2 Pac

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In -pac's song "Dear Mama" he thanks his mother for all the good that she has done for him throughout his struggling life. He begins the song talking about his childhood days when he was seventeen and kicked out of the house. Even though his mother had kicked him out of the house for having their differences, he looks passed all of the mistakes and try's to reconcile for the tough times he had put her through. "Dear Mama" is a poem that was written from a son to a mother, thanking her for all the things that she had done for him in the past. -Pac brings his childhood experiences directly into the song, giving examples of the bad times that he had endured as a youngster. Even though she was a drug addict and never was around for him, -pac still appreciates everything his mother tried to do to make his life better. He understands that his mother was just playing the cards she was dealt.

The theme of this song is very depressing considering all the bad that he talks about. -Pac writes about how dark his days were being in and out of jail and always struggling to make a dollar. Even though he encountered a lot of obstacles as a child, he always new how to deal with them. He understood that his mother wasn't perfect and never really had a straight head on her shoulders, but he did all he could to help her when she was down. Whatever it was he did, from selling drugs on the streets to running from the cops, he always gave his mother the recognition she deserved. This song is a thank you letter to his mother for bringing him to this world. -Pac respects his mother very much for all that she had done for him as a child. In his first verse he thanks her for all the little things that she put up with, and even though she wasn't always there, he forgives her and understands she did what she could.

In the second verse -Pac talks about how he never had a father. He never had someone that he could look up to and follow in his footsteps. This was hard for him as a child being raised in the streets. There was nobody in his life to tell him right from wrong, so he resorted to drugs and crime. This was the easiest way to make money for him and his family, so he had to take on responsibilities as a father. He always appreciated bringing money home and paying for the rent. -Pac portrays himself as a lonely misunderstood kid that did what he had to do to survive. He realizes that for a single mother it was not easy raising two children on welfare. So the only possible option was selling dope; something most people do to make easy money. He lets us know that his mother was a "crack fiend", but in his eyes she will always be a "black queen". He was a very understanding person and I respect him for his strive to make life better for himself and his mother. I believe there's nothing more rewarding than love from a mother.

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Accident

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Accident

I had obtained my driving license some five months ago. I was only 1. Young, naive, careless, and inexperienced. Having never been involved in any kind of traffic accident or citation, I had full confidence in my driving skills and abilities. When one lacks experience, he does not really understand what he is getting into; until its too late.

My worst nightmare came true on that evening. It was a busy Friday evening. The traffic on Memorial street was heavy. And I was late, once again, from work. I had received a pretty lengthy confrontation with my manager just a day earlier about not being on time for work. And I did not need another one. So here I was , driving in rough traffic conditions, at 60 mph, cutting lanes, carelessly over taking other cars, avoiding the scoldings of my manager was my goal; and just trying to get to my job place as soon as possible.

While I was already thinking about how I'd face my manager when I get to work, I suddenly saw the traffic light right ahead of me turn from green to orange. In a rush of blood to the head, pressed the gas pedal when I should have actually applied the brakes. The vehicle ahead of me seemed to be trying to pass the signal before the light turned red. But at the last moment, the driver in that vehicle seemed to have a change of heart. He applied sudden brakes. I tried to my apply brakes, but I was driving way too fast, and I realized that my car wouldn't actually stop without colliding with the vehicle right ahead of me.

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The next 5 seconds were the worst time of my life. My car hit the vehicle right in front of me. The airbag was released on the driver side of my car. All I could see was smoke inside. .The car that I hit, swirled and fell into a ditch on the roadside. Not being able to control my car I was also forced into the ditch by the shifting momentum of the automobile. I was sitting scared inside my car, not knowing the fate of the passengers in the car that I had collided with. I had all these weird thoughts going through my mind. What have I done? What if I had killed someone in the other car? What if there were kids in that car, and were seriously injured. If there was anyone else involved, the thought of my car being poorly damaged also crossed my mind. Instead of thinking about what consequences I would suffer came to my mind the terrible situation that the other driver may be in and if there were any fatal injuries attached to this accident. I couldn't actually imagine forgiving myself if I had done something of such high magnitude.

With all these thoughts running through my head, I opened the door and stepped out of my vehicle. My heart was racing, and I could almost hear it was almost pounding out of my chest as I walked towards the other car. Suddenly someone opened the door from that vehicle, the door opened to a splash of the window shatter and out came this man with a few cuts. I was relieved to see that he was walking on his own feet and not complaining of any major injuries. I asked him if he was okay, and fortunately he wasn't nearly as hurt as I thought he was. As I looked in his car for any others passengers I took a big sigh of relief as I discovered that he was the sole person in the car. I expected him to start screaming at me coming at me with "what the hell were you thinking, kd" , but he was patient. He did not even show a slightest of the hint that he was mad at me.

