooperation durch den Risikograd geprägt wird, sowie durch die Art, wie Risikoinformationen erworben werden (durch Beschreibung oder Erfahrung), im Vergleich zu nicht-sozialen Situationen mit gleichem Risiko. Kooperation variierte systematisch mit dem Risikograd, aber wie Informationen erworben wurden war nur in den nicht-sozialen Situationen relevant. Prozessdaten und Teilnehmerberichte legen nahe, dass diese Diskrepanz auf Entscheidungsprozessen beruhen könnte, in denen Erwartungen über das Verhaltens anderer sowie die Höhe der Verdienste wichtiger sind als deren Wahrscheinlichkeit. Das dritte Projekt vergleicht Urteile in moralischen Dilemmata, in denen der Verlauf der Ereignisse noch ungewiss ist (Vorschau), mit Situationen, in denen bereits sicher ist, ob negative Nebeneffekte auftreten oder nicht (Rückschau). Die Ergebnisse zeigen einen Rückschaufehler in moralischen Urteilen sowie bei der Wahrscheinlichkeitseinschätzung der Nebeneffekte. Der Unterschied zwischen moralischen Urteilen unter Sicherheit und Unsicherheit wirft die Frage auf, inwieweit sich empirische Ergebnisse generalisieren lassen, die auf den üblicherweise untersuchten moralischen Dilemmata basieren, wie z.B. den „Trolley“-Fällen, in denen alles mit Sicherheit feststeht. / In this dissertation I investigate how people make judgments and decisions in moral situations under uncertainty. Theoretically, behavior in moral situations is analyzed from the perspective of bounded and ecological rationality, which emphasizes the interplay between cognition and the structure of the environment. Empirically, the goal is to investigate moral judgment and behavior under epistemic conditions people encounter in the real world. The first project discusses from the perspective of ecological rationality how the interaction of heuristics and the environment helps explaining moral behavior that appears inconsistent from accounts referring to characteristics of the individual. This view also emphasizes the importance of studying social environments, as judgment and behavior in moral situations under uncertainty may often not result from specifically moral rules but instead from morally neutral social heuristics which serve the coherence of social groups. The second project empirically examines decisions in social dilemmas. The key question was how cooperation is shaped by different levels of risk and by the way information about risk is acquired (from description or from experience), compared to nonsocial situations with equivalent risks. Cooperation systematically varied with different levels of risk, yet the way in which information was acquired only mattered in nonsocial situations. Process data and self-reports indicated that this discrepancy may have resulted from decision processes that are more sensitive to expectations about others’ behavior and the size of rewards than to reward probabilities. The third project compared judgments in moral dilemmas when the course of events is still uncertain (foresight) with situations when it was already certain whether negative side-effects did or did not occur (hindsight). Results showed a hindsight effect for moral judgments, as well as for probability estimates of negative side-effects. As moral judgments differed under certainty and uncertainty, this raises concerns about generalizing empirical results from commonly investigated moral dilemmas, such as the “trolley” cases, in which everything is certain.

In der betriebswirtschaftlichen Realität werden riskante Entscheidungen meistens nach vorherigen Gewinn- oder Verlusterfahrungen getroffen und empirische Beobachtungen legen nahe, dass vorherige derartige Erfahrungen das aktuelle Risikoverhalten beeinflussen. Da dieser Umstand in den existierenden Ansätzen der normativen und deskriptiven Spieltheorie bisher jedoch nicht abgebildet wird, beschäftigt sich diese Arbeit zunächst mit der Erarbeitung der theoretischen Grundlagen für das Verhalten nach entsprechenden Erfahrungen in riskanten Entscheidungssituationen mit und ohne strategische Interaktion. Dabei wird für Einpersonenspiele das gesamte Framework der Kumulativen Prospekttheorie um einen "Aggregationsaxiom" erweitert und es werden anreizkompatible Mechanismen abgeleitet, die in zwei experimentellen Studien überprüft werden. Die Aggregationshypothese konnte dabei bestätigt werden, wenn auch die Risikowahrnehmung eher im Einklang mit normativen Ansätzen und nicht mit der Kumulativen Prospekttheorie zu stehen scheint. Für Mehrpersonenspiele wurde der klassische Ansatz um den Aggregationsaspekt, eine Auszahlungstransformation gemäß der Kumulativen Prospekttheorie und um die Annahme der Sozialen Projektion erweitert. Die Verhaltensprognosen für zwei einfache Koordinationsspiele resultieren dann insbesondere aus den beiden Verfeinerungskriterien "Risikodominanz" und "Perfektheit" der allgemeinen Gleichgewichtsauswahltheorie von Harsanyi und Selten. Die gemachten Vorhersagen konnten für die Teilnehmer (wenn auch mit überraschenden geschlechtspezifischen Unterschieden) in zwei Experimenten bestätigt werden. / In the real world, risky decisions are typically made after previous experiences, which include prior gains and losses. Although there are empirical findings suggesting an influence of such prior experiences on the subsequent behavior, normative as well as descriptive decision and game theory have not provided yet a thorough theoretical treatment of these effects. Therefore, this thesis develops first a theoretical fundament. It is based on the Cumulative Prospect Theory, which is extended by an "aggregation axiom". For single-person-games an incentive compatible mechanism to elicit true evaluations of risks is developed and finally tested in two laboratory experiments. The relevance of the aggregation axiom gets full support whereas a risk perception according to the Cumulative Prospect Theory has to be rejected. In multi-person-games the additional assumption of Social Projection has to be incooperated. Equilibrium selection according to "perfectness" and "risk dominance" are used to derive predictions, which are empirically validated in two experiments. Furthermore, gender-specific differences are observed.

