The present study aimed to apply the framing effects in prospect theory to examine college students¡¦ risk tendencies under the context of educational marketing. Prospect theory proposes that framing effects result in a preference for risk-averse choices in gain situations and risk-seeking choices in loss situations. Frame in this research was treated as a between-subjects factor. Participants were randomly assigned to either the gain or loss condition with the counter-balanced method. The decision tasks consisted of four domains of marketing mix, i.e., product, price, place, and promotion. The results showed that participants¡¦ risk tendencies were in accordance with the predictions from the perspective of framing effects. Reference points were employed by participants to determine gain or loss framing. Specifically, risk-averse tendencies were more prominent in gain situations than those in loss situations, whereas risk-seeking tendencies were more pronounced in loss situations than those in gain situations. Hence, in order to produce desirable outcomes of marketing mix in educational marketing, marketers in higher education should take the influences of reference point and framing effects on decision-making into consideration.

Addressing fairness among users and risk mitigation in the context of resource allocation in transportation systems under uncertainties poses a crucial challenge yet to be satisfactorily resolved. This dissertation attempts to address this challenge, focusing on achieving a balance between system-wide efficiency and individual fairness in stochastic transportation resource allocation problems. To study complicated fair and risk-averse resource allocation problems - from public transit to urban air mobility and multi-stage infrastructure maintenance - we develop three models: DrFRAM, FairUAM, and FCMDP. Each of these models, despite being proven NP-hard even in a simplistic case, inspires us to develop efficient solution algorithms. We derive mixed-integer linear programming (MILP) formulations for these models, leveraging the unique properties of each model and linearizing non-linear terms. Additionally, we strengthen these models with valid inequalities. To efficiently solve these models, we design exact algorithms and approximation algorithms capable of obtaining near-optimal solutions. We numerically validate the effectiveness of our proposed models and demonstrate their capability to be applied to real-world case studies to adeptly address the uncertainties and risks arising from transportation systems. This dissertation provides a foundational platform for future inquiries of risk-averse resource allocation strategies under uncertainties for more efficient, equitable, and resilient decision-making. Our adaptable framework can address a variety of transportation-related challenges and can be extended beyond the transportation domain to tackle resource allocation problems in a broader setting. / Doctor of Philosophy / In transportation systems, decision-makers constantly strive to devise the optimal plan for the most beneficial outcomes when facing future uncertainties. When optimizing overall efficiency, individual fairness has often been overlooked. Besides, the uncertainties in the transportation systems raise serious questions about the adaptability of the allocation plan. In response to these issues, we introduce the concept of fair and risk-averse resource allocation under uncertainties in this dissertation. Our goal is to formulate the optimal allocation plan that is both fair and risk-averse amid uncertainties. To tackle the complexities of fair and risk-averse resource allocation problems, we propose innovative methods and practical algorithms, including creating novel formulations as well as deriving super-fast algorithms. These solution approaches are designed to accommodate the fairness, uncertainties, and risks typically in transportation systems. Beyond theoretical results, we apply our frameworks and algorithms to real-world case studies, thus demonstrating our approaches' adaptability to various transportation systems and ability to achieve various optimization goals. Ultimately, this dissertation aims to contribute to fairer, more efficient, and more robust transportation systems. We believe our research findings can help decision-makers with well-informed choices about resource allocation in transportation systems, which, in turn, lead to the development of more equitable and reliable systems, benefiting all the stakeholders.

No description available.

Taiwan has an ageing population, with more people in the concern of not having sufficient income stream during retired life. This study investigates the ageing issue through socioeconomic perspectives. It is recommended that apart from low interest-bearing term deposits, Interest-Sensitive Annuity is the most suitable solution for countering longevity risk. Through case studies, it has been found that 1, due to Annuity Puzzle sentiment, term depositors will continue to invest in Interest-Sensitive Annuities, regardless of the low interest rate environment. 2, Interest-Sensitive Annuity investors are as risk-averse as term depositors, implying that they do not necessarily choose the surrender option upon expiry. 3, due to customer sentiment, the Interest-Sensitive Annuity policy fees charged are inversely correlated to customers¡¦ willingness to invest. 4, by selling low-commission products, namely one- and two-year Interest-Sensitive Annuities through bancassurance channel, insurance companies enjoy the benefit of low cost capital and are able to reduce interest spread risk through efficient investments. Moreover, customers have their retirement needs covered while insurance salespeople of different channels are able to meet respective sales targets. It is therefore shown that Interest-Sensitive Annuities have the following benefits. For investors, it is the product type that best meets their needs. For insurance salespeople, they enjoy a diverse and complete product portfolio and for insurance companies, it maximizes operation efficiency. Unfortunately, after the termination of one- and two-year Interest-Sensitive Annuities on the market, insurance company capital costs have been negatively impacted, with retirement and longevity risks unsatisfied and insurance salespeople having less products to choose from. It is suggested that the regulator considers re-introducing one- and two-year Interest-Sensitive Annuities, using Risk-Based Capital as a complement in monitoring insurance companies.