With the quickness the tow trucks arrived fighting over who saw us first and who was going to tow the cars. The ambulance arrived shortly along with the cops. I was still in a state of disbelief, and shock. I couldn't just get over it. It seemed so unreal. I had never even imagined to ever be in such a situation. I received a couple of citations, and had totaled my car. I was so careless, and to top it off at the time I didn't have proof of insurance. So a rush of blood to the head costed me around $5000 in fixing my car, and the other vehicle. This was in addition to the endless round of court hearings.

I was really angry at myself for a long time after this incident. Everybody around me had told me to drive carefully, but I never really took that advice very seriously. I thought that I wasn't like the other kids who got into such accidents. I thought that I was a much better driver. But I guess time is the greatest teacher. This incident taught me many valuable lessons. It actually helped in many ways. It helped me in becoming more careful about everything. It taught me to not take life for granted. And it taught me how an adrenaline rush can cost u all of your savings.

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Rewarding learning

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This paper will be evaluating and reflecting on two conflicting arguments. The first, by Paul Chance, seeks to convince the reader that learning is best facilitated by shaping the actions of individuals through positive reinforcement contingent on success. Alfie Kohn disagrees with this assessment saying that rewards decrease motivation and lead individuals to see learning as means rather than an end. Both have various strengths and weaknesses, which invariably lead the reader to their own conclusion.

The first article, The Rewards of Learning, champions the argument that by rewarding individuals, specifically students, learning can be facilitated. Paul Chance is of the belief that not only is some reinforcement needed in the classroom setting, but a lot of it. In these opening pages he argues that the American system vastly under uses reinforcement and lists several examples of why this is.

The heart of Chance's article seems to center around motivation, rewards, and learning. He acknowledges that if rewards do reduce interest than it is significantly important to classroom teachers (Chance, 1, in Slife). He refers here to findings that too many extrinsic rewards can reduce future motivation in the same tasks. The best predictor of this, the author argues, is the reward contingency. He reflects on research done by Alyce Dickinson and the three kinds of reward contingencies that she came up with and concludes that 'success-contingent' rewards are the most effective.

The author finds things like encouragement and intrinsic rewards as motivating, but not teaching and as less rewarding than say the intrinsic pleasure created from friend's laughter. He concludes his article with some guidelines for how to reward without stifling motivation. He recommends teachers do things like give the weakest reward possible, reduce the rate of rewards as time goes on, and reward only behavior they want repeated. He sees extrinsic reinforcement as one of the most powerful tools available to teachers.

The second article was written as a direct response to Paul Chance. Its author, Alfie Kohn, argued against the manipulating punishments and rewards endorsed by Chance. The premise of his paper, besides a complete refutation of Chance's work, is that rewards can get people to do what one wants for a while, but the effect is short-term and actually detrimental over time to those being rewarded.

The bulk of his paper is concerned with proving four points that rewards are inherently controlling, are ineffective, make learning less appealing, and ignore curricular questions. He sees applied behaviorism as a technique to control people, or do things to them instead of working with them (Kohn, 1, in Slife). Kohn desires learning to be more reliant on mutual problem solving than control. He defends his argument that rewards are ineffective through a wide variety of cited research and example, including the failures of the token economy.

The purpose of Kohn's article seems to be that contrary to all of the evidence that Chance and other authors like Dickinson, bring forth, rewards feel controlling and do reduce interest. As Kohn says, getting children to think about learning as a way to get a prize, can turn learning from an end into a means (Kohn, 1, in Slife).

Although part of a minority group of thinkers, Paul Chance does raise some good points in his piece. Chance took a holistic approach and recognized the importance of taking a long-term view of learning. An argument of strength that Chance presents and even Kohn recognizes, is that learning should be fun and interesting. He argues that extrinsic rewards can help people learn and make it not seem to be a boring, yet expected process. Another strong insight is the fact that punishments outnumber rewards. A society or classroom built on fear and avoidance does not promote positive conducive atmosphere.

Even with the as stated strengths, Chance presents an argument lacking in uniformity and general persuasiveness. His primary point was that success-contingent rewards work best. This argument was weakened by his example where students who already enjoyed an activity, were given a prize if they completed it. Weeks later the prize was no longer given on completion and they still continued to do the task. Imagine that, students continued doing something they had already enjoyed! It seems like Chance's sources were incomplete, his detractors many, and that he was not entirely confident or complete in his own argument.

The argument that Alfie Kohn presented was shorter, but raised a lot of good points that strengthened his side. Referring to directly to Chase's sources, namely Dickinson, and highlighting their weaknesses and inconsistencies worked very well for Kohn. He also did a good job of diminishing Chance's behavior arguments by showing potentially uniformed readers that the token system, a behavior modification landmark, has been discredited because subjects revert back to old behavior upon withdrawal of rewards.

A weakness of Kohn's argument is that even though he wants parents and educators to do everything that they can to get students to forget that grades exist and focus on working relationships he does not leave any room for rewards to act in a positive manner. Passer & Smith (5, 001) show that applied behavior analysis can be used to target specific problems, which may be detrimental to the learning process. This absence may be due to the specific question he was addressing, but limited his argument.

