本研究之目的在於探討：（1）管理才能測驗與360度回饋之有效性，以及360度回饋之遞增效度；（2）以管理才能測驗及360度回饋為基礎之「個人—工作契合」對學習反應（受訓滿意、受訓壓力）及學習成效（受訓表現、學科測驗、實務驗收、部隊見習、總成績）的預測效果。 本研究樣本為接受短期軍官養成教育之義務役預官，共358人進行管理才能測驗評量，其中實習幹部41人接受360度回饋之評量。研究結果顯示：（1）管理才能測驗能預測受訓滿意、受訓壓力、受訓表現及實務驗收等效標，而360度回饋能預測受訓表現、學科測驗、實務驗收、部隊見習、總成績等效標；（2）控制管理才能測驗的效果後，360度回饋對於學科測驗、實務驗收、部隊見習、總成績等學習成效效標具有遞增預測效度（△R2=.14~.23）；（3）管理才能測驗之差距契合|D|測驗對受訓滿意、受訓壓力、受訓表現、實務驗收及總成績等具有預測力，而相關契合Q測驗對受訓表現、部隊見習等具有預測力。然而，無論是差距契合|D|測驗或相關契合Q測驗，均未能比管理才能測驗具有更高的預測力；（4）360度回饋之差距契合|D|360對受訓表現、學科測驗、實務驗收、部隊見習及總成績等具有預測力，而相關契合Q360則對任何效標均不具預測力，且不論是差距契合|D|360或相關契合Q360，均未能比為360度回饋具有更高的預測力；（5）差距契合|D|測驗對受訓滿意、受訓表現等效標的預測力高於相關契合Q測驗；差距契合|D|360對總成績的預測力高於相關契合Q360。 最後，研究者並進一步提出本研究之限制、後續研究方向，以及實務應用之建議，以提供軍事及企業組織加以參考運用。

一九九七年複製羊桃莉之出現，曾引發當時國際社會之震驚，由於不同動物之複製技術相似，人們開始顧慮複製人可能於未來出現，導致現有社會倫理及法治規範受到衝擊。因此，本文研究方向主要有二，其一係探討如何有效管制複製技術之實施，其二則是研究倘若複製人誕生，其法律上之身分關係應如何認定。而研究範圍又可分為三個部分：（一）闡述血緣之意義，並就現行民法婚生制度相關爭議加以介紹；（二）探討複製人引發之宗教、倫理爭議，同時比較國內外相關法規，以尋求合理之管制模式；（三）探討人工生殖下子女之婚生性認定標準，並以此為基礎研究複製人身分如何認定。 　　在章節安排上，第一章緒論為研究動機及研究方向之介紹，並對論文結構進行概要之說明。第二章分別由生物學及社會學角度，說明血緣關係之認定標準、血緣對於生物行為之影響，以及生物個體間如何辨別血緣關係。同時，本章亦將介紹血緣與社會團體之關係、血緣對於社會行為之影響， 並以我國社會制度作為血緣影響力之檢驗對象。最後，本章就與血緣相關之婚生推定及否認制度予以介紹。第三章先就不孕症與現行人工生殖技術之意義及態樣加以介紹，並闡明相關專業名詞，接著則是介紹近年來熱門之複製技術，其中除了技術原理及操作過程之解釋外，亦將對其發展過程及可能用途予以說明。 　　第四章針對宗教及倫理爭議加以探討。在宗教爭議方面，分別從天主教、基督教、佛教及伊斯蘭教之觀點，闡述其對於複製人之看法；而在倫理爭議方面則將就醫療倫理、人性尊嚴、家庭倫理以及社會秩序四個方向進行問題整理研究。最後再以我國宗教及倫理觀念來探討複製人之相關問題。第五章先就國際間之複製技術法規進行介紹，並對其管制模式及效果加以比較，再來則針對我國「人工協助生殖技術管理辦法」、「胚胎幹細胞研究的倫理規範」、「醫師法」、「醫療法」、「人工生殖法草案」及「人權基本法草案」等法規及法律草案進行檢討，並比較國內外立法例之優缺點。 　　第六章先就血統、分娩與婚生推定等傳統婚生性標準之主張，以及人工生殖目的、當事人意願、契約與子女最佳利益等事項加以比較，並歸納人工生殖子女之最佳婚生性依據。之後，基於複製技術與人工生殖技術之相似性，就先前歸納結果析述複製人之身分認定問題。最後則試圖從複製技術之正面意義，探討其作為合法人工生殖技術之可行性。第七章為結論，乃歸納先前各章討論之結果，對我國婚生制度為整合性之闡述，並對複製技術之規範方式及身分認定進行總結。

選舉是實行民主政治最有效的方法之一，而選區劃分的方式將直接或間接的影響投票結果與民主政治理念的施行。 然而在選舉法規或行政區域發生變動時，舊有的選區劃分方式需要隨之調整。而傳統人工的方式具有許多缺點，如：耗費人力資源、人口分配不均、難以兼顧形狀及行政區完整等等。若每次行政區域發生變動，都需要重新劃分，將花費許多不必要的人力、物力及時間，因此利用電腦以完成自動劃分的技術逐漸受到重視。 本論文中我們打破現有的政治與人文鴻溝，嘗試以系統化的方法對選區劃分作全面性的查驗。我們利用計算幾何學的特性與人工智慧搜尋的技巧，儘量找出可能的劃分方式再進行評估。我們依據中選會的建議採用村裡為劃分之最小行政區域，從數以十萬計之合理解中，根據形狀等客觀條件篩選出較佳之劃分方式，進而將歷史投票行為加入考量，以對篩選出的劃分方式作進一步評估與分析。 實作中我們以台南市為對象，在不同的人口限制及形狀條件下，分別比較所能找到的合理解數目。同時選出一部分的劃分方式，和中選會的劃分方式比較，結果顯示我們的方法可以全面性的分析選區劃分，不同的劃分方式可能產生不同的選舉結果。 / Election is one of the most effective way of conducting democratic politics, and mean of electoral redistricting shall post effect, either directly or indirectly, on electoral outcome as well as delivering ideas of democratic politics. As election regulations or administrational districts experience alterations, the present electoral districting is forcefully accompanied with adjustments. Electoral redistricting using traditional human labor works reveal several flaws such as: human resource wastage, uneven population distributions, hard to maintain shape contiguity and compactness, as well as the completeness of administration districts. Every single alteration experience in administration district requires redistribution, thus expensing on unnecessary human labor, resources and time. As such, it had brought great attention on techniques of automatic redistribution by means of modern computer technologies. In this thesis, we shall breakthrough a giant gap between politics and humanity; conduct a thorough examination on systematic approach on electoral redistricting. We are going to utilize characteristics of computational geometry and artificial intelligence searching techniques to find out every conceivable means of redistricting then evaluation the performance of them. By recommendation of Central Election Commission (hence CEM), we will adopt the classification of township as basic unit of administrational district, from counts of thousand adequate explanations, by objective factors of shape accordance and others, select the better means of redistricting methods, and afterward put into concern of historical voting behavior, conduct a further evaluation and analysis upon chosen redistricting method. In actual practices we had selected Tainan City as the experiment target, under different population limitations and factors of form, compare the searchable numbers of decent explanation respectively. We choose some redistricting outcomes, and put into comparison with redistricting method of the CEM. The results indicated our approach is able to conduct a thorough redistricting analysis, as well as more diversified comparing to CEM's outcome. The result of this experiment also reveals different election outcome with adoption of different redistricting methods.

