課程資訊
課程名稱
管理決策
Managerial Decision Making 
開課學期
104-2 
授課對象
管理學院  工商管理學系  
授課教師
陳文華 
課號
MBA5002 
課程識別碼
741 U0220 
班次
 
學分
全/半年
半年 
必/選修
選修 
上課時間
星期四7,8,9(14:20~17:20) 
上課地點
管一405 
備註
本課程中文授課,使用英文教科書。
限本系所學生(含輔系、雙修生)
總人數上限:25人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1042Decision_Making 
課程簡介影片
 
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課程概述

We all make decisions, all the time. Some of them are not particularly important, but others are. Imagine how much better your life would be if all of your important decisions were as good as they realistically could be. Although this is an unattainable goal, we can all learn to improve our decision making. And if we can improve by even a small amount, we will be better off and so will many other people. That is what this course is about: making your decisions better so that you and everyone else can benefit.

As your career grows, your decisions will become more and more important. Many of them will entail considerable risk. Many will also involve potential competition. This course is designed to give you the opportunity to make a variety of risky, competitive, and difficult decisions so that you can learn, first hand, (1) how you make decisions and (2) how you can make them better. The goal of this course is to open your eyes both to yourself and to a variety of methods than will allow you to improve your decision making, a lot.

We will touch on all kinds of decisions - financial, person, and professional - with a primary emphasis on how we can make these decisions more effectively. Each class will be a combination of exercises, discussions, and analysis. By asking you to make many decisions, we will try to highlight some of the underlying tendencies that many people - including you and me - often display. This way we can build a deep understanding of the decision process while we sharpen our analytical skills. 

課程目標
The main objectives of this course are as follows:
(1) help you to learn how to utilize smart decision making process and decision making tools
(2) help you to understand how decisions are made
(3) help you to improve your judgmental capabilities
(4) help you to improve the quality of your decisions 
課程要求
Attendance Policy

As this is a professional school, I expect that you will all act professionally in this class. Thus, I expect that you will attend each class, on time, and notify me in advance if you must miss a class. If you think of our meetings as work meetings and part of your job, you'll have an idea of what I expect.
 
預期每週課後學習時數
 
Office Hours
每週四 12:30~14:00 
指定閱讀
1. Max H, Bazerman, and Don A. Moore, Judgment in Managerial Decision Making, Wiley, 2012.
2. John S. Hammond, Ralph L. Keeney, and Howard Raiffa, Smart Choices: A Practical Guide to Making Decisions, 2007.
3. Articles & Cases
 
參考書目
1. Robert E. Gunther, Stephen J. Hoch, and Howard C. Kunreuther, Wharton on Making Decisions,
Aug 20, 2004.2. Peter Ferdinand Drucker, John Hammond, Ralph Keeney, Howard Raiffa, Alden M. Hayashi,
Harvard Business Review on Decision Making.
3. James G. March, A Primer on Decision Making: How Decisions Happen, May 23,
1994.
4. Lewis, M. (2004). Moneyball: The art of winning an unfair game. New York: Norton.  
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Class Participation 
25% 
Quality of class Participation: Active discussion is a very important part of the learning process in the course and is also part of what will make the course interesting for you and your fellow students. You will be evaluated on the quality of you contributions and insights. Quality comments possess one or more of the following properties: - Offer a different and unique, but relevant, perspective - Contribute to moving the discussion and analysis forward - Build on other students' comments - Transcend the "I feel" syndrome. (That is, they include some evidence, argumentation, or recognition and demonstrate some reflective thinking.) - Provide a real life example of a course concept  
2. 
Home assignments 
45% 
1. Individual assignments should be a maximum of two typed pages in length. (Format should be Times Roman, 12 point font, 1.5 line spacing, 1 inch margins.) They should describe a decision that you have made in the past that did not go well or a decision that has been reported in the press that also did not go well. You should then analyze and critique the decision using the material that we have covered in class and show how, in retrospect, the decision might have improved markedly. I expect pointed, insightful analyses that are both deep and comprehensive. 2. Case study 
3. 
Group paper 
30% 
The paper should analyze a current or previous work experience or a newspaper or a magazine article that illustrates one or more principles that were covered in the course. If the source is a personal experience; provide a brief description of the situation (two pages maximum in addition to the analysis). The paper should analyze the case and describe its relevance to material that was covered in the course. The paper should be both descriptive (Why did the decision maker behave as they did in the specific situation) as well as prescriptive (How could they have acted better?). In analyzing the problem you need to chose a model. The paper will be graded according to (1) whether the insights generated from your analysis are based on the course concepts (integration); (2) the relevance of the analysis conclusion to the decision makers involved in the case (relevance); and (3) the originality and depth of your analysis and prescriptions (originality).  
 
課程進度
週次
日期
單元主題
第1週
02/25  Course Introduction
Rational Choice and Managerial Decision Making 
第2週
3/03  Decision Process 
第3週
3/10  Decision Tree, Value of Information 
第4週
3/17  Exercises on Tradeoffs and Conflicting Objectives 
第5週
3/24  Ranking of Alternatives 
第6週
3/31  Heuristics and Cognitive Biases 
第7週
4/07  Negotiation 
第8週
4/14  Group decision making 
第9週
4/21  Power of Noticing 
第10週
4/28  Who made the Decision? 
第11週
5/05  Power of Influencing  
第12週
5/12  Decision Making in Crisis 
第13週
5/19  McKinsey Way: Problem Analysis and Solution Skills 
第14週
5/26  Big Data and Business Analytics-1 
第15週
6/02  Big Data and Business Analytics-2 
第16週
6/09  Big Data and Business Analytics-3 
第17週
6/16  Course Wrapups