近年來，在電腦動畫的應用中，虛擬人群模擬的需求越來越多；但人群運動的模擬對於動畫設計師而言，仍是一件十分繁瑣耗時的工作。過去有許多研究曾以虛擬力場模擬簡單的生物群聚行為，但所模擬出的動畫品質與虛擬力場的參數及虛擬環境息息相關，因此經常需要以人工的方式耗時地調整出適當的虛擬力場參數。因此，我們提議以此問題定義成一個基因演算法的問題，針對不同的移動行為，定義適切的適應函數，再由系統根據不同環境自動演化出適當的虛擬力權重組合，以供產生不同人群移動行為之動畫時參考。在本篇論文中，我們已完成基因演算法的設計及人群動畫模擬系統，並設計了不同的典型環境進行電腦模擬實驗，以驗證此方法的可行性。 / The demands for virtual crowd simulation have been increasing in recent years but creating realistic crowd motions remains a complex and time-consuming task for a computer animator. In the literature、much work has been proposed to use virtual forces to simulate the motion of a group of virtual creatures such as birds and fishes. However、the quality of the simulations largely depends on the weights of the component virtual forces as well as the scene where the agents are situated. Usually it requires the animator to tune these parameters for a specific scene in order to obtain the desired result. In this thesis、we propose to use genetic algorithm to generate an optimal set of weighting parameters for composing virtual forces according to the given environment and desired movement behavior. We have implemented the proposed genetic algorithm as well as the crowd simulation system. Extensive experiments have also been conducted to study the effects of typical scenes and behaviors on the parameter sets and verify the feasibility of the approach.

基於SEC之要求，越來越多美國金融機構於其財務報表附註中揭露金融交易資產之風險值；然而計算風險值涉及到許多假設，於是導致過去部分文獻對於風險值資訊揭露之可靠性產生質疑。本研究以風險值之揭露對於報表使用者之資訊價值作為研究課題；為求與附註揭露之風險值資訊比較，本研究以公司帳列之金融交易資產(Trading Assets)、金融交易收入(Trading Revenue)為基礎，利用蒙地卡羅模擬法模擬帳列金融交易資產於次期可能產生之最大潛在損失，並且透過OLS regression及panel data model探討： (1)風險值及金融交易資產潛在可能損失是否可以預測次期金融交易收入波動 (2)風險值與金融交易資產潛在可能損失資訊之提供是否影響次期股票交易量 (3)風險值與金融交易資產潛在可能損失資訊是否可以有效預測次期股價報酬 率變異。 實證結果顯示，風險值之揭露與金融交易資產潛在可能損失之資訊對於預計次期金融交易收入之波動與股價報酬率變異均呈現顯著正相關；易言之，上述兩種資訊之揭露均提供增額之資訊價值。惟另方面，風險值之揭露與金融交易資產潛在可能損失之資訊卻與次期股票交易量呈正相關，也就是說上述兩種資訊的揭露反而會造成投資人降低長期投資持有的意願。 / Financial institutions in the United States are required by the Securities and Exchange Commission to disclose their Value at Risk (VaR) in the footnotes of the financial statements. Over the years, VaR has been used by institutional investors, industry consultants, and regulators as one of the key measures of market risk. However, there are a number of approaches to calculating VaR and some of them may involve various statistical models and assumptions. Due to the fact that different models and assumptions may be used, the VaR numbers produced by different institutions are difficult to compare with one another. The usefulness of these numbers is therefore decreased. This thesis examines the usefulness of disclosures of VaR information. In order to compare with VaR disclosures, the implied potential maximum losses of trading assets are simulated by using Monte Carlo simulation. We then employ the OLS regression and the panel data models to investigate the following research questions: (1)whether VaR and implied potential maximum losses of trading assets disclosed by financial institutions are instrumental in predicting the variability of trading revenue for the next quarter; (2)whether VaR and implied potential maximum losses of trading assets disclosed by financial institutions affect investors' investing decision; (3)how useful are VaR and implied potential maximum losses of trading assets in predicting the volatility of daily stock return next quarter. The empirical results indicate that VaR and implied potential maximum losses of trading assets are significantly related to the variability of trading revenue and the volatility of daily stock returns next quarter. This evidence suggests that both types of disclosures provide incremental information on predicting the variability of trading revenue and investment risk in the future. Nevertheless, we also find that both VaR disclosures and implied potential maximum losses of trading assets are positively associated with future average stock trading volume, implying that investors tend to trade stock more frequently when the market risk information is disclosed.

