Compared to when I just came out of grad school, one of the areas that I’ve improved the most is in turning interesting but vague research questions into bite-sized manageable pieces, which then can be tackled scientifically.

But this is just an excuse to talk about my favorite equation—the Drake Equation. And by “favorite" I mostly mean it is underrated and deserving of more praise, like how one would talk about an obscure “favorite” athlete or book. Because really, favorite equation?!

Part of what I like about the Drake Equation is its seeming absurdity at first glance. It is "a probabilistic argument used to arrive at an estimate of the number of active, communicative extraterrestrial civilizations in the Milky Way galaxy.”


On top of being about something quite out there (oh!), it also not actually all that practically useful. Recently in an In Our Time podcast about extremophiles and astrobiology, one of the guests described in the equation in the most delightful way, that, “The number N depends on

• The average number of planets that potentially support life, which WE DO NOT KNOW.
• The fraction that develop life at some points, which WE DO NOT KNOW.
• The fraction that develop intelligent life, which WE DO NOT KNOW.
• The fraction releasing detectable signs into space, which WE DO NOT KNOW.
• And the length of time releasing detectable signals, which WE DO NOT KNOW.”

But far from being a failure, it has in fact been incredibly influential. Why? Because it breaks up a daunting problem into more manageable pieces, some more manageable than others. A generation ago, we did not know the average rate of star formation, or the fraction with planets, but now we have decent ideas of those. And one can imagine that we will either find out what some of the other terms are, or break those into manageable problems that we can solve.

Simply put, the Drake Equation provides the scientific community with a strategy to make systematic progress on a difficult question. More than difficult in fact, on what is an impossible question to address with current technology.

So when I hear blanket criticisms of applying basic sciences to business or societal problems, I take heart in the fact that Frank Drake and his coauthors were able to provide a sensible scientific framework for such a seemingly absurd question.

One of the perks of being an academic is the ability to take a sabbatical and explore novel directions that we normally wouldn’t have the time or opportunity to do so. This semester, I am spending my sabbatical at UCSF Neurology, and hope to document some of the experiences here. I will be exploring some of the ways that our research in neuroeconomics can help neurologists in their diagnosis and treatment of behavioral dysfunction that often accompany neurological disorders.

As importantly, I am looking forward to learn about how neurologists deal with the immense uncertainties facing them and their patients. This is analogous to the uncertainties that marketers face with regards to their customers. I hope that I can take some of those lessons and apply them to marketing in general and neuromarketing specifically.

If You Can’t Measure It, You Can’t Manage It.
Many of us have heard of the phrase, "If You Can’t Measure It, You Can’t Manage It." It's a fun quote, and is becoming increasingly popular in this age of big data. It also has a halo of authority to it, having been attributed to gurus like W.E. Deming or Peter Drucker. I was going to use it in a paper, and casually figured I'd find out who (if either of them) actually coined the phrase.

Little did I know that in fact: (i) Drucker didn't say it, (ii) Deming didn't say it, (iii) Deming said something similar, but meant something completely the opposite.

The actual quote, by Deming, is, "It is wrong to suppose that if you can’t measure it, you can't manage it—a costly myth".

I'm sure there are multiple constructive takeaways here, but what I want to know is who the !@#$% is responsible for twisting this into its current meaning?!

New study from the lab on using neuroscience to address marketing problems. Some select quotes from the media release.

Researchers at UC Berkeley’s Haas School of Business are using functional magnetic resonance imaging (fMRI) to see if what people say about brands matches what they are actually thinking.

“Surveys and focus groups are the work-horses for generating customer insights. They are fast, inexpensive, and offer tremendous value for marketers,” explains Hsu, who served as senior author of the study, “However, the inherent subjectivity of these measures can sometimes generate skepticism and confusion within companies, often leading to difficult conversations between managers within marketing and those outside.”

“We were able to predict participants’ survey responses solely from their brain activity,” says Chen. “That is, rather than taking participants’ word at face value, we can look to their neural signatures for validation.”

Although conducting fMRI studies on a routine basis is still likely to be cost prohibitive for most companies, the current findings point to a future where marketers can directly validate customer insights in ways that were not possible before.

[Full release] [Full Study]