Being relatively uniformed about the larger debate involving this issue, this author found himself persuaded by the clarity, confidence, and knowledge that Alfie Kohn presented in his article. This author would argue that while it is virtually impossible to avoid a rewards/punishment reinforcing system, it is possible to limit this activity in a structure like school. Both authors argue for a more interesting and student-relative system where individuals can have fun and be attached to the learning process. Kohn argues this better and effectively counters the general reward-contingent argument.

Rewards should not be solely relied upon to reinforce behavior, yet there is a place for them and in the current grades-oriented system they won't soon disappear. While it would take more work for the teacher and require extensive targeting, a new style of learning could emerge where students are targeted based on individual interests and strengths and a curriculum organized and playing off of these. Students must become attached to the learning process and as Kohn and others have shown, rewards only have a short-term answer for this.

In conclusion, the question addressed leaves more questions than answers. If the whole concept of learning was narrowed down to a simple 'do rewards facilitate learning?' than the answer, based solely upon the readings reviewed would be, no it does not. The definition of learning is to acquire knowledge in the area being taught. Not only acquire, but also remember. Kohn shows that not only do rewards create short-term results; they decrease motivation needed for later learning processes. Learning must be seen as engaging and fun, not as a simple chore to be performed for a prize.

Please note that this sample paper on Rewarding learning is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on Rewarding learning, we are here to assist you. Your cheap custom college paper on Rewarding learning will be written from scratch, so you do not have to worry about its originality.

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Effects of rising sports salaries

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One does not have to be a genius to understand that professional sports are losing popularity and that players' salaries have become ridiculous, when comparing wages of regular citizens with unpretentious jobs. It seems absurd that the starting base salary of a Major League Baseball player is the same as what the president of the United States reportedly makes in a year. Rapidly increasing players' salaries is having a devastating impact in the industry of professional sports because teams are losing money, game attendances are down, and fans are losing respect for athletes and professional sports.

With all the money thrown around in professional sports, it seems unbelievable for us to accept that owners of teams are losing money, but it is true. Players' salaries have increased so rapidly over the past five years that almost half the teams in the Major League of Baseball have reported losses for the 001- 00 season. Additionally, the Pittsburgh Penguins of the National Hockey League were unable to pay Mario Lemieux the remainder of his contract when he retired in 16, so they decided to give him majority shares of the team. It is not surprising that owners of professional sports teams are having to declare bankruptcy as the only way to escape their financial burdens, such as the Ottawa Senators and Montreal Expos. All Players' contracts are negotiated in United States currency; therefore, Canadian teams have to forfeit more money because of the weakening Canadian Dollar. If these outrageous salaries keep inflating, professional sports in Canada will no longer exist because team owners will no longer be able to afford quality players and fans will not want to support a losing team

The key factor to a successful sports team making money is to over crowd stadiums with as many people as possible, but the last few seasons, most pro sports leagues have been reporting that fan attendance is at an all time low. Major League Baseball reported 6.1 % drop in fan attendance for the 001- 00 season. With ticket prices rising every year, most people cannot afford to support his or her favorite sports team. In order for an average family of four to attend a Washington Capitols hockey game, it will cost them around $7.50 US dollars; whereas, most families are not willing to give half a weeks net wages, in order to view a live game. In addition, outrageous concession prices have soared to help compensate for poor fan attendance and over inflated player's earnings. Fan base is the main source of revenue for professional sports teams and the main factor for the success or failure of any event. Owners had better recognize that loyal fans are fed up with the increasing costs of attending games; therefore, better start dropping prices in order to fill stadiums back up to capacities again and restore the interest back into fans.

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Another considerable effect from players enormous incomes is that fans are losing respect for professional sports and the athletes. Firstly, community property taxes are constantly increasing because of new gigantic complexes needed to house and support the growing demand of professional sports. Fans are also sick of hearing how underpaid pro athletes are. How can athletes declare they are underpaid? Alex Rodriguez of the Texas Rangers makes an astonishing $ million a year. Mathew Barnaby of the New York Rangers, who scored no goals, no assists, and received 70 minutes in penalties, earned $1.5 million last year. Lastly, players are not performing with the same amount of passion from that of previous years. If players want to win back the respect of the fans, they have to be prepared to compete everyday and show his loyalty to his team, instead of asking to be traded to teams with the most money. If fans were to have more influence managing their teams, you would see a severe improvement in their attitudes towards their team.

In conclusion, when looking at all the facts, it is the fans that ultimately decide the future of professional sports and not the players. We have a choice to rebel against over inflated ticket prices, in which we can boycott attending games and start watching them televised. The coverage is spectacular and the price is right free. It is to bad that the cost of watching professional sports is greater than the enjoyment. If current trends continue, professional sports might lose what remains of its integrity and become another industry auctioned off to the highest bidder.