本研究為探討網路演化過程中的群聚現象，說明網路是否能夠藉由訊息傳遞的過程而浮現出族群分離的現象，為此我們架構了一個電腦模型，同時加入了突現行為（mutation）的考量，藉此來瞭解網路形成過程中的不理性行為是否有影響族群群聚的功效存在，探討在不同的突現發生機率下，網路演化過程中的群聚現象是否會隨著突現發生機率的不同而產生不同的結果。至於，在連結形成方面，我們設定連結如果要形成的話，必須要雙方玩家都願意採取相同的投資動作才能成立，連結必須要雙方玩家都相互協調的情況下才能建立，所以玩家每期都會先選擇要進行的投資策略，其次才依照雙方所選擇的投資動作來決定連結是否建立，並不像以往的文獻中所述，完全依照玩家是否有連結誘因存在來決定連結是否建立，在本研究中玩家是依照雙方所選擇的投資動作來決定連結是否形成。 在此研究中，我們以富人和窮人的投資合作過程為故事背景，假定玩家都有兩種投資策略可以選擇，玩家會從中擇一採用，當雙方玩家協商之後，如果雙方都願意採取相同的投資動作的話，則連結就會建立。設定富人選擇投資策略的自由度較高，而窮人選擇投資策略的自由度較低，因此當玩家開始去尋找適合的合作伙伴時，窮人所受的限制較多，而富人所受的限制較少，但是為了避免設定了太強的假設，所以本研究盡量使用較為薄弱的假設，盡可能使窮人與富人之間的差異性不大且符合現實。在這種行為模式的設定下，我們利用兩個群聚比例來計算每期所形成的同性質連結數目，藉由這兩個群聚比例的變化來探究網路形成過程中的群聚分離狀況，因此我們利用了一個較為間接的方式來瞭解所要討論的議題。 本研究透過演算模擬得到幾個結論：（1）平均群聚比例會隨著突現發生機率趨於0而不斷提升，代表網路隨著突現發生機率的下滑，族群分離的程度會隨之趨於明顯。（2）當突現發生機率很低時，平均群聚比例會隨著玩家每期能夠建立的最大連結數目增加而下滑，網路內的族群分離現象逐漸不明顯。但是當突現發生機率很高時，卻會維持在平均的水準。（3）當玩家的人數增多時，突現的發生對群聚比例會有提升的效果存在，不管最大連結數目為何都會有如此的結果。（4）如果市場上處於一個貧富人數不均的環境下，則隨著網路的演化最後所計算出來的族群現象就會越明顯，富人自然會和富人相連，而窮人自然會和窮人相連，群聚現象是自然演變出來的。（5）若搜尋時採隨機模式，則平均群聚比例在突現發生機率很高時會較一般模式差，而在突現發生機率很低時則與一般模式差異不大。（6）若突現僅限於連結的斷裂，且窮人的動作選擇僅限於投資策略二，則平均群聚比例則會趨近於一。

過去的文獻中，資產配置的方法不外乎效率前緣、動態資產配置等方式，但是，單獨針對銀行探討的文章並不多見，所以本文的貢獻在於單獨針對銀行的資產配置行為進行研究，希望能利用『演化策略演算法』，進行『模擬最佳化』來解決銀行資產配置的問題。基本上這個方法是由兩個動作結合而成，先是模擬，再來尋求最佳解。所以，資產面我們選擇了現金、債券、股票、不動產四項標的，而負債面則模擬了定存、活存與借入款這三項業務，然後透過重複執行模型的方式來求出最適解。並與單期資產配置方法下的結果作一比較，發現運用演化策略演算法有較佳的結果，此外，在不同的亂數下，仍具有良好的穩健性，可作為一般銀行經理人參考之用。 / We focus on the bank’s asset allocation problem in this thesis. We use simulation optimization to solve the problem by evolution strategy, which is relatively new in the financial field. Simulation optimization consists of two steps: simulate numerous situations and search for the optimal asset portfolios. In the simulation, we set up four assets, including cash, bond, stock, and real estate and three business lines, including demand deposits, time deposits, and borrowings. Then we search for the optimal solution by running the ES algorithm. The results show that simulation optimization generates better results than one-period asset allocation. Furthermore, the evolution strategy method generates similar results using different random numbers.

