The Superforecasters

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Web player: https://podcastaddict.com/choiceology-with-katy-milkman/episode/1587428
Episode: https://chtbl.com/track/224G4/https://dts.podtrac.com/redirect.mp3/cdn.simplecast.com/audio/46d9ff78-39b5-4502-a5e9-0df217e1b3a7/episodes/1977547c-2cd4-4ad2-ac0c-def32704e478/audio/3684c6ab-0229-43c3-9c38-14995005c7ea/default_tc.mp3?aid=rss_feed&feed=66QlUXEg

There are moments in life where it seems as though everything is riding on one important decision. If only we had a crystal ball to see the future, we could make those decisions with greater confidence. Fortune-telling aside, there are actually methods to improve our predictions—and our decisions. In this episode of Choiceology with Katy Milkman, we look at what makes some people “superforecasters.” In 2010, the United States government had been looking for Al Qaeda leader and perpetrator of the 9/11 attacks, Osama bin Laden, for nearly a decade. Years of intelligence gathering all over the world had come up short. It seemed every new tip was a dead end. But one small group of CIA analysts uncovered a tantalizing clue that led them to a compound in Pakistan. Soon, the president of the United States would be faced with a difficult choice: to approve the top-secret mission or not.

We will hear this story from two perspectives. Peter Bergen is a national security commentator and author of the book The Rise and Fall of Osama bin Laden. He interviewed Osama bin Laden in 1997. Former CIA director Leon Panetta led the United States government’s hunt for bin Laden and describes the night his mission came to a dramatic conclusion.

Next, Katy speaks with Barbara Mellers about research that shows how so-called superforecasters make more accurate predictions despite facing uncertainty and conflicting information. You can read more in the paper titled “Identifying and Cultivating Superforecasters as a Method of Improving Probabilistic Predictions.” Barabara Mellers is the I. George Heyman University Professor of both marketing at the Wharton School and of psychology at the School of Arts and Sciences at the University of Pennsylvania.

Black Flies Matter

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This probably represents the how most people in New York City think folks in the Adirondacks live. Piseco Road in Stratford. 

Taken on Sunday June 20, 2021 at Piseco-Powley Road.

R isn’t that awful 🗺

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I keep telling myself that I should do more Python programming as it’s the future and R is a dying language. R isn’t the most popular language compared to Python.

But the thing is Python remains far behind R when it comes to map making and graphics. And there is a ton of useful packages out there for R, sometimes much better packages for R then Python especially when it comes to graphics and light manipulation of data, especially Census data. PANDAS might be better for heavy lifting then tidyverse but for many things the tidyverse is simpler.

Yet I concede R is a like adopting the Macintosh System 7 platform decades ago in the era of Windows 95. Your simply not using what the masses are using and you are somewhat locked out of benefits of a popular platform. Moreover, the underlying code in R is often slow and inefficient, with a legacy of 50 year old designs unlike the relatively modern clean and elegant of Python. Much like Macintosh System 7 compared to Windows 95. Macintosh System 7 did a lot of things good in graphics and user interface but the underpinning were a hot mess of hacks built on code from the early 1980s. Windows 95 had protected memory and preemptive multitasking while System 7 was stuck in the era of shared memory and cooperative multitasking.

But R is different than Macintosh System 7. R might be creaky and old but it’s actively maintained and unlikely to be killed off with a single shot by a corporation like Apple did with Macintosh System 7 with the release of Mac OS X. R programming will last forever even if it eventually dies out to Python as it’s open source and not controlled by a profit seeking corporation. Old R code is unlikely to stop working, as there is enough existing code base that interpretive environments are likely to be maintained just like how GNU FORTRAN still is a thing despite little new FORTRAN code written anymore.

Yet my bigger fear is that every time I use R programming language not only am I not writing truly future compatible code, I’m not practicing a skill that is beneficial for my future. I’ve read a lot of books on Python code and I’ve written a lot of Python but the way to be truly good at something is to use it a lot and practice. It’s great to be a skilled R programmer but if Python is the future it’s what for naught. Yet, I constantly find when I write code in Python the weakness of the graphics, geospatial and even data wrangling capacities come back to bite my compared to what I can easily do in R no matter how much research I do into libraries and best practices. And that troubles me to keep going back to the second fiddle known as R programming.