804.424.1933 brad@bbbox.io

1 – Coding Up the Curve

While a lot of money gets funneled into measuring programmers’ productivity, says programmer and author Isaac Lyman, doing so is a lot easier said than done. Sure, you could use lines of code, but that’d be ludicrous as there’s no control for quality or purpose. Money? While programmers would certainly give an emphatic, “ABSOLUTELY!” there’s diminishing return there. Generally, a good measure would be more holistic, taking into account contributions to a team environment, leadership when needed, creativity, flexibility, and a whole host of immeasurable things.

That said, for the sake of the assignment, I’m going to go with lines of code, making the assumption that our dear programmer is, in fact, conscientious about his output, not starting fights in the workplace, and taking care of himself. According to DZone blogger Dalip Mahal, citing Capers Jones and Olivier Bonsignour’s The Economics of Software Quality, the average programmer writes 50 lines of code per day. Let’s say our programmer, I’ll call him Kody, just graduated college and started his first programming job. He currently codes two standard deviations below the population mean, which would be 41 lines of code per day. CRM.org writer Christopher Sirk calculates that there are 261 working days this year, which we’ll go with for just about every year to keep things simple; therefore, Kody can expect to code 10,701 lines of code per year. So let’s see how long it takes Kody to get to his goal of 21,402 lines of code per year.

ANSWER: 13 years. It’s pretty easy to see that Kody will exceed his goal by coding 21, 532 lines of code per year after coding for 13 years. The spreadsheet used to create this chart is linked here.

2 – Project Cogitations

Throughout the pandemic, I’ve had plenty of time to explore a variety of podcasts and thought, “Hey, I can do that!” I’m sure it’s a lot easier said than done, but it seems like a fun, useful skill to pick up. So I plan to do an eight-episode podcast. I’m thinking about calling it “GOOD IDEAS about Our Future,” which will focus on how current and emerging technologies can be used to build healthier, more prosperous communities. This is clearly aligned with the work I’ve been doing for the past decade. It also seems relevant to the thesis project I’ve started planning with Melissa Bridges, performance and innovation coordinator for The City of Little Rock, which will be related to the city’s proposal to the Bloomberg Mayors Challenge initiative and focus on increasing resources and support available to entrepreneurs in Central Arkansas. What’s more, if and when I officially release this podcast, it would certainly help with business!


3 – Putting the Delphi Method to Work

Part A

My question was, “When will cybernetic implants be as common as cosmetic surgery?” My range was 2030 – 2040, the median was 2035 — see graph below.


Part B

The Corporate Financial Institute describes the Delphi Method based on the qualitative information it provides, focuses on problem-solving (as opposed to predictions), and includes a group of experts and specialists related to the objective at hand.

In Forecasting: Principles and Practice, Rob J. Hyndman and George Athanasopoulos describe the method as following five steps:

  • A panel of experts is assembled.
  • Forecasting tasks/challenges are set and distributed to the experts.
  • Experts return initial forecasts and justifications. These are compiled and summarised in order to provide feedback.
  • Feedback is provided to the experts, who now review their forecasts in light of the feedback. This step may be iterated until a satisfactory level of consensus is reached.
  • Final forecasts are constructed by aggregating the experts’ forecasts.

A cursory review of Google results also shows that the Delphi can be a useful method for predicting results in medical science research.

Generally, the most notable difference in proposed approaches compared to what we did in class is the intentional inclusion of experts and specialists. Also, most other sources recommend using the method to solve certain problems rather than to just predict when something will happen.

Part C

I think our focus group could have included experts/specialists in the field of futurism. Also, we were extremely limited on time — actual implementation of the Delphi method may be more effective if participants have more time to participate and focus on a specific topic. Additionally, I can imagine this process would generate much richer conversation if it was focused on solving a problem rather than predicting when a fairly technology-related, random event will occur.

4 – More about Weapons of Math Destruction

On Goodreads.com, 48% of readers gave this book four to five stars (17,000+ ratings, 2,200 reviews). Individuals’ critiques can be summarized as essentially saying that the author, Cathy O’Neill covers a lot of ground with limited depth, comparing her to Malcom Gladwell in oversimplifying very involved subject matter. Some highlights from reviewers include:

While this book is nothing groundbreaking, it’s another reminder that tech HAS to do better if we are going to put more of our personal data – our lives – into its hands.

The author is onto something critically important when one reads the title of the book and goes through the first few pages. However, it is a tragedy to see the author falling in love with her own phrase WMD and completely losing the plot. The examples used are good in the beginning but soon turn ridiculous (they would be laughable if not so lamentable for the people involved). In the process, the author loses her credibility as a champion of the topic.

Book reviews are all about expectations, and honestly I, as someone doing data science and grappling with issues, expected more. With a data scientist writing a full length book inditing data science one expects a deep dive revealing real points.

O’Neil takes the idea of “garbage in, garbage out” and compounds it, and this book is an exploration of the pitfalls, both real and potential, involved in letting algorithms dictate our lives, in everything from teacher retention to prison recidivism to workplace wellness programs. 

As a peronal reflection on the criticism O’Neill receives, I think it’s inevitable that an ambitious, clever person goes for broad, provocative claims without going deep. If I was an internationally celebrated data scientist, my book would do the same because I would want to make money off of a bestseller. Honestly, I selected this book to have the opportunity to survey big data topics related to social justice issues as there are always plenty of other resources out there that “go deep.” And sure enough, the book includes about 290 citations of sources that go more in depth on the included topics and claims.