Alex Soojung-Kim Pang, Ph.D.

I study people, technology, and the worlds they make

Category: Design (page 1 of 7)

Are Post-its evil?

My friend Anthony Townsend turned me onto this entertaining rant against Post-Its and design thinking: Jamer Hunt argues that the Post-It filled wallboard has become a wrongheaded symbol of creativity.

The predominant image of design in the 21st century is that cliché of the empty conference room or studio–just after some feverish brainstorming extravaganza–plastered with Post-it notes … as if the act of design had suddenly morphed into some strange game of pin the Post-it on the mind map. How is it possible that the wonderfully complex process of design has devolved to the point that we now commonly represent it by the leftover artifacts of quickie ideation? Is that all there is?

As someone who's written pretty extensively on the use of paper media and paper spaces in collaborative creative work, naturally I was intrigued by Hunt's argument, but he seems a lot more concerned about the Post-it as symbol of design. He doesn't seem to be arguing that it's a tool that leads you to do bad work, or is too weak to support good work. So what's wrong with the Post-it as symbol?

The problem is that in serving as a substitute for the whole of design, the Post-it represents only a small fraction of what makes design uniquely effective. It papers over the fact that ideation without materialization is not design. Designers discover as they turn ideas into thing (even when those things have no physical form). We gain true insight in the act of making a mark on a page or pushing pixels on the screen. We don't need to over-hype those processes, but to ignore them means that we shortchange the practice of design. Clever ideas are a dime-a-dozen–about the cost of Post-its.

Fair enough. The idea that ideas are what matters, and that the actual thing is kind of an afterthought is one of the scourges of our age. This kind of creative Platonism downplays (or just misunderstands) the difficulty of actually making good things, and the role that solving production problems can play in innovation. (I was turned onto this by two very different sources: Bill Leslie's work on Bell Labs and Western Electric, and Matthew Crawford's Shop Class as Soul Craft) To the degree that those of us in the delirious professions treat brainstorming (and its material/visual expression) as an end-point rather than a stage in a bigger process, we sell ourselves short, and do a disservice to our clients.

Social bike sharing

Another example of bicycles becoming smarter and more social: Social Bikes.

For those who aren't familiar with how these resource-sharing services typically work, check out our story about the technology behind Zipcar. In a nutshell, there are little car lots (or in the case of B-Cycle, a company that will soon deploy shared bikes in Chicago, bike stations) located all over a city that are locked when not in use. A user can make a reservation online for a car or bike and then pick it up at the designated time.

There is no human interaction required: once the mode of transportation is reserved, the user identifies him or herself to the car or bike either by RFID (Zipcar) or PIN at the cycle station (B-Cycle), which then unlocks the car/bike. When the user is done, he or she returns the vehicle to the same lot so that others can make use of the car. For B-Cycle, users can return bikes to any B-Cycle station, not necessarily the one they rented from.

The SoBi system follows a similar path, but the technology is a bit more advanced than that of services like B-Cycle…. For one, there are no cycle stations: SoBi bikes are parked all over the city (starting in New York City) at regular old bike racks. This means that bikes could, in fact, be anywhere at any given time, and not just at a designated station that could be blocks away. You can pick up any bike that's not already reserved, and drop it off anywhere without having to hunt down a drop-off station….

Like a Zipcar, each SoBi bike is equipped with its own "lockbox" that communicates wirelessly with the SoBi servers via GPS and a cellular receiver (an H-24 module from Motorola, Rzepecki told Ars). When you make a reservation online or via smartphone, you see a map of all the bikes in the area based on their GPS data and are given the option to unlock a specific bike when you click on it….

Since the lockbox contains a GPS module, a cell chip, and a lock that works with a PIN pad, there has to be some way to power it. The SoBi team is still working out the kinks in power consumption, but plans to power the devices with a hub dynamo on the bike's rear wheel. The lockbox is essentially powered by your pedaling—no charging stations required.