「個案小教授」是一篇探討專家系統方法的研究與應用的探索性論文，主要的應用領域是企管個案教學的輔助教學工具，我們嘗試擴大專家系統的應用的領域，也嘗試去突破一些困難，我們發展出了一個「個案小教授」的雛型。由於專家系統在個案教學上的應用算是首創，如何利用有限的工具來完成千變萬化的個案教學是一大挑戰。本論文將依照知識工程的方法，逐步將個案教學的的精髓融入專家系統的方法中，並以此發現專家系統研究上的一些限制，提供給後續人工智慧與專家系統研究學者參考，使得專家系統能夠跨入更多的領域，幫助人類解決日常決策的問題。本論文採取的研究方法為 　　1.文獻探討：在於整理出發展專家系統的步驟與技術，包括知識擷取方法，知識表現與推理方式，以歸納出知識工程在個案分析教學上應用。 　　2.深入訪談法：知識擷取的工作以知識工程師為界面，透過知識工程師為主導，以交談與口語資料分析(Protocol analysis)等方式將專家知識擷取出來。 　　3.觀察法：利用專家工作的現場與情境實際觀察(使用錄影或是錄音)專家工作方式與推理過程，藉以了解專家知識表現的方式。本研究則是到個案研討的教室實地觀察並記錄司徒達賢教授上課之情形。 　　4.發展系統雛型：專家系統又稱為知識基礎系統(knowledge-based systems)，或知識系統。 　　其系統架構可分為五部份： 　　(1)知識庫(knowledge base)用以儲存專家用以解決問題之知識部份。 　　(2)推理機(inference engine)用以控制推理過程之機制。 　　(3)使用者界面(user interface)用以供使用者友善的解釋及諮詢功能介紹之界面。 　　(4)知識擷取界面(knowledgeacquisition interface)用以提供編輯，增修知識庫之界面。 　　(5)工作記憶區(working memory)用以儲存在推理過程中當時之事實之部份。本研究是以NEURON DATA公司所出品的NEXPERT OBJECT作為系統發展工具，將個案教學專家的知識與推理過程以專家系統加以表現。

全民健康保險自1995 年開辦以來，不但減輕國內民眾就醫的財務負擔，也使民眾獲得良好的醫療照顧，但2000年以後，隨著環境變遷，科技不斷進步、國內老年人口增加及人民所得及生活水準不斷提高，各種因素的驅使下使政府的健保醫療支出年年增加，造成政府的財政負擔。 在對民眾的保費，礙於民情及民眾心態調漲不易的情況下，政府不得不採取節流措施，以改變對醫療院所或醫師的健保支出及調降相關器材核價之法令，藉以控制健保收入及支出的平衡。雖政府用意在改善醫療支出的浪費，但造成的結果卻是使醫療院所或醫師引進新技術或新產品的意願降低，也開始縮減醫院人力，使得醫療環境逐漸惡化，而廠商也因下游醫療通路(醫院/醫師)不斷向其砍低產品價格，使得廠商入不敷出，面臨難以生存的窘境。 在此情況下，廠商不得不開始轉向自費型的市場，眾家廠商及本文討論之個案A公司在2006年配合眼科醫學會，不斷向衛生署健保局溝通及陳情，極力爭取白內障產品的自費項目，促使健保局2007年正式公告開放白內障特殊功能人工水晶體的給付差額。 個案A公司在此艱難的制度環境下，同時公司在市場上也進入較晚，面臨該市場激烈競爭，必須力圖改變才能突破，因此本研究主要以行銷策略4C架構來分析個案A公司在此市場下，如何改變公司策略以打破困境。

都市熱島效應帶給臺北盆地許多問題，如更炎熱的天氣、都市降雨增加、空氣污染、能源短缺，並造成環境惡化。為了瞭解熱環境及影響都市熱島強度的相關因素，本研究在2012年7月4日、11日、13日以機動觀測法進行溫度實測實驗。分別得到臺北盆地的中午(12:00~14:00)，晚上(19:00~21:00)和凌晨(02:00~04:00)溫度分佈數據和圖形。結果顯示，都市熱島環境下氣溫熱點出現在高度擁擠的交通節點、高人口密度地區、高建蔽率地區，而氣溫冷點出現在低人口密度地區、廣闊綠地附近和山邊。中午時段熱點散佈在都市各地，最大熱島強度為6.87。C；晚上時段熱點出現在具有人工發散熱的都市中心，因為人類活動使空氣升溫，成為扮演熱島效應的重要角色，同時其最大熱島強度為5.77。C；凌晨時段都市吸收的熱輻射和人為活動造成的熱能無法從市中心擁擠的建物群消散到郊區，此外從盆地邊緣吹來的風冷卻了郊區，造成其溫度急劇下降，因此熱區的面積明顯縮小，集中在臺北盆地中央部分，此時段最大熱島強度為4.38。C。 本研究同時進行CFD模擬實驗，取得臺北盆地CFD模擬溫度分佈圖。其過程係建立臺北盆地3D立體幾何結構場域，輸入相關邊界條件、人工熱與物件材質之熱屬性，經過電腦重覆演算至穩定狀態，得到CFD模擬臺北盆地夏季中午、晚上和凌晨之溫度分佈圖，其溫度分佈情形和實測溫度分佈相似，而三時段的都市熱島強度則分別為中午1.38。C、晚上1.35。C、凌晨1.35。C。 進一步比較臺北盆地2012年夏季各時段實測彩色溫度分佈圖和CFD模擬 溫度分佈圖，可得知臺北盆地中央熱四周冷，且都市熱島強度在人口密度高、建蔽率高的區域偏高，而在大型綠地區域偏低。CFD模擬圖中則呈現，在大型水域附近區域的都市熱島強度偏低。 為精確了解都市熱島強度受相關因子影響程度，本研究針對臺北市都市熱島強度與主要相關因子(人口密度、建蔽率、綠地比率)進行量化分析，經線性回歸圖形及相關係數得到印證，都市熱島強度確與人口密度及建蔽率呈正相關，而與綠地比率呈負相關。 綜合上述結論，為減少日益嚴重的都市熱島效應，可朝以下方法努力，如 減少人工熱排放(如汽車、空調)，改善鋪面材質如屋頂綠化、開發大型綠地、 疏浚維護大型水域周邊與改善都市內空氣流通量使蓄積的輻射熱或人工發散熱更容易排散。

The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents' beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understood / The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents' beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understood.

