It’s hardly surprising, at least in retrospect, that location-based social networking company Foursquare was founded (twice!) in New York City. Where else (at least in the United States) are there so many people with so many places to go and so many ways to get there? I’m not a social or environmental determinist, but clearly a startup needs hospitable conditions to thrive.
As a first-generation American who is married to a card-carrying Native American, I celebrate Thanksgiving the traditional way: a day of gluttony followed by yummy leftovers. But, trite as it may be, I do like to take the time to reflect on the countless things for which I am thankful. A wonderful family, of course, but also the great fortune to live in an age where some of the subjects that I find most intellectually stimulating have become highly relevant to our practical daily lives.
I’ve been reading Networks, Crowds, and Markets, a great textbook by David Easley and Jon Kleinberg. I’m very grateful to Cambridge University Press for surprising me with an unsolicited review copy. I’m more than halfway through its 700+ pages. Much of the material is familiar in this “interdisciplinary look at economics, sociology, computing and information science, and applied mathematics to understand networks and behavior”. But I’m delighted by much that is new to me, including a particularly elegant description of an information cascade.
Surprise is not a word that user interface designers typically like to hear. Indeed, the principle of least surprise (also called the principle of least astonishment) is that systems should always strive to act in a way that least surprises the user.
Like many interface design principles, the principle of least surprise reflects the premise that software applications exist to be useful. In utility-oriented applications, surprise means distraction and delay — negatives that good designers work to avoid.
Ideally, a search engine would read the user’s mind. Shy of that, a search engine should provide the user with an efficient process for expressing an information need and then provide the user with results relevant to the that need.
From an information scientist’s perspective, these are two distinct problems to solve in the information seeking process: establishing the user’s information need (query elaboration) and retrieving relevant information (information retrieval).
Last week, I had the pleasure of talking with CMU professor George Loewenstein, one of the top researchers in the area of behavioral economics. I mentioned my idea of using prediction markets to address the weaknesses of online review systems and reputation systems, and he offered two insightful pointers.
The first pointer was to the notion of pluralistic ignorance. As summarized on Wikipedia:
Following the Supreme Court’s decision in Bilski v. Kappos, the United States Patent and Trademark Office (USPTO) plans to release new guidance as to which patent applications will be accepted, and which will not. As part of this process, they are seeking input from the public about how that guidance should be structured. The following is an open letter than I have sent to the USPTO at Bilski_Guidance@uspto.gov. More information is available at http://en.swpat.org/wiki/USPTO_2010_consultation_-_deadline_27_sept and http://www.fsf.org/news/uspto-bilski-guidance. As with all of my posts, the following represents my personal opinion and is not the opinion or policy of my employer.
To whom it may concern at the United States Patent Office:
A guiding principle in information technology has been to enable people to perform tasks at the “speed of thought”. The goal is not just to make people more efficient in our use of technology, but to remove the delays and distractions that make us focus on the technology rather than the tasks themselves.