The developing confusion of overly-informed people What’s the problem? Considering base rates. Visualizing the issue Closing The developing confusion of overly-informed people Science is often mistakenly touted as an undeniable collection of facts. But the availability of information about COVID-19 has introduced people to the inevitable uncertainty of scientific discovery. Those not familiar with the process have begun to use this feature as a reason to doubt science as a whole, but complex issues like a pandemic are full of complicated layers that scientists have to navigate and re-evaluate.
A bit of background The Essay References A bit of background I’m finally at the point of my PhD candidacy exam. The way it works in my department at BU is straightforward: you and 3 professors come up with a list of 75 papers (divided into 3 topics, each pertaining to their expertise and related to your research). The exam is to write two essay responses for each set of 25 papers within 2 hours (closed-book, obviously).
So you signed up for your first hackathon… Ok ok, but what did you actually work on? Just a tad more So you signed up for your first hackathon… About a year ago I was (luckily) invited to attend Neurohackademy at the University of Washington, Seattle. This was my first time even hearing about hackathons in neuroimaging, so I was definitely excited to check it out. The format of the 2-week event was perfect for me: one week of broad introductions to tools and perspectives that we were encouraged to implement during the second week in collectively-idealized projects.
The biggest appeal of behavioral economics is its promise of practically regularizing the rational expectations imposed by traditional economics. Since the 70s psychology has catalogued increasing amounts of biases and circumstancial heterogeneities that go against the expectation of rationality in decision making. However, in my short years studying it, it seems like these discoveries are seldom applied to complex real-life economic environments (Nudge being a good example of this). That’s what attracted me to ‘A Crisis of Beliefs’, a book by Nicola Gennaioli and Andrei Shleifer that was published last year.
Why are you doing this? There are enough secret santa services So, how does it work? Shiny implementation Closing Why are you doing this? There are enough secret santa services Last Christmas my wife was left giftless on my side of the family due to a faulty secret santa generator. In trying to earn brownie points the arrogant nerd in me thought he could do better, so I decided to program one in R (I’d rather know exactly what’s going on in the background anyways).
A bit of background The Rand Index The Adjusted Rand Index Closing A bit of background A common need for researchers that rely on clustering algorithms, such as the organization of networks into cohesive node communities, is to evaluate the similarity of the partitions produced. In my case this problem takes the form of comparing the distribution of brain networks across individuals. While many tools have been developed to tackle the challenge (see Fortunato & Hric, 2016 for an initial survey), here I’ll give a superficial view on the adjusted rand index (ARI), hoping to better understand its behavior and ideal case usage.