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Compare The Awakening to Madame Bovary

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Compare The Awakening to Madame Bovary

Kate Chopins The Awakening and Gustave Flauberts Madame Bovary are both tales of women indignant with their domestic situations; the distinct differences between the two books can be found in the authors unique tones. Both authors weave similar themes into their writings such as, the escape from the monotony of domestic life, dissatisfaction with marital expectations and suicide. References to fate abound throughout both works. In The Awakening, Chopin uses fate to represent the expectations of Edna Pontelliers aristocratic society. Flaubert uses fate to portray his characters compulsive methods of dealing with their guilt and rejecting of personal accountability. Both authors, however seem to believe that it is fate that oppresses these women; their creators view them subjectively, as if they were products of their respective environments.

Chopin portrays Edna as an object, and she receives only the same respect as a possession. Ednas husband sees her as and looks, ...at his wife as one looks at a valuable piece of personal property which has suffered some damage. (P The Awakening) Chopin foils their marriage in that of the Ratignolles who, ...understood each other perfectly. She makes the classic mistake of comparing ones insides with others outsides when she thinks, If ever the fusion of two human begins into one has been accomplished on this sphere it was surely in their union. (P 56 The Awakening) This sets the stage for her unhappiness, providing a point of contrast for her despondent marriage to Mr. Pontellier. She blames their marriage for their unhappiness declaring that, ...a wedding is one of the most lamentable spectacles on earth. (P 66 The Awakening) She sees their lifetime pledge to fidelity and love as merely a social trap; the same forces that bind them oppress her.Buy custom Compare The Awakening to Madame Bovary term paper

Simultaneously, Mademoiselle Reisz, who ...sent a keen tremor down Mrs. Pontelliers spinal column... which perhaps is the tremor that marks the beginning of Ednas self discovery. A certain light was beginning to dawn dimly within her, - the light which, showing the way, forbids it. (P 1 The Awakening) As she explores her world, other men, swimming, and her other romantic pursuits, she experiences her epiphany; she finds that the world has much to offer and kills herself in the lamentation of that which she cannot truly have.

Edna finds herself filled with An indescribable oppression, which seemed to generate in some unfamiliar part of her consciousness...She did not sit there inwardly upbraiding her husband, lamenting at Fate, which had directed her footsteps to the path which they had taken. (P 6 The Awakening) Edna takes an active part in finding happiness within her world. She pursues her swimming and other men in the interest of ending the monotony she lives with as a result of her being confined into her aristocratic society.

Emma Bovary, being both protagonist and antagonist, by contrast experiences her epiphany solely at death. She takes the arsenic when she realizes all that she will not get from what she already has. Her light of discovery is found only in the darkness of her death. She laments not what she does not possess, but what happiness her world does not give her. Hers is a story of spiritual emptiness and foolish idealism. ...Emma tried to find out what one meant exactly in life by the words bliss, passion, ecstasy, that had seemed to her so beautiful in books. (P 4 Madame Bovary) She searches for that which is found in the fantasy world of books in her own world and falls short of her expectations. Charles, her husband, she takes for granted as She would have done so to the logs in the fireplace or to the pendulum of the clock. (P 44 Madame Bovary) Flaubert allows her to see Charles as an object just as Mr. Pontellier sees his wife as an object. Although the characters are of the opposite sex, leaving both of the women displeased with their men, and moreover, their lives. Edna and Emma both use people (Emma is also used herself) when needed, and are discarded when they have outlived their usefulness Charles was someone to talk to, an ever-open ear, an ever-ready approbation. She even confided many a thing to her greyhound! Emma treats Charles as her personal dog, she uses him as she uses everyone else in the book. Perhaps it is because of her antagonistic nature that, She would open his letters, spy on his whereabouts, and listen behind the partition when there were women in his consulting room. (P 5 Madame Bovary) It is ironic that she would do these things, as she is the adulterer, searching to assure herself that he is not doing the same harm to her which she is doing to him. Through this paranoia, Once lively, expansive, and generous, she had become difficult, shrill voiced, and nervous as she grew older, like uncorked wine which turns to vinegar. (P 0 Madame Bovary) As she sours in her downward spiral she takes those from whom she would reap happiness with her. Both women indulge in their new findings, and subjectively fall into their desires.

Flaubert compares Emma with a martyr as, ...she looked at the pious vignettes edged in azure in her book, and she loved the sick lamb, the Sacred heart pierced with sharp arrows, and poor Jesus stumbling as He walked under His cross... She attempted to think of some vow to fulfill. Emma indeed carries her own cross, but she does not stand for anything but her own greed; ...she stays home darning his socks. And so bored! Longing to live in town and dance a polka every night. Poor little woman. Gasping for love, like a carp on a kitchen table gasping for water. Indeed, Emma has almost as much sense as the carp, her mind reduced to only fulfilling her carnal desires. She wants to feel nothing She was in a blissful state of numbness. Her soul sank deeper into this inebriation and was drowned in it... (P 188 Madame Bovary) Because living brings her only disappointment she is only pacified when she is comfortably numb. When she finally discovers that her feelings are as empty as her desires and that her desires are as empty as her relationships she kills herself.

