Overconfidence bias- the premature end to information search
Ever since I learned that I was accepted to the MIT Hackathon, I had been running around like a headless chicken trying find an idea, code and idea, and make an idea.
Yesterday was my final burst of energy that I had to get out in my system. I decided to try to attend an Apple networking event to understand innovation and design better. More specifically, I was talking to one of my friends, Keegan, and we were talking about innovative ideas. Keegan told me that once at a entrepreneur talk, the speaker said that if you improve an ordinary product you will make profits, but your profits will just be marginal. However, if you can revolutionize the way people do ordinary task and makes a product that fits that, those are the ideas that make billionaires.
I wanted to go to the Apple networking event to broaden my horizon and understand the way designers for this company frame their creative efforts when they make products. I just wanted to talk to someone to feed my curiosity. I know with change especially in innovation there is always resistance from consumers because people are creatures of habit, and change is not intuitive. Anyway I waited in a long line, in a suit, resumes in hand for two hours, and then finally it was my turn to speak to an employee. Since I wasn’t looking for a job, I casually introduced myself and my educational background/ work experience, before transitioning into asking the employee about his background. He told me he was an electrical engineer by trade and he was part of the design team that made the air pods for apple. I asked him specifically how his team came up with this idea and I asked him about what kinds of things they kept in mind when they designed those air pods. The response I got was “People buy cool stuff,” as he turned my resume over signaling that my time was coming up.
I left the event with an empty and sick feeling in my stomach. I had to lighten up for my meeting coming up with Professor Malis Oana. She was the professor, Arpitha introduced me to who was a physicist in infrared science. I was meeting with her to learn more about the technology available and get feedback on my idea from last week(Quantifying Vibes).
Professor Malis Oana was such a positive part of my day in contrast to the short moment I got to interact with the Apple employer. She actively listened to my idea about thermo mapping the court, and we actually stood up at one point and test to see if there was enough heat transfer from friction of our foot to the floor to quantify, like I planned to do in my idea. We figured out that because the sport was so fast-paced that space even with a time lapse there would not be any significant numbers to analyze and calculate. Professor Oana advised me to focus on a certain player or movement and thermo map that. That’s when she added when someone is agitated and their blood is pumping faster, their body id warmer. That comment made me think of quantifying the style of coaching vs a team and fitting which team with a “hot headed”(literally) coach or not.
However, given yesterday was Valentine’s Day and all I had been doing is running around in a suit on an empty stomach, I was honestly too exhausted to be excited or think more. I just wanted to see Scott and enjoy the rest of my night relaxing with him. So I ended thinking about the coaching style idea.
Fast forward, to this morning, I received an email from the MIT Hackathon Contact about how each candidate is receiving access to a coding boot camp basically to brush up our coding skills, and also that we will be receiving our data set to use for the hackathon.
Ironically, in this first morning class I had we were learning about biases and yesterday when I decided to stop and settle for the “coaching style” idea, that was a prime example of overconfidence bias. I was happy because i felt a weight lifted off my shoulders. I thought of it in a way more complicated way than it actually was, I thought I had to get the data and present an app or physical thing by myself. At the same time, I also felt a little disappointed because I was thrown all the way back to square one. Brainstorming.
I ran into Keegan again today, and we looked into past winners and the format of the Hackathon. Turns out I get the dataset about a week early and then I have 3.5 hours to prepare my hack and 60 second presentation. In the last two hackathons, the datasets have been of NFL, but I don’t know anything about the kind of data ESPN will give me this year.
We also noticed in past the winners have made a relatively simple graphic such as the ones below:
The graphs look simple enough like the ones we coded in R in IE 332. I think I will be able to consult Professor Mario, but I wanted to go in with ideas. I also noticed these examples really dealt with a simple problem, and made visuals that made sense, instead of anything too groundbreaking.
For now, my idea is to make trend lines of busts in the NBA or NFL from a players raw ranking in their draft to their ranking in the ESPN at the end of the year based on that year’s performance. Simulating players’ performance to stocks’ performance, I wanted to represent the trends to use when deciding whether an investment is worth it.