Essays in Industrial Organization and Information Technology

Chapter 1 studies how consumers’ switching costs affect the pricing and profits of firms competing in two-sided markets such as Apple and Google in the smartphone market. When two-sided markets are dynamic – rather than merely static – I show that switching costs lower the first-period price if network externalities are strong, which is in contrast to what has been found in one-sided markets. By contrast, switching costs soften price competition in the initial period if network externalities are weak and consumers are more patient than the platforms. Moreover, an increase in switching costs on one side decreases the first-period price on the other side. Chapter 2 examines firms’ incentives to invest in local and flexible resources when demand is uncertain and correlated. I find that market power of the monopolist providing flexible resources distorts investment incentives, while competition mitigates them. The extent of improvement depends critically on demand correlation and the cost of capacity: under social optimum and monopoly, if the flexible resource is cheap, the relationship between investment and correlation is positive, and if it is costly, the relationship becomes negative; under duopoly, the relationship is positive. The analysis also sheds light on some policy discussions in markets such as cloud computing. Chapter 3 develops a theory of sequential investments in cybersecurity. The regulator can use safety standards and liability rules to increase security. I show that the joint use of an optimal standard and a full liability rule leads to underinvestment ex ante and overinvestment ex post. Instead, switching to a partial liability rule can correct the inefficiencies. This suggests that to improve security, the regulator should encourage not only firms, but also consumers to invest in security.

Development of a patient-specific model for stroke risk assessment in atrial fibrillation patients

Atrial fibrillation is associated with a five-fold increase in the risk of cerebrovascular events,being responsible of 15-18% of all strokes.The morphological and functional remodelling of the left atrium caused by atrial fibrillation favours blood stasis and, consequently, stroke risk. In this context, several clinical studies suggest that stroke risk stratification could be improved by using haemodynamic information on the left atrium (LA) and the left atrial appendage (LAA). The goal of this study was to develop a personalized computational fluid-dynamics (CFD) model of the left atrium which could clarify the haemodynamic implications of atrial fibrillation on a patient specific basis. The developed CFD model was first applied to better understand the role of LAA in stroke risk. Infact, the interplay of the LAA geometric parameters such as LAA length, tortuosity, surface area and volume with the fluid-dynamics parameters and the effects of the LAA closure have not been investigated. Results demonstrated the capabilities of the CFD model to reproduce the real physiological behaviour of the blood flow dynamics inside the LA and the LAA. Finally, we determined that the fluid-dynamics parameters enhanced in this research project could be used as new quantitative indexes to describe the different types of AF and open new scenarios for the patient-specific stroke risk stratification.