林憲德等人於1999年提出台北夏季午夜之都市熱島強度為4.5℃，至2012年簡子翔等人所提出夏季白天最大熱島強度6.18℃、午夜4.38℃，可以發現台北市的高溫化現象並未有顯著的差異，甚至還新增加了多個新興熱區。 本研究以健康新城為研究對象，以實測方式、CFD電腦模擬方法，釐清社區建築環繞下，社區內的高溫化現象，並與社區外的街道環境比較溫度差異，評估熱舒適性。研究結果顯示，建築環繞下的社區內部(社區中庭)，在日落後的確有高溫化現象，白天時則會因為各社區的遮蔽條件、綠化條件不同，而有不同程度的差別。而模擬結果亦顯示，社區開口條件、通風道配置不同，也會影響社區中庭與外部周邊街道環境之舒適性差異。 建議未來社區的建築規劃設計，除了增加綠化措施之外，尚能適度增加開放空間，增加通風性能；而公部門在訂定法定容積時，應考量都市環境因素(增加遮蔽、通風)，酌以調整已達優良的都市實質環境。 / According to Urban Heat Island’s studies by Lin et al. (1999) and Chien et al. (2012), urban heat island intensity (UHIs) of Taipei didn’t get an obvious improvement from 1999 to 2012. UHIs of Taipei was 4.5℃ in the midnight in 1999, 4.38℃ in the midnight in 2012 and 6.18℃ in the daytimes in 2012. Obviously, there are several high temperature area appeared in the years. This study tries to measure the thermal comfort between the area inside Jian-Kang community and the streets’ environment around Jian-Kang community. Furthermore, this study utilize CFD simulation that can help the study knows the reason effects thermal comfort. As the result of the study, the area inside Jian-Kang community has higher temperature after sunset. In the morning, it will have difference due to the shadow and green situation. Also, the simulation results show that the draft condition of the community will influence the thermal comfort. In the future, this study suggests some strategy to have a better urban environment. First, increase much more greening measures. Second, preserve open spaces to improve the ventilation when deciding the community’s design. Third, consider the urban environmental factors when rule the building’s height.

臺灣各縣市人口結構差異明顯，各縣市的人口出生、老化程度都不盡相同，而且在醫療分配及社會資源的使用也有很大的差異，因此各縣市應因應各地特性發展不同的小區域人口推估方法。由於樣本數與變異數成反比，人數較少者的死亡率（像是高齡人口）通常震盪較大，藉由適當的修勻(Graduation)調整，通常可降低年齡層間的死亡率震盪。然而，當縣市層級的人數太少時，只依賴修勻往往不足，多半會再參考人口較多的大母體之死亡率。例如：傳統的的貝氏修勻，使用Lee-Carter之類的參數死亡模型(Lee and Carter, 1992)，或是透過小區域及大母體的死亡率比值（王信忠, 2012）。然而過去研究較少全面性的比較這些方法，尤其是用於人數較少（如：十萬人）的地區。 本文以探討小區域生命表及死亡率推估為目標，著眼於人數不多於五萬人，尋求較為適合臺灣及類似國家的死亡率編算方法。由於修勻或貝氏等方法可視為增加樣本數，本文將擴大樣本分為四種方式：「同地同時」、「同地異時」、「異地同時」、「異地異時」，亦即將死亡資料的整併分成是否限定於小區域，以及是否可擴及其他年度。本文藉由電腦模擬測試，提供在各種限制之下，最合適小區域生命表建構的準則。其中，本文假設大、小區域的死亡率間存有三種情境的關係：定值、遞增、V字型，藉由調整大小區域死亡率比值間的幅度，探討大母體及小區域間的差異對實務使用的影響。研究發現，Partial SMR方法是一個值得參考的方法，當大小區域死亡率類型接近時的效果不錯，甚至可用於人數小於一萬人，但若死亡率類型差異過大，修勻方法會有限制，使用時需格外謹慎。 / The population structure, life expectancy (and age-specific mortality rates), and the speed of population aging vary a lot in different county of Taiwan. Each county has its own policy planning according to the needs. However, the county level population is usually not enough to provide stable estimates, such as of the life expectancies and mortality rates at the county level. Thus, certain graduation methods are applied to stabilize these estimates. However, only a few studies focus on comparing different types of graduation methods, including traditional graduation methods, Bayesian methods, and parametric mortality models. In this study, we separate the graduation methods into four types, according to if using only the small area data and if one year or multiple years of data are used, and explore which methods are appropriate to the areas with population fewer than 100,000. We use computer simulation to evaluate the graduation methods. We found that the Standard Mortality Ratio is promising when the mortality profiles of small and large populations are similar, and it is a feasible solution even for the areas with population fewer than 10,000. However, if the mortality profiles differ significantly, all graduation methods need to be applied with care.