The Awakening and Madame Bovary both have nearly identical subject matter; distinct from one another only by the authors tones. Two passive women are subjected to situations where they feel oppressed and constrained. They have extramarital affairs and explore their worlds. At the ends, they die at their own hands. Chopin sees her protagonist in the light of sympathy, using literature as a device portraying her characters in a sympathetic light. Flaubert, using nearly the same characters, produced a 00-page soap opera, having once described literature as Athe dissection of a beautiful woman with her guts in her face, her leg skinned, and half a burned-out cigar lying on her foot (http//mchip00.med.nyu.edu/lit-med/lit...docs/webdescrips/flaubert11-des-.html); his tone is apparent in his commentary. The two stories are actually quite identical, as if two different narrators had told the same tale.

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Dss

If you order your custom term paper from our custom writing service you will receive a perfectly written assignment on dss. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality dss paper right on time.

Our staff of freelance writers includes over 120 experts proficient in dss, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your dss paper at affordable prices!

MIS-745 Decision Support Technologies

School of Management, Syracuse University

Fall 00, Wed. 700 - 45

ProfessorOffice Hours

e-mail

Telephone

URL

Michel Benaroch

Wed. & Thur. 40-50 (or by appointment) room 46

mbenaroc@syr.edu

(15) 44-4/18

http//sominfo.syr.edu/facstaff/mbenaroc/courses/mis-745/syl-f0.htm

Course_Objectives Readings Grading Tentative Schedule

Team Project Group-Case Assignments Downloadable Software Links to Useful Resources

Last updated 8/5/00

• NEW …

Whats new until 8/15/0?

Course Objectives

This course studies the use of advanced technologies in managerial decision-making and business data mining. The

technologies covered include Genetic Algorithms, Neural Networks, Decision Tree Induction, Expert Systems, and

Fuzzy Logic. The course looks at how the theoretical foundations of these technologies apply to business decision

problems involving quantitative models, heuristic (symbolic) models and data-driven models, and focuses on

providing hands-on experience with commercial Excel add-ons and PC-based software tools that employ these

technologies. Attention is also given to key issues organizations face in developing and deploying DSS applications

using these technologies.

Prerequisites only basic spreadsheet skills; no programming skills are required.

By the end of this course, students will

1. understand the process of developing a DSS application;

. know the theoretical foundations of the target decision-support technologies;

. understand the practical basis for applying these technologies to business decision-making problems that

involve reasoning with quantitative models, data-driven models, and heuristic symbolic models. (Sample

problem areas include risk analysis, investment analysis, trading, economic prediction, target/direct

marketing, marketing research, learning consumer choice models, product design, production planning,

scheduling, distribution under constraints, human resource evaluation, engineering, and so on.);

4. gain hands-on experience with several commercial software tools implementing the target technologies

o Excel -- advanced spreadsheet capabilities,

o Generator genetic algorithms,

o ThinkPro, MATLAB neural networks,

o See5 decision trees induction,

o VP-Expert rule-based expert systems,

o QubiQuick, MATLAB fuzzy logic;

5. build a full-scale DSS prototype using one (or more) of the above tools.

The course achieves these objectives through class lectures, computer demonstrations, student presentation and class

discussion of case applications, homework assignments, and a group term project entailing the development of an

actual DSS prototype.

Readings

• Benaroch M., Advanced Decision Support Technologies From Spreadsheet Models to Web Mining, selected

draft chapters will be distributed online.

• Additional lecture notes, cases and articles will be distributed online.

Grading (tentative)

Assignments (individual)

Case presentation (team)

Final Project (team)

Bonus

40%

0%

40%

10%

• Case Presentation. Each group consisting of - students will present 1- cases. The presentation should be

about 0-0 minutes long. The presenting group is not required to submit a written analysis.

General suggestive case questions (try to address question 5, if possible)

1. What is the "business" problem being addressed and why is it challenging?

. Why was it necessary to use the solution approach (or advanced technology) discussed in the case?

. What are the strengths and weaknesses of the solution approach presented in the case?

4. If relevant, how "scalable" is the solution approach presented in the case to real-world setting?

5. How "costly" (time, effort, money) is the solution approach to the particular context discussed in the

case?

• Final Project. The final team project is described in the last part of the syllabus. Students are to work on the

project in their pre-assigned teams. Except for extremely unusual circumstances, no extensions will be given

past the 1/5/00 submission deadline.

• Bonus. A 10% bonus will be given to every individual who finds an "interesting" web-based DSS

application that is described at a sufficient level of detail for everybody to understand its "workings".

