How to decide which country will host the World Cup?

Early World Cups were given to countries at meetings of FIFA's congress. The choice of location gave rise to controversies, a consequence of the three-week boat journey between South America and Europe, the two centres of strength in football. The decision to hold the first World Cup in Uruguay, for example, led to only four European nations competing. The next two World Cups were both held in Europe. The decision to hold the second of these, the 1938 FIFA World Cup, in France was controversial, as the American countries had been led to understand that the World Cup would rotate between the two continents. Both Argentina and Uruguay thus boycotted the tournament.

Since the 1958 FIFA World Cup, to avoid future boycotts or controversy, FIFA began a pattern of alternating the hosts between the Americas and Europe, which continued until the 1998 FIFA World Cup. The 2002 FIFA World Cup, hosted jointly by South Korea and Japan, was the first one held in Asia, and the only tournament with multiple hosts. In 2010, South Africa will become the first African nation to host the World Cup. The 2014 FIFA World Cup will be hosted by Brazil, the first held in South America since 1978, and will be the first occasion where consecutive World Cups are held outside Europe.

The host country is now chosen in a vote by FIFA's Executive Committee. This is done under a single transferable vote system. The national football association of a country desiring to host the event receives a "Hosting Agreement" from FIFA, which explains the steps and requirements that are expected from a strong bid. The bidding association also receives a form, the submission of which represents the official confirmation of the candidacy. After this, a FIFA designated group of inspectors visit the country to identify that the country meets the requirements needed to host the event and a report on the country is produced. The decision on who will host the World Cup is usually made six or seven years in advance of the tournament. However, there have been occasions where the hosts of multiple future tournaments were announced at the same time, as will be the case for the 2018 and 2022 World Cups.

For the 2010 and 2014 World Cups, the final tournament is rotated between confederations, allowing only countries from the chosen confederation (Africa in 2010, South America in 2014) to bid to host the tournament. The rotation policy was introduced after the controversy surrounding Germany's victory over South Africa in the vote to host the 2006 tournment. However, the policy of continental rotation will not continue beyond 2014, so any country, except those belonging to confederations that hosted the two preceding tournaments, can apply as hosts for World Cups starting from 2018. This is partly to avoid a similar scenario to the bidding process for the 2014 tournament, where Brazil was the only official bidder.

Analysis of Decision Making Process

http://www.youtube.com/watch?v=JvtDuJQlO88&NR=1

Obama's decision making process

http://www.youtube.com/watch?v=H3HmwVOMVFo

Don't rely on any data you see

You wouldn't buy a car or a house without asking some questions about it first. So don't go buying into someone else's data without asking questions, either. Here are a few standard questions you should ask any human beings who slap a pile of data in front of you and ask you write about it.

1. Where did the data come from? Always ask this one first. You always want to know who did the research that created the data you're going to write about.

   You'd be surprised - sometimes it turns out that the person who is feeding you a bunch of numbers can't tell you where they came from. That should be your first hint that you need to be very skeptical about what you are being told.

   Even if your data have an identifiable source, you still want to know what it is. You might have some extra questions to ask about a medical study on the effects of secondhand smoking if you knew it came from a bunch of researchers employed by a tobacco company instead of from, say, a team of research physicians from a major medical school, for example. Or if you knew a study about water safety came from a political interest group that had been lobbying Congress for a ban on pesticides.

   Which brings us to the next question:
   

2. Have the data been peer-reviewed? Major studies that appear in journals like the New England Journal of Medicine undergo a process called "peer review" before they are published. That means that professionals - doctors, statisticians, etc. - have looked at the study before it was published and concluded that the study's authors pretty much followed the rules of good scientific research and didn't torture their data like a middle ages infidel to make the numbers conform to their conclusions.

   Always ask if research was formally peer reviewed. If it was, you know that the data you'll be looking at are at least minimally reliable.

   And if it wasn't peer-reviewed, ask why. It may be that the research just wasn't interesting to enough people to warrant peer review. Or it could mean that the research had as much chance of standing up to professional scrutiny as a $500 mobile home has of standing up in a tornado.

3. How were the data collected? This one is real important to ask, especially if the data were not peer-reviewed. If the data come from a survey, for example, you want to know that the people who responded to the survey were selected at random.

