Quarter 10 - Week 7

You'd expect that as the finish line draws nearer, you'd get re-energized to run faster. I do have another quarter left (oh, the choices one makes...), and I'm looking forward to a couple of really cool courses (New Enterprise Financing and Reinvention through Entrepreneurial and Intrapreneurial Leadership) *fingers crossed for no schedule clash*, but still it's almost a feeling of relief, and the 'slow down, whats the hurry' syndrome. I want to think that I'm going to miss college once I get out, but I'd be lying. I can't wait to be done with the busy Fridays and Saturdays, the sleepless and least-efficient weeknights. Yes, I know I'll miss the refreshing gyaan (in most cases) that's being poured on us, which we then go and spout to our colleagues as if we came up with it... but hey, three years is a LONG time in an MBA programme. Let's get it done.

Business Data Mining & Decision Models

This week continues with more lab sessions... so we do Artificial Neural Networks and Market Basket Analysis. The funda of the first is that the model slowly learns how to set weights to the various inputs such that the output can be predicted. It still uses the concept of training data (the set that is used to prepare the model) and the testing data (the set on which you try to confirm if the model does indeed work). We got to see that sometimes models need to be 'tricked'. You'd oversample one particular segment of input records, to balance their weightage towards getting the final answer.

Why? Let's take an example. Let's say you want to understand attributes of the people that are likely to use an online dating service in India. So ASSUME you run an analysis on your users (OMG, you have no privacy concerns, you have no ethics etc. etc...) and ASSUME you find out that a majority of the participants like to watch TV shows about war, wrestling and cricket. ASSUME you also find that the majority would still browse through the profiles of the opposite gender, EVEN if they were already hooked up. Now you're wondering if something's off, even the so called sensible women, epitomes of loyal partners in relationships *cough*, appear to be going nuts. What you don't get immediately is this. Anyone who uses chatrooms in India knows that the ratio of men to women in such sites is *very* high. It would have been much higher, had we not had some nutty Indian guys parading as women online, for whatever psycho reasons they have. So your results are actually dominated by male attributes. Therefore, you try to oversample the women in the group, to get a more fair representation amongst the genders. Then, the results you get are more likely to be attributes of the psychographics that visit online dating sites. Get it? No? Remind me not to join the faculty at the IIMB.

The next session was about identifying what options in a set go together. For e.g. when you visit a supermarket, is the purchase of bread and butter highly correlated, or is the purchase of milk and vermicelli highly correlated (yum, payasam!). Therefore, the Market Basket Analysis tries to see if you can identify any trends in co-purchases/activity in a particular field. Fun analysis, lots of weird looking graph, but some interesting thoughts from our lab guide on what could be the possible reasons for the correlations we seemed to find. For e.g. milk and cheese. A possible reason could be that they're both being refrigerated, hence stored together...). Incredibly fun stuff.

Strategic Thinking and Decision Making

We go even further down the emotions route, we add the concept of affect this week. There were a lot of interesting principles.. stuff like how group decision making appears to be as flawed as deciding by yourself, since people tend to go with consensus more than rational debate. Or how you're likely to think more about how a particular decision benefits you rather than the company. Or why people who're advertising about 'Feed a child' always show you an EXTREMELY impoverished kid, to bias a rational decision making process. It's not a bad thing, it's just that people appeal to your emotions, instead of your logic, in some scenarios.

Or even the fact how you don't understand the scale of certain problems. For e.g. if there are two jars, a really big one with a lower percentage of green to red gems, and the other is a much smaller jar with a higher percentage of red to green gems, you're more likely to pick from the big one when asked to get more than 10 green gems in your hand (because you seem to be overawed by the magnitude). Or even when you're gambling, you're given a chance to win really big with lower probability, or win very small amount with a much higher probability, and you pick the latter because you enjoy the concept of winning more than the value of what you're winning. So the point is that the way elements AFFECT your decision making process is not visible to you, but it should hopefully be visible to others. So try to get groups to think together, and question each other, rather than letting one person dominate the discussion and then sway the group in his/her favour.

Next week's a break for Diwali, so it should be quite chilled out. I so look forward to being waken up by noisy crackers at 5 in the morning. My alarm clock chime just doesn't convince me that its worth waking up early anymore, maybe this will change that perception...

Quarter 10 - Week 6

It's been a reasonably calm week, not much out of the ordinary. The profs continue to remind us about the upcoming projects so that we don't go crazy in the last one week. They're still very optimistic. Their faith in us is troubling... or rather guilt-inducing. Whatever works, right?

Business Data Mining & Decision Models

We continue with labs this week, we've been doing stuff on K-clusters and Decision trees. The funda of the first is simple, take reams of data and let the software (Clementine in this case) work its magic on it to bundle them into clusters. The more clusters you decide to have, the more 'internally aligned' the clusters are supposed to get. So, after a little hulabaloo, you get 4-5 clusters with descriptions like (Male, under 30's, <50000$ income), (Females, over 40s, >30000$ income)... etc etc. The second session had us doing decision trees, where we feed in a bunch of inputs to the software, drag in a model and 'train' it with one part of a data set. We then just apply the trained model to the rest of the dataset and check if it still accurately identifies the type of output we're expecting (based on its understanding of the training data set). Totally takes the fun out of number crunching, but saves us a heck of a lot of time. Yay. And here I thought, the point of number crunching was to identify some human aspects of what were evidently just becoming statistics.

Strategic Thinking and Decision Making

A very creative midterm, if you ask me. The prof tries to compare a Prisoner's Dilemma with a marriage, and asks us what strategies go where. Or he asks us why Apple comes out with an iTunes store, what's the point of it? Was it strategic, or a hint from God? Some questions like that, which required us to think, assume and chew on. Finally when we start discussing the solution, heated debates about what assumptions are right, what weren't. Why one answer is as good as another... sigh, that's probably why some IIMB profs stopped getting creative with their questions. It's difficult to 'hold on' to assumptions...

We were supposed to do a case in the next session, but apparently EVERYONE forgot there was a case, and didnt even realize that the handout wasn't in the book. The perplexed prof has come totally prepared for a heated case discussion, and then realizes to his consternation that the guys in class didn't even realize there was a case (though in our defense, we had a friggin' midterm!). So already tired from the debate, and the futility of it all, the class gets cancelled and for the first time... in our time at IIMB... we were left with a free period. I should be happy! Wonder why it doesn't feel good then.

Quarter 10 - Week 5

It's the usual calm before the storm... midterms are beginning this week, project proposals are also expected to be turned in. The guys working on their final projects are frantically running around trying to give some shape to their mid-term submissions. The profs have now stopped chiding us on the fact that we're not reading the readings, and have not sarcastically taken on to the 'In the reading you SHOULD have read, the saying goes...'. Everything's normal, we're in the midst of the sea and there are no rocks to crash against, yet.

Business Data Mining & Decision Models

We're coming to the more complex data mining modes - we just began with text mining this week. The prof takes examples of how words by themselves are not as important as n-grams. These n-grams are certain combination of words that actually have a sensible meaning. For e.g. 'great' is a good word, but 'not great' implies sometihng else entirely, while 'not great aesthetics' implies yet another meaning. Hence, he goes on about a case where they had to identify trends based on feedback left behind on something. I'd have been more specific, but I'd fallen asleep thinking of n-grams.

The second session was our first practical session on Clementine... which does some pretty cool stuff with data. It's like excel, only that you can do some cool graph-y things with it. Combine data sets, filter stuff out, sort it, sample it... and finally display or extract stuff. And we got to work on a cool data set too! One of login data of students and profs.. probably to check attendance and other such stuff. Good fun...

Strategic Thinking and Decision Making

We continue down the emotion route... further refining our understanding of Game Theory. Apparently, humans are lazy, that's a shocker! They tend to make decisions based on incomplete information and rely a helluva lot on their intuition. Again, such a shocker. The funda appears to be bounded rationality. We tend to look for answers, and will continue looking till we get what we deem to be a satisfying answers, and not the optimal one. We also seem to have some sort of an opinion bias? We are more likely to think our harebrained ideas are the awesomest, while some brilliant ideas from others are downright crappy. Apparently, our intuitive part of the brain is different from the reasoning part of the brain. What makes it worse, is that we can't realize when our intuition is suggesting something downright wrong.

We're also quite risk averse! If we're getting a chance to look at good things happening, and are confronted with two options, we're more likely to take the less risky one. And if we're getting a chance to look at bad things happening, and are offered two options, we think 'what the hell' and are willing to take much riskier options. So, interestingly, the way a problem is phrased to you ends up with you taking different decisions. Good tip to have, no? It's one of the reasons we stay invested in a market that's obviously going down (because we *think* it will come back up again anyway), or why we don't invest in markets when they're going up (what if it goes down?). Interesting to know how nutty we can get!

Quarter 10 - Week 4

It's about that time of the quarter when people suddenly realize that they're supposed to settle down on a project topic, and then heated debates rise on what to study. The debates essentially come down to convenience vs. interest... 'my topic is easy', 'yes, well, mine is easier!' or the 'Man, let's study social networks!', 'Are you crazy?! Let's do a system that can rival S & P's credit ratings of countries!' to the ever glorious 'The prof doesn't understand your topic, we'll get less marks', 'The prof doesn't understand my topic, we'll get more marks!'

Business Data Mining & Decision Models

This week was supposed to be about pruning/cleaning data. It might come as a shocker to you, but people aren't collecting stats on their companies/divisions/teams so that data analysts can come and do one click with their mice with a magical flourish and charge them a bomb. Everyone wants to make everyone else work, so they deviously collect some data in one form, some related data in the other, jumble the two up and generously offer the analyst some tossed salad. If that weren't enough, they will specifically allow users to half-enter their data (in response to surveys etc.) and submit it, without offering the 'Fill everything, or you dont go further!' clause. Hence, the analyst has the mind-numbing job of having to sift through the data, find out what goes where... neatly order it, find some intellectual way of filling the missing data.... and THEN he clicks the magic button. The prof takes us through multiple examples where given a subset of data, how would one go about gleaning enough to complete the dataset. Appeared to be fun, will know for sure when we start.

Strategic Thinking and Decision Making

We had a quiz worth 20 percent of the overall count, so we justifiably had a reason not to go through the readings for the day. The prof enters the class, and is eagerly getting ready to talk of the Cuban missile crisis, and describe the various mechanics at game theory at work! And he asks 'What do you guys think??'... Pin-drop silence. Even the crickets that typically make an appearance here and answer in an unknown 'crik-crik's were silent, presumably since they were studying for the quiz as well. The prof fumes on how we've got to catch up, this is nothing, the readings are tiny compared to what we'll get later... and I'm guessing he doesn't realize that it is not really a motivating line to begin with.

In any case, we continue discussions around game theory, about how the Cuban missile crisis was an example of how the two most publicly powerful people at the time signalled their intentions to each other and how the world was at the brink of a nuclear war. Then, for the first time there was a little addition of emotion to rationality. The prof makes us play a few games, and then he questions why we don't behave rationally... and then brings in a sense of fair-play etc. to the table. Apparently, if I was offered a 100 bucks, on the condition that I share it with someone... I could come to you and say here's 10 bucks, I'll keep 90 and you're likely to show me the finger, maybe give me two rupees due to my apparently dismal state. What you don't realize is that if you don't accept the 10 bucks, you get nothing anyway. So you have nothing to lose even if I were to give you ONE buck. By choosing to walk away, you show how you bring a sense of fairness to the game. You'd rather take no money than take a pittance or unequal share. And hence, we begin our descent into the nature of the human aspects of the decision making process.