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近年線上教學平台有著很大的發展，不管是國內的均一教學平台，或國外知名的可汗教育平台，都提供各種學科便利學生自主學習。而在高中化學計算的領域中，這些平台上均提供各種教學課程。美中不足的是在線上的練習系統中，往往題目數量少、題目變化少、無詳細解題步驟，這樣將不足以透過題目衡量一個學生在各個主題的學習上有無明顯的進步。 本論文的目的是改善上述問題。我們設計並實做一系統，只要使用者輸入簡單需求，即可自動產生高中化學問題以及伴隨詳細解答，可方便出題者快速產生各式不同主題的高中化學應用題目。我們的系統提供一個Web前端供使用者輸入所需要生成的題目之資訊。系統由此收齊相關參數之後，接著即可依據參數產生符合題目限制條件的化學問題生成模型。此問題模型為一hypergraph，節點代表已知或未知相關化學量，超連結(hyperedge)則代表數個化學量間的相依關係。有了此一以ASP(Answer Set Programming)表達的問題模型之後，系統即可利用ASP求解器(Solver)進行單一或多個題目生成，後續工作則是驗證每一生成題目之可行性並產生解題步驟，最後經由Django整合呈現於Web上。 / In recent years there has been great progress in the development of online learning. Well-known platforms such as international Khan Academic or local Junyi Academy in Taiwan provide courses in various subjects allowing interested students to study in a very convenient and autonomous way. As expected, courses on common subjects such as high school chemistry are offered with rich content by these platforms. However, there are shortcomings in these courses about the problems they provide for the students to practice or test. In addition to rich content, an ideal course should provide abundant problems of all possible topics, with each given detailed solution, so that students can evaluate their achievement of study by practicing or testing themselves with these problems. Unfortunately, no courses on these platforms meet the above requirements. The purpose of this thesis is to improve the above shortcoming by providing a system which can generate automatically word problems on various topics of high school chemistry, together with detailed accompanied solutions. Our system is a web-based application implemented using Django. It provides a front-end enabling the users to enter related information for the word problems they want the system to generate. According to the parameters collected from the front-end, our system will generate a corresponding chemical problem model. The model is a hypergraph with nodes representing known or unknown chemical quantities related to the problem and hyperedges representing relations or dependencies among these quantities. After the model is generated as a logic program of ASP(Answer-set Programming), the system will use an ASP solver to generate one or more candidate problems. Subsequent works are then used to verify the feasibility of each problem and produce a solution for the feasible one. Finally the generated problems as well as solutions are wrapped in the server side and then sent to and presented friendly in the client's browser.