[thanks, Heather]

The Copenhagen Wheel

For a long time, I've been interested in getting an electric bike, especially after I saw the Optibike at the California Academy of Sciences. Via the Daily Dish, I came across an MIT hybrid bicycle project that looks like just the thing: the Copenhagen Wheel. Check out the video:

Not completely clear from the video exactly how it works, but I like how elegantly it attaches to a bicycle (some bike motors look like real kludges), and that it also is a smart device:

Dyson Award-winning design:

Smart, responsive and elegant, it transforms existing bicycles quickly into hybrid electric-bikes with regeneration and real-time sensing capabilities. Its sleek red hub not only contains a motor, batteries and an internal gear system – helping cyclists overcome hilly terrains and long distances – but also includes environmental and location sensors that provide data for cycling-related mobile applications. Cyclists can use this data to plan healthier bike routes, to achieve their exercise goals or to create new connections with other cyclists. Through sharing their data with friends or their city, they are also contributing to a larger pool of information from which the whole community can benefit.

It's called the Copenhagen Wheel because the bike-friendly wants to increase the number of people who cycle, and worked wit the team to

to investigate how small amounts of technology could improve the cycling experience and how the four main obstacles to getting people on bikes – distance, topography, infrastructure and safety – could be overcome. What has resulted is the Copenhagen Wheel: a new type of electric smart-bike which utilizes a technical solution for overcoming distance and topography (a motor and batteries with regeneration capabilities that can provide riders with a boost when needed) and a real-time data network and series of applications to support infrastructure creation and foster a sense of safety.

Trading intelligence for resources; encouraging mergers of people and devices on human terms rather than device terms; bringing information to users in context– all great examples of an end of cyberspace device.

Futures Company on Wordle

A good critique by Russ Wilson of the Futures Company of the limitations of tag clouds to actually explain things:

I have two main issues with Wordles [a tag cloud generator], and they’re exemplified in the wordle above, based on David Cameron’s coalition speech. First, they remove the word from its immediate context. Take the word interest, represented as one of the more frequently occurring words. But it could equally indicate curiosity and engagement or interest payments.

The second issue is that frequency is being proposed as an indicator of importance, but that’s not how we actually interpret speech…. Frequency of use is simply that – frequency of use.

Both of these are classic problems in information management– or to use the old school term, indexing. When I was at Britannica, people often used the example of "depression" in the same way Wilson talks about interest: the Britannica had articles on the geological kind, the economic phenomenon, and the psychological condition, but it was hard for most automated systems to distinguish between them. Users, in contrast, could do so quickly– because they brought (or could quickly surmise) the specific context in which an instance of the word appeared.

And a moment's reflection should make clear that frequency and importance are not the same thing. Sometimes they are. But how many times is the word "like" over-used to the point of meaninglessness– ether as a verbal tic, or, thanks to the flattening of the term by Facebook, as the Web equivalent of Valley girl-speak?

Racing, innvation, and the Automotive X Prize

Great article in Slate about attempts to build cars that will claim the Progressive Automotive X Prize, and how these efforts benefit from a quirk of recent history. For most of the history of cars, automobile racing and everyday innovation were connected:

The track wasn't just a marketing tool; it was a proving ground, a place where engineers learned new tricks that filtered down to the American consumer. Well into the 1960s, when Ford challenged Ferrari in the European endurance race known as the 24 Hours of Le Mans, automakers lavished money on their racing teams, believing they'd earn it back in expertise and sales. The link between motor racing and the cars in our driveways turned into a mantra for the industry: Win on Sunday, sell on Monday.

Through the 1980s and '90s, that connection eroded. The IndyCar and NASCAR circuits were flooded with sponsorship cash from tobacco and beer companies, which didn't care about automotive innovation. They just wanted the races to be entertaining…. It used to be that the goals of racing and consumer R&D were one and the same—to make better road cars. Now the automakers' consumer divisions are searching for the holy grail of fuel efficiency while the brilliant engineers in their racing divisions make tweaks to the latest gas-guzzling V8s. It's a tragic waste of human capital, as if Silicon Valley's elite programmers had spent the last two decades optimizing video-game code instead of creating search engines.

