生活中許多決策情境是「不確定下的決策（decisions under uncertainty）」，只瞭解選項的結果，不知道結果發生的機率。人們會累積經驗，以學習到適當的決策。許多證據支持，自身會透過增強學習（reinforcement learning）機制學習，根據每次獲得的經驗，調整對於選項的期望，之後選擇期望最大的選項，幫助做出適當的決策。經驗可以透過自身決策或觀察他人決策所獲得，然而，過去較少研究探討觀察學習。因此，本研究欲探討決策中的觀察學習，並釐清同理心對於觀察學習的調節作用。實驗一中，改善過去了研究限制，量測膚電反應、學習速率與行為表現，讓參與者在自身學習、觀察他人與觀察電腦情境進行作業，並透過同理心問卷測量參與者的同理心特質。結果顯示，觀察學習在正向學習與負向學習不同，正向學習為趨向優勢選項，負向學習為避開劣勢選項。正向學習在三種學習情境中無任何差異，負向學習在觀察他人學習時，會受到同理心的調節作用。同理心分數越高，觀察他人的負向行為表現越好，觀察他人負向回饋的膚電反應越大。實驗一只透過問卷測量同理心，無法推論因果關係，因此實驗二直接操弄了不同的同理程度。回饋呈現的同時，呈現他人的情緒或中性臉孔圖片，以引發參與者的同理程度高或低。實驗中，量測回饋相關負波（Feedback-Related Negativity，FRN）、學習速率與行為表現。如同實驗一，只有負向學習受到同理程度不同的影響。同理程度高時，負向學習表現較好。FRN則顯示了同理程度與預期性的交互作用，同理程度低時，與過去研究一致，非預期FRN比預期FRN更加負向；同理程度高時，則無此預期性效果。雖然FRN無預期性差異，但依然能學習到符號機率，行為表現不受影響，推測可能有其他系統參與決策學習。綜上所述，本研究顯示，只有負向學習中，觀察學習會受到同理心的調節，同理心越高，行為表現越好。 / In daily life, we made many decisions under uncertainty. In each decision, we know only the outcome but no probabilities of the outcome. We have to accumulate the experience to learn adaptive decisions. Bunches of studies have shown that people may learn adaptive decisions by reinforcement learning. People modified the expectation for each option according to decision feedbacks, and, in the next time, chose the option with the maximum expectation. People can receive feedback from decisions making by self or others. However, fewer studies examined observational learning in decision making. Therefore, present research would clarify observational learning in decision making, and examine how empathy modulated observational learning. In experiment 1, skin conductance response, learning rate and behavioral performance were recorded and analyzed. Participants would learning decisions in different situations of self learning, observing others and observing computer. The questionnaire of empathy was also measured to examine its modulation in observational learning. The results showed that there were difference in positive learning and negative learning. Positive learning is to approach to the advantageous option, while negative learning is to avoid from the disadvantageous option. In positive learning, there were no difference among the three learning situations, but, in negative learning, empathy would modulate learning by observing others. The higher the empathy score was, the better the behavioral performance of negative learning was. Moreover, the skin conductance response when participants observing others’ negative feedback positively correlated with the empathy score. In experiment 2, the empathy level was manipulated by display pictures of others faces with feedback. Displaying the emotional faces or neutral faces would induce high or low empathy level for others, respectively. The feedback-related negativity (FRN), learning rate and behavioral performance were recorded and analyzed. Similar to experiment 1, only the negative learning was modulated by the empathy level. When participants were induced high empathy level, the behavioral performance was better. The results of FRN showed the interaction between empathy levels and expectancy of feedback. When participant’s empathy level was low, unexpected FRN was more negative than expected FRN. This result was consistent with previous studies. Nevertheless, when participant’s empathy level was high, there was no difference between unexpected FRN and expected FRN. Although FRN didn't show the effect of expectancy, participants could still learn the probabilities of each signs and made adaptive decisions. This result may result from other systems involved in observational learning. From the results of experiment 1 and 2, present research showed that, only in negative learning, observational learning was modulated by empathy, and the higher the empathy level was, the better the behavioral performance was.

[pt] Esta tese é composta por quatro artigos que descrevem diferentes formas de inclusão de aversão a risco em problemas dinâmicos, ressaltando seus aspectos teóricos e consequências práticas envolvidas em técnicas de otimização sob incerteza aplicadas a problemas financeiros. O primeiro artigo propões uma interpretação econômica e analisa as consequencias práticas da consistência temporal, em que particular para o problema de seleção de portfólio. No segunfo artigo, também aplicado à seleção de portfólio, é proposto um modelo que considera empréstimo como variável de decisão e uma função convexa e linear por partes que representa a existência de diversos credores com diferentes limites de crédito e taxas de juros. A performance do modelo proposto é melhor que as aproximações existentes e garante otimalidade para a situação de vários credores. No terceiro artigo, desenvolve-se um modelo de emissão de títulos de dívida de uma empresa que seja financiar um conjunto pré-determinado de projetos. Trata-se de um modelo de otimização dinâmico sob incerteza que considera títulos pré e pós-fixados com diferentes maturidades e formas de amortização. As principais contribuições são o tratammento de um horizonte longuíssimo prazo através de uma estrutura híbrida dos cenários; a modelagem detalhada do pagamento de cupons e amortizações; o desenvolvimento de uma função objetivo multi-critério que reflete o trade-off entre risco-retorno além de outras medidas de performance financeiras como a taxa de alavancagem (razão passivos sobre ativos). No quarto artigo é desenvolvido um modelo de programação estocástica multi-estágio para obter a política ótima de caixa de uma empresa cujo custo de investimento e o custo da dívida são incertos e modelados em diferentes regimes. As contribuições são a extensão de metodologia de equilíbrio dual para um modelo estocástico; a proposição de uma regra de decisão baseada na estrutura de regime dos fatores de risco que aproxima de forma satisfatória o modelo original. / [en] This PhD Thesis is composed of four working papers, each one with a respective chapter on this thesis, with contributions on risk averse stochastic programming models. In particular, it focuses on analyzing the practical consequences of certain theoretical concepts of decision theory, finance and optimization. The first working paper analyzes the practical consequences and the economic interpretation of time consistent optimal policies, in particular for well known portfolio selection problem. The second paper has also a contribution to the portfolio selection literature. Indeed, we develop leverage optimal strategy considering a single-period debt with a piecewise linear borrowing cost function, which represents the actual situation faced by investors, and show a significant gap in comparison to the suboptimal solutions obtained by the usual linear approximation. Moreover, we develop a multistage extension where our cost function indirectly penalizes the excess of leverage, which is closely related to the contribution of the next working paper. The contribution of the third working paper is to penalize excess of leverage in a debt issuance multistage model that optimizes over several types of bonds with fixed or floating rate, different maturities and amortization patterns. For the sake of dealing with the curse of dimensionality of a long term problem, we divide the planning horizon into a detailed part at the beginning followed by a policy rule approximation for the remainder. Indeed, our approximation mitigates the end effects of a truncated model which is closely related to the contributions of the forth working paper. The forth paper develops a multistage model that seeks to obtain the optimal cash holding policy of a firm. The main contributions are a methodology to end effect treatment for a multistage model with infinite horizon and the development of a policy rule as approximation of the optimal solution.