資訊科技(Information Technology, IT)對我們的生活與企業帶來極大的影響與改變。在企業中，資訊科技經常扮演不同的角色，這些不同的資訊科技角色(IT Role)可以自動化企業流程、支援決策制定、整合資源，甚至實現轉型與創新，對於企業的決策帶來不同層面的影響。而我們從近年來新興的資訊科技─大數據與人工智慧技術中，發現了不同於過去的新資訊科技角色。為了近一步了解這個新角色，本研究選擇人工智慧應用之一的自動駕駛車作為研究案例。本研究目的是探討自動駕駛車的資訊科技所扮演的新資訊科技角色；研究問題包含 (1) 自動駕駛車的資訊科技如何影響駕駛決策制定 (2) 在決策制定過程中，人與資訊科技分別扮演何種角色與職責。 本研究採用多個案研究法，分為兩個階段。首先，為解構資訊科技的決策制定流程，本研究依據決策理論與系統理論建構一研究架構。於文獻探討的章節中，本研究根據過往文獻與案例，提出四種企業常見的資訊科技角色─Automation、Supporter、Mentor與Enabler，並將研究架構應用於以上資訊科技角色以進行調整與驗證。接著，本研究選擇Google (Waymo)與Tesla作為自動駕駛車的研究個案，並將研究架構套用於兩個個案研發的自動駕駛車。由於不同的自動駕駛車研發理念與實現方式，Google與Tesla自動駕駛車的資訊科技分別扮演兩種不同的資訊科技角色─Autonomer與Smart Automation，本研究進一步比較所有資訊科技角色的研究架構結果，了解資訊科技角色的特性、影響與適用的決策類型。 自動駕駛的決策問題與環境與過去有極大的不同。為了實現安全的自動駕駛，資訊科技需要的資料類型更加多元，除了傳統數位類型資料，也需要收集周遭環境的3D影像等資料；另外，由於決策從過去的靜態問題轉移到動態與快速變化、擁有爆炸性資料與資訊的環境中，資訊科技需要更多的應變能力以制定更即時與適當的決策。由於資料、決策問題與環境的改變，企業對於資訊科技能力的需求也隨之改變，從自動駕駛車的個案中，本研究發現原本的資訊科技角色(Automation、Supporter、Mentor、Enabler)並不具備能應對如此動態與快速變化的決策問題與環境的能力，因而根據個案提出有能力實現動態即時決策制定的兩種新資訊科技角色。 使用人工智慧技術的Google無人駕駛車扮演著Autonomer的角色。資訊科技角色Autonomer能夠與外界進行互動，並且能夠不斷地追蹤、反饋與修正以實現自我成長；此外，面對各種駕駛決策情境，也能夠在無人為干預的情況下獨力完成駕駛決策的制定。資訊科技的學習能力是面對未知與難以預測的問題的最大優勢，而Autonomer的自我學習與決策制定能力也是與其他資訊科技角色最大的不同之處。使用大數據技術的Tesla自動駕駛車的Autopilot系統扮演著Smart Automation。資訊科技角色Smart Automation擁有更進步的資料收集與分析能力，能夠在動態與快速變化的環境中處理更為複雜的決策問題；此外，面對各種駕駛決策情境，Autopilot系統能在駕駛人保持監督的條件下進行自動駕駛以駕駛輔助的方式減輕駕駛人的負擔。最後，我們發現對於決策制定，資訊科技不僅能扮演一個完全獨立的角色，也能夠扮演一個與人互補的角色。大部分的人工智慧如同Google無人駕駛車做為一個Autonomer的角色，但同時更多企業目前使用的資訊科技屬於Supporter、Mentor與Smart Automation以支援或強化決策者的能力。 本研究探討在自動駕駛過程中不同資訊科技角色如何影響決策制定，以及駕駛人與資訊科技的角色與職責。並且從決策類型與資訊科技能力的角度，協助決策者與使用者全面地了解每個資訊科技角色的特性與適用的決策類型。此外，科技不斷在進步，本研究也提供一個了解各種資訊科技角色的基石，透過本研究的研究架構與方法，協助企業與決策者了解不同資訊科技對於決策的影響，本研究結果也能延伸應用於其他自動化、大數據與人工智慧相關領域，如無人工廠、吾人航空載具、工業4.0與金融科技(Fintech)。 / Information technology (IT) has brought great changes to people and business. In various applications, IT plays diverse roles that can automate business processes, support decision-making, integrate resources, and enable transformation and innovation and brings the impacts on different aspect of decision-making in enterprises. However, with the emerging technology of big data and artificial intelligence (AI), there is a new role for IT. To understand this role, we chose the autonomous car, an application of AI, as a study case. The objective of the research is to understand the new roles played by IT in the autonomous car. We focused on two questions: (1) how IT impacts decision-making in the autonomous car; and (2) what roles do IT and humans play during the decision-making process. This study applies a multiple case study in two phases. First, we built a conceptual framework, based on decision theory and system theory, to deconstruct the decision process of IT. To adjust and verify the framework, we applied it to actual cases and proposed IT roles of Automation, Supporter, Mentor and Enabler. Second, we applied the framework to the chosen autonomous car case studies, Google (Waymo) and Tesla, to explore the new role of IT in the autonomous car. Because of the different philosophies, there were two distinct roles played by IT in Google and Tesla’s