The increasing need for nonlinear heuristics

Spending time in San Francisco, with its subtle but frequent earthquake reminders, makes me think of how poorly humans deal with nonlinear effects. Humans intuitively understand linear effects - a small change will result in a small effect and a large change will result in a large effect. Many change-effect relationships in nature are approximately linear. For example seasonal temperature change. In the Northern Hemisphere, temperatures typically wax during the spring and summer and wane during the fall and winter as a result of the Earth's axis tilt.

Nonlinear effects take us by surprise. Nonlinear natural events are more difficult to understand. Insert your most salient natural disaster.  If change-effect relationships deviants from linear, we fail to understand (or predict) the results.

In contrast to the natural world, the human-driven world is becoming increasing nonlinear because of interconnections. That means the total number and impact of the nonlinear events are increasing. To deal with this change in the world, new heuristics are needed. Our "natural" (linear) heuristics are no longer sufficient.

Start with The What; Then comes The Why

"The what" is the surface pheonmeom. How it appears. The data.

"The why" is the underlying mechanisms. What causes it. The reason.

Another life lesson gleamed from teaching biological psychology.

Algorithms & heuristics

I collect algorithms as I make my journey through a career as a Computational Cognitive Neuroscientist /Computer Programmer/Quantitive Analyst/Data Scientist. An algorithm is a rigorous method to solve a problem. The best ones are simple and elegant. My all-time favorite is the linear algebra behind the General Linear Model (GLM), which includes ANOVA and regression. A recent project was helped with modular arithmetic. It was a computational hot knife through programming butter.

I also collect heuristics, less rigorous methods to solve problems. The best ones are simple and elegant. My all time favorite heuristic is “past, present, and future.”“ Describe the past. Capture the present as it is (to the best of your ability). Envision a future. Thanks to Charlie Munger’s tome I started to use ”Always Invert." If you only have access to the properties that don’t work, you can invert them to find the properties that do work. For example if a project failed because of scope creep, try to limit the next project to the smallest meaningful, shippable unit.

Algorithms and heuristics are “force multipliers.” They can solve problems more quickly with less effort. They are tricks, ways to make the complex simpler. They free limited cognitive resources so the same problem doesn’t have to be solved twice.

Algorithms are powerful, but narrow and brittle. Heuristics are weaker, but wider and more robust. The art is knowing whether the problem should be solved with a algorithm or heuristic (or even an unique solution).

Flipping the yoga studio

There is a spectrum of yoga instruction from rigid (e.g., Bikram) to freestyle (e.g., Steve Ross). The evil genius of rigid instruction is “optimization,” always the same, perfectly constructed sequence.

If it is always same, what is the instructor’s role? They typically don’t demonstrate the poses; They usually just give the same rapid fire instructions every class.

Why not record the instructions and play them back? This would free the instructors to give individual feedback to the students.

A similar approach works in academic classrooms. No one should deliver another intro psychology lecture. They are all available online. It is a better use of class time to replace lecturing with feedback-dependent activities, for example presentations, projects, or testing (both formal and informal).

Returning to San Francisco

I’m excited to return to San Francisco. The art. The culture. The fog. The hipsters.

I spent 4 years, ’97-’01, in San Francisco while earning my undergraduate degree. I was focused on my studies and missed out on the dot-com boom (and bust). I moved down the California coast to follow my education dreams. During that time, my focus was research, and I missed out on Web 2.0.

Now is time of another internet revolution, aka Web 3.0. This one will be focused on the copious amount of data that is being generated. No one knows what it means or what can be done. It a great time to be "in the game."

All of my professional interests have been pointing to the field of data science, the union of statistics, programming, and communication. Now is the time to make the leap. San Franscisco is the epicenter of a future world that I want to contribute to.

Repost: The surprising seeds of a big-data revolution in healthcare

I am also passionate about solving meaningful problems with data. It wakes me up early and keeps me up late. It is "the why."

"The how" is becoming increasing easier. The tools of automatization are accessible to everyone (even hackers like me). I'm exploring how to automatize the data collection process for psychology experiments. My playground is Online Psychology Experiments.

Markdown for teachers

Markdown is a light-weight HTML syntax, primary used by web writers. It was my “killer app” during my last semester of teaching.

There are 3 places my classroom writing appears: handouts, slides and web. I use a Mac so my handouts are in Pages, my slides are in Keynote, and my webpages are in Canvas (the university’s chosen CMS). If I write all my materials in plain text with Markdown formatting, it trivial to translate into any or all of those mediums. Using Marked, I convert my plain text to rich-text formatting (Pages and Keynote) and HTML (Canvas). The same “stuff” appears in each place and is appropriately formated for each location.

Additionally, the content files are small, nonproprietary, and come with me everywhere.

Markdown is well worth the minimal time and effort it takes to learn.

PS The best way to learn Markdown.