Let's leave aside the question of whether Silicon Valley's elite programmers really have or haven't been optimizing video-game code, and whether that was a good thing (better simulations, anyone?), and note that this situation has created an opportunity to 1) acquire strong design talent, and 2) apply it in constructive and interesting ways. The article talks about one company that's doing just that, Edison2:

Edison2 was founded by a 48-year-old German real estate developer named Oliver Kuttner. Ever since he was a kid, Kuttner dreamed of running his own car company, and when the major automakers slashed their racing budgets to save costs during the recession, laying off thousands of engineers and mechanics, he saw an opportunity. He hired half a dozen of the most talented castaways, including Ron Mathis, a Brit who had designed champion F1 cars for Audi, and Bobby Mouzayck, a journeyman mechanic on Corvette, Viper, and Audi race cars.

edison2 cars, via flickr

As their Flickr photostream shows, the cars look pretty insane. But it's a very different kind of utopianism driving (as it were) Edison2 than, say, the Aspen Institute's hypercar:

Until now, much of the thinking about the future of transportation has been done by people who find cars irritating. For them, fuel efficiency means more people walking around and riding bicycles. They're busy drawing the chalk lines of a post-car America: high-speed rail, pedestrian-friendly neighborhoods, congestion pricing, bicycle lanes, sentient herds of Urban Smart Vehicles…. The car people who are now entering this conversation—the true gearheads—aren't utopian city planners. They're pragmatists who know that you can't transcend the car without building a better car first. And history tells us that a better car often starts with a dopey desire to go ridiculously fast.

Actually, prizes have been another significant source of innovation in the history of technology, and are more popular these days in promoting targeted innovation in science and technology (I had an elegant piece on this in Signtific, but for whatever reason IFTF took that million-dollar investment offline months ago and has never seen fit to put it back online).

[To the tune of Rush, "Red Barchetta," from the album Moving Pictures (a 3-star song, imo).]

Paper Spaces: Visualizing the Future

Years ago, I read Richard Harper and Abigail Sellen's Myth of the Paperless Office. For me, it's like Annie Hall or Houses of the Holy or David Brownlee's modern architecture class: it's one of those works that blows you away when you first encounter it, and still resonates years later. Almost immediately after reading the book, I started thinking about how paper media and their affordances are used– usually quite unself-consciously– by futurists in expert workshops.

The result is an article titled "Paper Spaces: Visualizing the Future." Like many of my articles, it's taken an unseemly amount of time to get into press, but it's finally coming out this spring in World Futures Review. A PDF of the latest draft is available here.

Here's the big argument, from the introduction:

We tend to think of space as irrelevant in creative work, or at best only indirectly influential: for example, architects may use a mix of open office plans, natural lighting, and bold colors to create stimulating, useful workspaces. But for workshops, and for the kinds of visual processes that many futurists use, the relationship between space, ideas, and creativity is much more intimate. Ideas are embodied in materials; they become cognitive and physical spaces that literally surround groups; and the process of creating those spaces can promote a sense of group identity and common vision for the future.

I use the term "paper spaces" to describe these environments, and to highlight several things. First, we're used to thinking of things made of paper as physical objects whose qualities help shape the experience of reading, but it's useful to pay attention to their spatial and architectural qualities as well. Large visuals aren't just things: they're spaces that possess some of the qualities of desks or offices. Workshops exploit their scale and physicality to promote social activity between workshop participants. In this case, the spatiality of paper is fairly self-evident; but many of our interactions with paper, books, and writing have a spatial quality. Scholars could gain much by analyzing print media using conceptual tools from architecture, design, and human-computer interaction, as well as literary theory and book history.

Second, it warns us against taking too passive or formal a view of visual tools in business, of treating them like paintings on a wall. In the way users interact with them– they're annotated, extended, argued over, and played with– they're more like Legos than landscapes. The process of creating maps, and the maps themselves, both reflect a set of attitudes about how to understand and prepare for the future, one that emphasizes user involvement, and the need for actors to develop and possess shared visions of the future. (Ironically, there may be more studies of large interactive displays and other digital media, than of the old media they're meant to displace. )

Third, the term "paper spaces" highlights their hybrid, ephemeral quality. They work because they're simultaneously interactive media and workspace, but their lives are brief and easy to overlook: they are designed to support idea- and image-making, but leave little trace of themselves…. [Despite this, though,] paper spaces are ubiquitous: most of our interactions with texts and other media have a spatial dimension that affects the ways we read, think, and create.

Car cost-sharing: finally around the corner?

Back in 2004, when I was a columnist for Red Herring, I wrote a piece about what would happen when reputation systems make their way into the world— that is, when they stop being things that we only consult in online transactions, and become things we can consult easily in real-world transactions. I talked about how they could jump-start car-sharing systems.