autonomous cars, Autonomer and Smart Automation, respectively. We furthermore compared the frameworks of Google and Tesla, as well as the existing and new IT roles, explained the differences regarding the IT roles and decision types, and found out the applicable decision-making type of each IT roles.. Compared to the past, there were the great differences for the decision problems and environment of autonomous driving. To realize the safe autonomous driving, the data IT required became more diverse including non-text or non-digit data; besides, the decision-making also changed from static decision problems into dynamic and rapid decision environment with the explosive data and information that IT required more resilience to make decision. Due to the changes of the data, decision problems and environment, the demand for IT capability also changed. From the cases of the autonomous car, we found the original roles including Automation, Supporter, Mentor and Enabler was not enough – they did not possess the capability to make the dynamic and instantaneous decision. Therefore, we proposed two new IT roles – Smart Automation and Autonomer in this research that these two new IT roles which were applicable to the dynamic and instantaneous decision-making. The computer of the Google driverless car using AI technology acted as an Autonomer that was responsible for interacting with the surroundings and being self-growing with continuous tracking and adjustment; furthermore, under driving decision circumstances, this computer could assume the entire decision-making process without human intervention. The self-learning and decision-making ability of Autonomer is the characteristic most different from other IT roles; additionally, the learning ability was the greatest strength for dealing with unknown and unpredictable circumstances. The Autopilot system of the Tesla self-driving car, leveraging big data technology, acted as a Smart Automation that could process more complex decision problems in the dynamic environment with the advancement of data collection and analysis ability; furthermore, under the driving decision circumstances, the Autopilot system of the Tesla self-driving car could temporarily take over the driving control to decrease the driving burden and provide assistance to make driving easier. According to the research results, IT can not only play a totally independent role but also a complementary role. Most AI played the same IT role – Autonomer, such as the computer of the Google driverless car; meanwhile, much of the IT introduced by businesses acted as Supporter, Mentor and Smart Automation to assist and complement humans. This research provided a perspective for identifying how the different IT roles impact decision-making while driving an autonomous car and clarify the responsibility of humans and IT in the driving experience; moreover, from the perspective of decision problems and IT ability, it also provided a comprehensive and general understanding for realizing the characteristics of diverse IT roles and the applicable decision problems.