none

In Chapter 1 I study the iterative strategy elimination mechanisms for normal form games. The literature is mostly clustered around the order of elimination. The conventional elimination also requires more strict knowledge assumptions if the elimination is iterative. I define an elimination process which requires weaker rationality. I establish some preliminary results suggesting that my mechanism is order independent whenever iterative elimination of weakly dominated strategies (IEWDS) is so. I also specify conditions under which the \undercutting problem" occurs. Comparison of other elimination mechanisms in the literature (Iterated Weak Strategy Elimination, Iterated Strict Strategy Elimination, Generalized Strategy Eliminability Criterion, RBEU, Dekel-Fudenberg Procedure, Asheim- Dufwenberg Procedure) and mine is also studied to some extent. In Chapter 2 I study the axiomatic characterization of a well-known bankruptcy rule: Proportional Division (PROP). The rule allocates shares proportional to agents' claims and hence, is intuitive according to many authors. I give supporting evidence to this opinion by first defining a new type of consistency requirement, i.e. union-consistency and showing that PROP is the only rule that satisfies anonymity, continuity and union-consistency. Note that anonymity and continuity are very general requirements and satisfied by almost all the rules that have been studied in this literature. Thus, I prove that we can choose a unique rule among them by only requiring union-consistency. Then, I define a bankruptcy operator and give some intuition on it. A bankruptcy operator is a mapping from the set of bankruptcy operators to itself. I prove that any rule will converge to PROP under this operator as the claims increase. I show nice characteristics of the operator some of which are related to PROP. I also give a definition for continuity of an operator. In Chapter 3 investigate risk-averse investors' behaviour towards a risky firm. In order to find Pareto Optimal allocations regarding a joint venture, I employ a 2-stage game, first stage of which involves a social-planner committing to an ex-post bankruptcy rule. A bankruptcy rule is a set of suggestions for solving each possible bankruptcy problem. A bankruptcy problem occurs when there is not enough endowment to allocate to the agents each of whom has a claim on it. I devise the game-theoretic approach posed in K1br1s and K1br1s (2013) and extend it further. In fact, that paper considers a comparison among 4 renowned bankruptcy rules whereas mine do not restrict attention to any particular rule but rather aim to find a Pareto Optimal(PO) one. I start with 2 agent case in order to give some insight to the reader and then, generalise the results to an arbitrary number of investors. I find socially desirable (PO) allocations and show that the same can be achieved through financial markets by the help of some well-known results.

