Background: Since the emergence of Covid-19 has it reaped and created havoc within every segment of society on a national and global scale. The financial market experienced significant declines and losses but some asset items handled the fluctuations better than others. Moreover, since some asset items are associated with different risk levels will various investors with contrasting risk attitude allocate dissimilar proportion of their disposable capital between these alternatives. Especially during low and high levels of economic uncertainty which is related to the volatile market of Covid-19. Although, little to no research has been conducted aimed at understanding how Covid-19 impacted Swedish students asset allocation prior and post the pandemic with different risk profiles.   Purpose: The purpose of this study is to investigate if students with different risk attitudes (risk-preference, risk-neutral and risk-averse) conduct statistically different asset allocation prior and post the Covid-19 pandemic. Furthermore, investigate shifts in asset holdings prior and post the pandemic. Moreover, in order to fill the identified literature gap and add to the current body of knowledge regarding asset allocation and variability concerning risk attitudes since its exclusion of Swedish student’s risk attitudes and impact of Covid-19 on preferable asset items.                                    Method: This investigative study concerns a quantitative survey of 81 different students attending Jönköping University. The survey was structured in a way to uncover whether students with different risk attitudes conduct asset allocation statistically different prior and post the Covid-19 pandemic. Moreover, incorporate sociodemographic factors of students in order to measure its relation to risk attitudes and uncertainty changes. This will be done through non-parametric tests (distribution free) such as the Chi-square, Kruskal-Wallis and Bonferroni adjusted p-value approach. The data is later discussed and interpreted through various academic sources and in the context of the frame of reference (expected utility theory).                              Conclusion: The impact of Covid-19 resulted into increased asset allocation of less risky and “safe” asset in order to deal with the declining stock market and future economic uncertainty. The study also suggest that students liquidated some of their current/fixed deposits and re-invested their disposable capital into a more conservative money management strategy, which was a continuous identified pattern.  Furthermore, the results indicate that students with different risk attitudes conduct significantly different asset allocation concerning commercial insurance, stocks/funds and various bond types prior to Covid-19. However, post the eruption has the statistical identified differences in bonds asset allocation reduced which refers to that the statistical power and dissimilar allocated proportion amongst asset items has diminished. Further multiple comparison reinsures this conclusion. Thusly, the study implies that the differences between asset allocation and student risk profiles are diminished post Covid-19 and therefore students perceived and allocated more similar capital proportions into various asset items. Hence answer the initial stated research question and empirically state that risk attitude of students impact how they conduct asset allocation prior to and to a lesser extent post Covid-19

The convergence of various technological and operational advancements has reinstated the interest in On-Demand Air Service (ODAS) as a viable mode of transportation. ODAS enables an end-user to be transported in an aircraft between their desired origin and destination at their preferred time without advance notice. Industry, academia, and the government organizations are collaborating to create technology solutions suited for large-scale implementation of this mode of transportation. Market studies suggest reducing vehicle operating cost per passenger as one of the biggest enablers of this market. To enable ODAS, an ODAS operator controls a fleet of aircraft that are deployed across a set of nodes (e.g., airports, vertiports) to satisfy end-user transportation requests. There is a gap in the literature for a tractable and online methodology that can enable ride-sharing in the on-demand operations while maintaining a publicly acceptable level of service (such as with low waiting time). The need for an approach that not only supports a dynamic-stochastic formulation but can also handle uncertainty with unknowable properties, drives me towards the field of Reinforcement Learning (RL). In this work, a novel two-layer hierarchical RL framework is proposed that can distribute a fleet of aircraft across a nodal network as well as perform real-time scheduling for an ODAS operator. The top layer of the framework - the Fleet Distributor - is modeled as a Partially Observable Markov Decision Process whereas the lower layer - the Trip Request Manager - is modeled as a Semi-Markov Decision Process. This framework is successfully demonstrated and assessed through various studies for a hypothetical ODAS operator in the Chicago region. This approach provides a new way of solving fleet distribution and scheduling problems in aviation. It also bridges the gap between the state-of-the-art RL advancements and node-based transportation network problems. Moreover, this work provides a non-proprietary approach to reasonably model ODAS operations that can be leveraged by researchers and policy makers.