Die Struktur der Landwirtschaft hat sich in vielen westlichen Ländern grundlegend geändert. In Westdeutschland, zum Beispiel, hat die Anzahl der Betriebe deutlich abgenommen, während die durchschnittliche Betriebsgröße von 7,4 ha im Jahr 1950 auf 42,9 ha im Jahr 2013 stieg. Um diese Entwicklung zu verstehen, müssen insbesondere die begrenzten Produktionskapazitäten berücksichtigt werden, die hinsichtlich der verfügbaren landwirtschaftlichen Nutzfläche oder der ehemaligen Milchquote existieren. Solche Kapazitätsbeschränkungen erzeugen einen direkten Zusammenhang zwischen dem Wachsen und Schrumpfen von Betrieben, da die Expansionsmöglichkeiten eines einzelnen Betriebes von frei werdenden Kapazitäten anderer Betriebe abhängen. Aus diesem Grund wird hier zunächst ein dynamisch stochastisches Gleichgewichtsmodell entwickelt, das auf mikroökonomischen Entscheidungen basiert und Markteintritt/-austritt für den Fall ausgeschöpfter Produktionskapazitäten darstellt. Der Kapazitätspreis ist dabei markträumend in dem Sinne, dass die Anzahl neuer Produktionseinheiten mit den aufgegebenen übereinstimmen muss. Die Industrie konvergiert unter diesen Bedingungen gegen ein stationäres Gleichgewicht, in dem Ein- und Austritt vorkommen, die Größenverteilung jedoch unverändert bleibt. Die Anpassung an diesen Zustand wird u.a. beschleunigt, wenn die fixen Eintrittskosten sinken. Eine Version des Modells wird dann auf den westdeutschen Milchsektor angewendet, um den Einfluss einer Quote auf den Strukturwandel zu untersuchen. Verglichen mit dem Szenario freien Wettbewerbs hemmt eine nicht-handelbare Quote die Anpassungsprozesse, während eine handelbare Quote Marktaustritte fördern und zu einer höheren durchschnittlichen Produktivität führen kann. Die Phase nach einer Quotenabschaffung ist geprägt von enormen Kapazitätsausweitungen und stark fallenden Preisen, falls die fixen Eintrittskosten niedrig sind. Diese Entwicklung ist weniger stark ausgebildet für höhere Eintrittskosten. / The agricultural industry in Western countries has undergone a substantial structural change. In West Germany, for instance, the number of farms declined notably during the last decades while the average farm size increased from 7.4 ha in 1950 to 42.9 ha in 2013. A factor that needs to be considered when explaining this development is the limited sectoral production capacity, which exists in terms of agricultural land or the former milk quota regime. This limited capacity generates a direct interrelation between farm growth and farm shrinkage as a single farm''s investment option depends crucially on the possible release of production capacity by competitors. The contribution of this thesis is twofold: First, a dynamic stochastic equilibrium model is developed that accounts for microeconomic decision-making and represents an industry operating at an upper capacity limit. The capacity price is determined endogenously such that it offsets the mass of entering and exiting firms in an equilibrium. It is proven that the industry tends to a stationary equilibrium in the long-run, in which entry and exit still occur but the firm size distribution remains constant. Moreover, the adjustment speed to this steady state increases if either the discount factor or the fixed entry costs decrease. Second, the impact of a production quota on industry dynamics is assessed with regard to the former milk quota regime. After calibrating the model to the West German dairy sector, the quota constrained industry performance is compared to a quota free setup. While a non-tradable quota slows down the adjustment processes within the industry, a tradable production quota can stimulate firm turnover and lead to a higher average productivity level. If the entry costs are rather low, the transition phase after a quota removal is characterised by an enormous expansion of production capacity and a considerable output price drop. This development is less pronounced though for higher entry costs.

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To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed. The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows. It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form. A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm.

To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed. The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows. It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form. A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm.