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.

The thesis focuses on the analysis of various extensions of the classical multi-period single-item stochastic inventory problem. Specifically, we investigate two particular approaches of modeling risk in the context of inventory management: risk-averse models and robust formulations. We analyze the classical newsvendor problem utilizing a coherent risk measure as the objective function. Properties of coherent risk measures allow us to offer a unifying treatment of risk averse and min-max type formulations. We show that the structure of the optimal policy of the risk-averse model is similar to that of the classical expected value problem for both single and multi-period cases. The result carries over even when there is a fixed ordering cost. We expand our analysis to robust formulations of multi-period inventory problems. We consider both independent and dependent uncertainty sets and prove the optimality of base-stock policies for the general problem formulation. We focus on budget of uncertainty approach and develop a heuristic that can also be employed for a class of parametric dependency structures. We compare our proposed heuristic against alternative solution techniques.

Denna studie bidrar till området behavioral finance och ger en ökad förståelse om hur kvinnliga aktieägare påverkas av stereotyper och igenkänning. Diskussionen kring att en könsdiversifierad styrelse och företagsledning kan bidra till en stabilare och ökad tillväxt uppmärksammas i allt fler länder, däribland Sverige vars jämställhet mellan kvinnor och män är hög. På dagens marknad är kvinnor underrepresenterade på högre företagsposter, vilket även gäller kvinnliga aktieägare jämfört med manliga. Detta beror på att män tenderar att ha en överdriven tilltro till sina egna uppfattningar och värderingar samt är mer riskbenägna och optimistiska än kvinnor. Beteendet hos aktieägarna påverkas av de stereotyper som förkommer. I jämförelse med andra är den demografiska egenskapen kön en central del i beslutsprocessen. Med utgångspunkt i teorier som prospektteorin, signalteorin och agentteorin kan kvinnliga aktieägares påverkan av stereotyper förklaras. Två fundamentala faktorer i studien är representativitet och konservatism eftersom människor har en tendens att kategorisera och hålla fast vid tidigare uppfattningar och stereotyper om andra individer och grupper. I enlighet med signalteorin bidrar kvinnor till minskad informationsasymmetri, på grund av ökade kommunikativa förmågor, vilket bidrar till stabilitet i företaget och intresserar enligt resultatet de kvinnliga aktieägarna. Ökad kommunikation till aktieägare minskar risken för att intressekonflikter uppstår mellan aktieägare och företagsledning. Teorierna hjälper till att undersöka det resultat som framställts av de analysmetoder som använts för att pröva hur aktieägare påverkas av en ökad könsdiversifiering. För att undersöka om en kvinnlig verkställande direktör påverkar kvinnliga aktieägare har en eventstudie genomförts. Vidare har en multivariat regressionsanalys gjorts för att kunna ta hänsyn till fler variablers påverkan, exempelvis könsdiversifiering i styrelse och ledning, samt företagsstorlek. Studiens resultat visar att en ökad könsdiversifiering intresserar kvinnliga aktieägare. Detta beror på att kvinnor är mindre riskbenägna än män och att igenkänning till individer med liknande demografiska egenskaper skapar en trygghetskänsla. Företag med en kvinnlig verkställande direktör har inte direkt påverkan på de kvinnliga aktieägarna, något som beror på den långsamma förmågan att ändra på tidigare övertygelser och stereotyper om kvinnors ledarskapsförmåga. Däremot sker en indirekt påverkan eftersom ledningen, vilken den kvinnliga verkställande direktören ingår i, har en påverkan på andelen kvinnligt ägda aktier. / The purpose of the study is to provide an increased understanding of how female shareholders are affected by stereotypes and comparison with other women. The discussion of gender-diversified boards and management is highlighted in several countries, including Sweden. Women are underrepresented as CEOs, board members and management. Likewise, women are underrepresented in the stock market, this because men tend to have excessive confidence in their own perceptions and values and are more risk taking and optimistic than women. Shareholders are affected by the stereotypes that occur. In comparison with others, the demographic characteristics are a key part of the decision-making process. The result of the study shows that increased gender diversification is interesting for female shareholders. This is because women are less risk-intensive than men and that recognition to individuals with similar demographic characteristics creates a sense of security. Female shareholders find similar characteristics in female board-members and management, this does not match comparison with female CEOs, due to the slow ability to change the perception of female leadership.

No description available.

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

Os processos de decisão de Markov (Markov Decision Process - MDP) têm sido usados para resolução de problemas de tomada de decisão sequencial. Existem problemas em que lidar com os riscos do ambiente para obter um resultado confiável é mais importante do que maximizar o retorno médio esperado. MDPs que lidam com esse tipo de problemas são chamados de processos de decisão de Markov sensíveis a risco (Risk-Sensitive Markov Decision Process - RSMDP). Dentre as diversas variações de RSMDP, estão os trabalhos baseados em utilidade exponencial que utilizam um fator de risco, o qual modela a atitude a risco do agente e que pode ser propensa ou aversa. Os algoritmos existentes na literatura para resolver esse tipo de RSMDPs são ineficientes se comparados a outros algoritmos de MDP. Neste projeto, é apresentada uma solução que pode ser usada em problemas maiores, tanto por executar cálculos apenas em estados relevantes para atingir um conjunto de estados meta partindo de um estado inicial, quanto por permitir processamento de números com expoentes muito elevados para os ambientes computacionais atuais. Os experimentos realizados evidenciam que (i) o algoritmo proposto é mais eficiente, se comparado aos algoritmos estado-da-arte para RSMDPs; e (ii) o uso da técnica LogSumExp permite resolver o problema de trabalhar com expoentes muito elevados em RSMDPs. / Markov Decision Process (MDP) has been used very efficiently to solve sequential decision-making problems. There are problems where dealing with environmental risks to get a reliable result is more important than maximizing the expected average return. MDPs that deal with this type of problem are called risk-sensitive Markov decision processes (RSMDP). Among the several variations of RSMDP are the works based on exponential utility that use a risk factor, which models the agent\'s risk attitude that can be prone or averse. The algorithms in the literature to solve this type of RSMDPs are inefficient when compared to other MDP algorithms. In this project, a solution is presented that can be used in larger problems, either by performing calculations only in relevant states to reach a set of meta states starting from an initial state, or by allowing the processing of numbers with very high exponents for the current computational environments. The experiments show that (i) the proposed algorithm is more efficient when compared to state-of-the-art algorithms for RSMDPs; and (ii) the LogSumExp technique solves the problem of working with very large exponents in RSMDPs

Os processos de decisão de Markov (Markov Decision Process - MDP) têm sido usados para resolução de problemas de tomada de decisão sequencial. Existem problemas em que lidar com os riscos do ambiente para obter um resultado confiável é mais importante do que maximizar o retorno médio esperado. MDPs que lidam com esse tipo de problemas são chamados de processos de decisão de Markov sensíveis a risco (Risk-Sensitive Markov Decision Process - RSMDP). Dentre as diversas variações de RSMDP, estão os trabalhos baseados em utilidade exponencial que utilizam um fator de risco, o qual modela a atitude a risco do agente e que pode ser propensa ou aversa. Os algoritmos existentes na literatura para resolver esse tipo de RSMDPs são ineficientes se comparados a outros algoritmos de MDP. Neste projeto, é apresentada uma solução que pode ser usada em problemas maiores, tanto por executar cálculos apenas em estados relevantes para atingir um conjunto de estados meta partindo de um estado inicial, quanto por permitir processamento de números com expoentes muito elevados para os ambientes computacionais atuais. Os experimentos realizados evidenciam que (i) o algoritmo proposto é mais eficiente, se comparado aos algoritmos estado-da-arte para RSMDPs; e (ii) o uso da técnica LogSumExp permite resolver o problema de trabalhar com expoentes muito elevados em RSMDPs. / Markov Decision Process (MDP) has been used very efficiently to solve sequential decision-making problems. There are problems where dealing with environmental risks to get a reliable result is more important than maximizing the expected average return. MDPs that deal with this type of problem are called risk-sensitive Markov decision processes (RSMDP). Among the several variations of RSMDP are the works based on exponential utility that use a risk factor, which models the agent\'s risk attitude that can be prone or averse. The algorithms in the literature to solve this type of RSMDPs are inefficient when compared to other MDP algorithms. In this project, a solution is presented that can be used in larger problems, either by performing calculations only in relevant states to reach a set of meta states starting from an initial state, or by allowing the processing of numbers with very high exponents for the current computational environments. The experiments show that (i) the proposed algorithm is more efficient when compared to state-of-the-art algorithms for RSMDPs; and (ii) the LogSumExp technique solves the problem of working with very large exponents in RSMDPs

In the field of VLSI circuit optimization, the scaling of semiconductor devices has led to the miniaturization of the feature sizes resulting in a significant increase in the integration density and size of the circuits. At the nanometer level, due to the effects of manufacturing process variations, the design optimization process has transitioned from the deterministic domain to the stochastic domain, and the inter-relationships among the specification parameters like delay, power, reliability, noise and area have become more intricate. New methods are required to examine these metrics in a unified manner, thus necessitating the need for multi-metric optimization. The optimization algorithms need to be accurate and efficient enough to handle large circuits. As the size of an optimization problem increases significantly, the ability to cluster the design metrics or the parameters of the problem for computational efficiency as well as better analysis of possible trade-offs becomes critical. In this dissertation research, several utilitarian methods are investigated for variation aware multi-metric optimization in VLSI circuit design and spatial pattern clustering. A novel algorithm based on the concepts of utility theory and risk minimization is developed for variation aware multi-metric optimization of delay, power and crosstalk noise, through gate sizing. The algorithm can model device and interconnect variations independent of the underlying distributions and works by identifying a deterministic linear equivalent model from a fundamentally stochastic optimization problem. Furthermore, a multi-metric gate sizing optimization framework is developed that is independent of the optimization methodology, and can be implemented using any mathematical programming approach. It is generalized and reconfigurable such that the metrics can be selected, removed, or prioritized for relative importance depending upon the design requirements. In multi-objective optimization, the existence of multiple conflicting objectives makes the clustering problem challenging. Since game theory provides a natural framework for examining conflicting situations, a game theoretic algorithm for multi-objective clustering is introduced in this dissertation research. The problem of multi-metric clustering is formulated as a normal form multi-step game and solved using Nash equilibrium theory. This algorithm has useful applications in several engineering and multi-disciplinary domains which is illustrated by its mapping to the problem of robot team formation in the field in multi-emergency search and rescue. The various algorithms developed in this dissertation achieve significantly better optimization and run times as compared to other methods, ensure high utility levels, are deterministic in nature and hence can be applied to very large designs. The algorithms have been rigorously tested on the appropriate benchmarks and data sets to establish their efficacy as feasible solution methods. Various quantitative sensitivity analysis have been performed to identify the inter-relationships between the various design parameters.

現實社會中，由於人並非如傳統學派所聲稱完全理性制定決策，自1980年以來即產生諸多傳統學派無法說明的現象，因此行為財務學派興起。本篇導入行為財務學的模型，探討當經理人具過度自信與過度樂觀特質時，經理人的特性會如何影響公司價值。研究假設當經理人為風險中立者，可達到公司價值極大化first-best value。若經理人為風險趨避，產生的效用成本將使其無法達成此目標，但此時經理人若具過度自信，則可抵消風險趨避帶來的公司價值減損，而達成股東所希望達到的公司價值極大。除此之外，根據Heaton(2003)模型所聲稱，過度樂觀的經理人無法達成公司價值極大，而本篇修改其模型，得出當公司經理人具過度樂觀特性，是有可能符合股東利益，而達到公司價值極大化的目標。 / In real world, people don’t make decisions depend on rationality. Therefore, there exists many facts that traditional researchers can’t explain since 1980’s and that’s why behavioral finance school arises. In this paper, we use behavioral finance models to discuss when managers are overconfident or optimistic, how their personality will affect the value of company. We find that when a manager is risk-neutral, he can maximize the firm value that we called “first-best value.” However, when a manager is risk-averse, the utility cost will be the huge obstacle to attain the goal. However, if the manager is overconfident, this characteristic will counterbalance the drawback that risk-averse will decrease company value. In addition, according to Heaton’s (2003) model, an optimistic manager can’t maximize firm value. This paper modifies Heaton’s model and finds that when managers are optimistic, it is likely that a manager can meet shareholder’s needs and maximize the firm value.