傳統上國人寄望養兒防老，但面臨少子化及壽命延長，家庭已無法獨力負擔照顧老年人的責任，必須仰賴個人（老年人自己）、國家及政府分擔人口老化造成的需求，這也是政府在過去二十年來積極投入更多資源，制訂與老年人有關的社會保險、福利及政策的原因。像是1995年開辦的全民健康保險提升了全民健康，其中老年人受惠尤多；2005年的勞工退休金條例、2008年的國民年金保險等，則是因應我國國民壽命延長的社會保險制度。對於未來費用的需求估算，需要依賴可靠的死亡率預測，但大多數預測並沒有將死亡率改善列入考量，勢必低估長壽風險的衝擊，影響個人的財務規劃、增加國家負債。 有鑑於此，本文研究常用的死亡率模型，評估哪些適合用於描述高齡死亡率的變化，且能用於計算年金商品的定價。本文考量的模型大致分成兩類：關係模型(Relational Models)及隨機模型(Stochastic Models)，第一類包括常用於高齡的Gompertz、Coale-Kisker模型，以及Discount Sequence模型，第二類則有Lee-Carter及CBD等模型。模型比較的方式以長期預測和短期預測，選用交叉驗證的方式驗證死亡率模型的預測結果與觀察值之間的差異。研究結果顯示Discount Sequence、Lee-Carter、CBD隨機模型較能準確描述台灣、日本與美國等三個國家的死亡率特性；但這三個模型在年金險保費並沒有很明顯的訂價差異。另外，若用於短期預測、長期預測比較，又以Discount Sequence的預測結果優於Lee-Carter模型的預測。 / Traditionally in Asia, families played the main role in caring their own elderly (i.e., parents and grand-parents), but the declining fertility rates and longer life expectancy make it difficult for the families to take care of the elderly alone. The elderly themselves and the government need to share the burden caused by the aging population. In fact, most Taiwan’s major social policies in the past 20 years are targeting the elderly, such as National Health Insurance, Labor Pension Act and National Pension Insurance. Their planning and financial solvency rely on reliable mortality models and their projections for the elderly population. However, many mortality models do not take into account the mortality improvements and thus underestimate the cost. In this study, we look for elderly mortality models which can reflect the mortality improvements in recent years and use them to price the annuity products. Two types of mortality models are of interest: relational models and stochastic models. The first group includes the Gompertz model, Coale-Kisker model and Discount Sequence; the other group includes the Lee-Carter and CBD models. We utilize these mortality models to project future mortality rates in Taiwan, Japan and U.S., along with the block bootstrap and ARIMA for projection. The model comparison is based on cross-validation, and both short-term and long-term projections are considered. The results show that the Discount Sequence, Lee-Carter model and CBD model have the best model fits for mortality rates and, for the short-term and long-term forecasts, the Discount Sequence is better than the Lee-Carter model.

壽命延長及生育率下降使得人口老化日益明顯，成為全球多數國家在21世紀必須面對的議題，由於各區域人口老化的速度不同，必須根據各地特性而調整因應對策。其中研究死亡率變化為面對人口老化的必備課題，尤其是高齡族群的死亡率，這也是近年高齡死亡模型廣受重視的主因之一。因為樣本數與變異數成反比，人口較少的區域或是高齡人口，死亡率的觀察值通常會有較大震盪，為了降低震盪多半會經過修勻，以取得較為穩定的死亡率推估值（王信忠等人，2012）。此外，Li and Lee (2005)的Coherent Lee-Carter模型也是另一種可行方法，透過參考大區域的資訊降低小區域的估計誤差。 本文探討結合上述修勻、死亡率模型的可能，希冀能綜合兩者的優點，提高小區域死亡率推估的精確性。因為Coherent Lee-Carter模型的想法類似增加小區域的人數（加入大區域的人數），本文探討人口數與Lee-Carter模型參數估計值的關係，再以修勻調整大小區域的差異，透過電腦模擬及資料分析，驗證本文提出方法是否有效。其中，仿造王信忠等人的作法，假設小區域與大區域死亡率間的七種可能情境，以平均絕對百分誤差(Mean Absolute Percentage Error)為衡量標準，找出調整修勻、相關模型的方法。另外，本文也以臺灣縣市為研究區域，驗證本文方法的估計結果。研究發現適當地使用修勻方法，可降低小區域的死亡率估計值，其效果優於Coherent Lee-Carter模型。

