Institute for the Future: An Interview about Quantified Self

ThIFTFe Institute for the Future in Palo Alto has a long history in Silicon Valley. A non-profit, it makes its living translating the futuristic visions of technical people into pragmatic frameworks for understanding possible futures.

IFTF hosted the second Quantified Self Show&Tell, and since then they’ve been curious about and supportive of this extended investigation into the meaning of what more academic observers call “personal informatics.” Recently, Bradley Kreit interviewed me about the implications of The Quantified Self for the IFTF Health Horizons Report. His interview and excellent editing helped me express what I think is happening in a fairly concise way. I’ve republished it with Bradley’s permission below.

IFTF Health Horizons

Gary Wolf is a contributing editor at Wired magazine and the co-host of The Quantified Self, a blog dedicated to self-knowledge through numbers ( At Wired, he has been the author of a number of the magazine’s most frequently cited articles, including “The Curse of Xanadu,” about Ted Holmes Nelson and the invention of hypertext; “The World According to Woz,” about Apple co-founder Steve Wozniak; and “The Wisdom of St. Marshall, Holy Fool,” about Marshall McLuhan. He has also written about Piotr Wozniak, creator of the memory program SuperMemo, and recently about Craigslist and its founder, Craig Newmark.

IFTF: The phenomenon of the quantified self is an early form of personal health forecasting. What is the idea behind it?

GW: Numbers play a key role in analyzing all kinds of phenomena, from the largest phenomena of the cosmos using radio telescopes to the smallest phenomena in the universe—the analysis, say, of subatomic particles. We have statistical tools of great sophistication for gathering data and finding meaning in it. It seems only natural that we would want to use some of these techniques to gain knowledge about ourselves.

This is so obvious that it might almost seem trivial, except when you realize that we usually associate self-knowledge not with numbers but with words—a kind of inner voice of consciousness and conscience. I think that supplementing that with quantitative tools is one of the most interesting trends emerging in our culture today. This interest is based on the highly practical results of experiments that people are doing in collaborative diagnosis and collaborative evaluation of treatments for chronic conditions, as well as experiments that involve the analysis and acceleration of learning.

IFTF: In some of your writing about the quantified self, you’ve talked about a concept called a macroscope. What do you mean by that, particularly as it relates to health?

GW: The word macroscope has been used quite a few times in quite a few contexts. It’s an interesting word; its meaning is trying to emerge and everyone’s taking a crack at it, but it’s finally settling down into a useful concept.

My meaning is taken from Jesse Ausubel, a climate scientist who is also a professor at The Rockefeller University. It simply refers to gathering data in nature through distributed methods, often through sensor networks, and then analyzing it on a computer. The particular pieces of technology for gathering this data are familiar; it is how they are now being combined that is interesting. We are beginning to see them being used in the context of a social process that produces data that would be inaccessible to an individual researcher trying to build this network from scratch.

The macroscope concept can be applied to the many individuals keeping track of some aspect or aspects of their lives. You have people tracking sleep, diet, exercise, productivity, symptoms, and so on. With all this tracking, a tremendous amount of health-related data is being produced. When that data is analyzed, you learn things that would be much harder to learn using the traditional methods of a clinical trial or a population study.

IFTF: Do you expect self-tracking will become widespread over the next ten years?

GW: I think it will become a mainstream, almost ubiquitous practice and at the same time will become invisible because it will be blend in with daily life. I think a good comparison is with the fate of computing. At one time, the people who used computers tended to be the kind of people who liked it. Over time, the process of computing has been incorporated into so many technologies and devices that many of the things we do that involve computing don’t seem like computing at all. Think of using a pedometer or step counter, or standing on a digital scale. The computing component is disappearing, and the self-tracking aspect will, too.