   In 1997, the Orlando Sentinel released the results of a poll in which more than 90 percent of those people who responded said that Orlando's National Basketball Association team, the Orlando Magic, shouldn't re-sign its center, Shaquille O'Neal, for the amount of money he was asking. The results of that poll were widely reported as evidence that Shaq wasn't wanted in Orlando, and in fact, O'Neal signed with the Los Angeles Lakers a few days later.

   Unfortunately for Magic fans, that poll was about as trustworthy as one of those cheesy old "Magic 8 Balls." The survey was a call-in poll where anyone who wanted could call a telephone number at the paper and register his or her vote.

   This is what statisticians call a "self-selected sample." For all we know, two or three people who got laid off that morning and were ticked off at the idea of someone earning $100 million to play basketball could have flooded theSentinel's phone lines, making it appear as though the people of Orlando despised Shaq.

   Another problem with data is "cherry-picking." This is the social-science equivalent of gerrymandering, where you draw up a legislative district so that all the people who are going to vote for your candidate are included in your district and everyone else is scattered among a bunch of other districts.

   Be on the lookout for cherry-picking, for example, in epidemiological studies looking at illnesses in areas surrounding toxic-waste dumps, power lines, high school cafeterias, etc. It is all too easy for a lazy researcher to draw the boundaries of the area he or she is looking at to include several extra cases of the illness in question and exclude many healthy individuals in the same area.

   When in doubt, plot the subjects of a study on map and look for yourself to see if the boundaries make sense.

4. Be skeptical when dealing with comparisons. Researchers like to do something called a "regression," a process that compares one thing to another to see if they are statistically related. They will call such a relationship a "correlation." Always remember that a correlation DOES NOT mean causation.

   A study might find that an increase in the local birth rate was correlated with the annual migration of storks over the town. This does not mean that the storks brought the babies. Or that the babies brought the storks.

   Statisticians call this sort of thing a "spurious correlation," which is a fancy term for "total coincidence."

   People who want something from others often use regression studies to try to support their cause. They'll say something along the lines of "a study shows that a new police policy that we want led to a 20 percent drop in crime over a 10-year period in (some city)."

   That might be true, but the drop in crime could be due to something other than that new policy. What if, say, the average age of those cities' residents increased significantly over that 10 year period? Since crime is believed to be age-dependent (meaning the more young men you have in an area, the more crime you have), the aging of the population could potentially be the cause of the drop in crime.

   The policy change and the drop in crime might have been correlated. But that does not mean that one caused the other.

5. Finally, be aware of numbers taken out of context. Again, data that are "cherry picked" to look interesting might mean something else entirely once it is placed in a different context.

   Consider the following example:

   My personal favorite was a habit we use to have years ago, when I was working in Milwaukee. Whenever it snowed heavily, we'd call the sheriff's office, which was responsible for patrolling the freeways, and ask how many fender-benders had been reported that day. Inevitably, we'd have a lede that said something like, "A fierce winter storm dumped 8 inches of snow on Milwaukee, snarled rush-hour traffic and caused 28 fender-benders on county freeways" -- until one day I dared to ask the sheriff's department how many fender-benders were reported on clear, sunny days. The answer -- 48 -- made me wonder whether in the future we'd run stories saying, "A fierce winter snowstorm prevented 20 fender-benders on county freeways today." There may or may not have been more accidents per mile traveled in the snow, but clearly there were fewer accidents when it snowed than when it did not.

It is easy for people to go into brain-lock when they see a stack of papers loaded with numbers, spreadsheets and graphs. (And some sleazy sources are counting on it.) But your readers are depending upon you to make sense of that data for them.

Use what you've learned on this page to look at data with a more critical attitude. (That's critical, not cynical. There is a great deal of excellent data out there.) The worst thing you can do as a writer is to pass along someone else's word about data without any idea whether that person's worth believing or not.

Monte Carlo Simulation

Monte Carlo simulation is a versatile method for analyzing the behavior of some activity, plan or process that involves uncertainty.  If you face uncertain or variable market demand, fluctuating costs, variation in a manufacturing process, or effects of weather on operations, or if you're investing in stocks, developing a new drug, or drilling an oil well -- you can benefit from using Monte Carlo simulation to understand the impact of uncertainty, and develop plans to mitigate or otherwise cope with risk. 