Today, I saw an article about RelayRides, a

person-to-person car-sharing service, which will be launching soon in Baltimore. Unlike fleet-based services—Zipcar, City CarShare, I-GO, and others—which maintain their own vehicles, RelayRides relies on individual car owners to supply the vehicles that other members will rent.

There are a couple other services like this, including Divvycar, but there seems to be a sense that these systems are ready to take off. So "why are peer-to-peer car-sharing services emerging now?"

Part of the answer might lie in the way online and offline services like Zipcar, Prosper, Netflix, and are training us to share our stuff—people are simply getting used to the idea. “‘Zip’ has become a verb to the point that we could ‘zip’ anything—they just happened to start it with cars. Close on their heels was Avelle (formerly Bag, Borrow Or Steal) and now SmartBike for bikes on demand. The next step seems to be a crowd-sourced version of Zipcar,” says Freed.

Another part of the answer might be found in our response to the ecological and economic crises Americans are facing. As Clark explains, “You just think of the number of cars on the road, and the resource that we have in our own communities is so massive… what the peer-to-peer model does is it really allows us to leverage that instead of starting from scratch and building our own fleet.”

From an individual’s perspective, peer-to-peer sharing is a means for owners to monetize their assets during times when they don’t require access to them. But peer-to-peer models can also be understood to utilize existing resources more efficiently—ultimately, to reduce the number of cars on the road—through shifted mentalities about ownership, the intelligent organization of information and, increasingly, through real-time technologies.

Since peer-based car-sharing companies don’t bear the overhead costs of owning and maintaining their own fleets, they don’t require the high utilization rates for vehicles that Zipcar and similar programs do—the result is comparatively fewer limitations for the size and scale of peer-to-peer operations.

Always satisfying for a futurist to see the future actually start to arrive.

PDF of social scanning piece

I've posted a PDF that pulls together my social scanning argument into a single document.

I'll add footnotes to every third word and send it to a journal in the near future.

The benefits of social scanning

In earlier posts, I made an argument for turning scanning into a more social activity; drew some lessons from my experience scanning at IFTF; and outlined how a system drawing on the community's use of Web 2.0 might work. Here, I talk about what such a system could deliver: in particular, functionalities that would deliver intellectual benefits; and the professional benefits that the system could deliver over time.

Intellectual benefits first. What could such a system deliver to practitioners that would help them improve their work in the near term? I can envision a couple things.

Heat Maps of the Future. This content could be presented in a variety of ways, at several time scales. A list of most popular subjects or citations from the last 24 hours, akin to the defaults lists on Technorati or Digg, would have the virtue of simplicity and familiarity. Citations and references in today's datastream can tell you what futurists think is interesting right now; but looking at the datastream over longer time periods– weeks or months, say– would give users a clearer picture of what issues are of enduring interest. New product announcements, elections, or disasters generate a frenzy of postings and repostings that die off quickly. In contrast, articles that are still cited after weeks or months are likely to deal with issues of more enduring importance. Looking at a longer stretch of the datastream will also help identify people who are good at spotting important trends early, and who can do so consistently. It will note who first identified the event, who subsequently picked it up, and what chains of influence connect people together.

Weak Signals. These heat maps would provide the background for what many people are really interested in: weak signals of disruptive change. Embedding the search for weak signals in social scanning would improve it greatly, by providing a standard against which the uniqueness of any signal can be measured. Today, the search for weak signals is pretty intuitive, and what counts as a weak signal is personal and subjective: my weak signal may be someone else's conventional wisdom, and vice versa. Aggregating signals from across the futurists' community would help individuals tune their intuition by letting them see when their weak signals are genuinely novel, and are actually well-known to people in other countries or experts in other specialties; and it would help the discipline as a whole by nudging the search for weak signals into something more rigorous and systematic.

Additional Functionalities. Identifying heat maps, trending topics, and weak signals would be basic functions of a social scanning system. Of course, it would be possible to develop additional functionalities based on this content. You could create tools for professional forecasters tools to benchmark and improve their practice, by showing users how their interests compare to those of the field as a whole; how often they identified weak signals that later were cited by others; and how important things they rated highly turned out to be over time.

Other tools could be used by groups. Top-rated topics could be flagged in a prediction markets system whose participants could more explicitly bet on the importance or timing of disruptions or future developments. Yet others could be used with clients. For example, interactive roadmaps based on content material from the system into an online presentation software system Prezi could be used in strategic planning workshops.