生活於數位時代，巨量的個人生命記憶使得人們難以輕易解讀，必須經過檢索或標籤化才可以進一步瞭解背後的意涵。本研究著力個人記憶裡繁瑣及週期性的廣泛事件，進行於「情節記憶語意化」以及「何以權衡大眾與個人資訊」兩議題之探討。透過生命記憶平台裡影像標籤自動化功能，我們以時空資訊為索引提出持續性概念重構模型，整合共同知識、個人近況以及個人偏好三項因素，模擬人們對每張照片下標籤時的認知歷程，改善其廣泛事件上註釋困難。在實驗設計上，實作大眾資訊模型、個人資訊模型以及本研究持續性概念重構模型，並招收九位受試者來剖析其認知歷程以及註釋效率。實驗結果顯示持續性概念重構模型解決了上述大眾與個人兩模型上的極限，即舊地重遊、季節性活動、非延續性活動性質以及資訊邊界註釋上的問題，因此本研究達成其個人生命記憶在廣泛事件之語意標籤自動化示範。 / In the digital era, labeling and retrieving are ways to understand the meaning behind a huge amount of lifetime archive. Foucusing on tedious and periodic general events, this study will discuss two issues: (1) the semantics of episodic memory (2) the trade-off between common and personal knowledge. Using the automatic image-tagging technique of lifelong digital archiving system, we propose the Coutinuous Reconceptualization Model which models the cognitive processing of examplar categorization based on temporal-spatial information. Integrating the common knowlegde, current personal life and hobby, the Continuous Reconceptualization Model improves the tagging efficiency. In this experiment, we compare the accuracy of cognitive modeling and tagging efficiency of the three distinct models: the common knowledge model, personal knowledge model and Coutinuous Reconceptualization Model. Nine participants were recruited to label the photos. The results show that the Continous Reconceptualization Model overcomes the limitations inherent in other models, including the auto-tagging problems of modeling certain situations, such as re-visiting places, seasonal activities, noncontinuous activities and information boundary. Consequently, the Continuous Reconceptualization Model demonstrated the efficiency of the automatic image-tagging technique used in the semantic labeling of the general event of personal memory.

無論在電腦動畫、電玩或電影產業，利用電腦自動產生虛擬人群已逐漸成為不可或缺的要素之一。這些虛擬人群，往往是系統先賦與每個虛擬代理人(agent)基礎智能，然後藉由個體之間的互動法則所自動產生。然而，過去因為普遍未考量真實群體情境中的傳播與互動模式，使得虛擬人群所表現的群體行為與現實情況仍有些差距。因此，我們引用社會心理學文獻，建立一個具有溝通機制的人群模擬平台(IMCrowd)，以期自動產生與現實群眾動態更相似的模擬人群。IMCrowd是多代理人(Multi-agent)基礎的系統，其中每個虛擬代理人都具有區域的感知範圍與自主能力，因此他們能夠自動地與環境中的其它物件互動與反應。由於我們為IMCrowd所建立的溝通模型考量了社會心理學的理論，因此虛擬人群能浮現真實群體動態中的社會互動模式，如情緒傳染與從眾效應。本研究以IMCrowd執行了多種情境下群眾暴動與群眾控制的模擬，藉此展現本系統的應用將不僅可提升群體模擬的真實度，亦可做為社會心理學家研究群體行為的工具。 / Using computer to automatically generate simulated crowd has become a trend in animation, computer game, and film productions. Many of these works were produced by modeling the intelligence of the agents in a crowd and their interactions with other nearby agents and the environment. However, the perceived facts or elicited emotions usually do not propagate in the crowd as they should in the real life. In this work we attempt to build up a communication model to simulate a large variety of crowd behaviors including the course of crowd formation. The proposed crowd simulation system, IMCrowd, has been implemented with a multi-agent system in which each agent has a local perception and autonomous abilities to improvise their actions. The algorithms used in our communication model in IMCrowd are based heavily on sociology research. Therefore, the collective behaviors will emerge out of the social process such as emotion contagion and conformity effect among individual agents. Several elaborate riot simulations and riot control simulations are demonstrated and reported in this thesis as the application examples of IMCrowd. Thus, we claim that IMCrowd may not only benefit on enhancing realism of crowd animation but also be useful in studying crowd behaviors such as panic, gathering, and riots.