The Monte Carlo method was invented by scientists working on the atomic bomb in the 1940s, who named it for the city in Monaco famed for its casinos and games of chance. Its core idea is to use random samples of parameters or inputs to explore the behavior of a complex system or process.  The scientists faced physics problems, such as models of neutron diffusion, that were too complex for an analytical solution -- so they had to be evaluated numerically.  They had access to one of the earliest computers -- MANIAC -- but their models involved so many dimensions that exhaustive numerical evaluation was prohibitively slow.  Monte Carlo simulation proved to be surprisingly effective at finding solutions to these problems.  Since that time, Monte Carlo methods have been applied to an incredibly diverse range of problems in science, engineering, and finance -- and business applications in virtually every industry.

Most business activities, plans and processes are too complex for an analytical solution -- just like the physics problems of the 1940s.  But you can build a spreadsheet model that lets you evaluate your plan numerically -- you can change numbers, ask 'what if' and see the results.  This is straightforward if you have just one or two parameters to explore.  But many business situations involveuncertainty in many dimensions -- for example, variable market demand, unknown plans of competitors, uncertainty in costs, and many others -- just like the physics problems  in the 1940s.  If your situation sounds like this, you may find that the Monte Carlo method is surprisingly effective for you as well.

To use Monte Carlo simulation, you must be able to build a quantitative model of your business activity, plan or process.  One of the easiest and most popular ways to do this is to create a spreadsheet model using Microsoft Excel -- and use Frontline Systems' Risk Solver as a simulation tool.  Other ways include writing code in a programming language such as Visual Basic, C++, C# or Java or using a special-purpose simulation modeling language.You'll also need to learn (or review) the basics of probability and statistics.  To deal with uncertainties in your model, you'll replace certain fixed numbers -- for example in spreadsheet cells -- with functions that draw random samples. And to analyze the results of a simulation run, you'll use statistics such as the mean, standard deviation, and percentiles, as well as charts and graphs.  Fortunately, there are great software tools (like ours!) to help you do this, backed by technical support and assistance.

If your success depends on making good forecasts or managing activities that involve uncertainty, you can benefit in a big way from learning to use Monte Carlo simulation.  By doing so, you can:Avoid the Trap of the Flaw of Averages.  As Dr. Sam Savage warns, "Plans based on average assumptions will be wrong on average."  If you've ever found that projects came in later than you expected, losses were greater than you estimated as "worst case," or forecasts based on averages have gone awry -- you stand to benefit!

Indifference Curve

An indifference curve is a line that shows all the possible combinations of two goods between which a person is indifferent. In other words, it is a line that shows the consumption of different combinations of two goods that will give the same utility (satisfaction) to the person. For instance, in Figure 1 the indifference curve is I1. A person would receive the same utility (satisfaction) from consuming 4 hours of work and 6 hours of leisure, as they would if they consumed 7 hours of work and 3 hours of leisure. Figure 1: An indifference curve for work and leisure An important point is to remember that the use of an indifference curve does not try to put a physical measure onto how much utility a person receives. The shape of the indifference curve Figure 1 highlights that the shape of the indifference curve is not a straight line. It is conventional to draw the curve as bowed. This is due to the concept of the diminishing marginal rate of substitution between the two goods. The marginal rate of substitution is the amount of one good (i.e. work) that has to be given up if the consumer is to obtain one extra unit of the other good (leisure). The equation is below The marginal rate of substitution (MRS) = change in good X / change in good Y Using Figure 1, the marginal rate of substitution between point A and Point B is; MRS = -3 / 3 = -1 = 1 Note, the convention is to ignore the sign. The reason why the marginal rate of substitution diminishes is due to the principle of diminishing marginal utility. Where this principle states that the more units of a good are consumed, then additional units will provide less additional satisfaction than the previous units. Therefore, as a person consumes more of one good (i.e. work) then they will receive diminishing utility for that extra unit (satisfaction), hence, they will be willing to give up less of their leisure to obtain one more unit of work. The relationship between marginal utility and the marginal rate of substitution is often summarised with the following equation; MRS = Mux / Muy It is possible to draw more than one indifference curve on the same diagram. If this occurs then it is termed an indifference curve map (Figure 2). Figure 2: An indifference map The general rule is that indifference curves further too the right (I4 and I5) show combinations of the two goods that yield a higher utility, while curves to the left (I2 and I1) show combinations that yield lower levels of utility. A Budget Line (budget constraints) The budget line is an important component when analysing consumer behaviour. The budget line illustrates all the possible combinations of two goods that can be purchased at given prices and for a given consumer budget. Remember, that the amount of a good that a person can buy will depend upon their income and the price of the good. This discussion outlines the construction of a budget line and how the change in the determinants will affect the budget line. Figure 3 constructs a budget line for a given budget of £60, £2 per unit of x and £1 per unit of y. With a limited budget the consumer can only consume a limited combination of x and y (the maximum combinations are on the actual budget line). A change in consumer income and the budget line If consumer income increases then the consumer will be able to purchase higher combinations of goods. Hence an increase in consumer income will result in a shift in the budget line. This is illustrated in Figure 4. Note that the prices of the two goods have remained the same, therefore, the increase in income will result in a parallel shift in the budget line. Assume consumer income increased to £90. Figure 4: An increase in consumer income If consumer income fell then there would be a corresponding parallel shift to the left to represent a fall in the potential combinations of the two goods that can be purchased. A change in the price of a good and the budget line If income is held constant, and the price of one of the goods changes then the slope of the curve will change. In other words, the curve will pivot. This is illustrated in Figure 5. Figure 5: A change in price The reduction of the price of good x from £2 to £1 means that on a fixed budget of £60, the consumer could purchase a maximum of 60 units, as opposed to 30. Note that the price of good y has remained fixed, hence the maximum point for good y will remain fixed. Indifference analysis combines two concepts; indifference curves and budget lines (constraints) The first stage is to impose the indifference curve and the budget line to identify the consumption point between two goods that a rational consumer with a given budget would purchase. The optimum consumption point is illustrated on Figure 6. Figure 6: The optimum consumption point A rational, maximising consumer would prefer to be on the highest possible indifference curve given their budget constraint. This point occurs where the indifference curve touches (is tangential to) the budget line. In the case of Figure 6, the optimum consumption point occurs at point A on indifference curve I3. Indifference analysis can be used to analyse how a consumer would change the combination of two goods for a given change in their income or the price of the good. The next section looks at the income and substitution effects of a change in price. If we assume that the good is normal, then the increase in price will result in a fall in the quantity demanded. This is for two reasons; the income effect (have a limited budget, therefore can purchase lower quantities of the good) and the substitution effect (swap with alternative goods that are cheaper). These two processes can be visualised using indifference analysis (see Figure 7). Figure 7: An increase in the price of good x (a normal good) Due to the price of good x increasing, the budget line has pivoted from B1 to B2 and the consumption point has moved. The decrease in the quantity demanded can be divided into two effects; The substitution effect The substitution effect is when the consumer switches consumption patterns due to the price change alone but remains on the same indifference curve. To identify the substitution effect a new budget line needs to be constructed. The budget line B1* is added, this budget line needs to be parallel with the budget line B2 and tangential to I1. Therefore, the movement from Q1 to Q2 is purely due to the substitution effect. The income effect The income effect highlights how consumption will change due to the consumer having a change in purchasing power as a result of the price change. The higher price means the budget line is B2, hence the optimum consumption point is Q2. This point is on a lower indifference curve (I2). Therefore, in the case of a normal good, the income and substitution effects work to reinforce each other.

What elective classes should I take in my MBA?

When I faced the issue to select electives classes that I would like to take in my MBA, I didn't realize how many different approachs I could use to do it. Now, after I took Decision Modeling class, I can thnk about at least 3 different aproaches: 

1) I have 2 concentrations I want to take for sure (Consulting and Operations). I could have given points to each course, higher points to courses in the Concentrations I want to have and lower points to courses outside those Concentrations. And then, I would maximize the number of points I would have.

2) On the other hand, I those are the 2 Concentrations I want to have, it's because I already have some knowledgement on them (at least I have an idea what they are about). So, to be a more complete and prepered employee, I should take courses outside my concentrations, to give me other perspectives and turn me in a more complete professional. Then I would give higher points to courses outside my concentrations.

3) The third approach I could use is have the constraint to select half of elective courses on my concentrations and half of elective courses outside my concentrations.