Electric power is one of the most critical parts of everyday life; from lighting, heating, and cooling homes to powering televisions and computers. The modern power grids face several challenges such as efficiency, sustainability, and reliability. Increase in electrical energy demand, distributed generations, integration of uncertain renewable energy resources, and demand side management are among the main underlying reasons of such growing complexity. Additionally, the elements of power systems are often vulnerable to failures because of many reasons, such as system limits, poor maintenance, human errors, terrorist/cyber attacks, and natural phenomena. One common factor complicating the operation of electrical power systems is the underlying uncertainties from the demands, supplies and failures of system components. Stochastic optimization approaches provide mathematical frameworks for decision making under uncertainty. It enables a decision maker to incorporate some knowledge of the uncertainty into the decision making process to find an optimal trade off between cost and risk. In this dissertation, we focus on application of three risk-averse approaches to power systems modeling and optimization. Particularly, we develop models and algorithms addressing the cost-effectiveness and reliability issues in power grids with integrations of renewable energy resources. First, we consider a unit commitment problem for centralized hydrothermal systems where we study improving reliability of such systems under water inflow uncertainty. We present a two-stage robust mixed-integer model to find optimal unit commitment and economic dispatch decisions against extreme weather conditions such as drought years. Further, we employ time series analysis (specifically vector autoregressive models) to construct physical based uncertainty sets for water inflow into the reservoirs. Since extensive formulation is impractical to solve for moderate size networks we develop an efficient Benders' decomposition algorithm to solve this problem. We present the numerical results on real-life case study showing the effectiveness of the model and the proposed solution method. Next, we address the cost effectiveness and reliability issues considering the integration of solar energy in distributed (decentralized) generation (DG) such as microgrids. In particular, we consider optimal placement and sizing of DG units as well as long term generation planning to efficiently balance electric power demand and supply. However, the intermittent nature of renewable energy resources such as solar irradiance imposes several difficulties in decision making process. We propose two-stage stochastic programming model with chance constraints to control the risk of load shedding (i.e., power shortage) in distributed generation. We take advantage of another time series modeling approach known as autoregressive integrated moving average (ARIMA) model to characterize the uncertain solar irradiance more accurately. Additionally, we develop a combined sample average approximation (SAA) and linearization techniques to solve the problem more efficiently. We examine the proposed framework with numerical tests on a radial network in Arizona. Lastly, we address the robustness of strategic networks including power grids and airports in general. One of the key robustness requirements is the connectivity between each pair of nodes through a sufficiently short path, which makes a network cluster more robust with respect to potential disruptions such as man-made or natural disasters. If one can reinforce the network components against future threats, the goal is to determine optimal reinforcements that would yield a cluster with minimum risk of disruptions. We propose a risk-averse model where clusters represents a R-robust 2-club, which by definition is a subgraph with at least R node/edge disjoint paths connecting each pair of nodes, where each path consists of at most 2 edges. And, develop a combinatorial branch-and-bound algorithm to compare with an equivalent mathematical programming approach on random and real-world networks.

The debt-to-equity ratio is useful for showing a company ́s financial capacity. As a result of the accompanying risks that a high level of leverage entails, most companies strive to be financed by equity as much as possible. However, tax benefits obtained through debt financing add complexity to the issue as a trade-off between risk and reward should be carefully considered. Previous empirical literature has shown that there is a relationship between the composition of the board, the gender of the CEO and the company’s capital structure.  The purpose of this study is to describe and analyze the relationship between the composition of the board and the CEO’s gender as well as the company’s capital structure in Swedish listed companies of Large, Mid and Small Cap between the years 2016 – 2020. This is done through a quantitative method where secondary data is analyzed through a multiple regression analysis. The result shows that there is no statistically significant relationship between the composition of the board and the company’s debt-to-equity ratio, while there is a negative statistically significant relationship between the CEO’s gender and the company’s debt-to-equity ratio. This leads to one of the study’s two hypotheses being rejected while the other hypothesis is accepted.

In this dissertation, we study the effects of emerging biomass markets on land use changes between alternatives of agricultural production, conventional timber production, and forest woody biomass production for energy use. Along with the uncertainty associated with woody biomass prices and rents, transaction costs incurred to land use play an important role in land allocation decisions and make this study distinct from other work. In Chapter 1, we introduce the background and objectives of our study. In Chapter 2, we analyze the behavior of a risk-neutral private landowner and social planner under uncertainty of woody biomass prices, assuming that there is a market emergence at some unknown time point in the future. Market emergence is characterized by a price jump and a certain timing of the price jump. Six different price jumps and five different timings of bioenergy market emergence are adopted to study their collective effects on land use change between agriculture and forestry. Chapter 3 studies this problem for a risk-averse private landowner. Two measures of relative risk aversion are used to examine how a landowner's preference may affect his or her land use decision. In Chapter 2, we find that, for three different quality categories of land, land rents from forestry increase significantly for higher price jumps and decreases in the length of time until bioenergy market emergence. One of the most important results is concerned with the presence of transaction costs. Here, we find that these costs may require unrealistic market emergence scenarios to lead to bioenergy adoption on any large scale. This result is even more likely with nonlinear transaction costs. Land allocation decisions in Chapter 3 are distinctly different from those in Chapter 2, due to the introduction of landowner risk aversion. In certain market emergence cases, some land units retain in agriculture entirely when the landowner is risk averse . The Chapter 4 studies a stochastic optimization problem of land use, assuming that woody biomass rents follow a stochastic diffusion called geometric Brownian motion that is later discretized by a binomial option pricing approach. The problems in Chapters 2 and 3 assume that the landowner must make all decisions at the beginning of his or her time horizon. This assumption is relaxed in Chapter 4. Now, the landowner is allowed to revise his or her land allocation decision among three alternatives over time as information about market emergence is collected. We observe that the different forms of transaction costs are not as significant as in Chapters 2 and 3. However, different values of volatility of forest biomass rents give rise to different land allocation decisions, especially for the land of high quality. / Ph. D.