Academic Integrity Policy

Students are expected to abide by principles of academic honesty.

The faculty of the School of Management has adapted an Academic Integrity Policy emphasizing that

honesty, integrity, and respect for others are fundamental expectations in our School. The Policy

requires all students who take SOM courses to certify in writing that they have read, understand, and

agree to comply with the Academic Integrity Policy. SOM students will soon receive information

regarding this procedure. All non-SOM students enrolled in this course are required to complete a

certification statement available in the Office of Graduate Programs (Suite ). Completed

statements will be kept on file in the Office of Graduate Programs.

Tentative Schedule

Class Topic & Readings

1 8/7 Introduction to Decision Support Systems (DSSs)

• notes-1

• Benaroch M., Chapter 1 Decision Support Technologies, Draft version

• Benaroch M., Chapter Decision Support Framework, Draft version

• (optional) Dutta S., Wierenga B, and Dalebout A., Designing Management Support Systems Using an Integrative Perspective,

Communications of the ACM, Vol. 40, No. 6, pp. 70-7, 17.

/ Traditional decision support models and how advanced technologies fit HW #1-a Due /10

• notes-

• Benaroch M., Chapter 4 Formal Models in Decision Support, Draft version

• (case) Moynihan G.P, Purushothaman P., McLeod R.W., and Nichols W.G., DSSALM A decision support system for asset and

liability management, Decision Support Systems, Vol. , pp. -8, 00.

• (case) Recio B., Rubio F., Criado J.A., A decision support system for farm planning using AgriSupport II, Decision Support System, 6

(00) 180

/10 ----------------- ------------------------- HW #1-b Due /17

4 /17 Genetic Algorithms (GA) & (Generator) HW #-a Due /4

• notes-

• Benaroch M., Chapter 5 Evolutionary Algorithms and Applications, Draft Chapter

• (case) Vergara E.F. et al., An evolutionary algorithm for optimizing material flow in supply chains, Computers & Industrial

Engineering, Vol. 4, pp. 407-41, 00.

5 /4 --------- " -------------- & Genetic Programming (GP) HW #-b&#c Due 10/1

Project abstract due /4

• (case) Sarker R. and Newton C., A genetic algorithm for solving economic lot size scheduling problem, Computers & Industrial

Engineering, Vol. 00, pp. 000-000, 00.

6 10/1 Data Mining Overview

• notes-4-0

• (case) Daskalaki S., Kopanas I., Goudara M., Avouris N., Data mining for decision support on customer insolvency in

telecommunications business, European Journal of Operational Research, Vol. 145, pp. �55, 00.

• (case) Cooper L.G. and Giuffrida G., Turning Data Mining into a Management Science Tool New Algorithms and Empirical Results,

Management Science, 000.

7 10/8 Supervised Neural networks (NNs) & (ThinkPro, MATLAB) HW #-a Due 10/15

Data for HW #

• notes-5

• Benaroch M., Chapter 11 Supervised Neural Networks, Draft Chapter

• (case) Leigh W. et al, Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic

algorithm a case study in romantic decision support, Decision Support Systems, Vol. , pp. 6177, 00.

8 10/15 ----------------- ------------------------- HW #-b Due 10/

10/ Supervised & Unsupervised Neural networks (NNs) & (ThinkPro, MATLAB)

• notes-5-1

10 10/ Decision Tree Induction & (See5, CART) HW #4-a Due 11/5

Full project proposal due

• notes-6

• Benaroch M., Chapter 10 Decision Tree Induction, Draft Chapter

11 11/5 ----------------- ------------------------- HW #4-b Due 11/1

• (case) Kim J.W. et al., Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts,

International Journal of Electronic Commerce, Vol. 5, No. , pp. 45�6, Spring 001.

1 11/1 Expert systems (ESs) & (VP-Expert) HW #5 Due 11/1

• Notes-7

• (case) Lazarov A. and Shoval P., A rule-based system for automatic assignment of technicians to service faults, Decision Support

Systems, Vol. , pp. 460, 00.

1 11/1 Fuzzy Logic & (CubiQuick, MATLAB)

• notes-8

14 11/6 Thanksgiving no class

15 1/ Student project presentations and course summary Complete project due 1/5

Team Project

The term project is to be done in groups of -4 students. Students are free to choose the topic for the project based on their interests,

experience, majors, etc. Important mile-stones are

1. One-page project abstract due on /4/00

The abstract should include a short description of the decision problem to be addressed, explanation of why this problem

is suitable for a DSS application, in what ways is the proposed DSS supposed to be used and by who, and what decisionsupport

technologies and software package are likely to be suitable for implementation.

After the project abstract is approved, each group will work closely with the instructor to expand and refine the abstract

into a full proposal (i.e., conceptual formulation of the problem, data requirements, data collection plan, implementation

strategy, testing and validation, etc.).