Self-tracking will disappear because it will be taken for granted. The quantitative tools in our lives will produce data that will be incorporated into some feedback mechanism; we will look at those mechanisms and they will influence us in some way. For instance, we will get biometric data in the form of feedback about how well we’re eating and sleeping, but we won’t have to peel back that information and do the analysis ourselves. Of course, the people who will be making these products and services will be highly aware of their tracking components, but if they’re successful, users won’t think about those aspects.

IFTF: Do you foresee any difficulties with privacy or concerns over control of information? Will individuals not want to share the detailed and intimate information that will be collected about them?

GW: Although gathering personal data will become mainstream, I don’t think most people will want to share their data. We can identify some people as sharer types with respect to their health and biometric data; they are closely linked to the pioneer type because they have a vision of what sharing may bring. But for the most part I think the benefits of the macroscope will be very hard to achieve under a system in which people can be punished harshly on the basis of their numbers. And we live in a world where if you have bad numbers, you will be punished.

IFTF: Isn’t one of the core challenges that the data is most useful in large-scale aggregations, but to get that you have to be able to get people to share their data?

GW: Let’s back up a bit: useful to whom? The data is very useful to you, whether or not it’s aggregated. You can see the macroscope as having multiple guises: there’s the social macroscope, which aggregates data across individuals, and that’s where the privacy issues come in, but you can also interpret the macroscope on an individual level. I can have multiple sensors at multiple times, all aggregating the data for me; I can do experiments of one, and the data never has to leave my computer.

IFTF: So how do you bridge that gap to make the social macroscope feasible?

GW: We need to articulate as clearly as possible that there must be a transformation in terms of how we look at what health and health care mean. As long as health care is considered from the perspective of the individual, there are many benefits that we’ll be missing.

The power of false remembering

[Reposted from QS]
Deep mysteries of human nature will be exposed by self-tracking, aspects of our behavior so disconcerting and bizarre that they will lead us to question whether we understand ourselves at all. I know this is true because such disconcerting results are already being produced at a rapid pace by experimental psychologists, and self-tracking brings the methods of experimental psychology into our daily lives; if, that is, we think we can stand to learn the lessons they teach.

Watch this video published from a story in New Scientist by Lars Hall and Petter Johansson.

Here is the explanation from Hall and Johansson:

[I]n an early study we showed our volunteers pairs of pictures of faces and asked them to choose the most attractive. In some trials, immediately after they made their choice, we asked people to explain the reasons behind their choices.

Unknown to them, we sometimes used a double-card magic trick to covertly exchange one face for the other so they ended up with the face they did not choose. Common sense dictates that all of us would notice such a big change in the outcome of a choice. But the result showed that in 75 per cent of the trials our participants were blind to the mismatch, even offering “reasons” for their “choice”.

This is troubling enough, but there’s more. When people are fooled into thinking they made a different choice than the one they actually made, and then articulate their “reasons” for this supposed choice, they then may actually change their future preferences to conform to their confabulated preference.

Importantly, the effects of choice blindness go beyond snap judgments. Depending on what our volunteers say in response to the mismatched outcomes of choices (whether they give short or long explanations, give numerical rating or labeling, and so on) we found this interaction could change their future preferences to the extent that they come to prefer the previously rejected alternative. This gives us a rare glimpse into the complicated dynamics of self-feedback (“I chose this, I publicly said so, therefore I must like it”), which we suspect lies behind the formation of many everyday preferences.

Lars Hall and Petter Johansson lead the Choice Blindness Laboratory at Lund University, Sweden. At the end of their New Scientist piece, they suggest that learning about this experiment should make people better at understanding their own choices.

In everyday decision-making we do see ourselves as connoisseurs of our selves, but like the wine buff or art critic, we often overstate what we know. The good news is that this form of decision snobbery should not be too difficult to treat. Indeed, after reading this article you might already be cured.

Unfortunately, this is not convincing. It is common for biases persist even when we are warned about them. I suspect we are in no position to stand guard over our judgments without the help of machines to keep us steady. Assuming, that is, that deliberative consistency is a value we care to protect.