The main objective of this thesis is to investigate risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems. The purpose of hydrothermal system operation planning is to define an operation strategy which, for each stage of the planning period, given the system state at the beginning of the stage, produces generation targets for each plant. This problem can be formulated as a large scale multistage stochastic linear programming problem. The energy rationing that took place in Brazil in the period 2001/2002 raised the question of whether a policy that is based on a criterion of minimizing the expected cost (i.e. risk neutral approach) is a valid one when it comes to meet the day-to-day supply requirements and taking into account severe weather conditions that may occur. The risk averse methodology provides a suitable framework to remedy these deficiencies. This thesis attempts to provide a better understanding of the risk averse methodology from the practice perspective and suggests further possible alternatives using robust optimization techniques. The questions investigated and the contributions of this thesis are as follows. First, we suggest a multiplicative autoregressive time series model for the energy inflows that can be embedded into the optimization problem that we investigate. Then, computational aspects related to the stochastic dual dynamic programming (SDDP) algorithm are discussed. We investigate the stopping criteria of the algorithm and provide a framework for assessing the quality of the policy. The SDDP method works reasonably well when the number of state variables is relatively small while the number of stages can be large. However, as the number of state variables increases the convergence of the SDDP algorithm can become very slow. Afterwards, performance improvement techniques of the algorithm are discussed. We suggest a subroutine to eliminate the redundant cutting planes in the future cost functions description which allows a considerable speed up factor. Also, a design using high performance computing techniques is discussed. Moreover, an analysis of the obtained policy is outlined with focus on specific aspects of the long term operation planning problem. In the risk neutral framework, extreme events can occur and might cause considerable social costs. These costs can translate into blackouts or forced rationing similarly to what happened in 2001/2002 crisis. Finally, issues related to variability of the SAA problems and sensitivity to initial conditions are studied. No significant variability of the SAA problems is observed. Second, we analyze the risk averse approach and its application to the hydrothermal operation planning problem. A review of the methodology is suggested and a generic description of the SDDP method for coherent risk measures is presented. A detailed study of the risk averse policy is outlined for the hydrothermal operation planning problem using different risk measures. The adaptive risk averse approach is discussed under two different perspectives: one through the mean-$avr$ and the other through the mean-upper-semideviation risk measures. Computational aspects for the hydrothermal system operation planning problem of the Brazilian interconnected power system are discussed and the contributions of the risk averse methodology when compared to the risk neutral approach are presented. We have seen that the risk averse approach ensures a reduction in the high quantile values of the individual stage costs. This protection comes with an increase of the average policy value - the price of risk aversion. Furthermore, both of the risk averse approaches come with practically no extra computational effort and, similarly to the risk neutral method, there was no significant variability of the SAA problems. Finally, a methodology that combines robust and stochastic programming approaches is investigated. In many situations, such as the operation planning problem, the involved uncertain parameters can be naturally divided into two groups, for one group the robust approach makes sense while for the other the stochastic programming approach is more appropriate. The basic ideas are discussed in the multistage setting and a formulation with the corresponding dynamic programming equations is presented. A variant of the SDDP algorithm for solving this class of problems is suggested. The contributions of this methodology are illustrated with computational experiments of the hydrothermal operation planning problem and a comparison with the risk neutral and risk averse approaches is presented. The worst-case-expectation approach constructs a policy that is less sensitive to unexpected demand increase with a reasonable loss on average when compared to the risk neutral method. Also, we comp are the suggested method with a risk averse approach based on coherent risk measures. On the one hand, the idea behind the risk averse method is to allow a trade off between loss on average and immunity against unexpected extreme scenarios. On the other hand, the worst-case-expectation approach consists in a trade off between a loss on average and immunity against unanticipated demand increase. In some sense, there is a certain equivalence between the policies constructed using each of these methods.