. Full project proposal due on 10//00

The structure and content of the full proposal usually depends on the type of problem solved, the problem-solving

approach, and technologies used. Therefore, each group must work closely with the instructor to develop the full

proposal. However, you can get an idea of what is the full proposal might include by looking at the following outline.

. Complete project due on 1/5/00

Each group has to hand in the disk(s) containing the entire working DSS prototype, and an accompanying 10-15 pages

paper (proposal enhanced by implementation details, users' manual, etc.). For the same reason explained above, each

group will have to work with the instructor to decide on the exact structure and content of the accompanying paper. Note

since the instructor reserves the right to keep both the disk(s) and paper, each group must make sure that they keep a copy

of these.

Each group will give a 15 minutes presentation of their project, so that the other students can benefit from their experience

and insights.

Downloadable Software

Software Tools

• Generator genetic algorithms (download, unzip, and run file install.exe)

• ThinkPro neural networks (download, unzip, and run file install.exe)

• See5 decision trees induction

• VP-Expert rule-based expert systems (download, and unzip into a directory named VPX)

• QubiQuick, MATLAB fuzzy logic

Spreadsheet Files Class Examples & Assignments

• Local-Optimum demo illustrating how Excel's Solver gets stuck in a local optimum

• Optimization Examples all the optimization examples provided in class

• Worker-Task assignment assignment-type problem to run with GAs.

• Portfolio Balancing for problem in the GA homework assignment.

• Trading-Rules demo illustrating how GAs are used to find optimal trading rules

Links to Optional Readings and Useful Resources

Traditional Decision Support Systems

• (case) Forgionne G.A., Selecting rail grade crossing investments with a decision support system, Information Sciences, Vol. 144, pp. 75�

0, 00.

• (article) Plane D.R., How to Build Spreadsheet Models, MSOR Today, February, 17.

• (article) Bhargava H.K., Sridhar S., and Herrick C, Beyond Spreadsheets Tools for Building Decision Support Systems, Computer, Vol.

, No. , March, 1.

Genetic Algorithms and Genetic Programming

• (case) Suer G.A., Vazquez R., Santos J., Evolutionary programming for minimizing the average flow time in the presence of non-zero

ready times, Computers & Industrial Engineering, Vol. 45, pp. 1-44, 00

• (case) Balasubramanian R. and Bui T., GPR A Data Mining Tool Using Genetic Programming, Communications of the Association for

Information Systems, 5(6), 001.

• (case) Karuga G.G. et al., AdPalette an algorithm for customizing online advertisements on the fly, Decision Support Systems, Vol. ,

pp. 85�106, 001.

• (case) Oliver J.R., A Machine Learning Approach to Automated Negotiation and Prospects for Electronic Commerce, Working paper,

Wharton, U. of Penn, 000.

• (case) Allen F., and Karjalainen R., Using Genetic Algorithms to Find Technical Trading Rules, Journal of Financial Economics, Vol.

51, pp. 45-71, 1.

• (tutorial) Introduction to Genetic Algorithms (general overview & Java applets with good visuals)

• (tutorial) Experimenting with the parameters of Genetic Algorithms (Java Applet -- very effective)

• (tutorial) The Genetic Programming Notebook (useful introduction and links to rich sources)

Data Mining

• (case) Chakrabarti S. et al., Mining the Web's Link Structure, IEEE Computer, pp. 60-67, August 1.

• (case) Gerritsen R., Assessing Loan Risks A Data Mining Case Study, IEEE IT Pro, pp. 16-1, December, 1.

• (article) Spiliopoulou M., Web Usage Mining for Web Site Evaluation, Comm. Of the ACM, 4(8), 000.

Neural Networks Supervised Learning

• (case) An-Sing Chen, Mark T. Leung, Hazem Daouk, Application of neural networks to an emerging Financial market forecasting and

trading the Taiwan Stock Index, Computers & Operations Research, 0 (00) 01�

• (case) Montagno R. et al., Using neural networks for identifying organizational improvement strategies, European Journal of Operational

Research, Vo. 14, pp. 8-5, 00.

Neural Networks Unsupervised Learning (self-organizing maps)

• (case) Restaurant Location in Hoteling Business

• (case) Voice Recognition

Knowledge Induction (and Machine Learning)

• (case) Sorensen et al., The Decision Tree Approach to Stock Selection, Journal of Portfolio Management, 000.

• (case) Theusinger C. and Huber K.P., Analyzing the footsteps of your customers, WEBKDD'000, 000.

• (case) Fawcett T. and Provost F., Adaptive Fraud Detection, Data Mining and Knowledge Discovery, pp. 1-8, 17.

Expert and Knowledge-Based Systems

• (case) Mustafa Ozbayrak, Robert Bell, A knowledge-based decision support system for the management of parts and tools in FMS,

Decision Support Systems, Vol. 5, pp. 487-515, 00.