But there are larger, longer-term professional benefits that social scanning could provide. It would facilitate better scanning by converting private work into public goods. Social scanning would provide a social platform connecting the field together. The system would identify people who are good broad scanners, who are good at seeing trends early, who can spot weak signals, or who don't know each other but share research interests. Finally, social scanning could improve the profession of futures by giving practitioners incentives to share their work and systematically improve their forecasting.

Social scanning would be better scanning. It would generate a continuously-updated, community-wide and collective view of what trends are shaping the future, and what signals suggest the emergence of new trends. We can see what various futurists (somewhat independently) consider important, by comparing input from multiple sources. In other words, our collective reading patterns may reveal some insights that we could not create individually. At the organizational level, it would reduce the work of starting new scanning platforms for projects; instead, researchers could draw on existing, automatically-updated scans, augmenting them with additional work when necessary.

It would make scanning more efficient at an individual level, too. Today there's a lot of repetition in scanning, since futurists don't have a way to systematically share the work of scanning. If we could pool the results of our work, and trust the whole community to keep up with the most popular (and, one hopes, most critical) trends, individuals would have more time to spend looking through specialized or offbeat sources– a diversification which would enrich the discipline as a whole– as well as working on synthetic, interpretive activities. To draw a parallel to the academic world, most scholars focus their own energies and writing on specialized subjects, and work with colleagues to evolve new approaches, schools of thought, etc. This latter work doesn't always happen formally: it emerges through literature reviews, thematic essays, conferences, and conversations– a whole infrastructure for producing collective knowledge that futurists haven't really replicated.

Social scanning would encourage useful specialization. Social scanning would allow practitioners to build professional reputations for more kinds of work and insight. Today the fastest way for a futurist to build professional capital is to make flamboyant public pronouncements; doing the more mundane work of identifying less flashy trends, or assembling evidence that others can use, receives virtually no credit. There are currently no mechanisms for recognizing researchers who are terrific scanners but lousy forecasters, or who have a brilliant eye for weak signals but no public presence. By awarding users points for each item them contribute to the datastream (i.e., writing posts on their blogs, adding bookmarks to their account, etc.) and additional points for work they do within the system (e.g., tagging content, associating different pieces of content, or rating contributions), it would quickly become possible to identify people who are community-minded and generous with their ideas. Some of these users may turn out to be well-known names in the field; others may not. (Because the system can also analyze the importance of contributions, it could distinguish people who's work is defined by quantity rather than quality.) But by making it public, the system would give scanning and sharing the recognition they deserve.

This in turn will enrich the professional ecology, by making it possible to practitioners to build social capital from a wider variety of intellectual and professionally constructive activities. This would make futures more like better-developed and -organized disciplines like physics, where people can specialize in particular subjects (high-energy physics, cosmology, condensed matter, etc.), but also make careers as theorists, experimentalists, instrument designers, or computational experts. This is not to say that some of these specialties aren't higher-profile than others, but what matters is that the field has mechanisms for recognizing and rewarding all kinds of contributions to science. This is missing in futures, but there is an opportunity here, thanks to the fact that very few futurists make any money from scanning, but instead make money from the things that scanning enables. Turning this largely invisible private activity into a public good would raise the overall quality of scanning, and recognize and reward good scanners for their contributions to the field.

Social scanning could bring gentle coordination to the discipline. The field lacks the centralized, gatekeeping institutions– a few dominant graduate training programs, a strong professional society, government certification– the give shape to other professions like law and medicine. Nor does it have the canonical literature, moral codes, and daily practices that define members of religious orders. Futurists are spread in corporations, government agencies, consulting companies, one- or two-person groups, and academia, and most of us spend much more time talking to clients than to each other. As a result, the field is physically dispersed and intellectually decentered. Social scanning would help build a more cohesive sense of identity by making the community's interests visible to itself; allow far-flung practitioners who share common interests to find each other, and let them build on each other's work in ways we cannot now.

Social scanning would raise the quality of the discipline. It would provide clear benchmarks for practitioners: it would let me compare what I've been reading to my colleagues. Social scanning would also contribute to the development of more solid and rational professional standards. Today, the market rewards the most public futurists for being provocative more than for being useful or right. The upside to analytical rigor and correctness is low, and the downside to being wrong is even lower. Social scanning would begin to shift the economics of professional reputation, and provide a system that ignored flamboyance, gave less credit to single dead-on predictions, and rewarded less spectacular but more consistent performance.