• (case) Lazarov A. and Shoval P., A rule-based system for automatic assignment of technicians to service faults, Decision Support

Systems, Vol. , pp. 460, 00.

Fuzzy Logic

• (case) L. Rizzi, F. Bazzana, N. Kasabov, M. Fedrizzi, L. Erzegovesi, Simulation of ECB decisions and forecast of short term Euro rate

with an adaptive fuzzy expert system, European Journal of Operational Research, Vol. 145, pp. 6-81, 00

• (case) Yuan Y. et al., The development and evaluation of a fuzzy logic expert system for renal transplantation assignment Is this a useful

tool?, European Journal of Operational Research, Vo. 14, pp. 15�17, 00.

• (case) Deinnichenko V., Bikeshera G., and Borisov A., Fuzzy Approach in Economic Modeling of Economics of Growth, in Artificial

Intelligence in Economics and Management, Ein-Dor P. (Ed.), Springer-Verlag, 16.

• Machacha L.L. and Bhattacharya P., A Fuzzy-Logic-Based Approach to Project Selection, IEEE Transactions on Engineering

Management, Vol. 47, No. 1, pp. 65-7, FEBRUARY, 000.

• Kunsch P.L. and Fortemps P., A fuzzy decision support system for the economic calculus in radioactive waste management, Information

Sciences, Vol. Xxx, pp. xxx-xxx, 00.

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PE17 Fellows,

On a special occasion like this, I urge you all to lie on your back for a sec, free your minds, and think back to the first day you step in here. Your memories are definitely varied, your expectations might be totally diverse, but I firmly believe there are two things in mind we have in common. One is the impression on people and place; and another is the impression on the steady, freshly-permed look on "Mr. K" hair. Isn't it nice to know that in this ever-changing world there is still one thing you can rely on? Well, I will get back to that later.

First thing first. I tried to think hard of all the academic places I have seen, and just could not imagine any single one similar to ours. Nowhere else I known would offer such a luxurious dinner in the five star hotels, absolutely free of charge, to the notoriously "eat-till-you-drop" students on a monthly basis. I tried to dig deeper and the numbers of remembrances just keep coming up. Could you think of any Economy course constructed by such a professor who is heavenly kind enough to let you have an all-day final test and still be able to call it off to the night? Or even the time after done something terribly disrespectful in Well Test class (you know, that time) and still we have a second chance to redeem ourselves? Not to mention the soda we got bought in every session of Advance Topic course, despite knowing that we would surely pass out after a grand dim-sum lunch buffet (shame on us). Yes, we have those times here in our second home.

The biggest pride of ours must not be far off from that we were the last in the breed to learn Nat Gas with Ajarn Kung. Though not realizing it at the time, but it did expand our horizon and safe our butt in the real world. As well, one of the strongest memories must be on that burgundy-red sleeping sack we are all acquainted with. I suppose there is nobody here to argue it really did serve its function. I recall one time The Giant (Mr. First) was in the middle of his afternoon nap right in the corner of the room, and that was exactly the moment Ajarn Yingyos just walked in. For an indefinite breathless moment of us - he just laughed, and let us out of the hook. If I may say, we were indeed embraced by all the teachers and personnel here.

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As three years went on, we did share the time, unavoidably, on the pace of study. I have tried, thus far, to portray the entire cast and crew characteristics, and yet a long way to go. We got an Arabian Girl, who by the way is dazzling pretty that guys standing in line just to talk to her. Vise versa, we have the sassy boy who just could not tie down with one girl (or that is just the cover? Only God knows he is straight or not). We got the three stooges who were playing the computer games all night long and ironically only one could not attend the class in the morning (yes, the Egyptian SLB labor). It is devastating to say that one of them have already been a Vietnamese roustabout, a serious contradiction to his behavior given that he could not find the place to take a good bath over there. The last guy has finally found his ultimate love in PTTEP (don't look around Joe, I mean you, the secretary-loving stereotype). We also got a loud girl who tends to complain a lot, but she surely gets the job done. We all owe her a thank you for a number of times. More on that, we surely need "The Pole" who can be all in one a photographer, webmaster, and guitarist to sort things out for us. We have the bloody genius that might be the only one that has a smooth relationship with his girl (since nobody else got one). And me, who can be a pain in the butt sometime, hope I got your excuses. Let's see, are those all we have? Did I leave out someone? Just take a glance at the first paragraph again and you will find out. Yeah, he is beloved - just when you need someone to throw the trash on. You are in our great help for a stress relief. No hard feeling?

I have to confess, I am not good at doing these touchy things at all. This article does not mean to get you sobbing or longing for the past when the dreams were young and the future awaits. Instead, the sake of it is rather more to remind us how immeasurable the value we gain here and how can we pay them back, just to make our community the better, the stronger, and maybe the greatest among all. So where can we get started? Well, one thing I always wish for is a nice, cozy couch for the students to lie on after lunch and hide away from the class…

Just an opinion, after all.

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