Social scanning would be a lightweight infrastructure. A social scanning platform would do all this without requiring something as elaborate as a World Brain (appealing though that idea might be), or requiring all futurists to adopt common software packages. Like all good knowledge tools (as Mike Love and I argued in a 2008 IFTF report), it lets people do what they're best at, and computers do what they're best at. It can be easily adapted by users and integrated into their existing workflows and habits. We can harvest work that people are already sharing. Nobody who already has a blog or thousands of bookmarks has to switch systems, learn a new tool, or abandon legacy content. They just keep doing what works best for them.

[This is extracted from a longer essay on social scanning. A PDF of the entire piece is available.]

Building new scanning capabilities

Today, futurists using Twitter, Delicious, Digg and other Web 2. 0 services publish a flow of content that is probably already too large for any person to follow, and is growing rapidly.

For example, Twitter publishes roughly 600-700 tweets per day marked with the #future hash tag. The futurists I follow post 70-80 tweets per day (though some of those posts are personal or auto-generated by other agents). Futures-oriented lists on Twitter follow anywhere from a dozen to three hundred people, and almost those lists are all available via RSS.

Other systems generate equally substantial bodies of content. Users on Delicious, the oldest social bookmarking service, post about 350 bookmarks per day with the tag "future." My network (which includes a select few futurists) posts about 220 bookmarks per year. That translates into about 1120 separate data-points per day, or over 400,000 signals per year — just from three services. Futurists' blogs publish between 100 and 200 posts per week.

Casting one's net wider, one can rapidly capture an enormous number of potential signals. Consider Tweet the Future, a Web site that monitors Twitter for tweets containing the word "future." It finds about 30 tweets every minute– over 40,000 a day– though the vast majority of these tweets have nothing to do with futures or forecasting.

So many if not most futurists, consulting companies, and futures-oriented nonprofits are using one or more these systems. Most of these datastreams are real time-reflections of what people are reading. These datastreams represent a vast but untapped resource that could be used to build a picture of the collective attention of the futures community, and detect weak signals: indeed, it can largely replace the kind of commissioned content that fed Delta Scan and Signtific. We no longer have to work alone to find interesting things. Instead, we can detect patterns in our and our colleagues' datastreams.

How would a social scanning platform work? Here's what I imagine a very simple but useful system doing.

Its core functionality would be an engine that gathers signals from the free and nearly real-time content produced by futurists and subject-matter experts on blogs, Twitter, and other social media platforms; analyzes this content to find subjects and citations that are of greatest interest to the futures community; and clusters together material that shares unusual terms, keywords, or links to common references. This would let us identify both popular subjects and outlying wild cards, and create a body of data that could support others tools or services.

The system would harvest RSS feeds generated by a list of blogs, Twitter,, Digg and other services generated by the system's managers. The list would have some simple metadata about sources, most notably their authors; it would also record metadata from its sources, particularly the publication date and time of posts and articles, and whatever tags attach to the content.

What would the system it do with this datastream? The first key task would to filter it. By gathering information about the author of each feed, it would be able to associate multiple feeds with the same author. If the same author has several different sources that the system is following, the system would look across those and filters out repeats. For example, if I have a blog and account, and both automatically push updates to a Twitter account, the system knows to look for cross-posts between those services, and count a blog post that generates a Tweet only once.

The second key piece of filtering involves associating multiple hits on the same subject. Different people may talk about the same event but reference articles published in different places, or the same article published in multiple places– a wire service article that appears in several newspapers, or an article that is reblogged. The system would also need to be able to identify different URLs as pointing to the same article—e.g., the full URL or an article and a shortened URL. Identifying these sources could be done by software, by users, or both. So while repetition by an individual would be controlled for, multiple citations and references are recorded. The former is noise in the system, but the latter is signal: the more people who tag or blog about a subject, the more important it is. (Citation and referencing also filters out non-professional noise. Many Twitter users combine references to major new articles with announcements like "I am eating a sandwich;" the latter are far less likely to be referenced by others than the former.)

In Delta Scan and Signtific, contributors or community members were supposed to formally rate the importance of different trends. In this system, we can simply assume that if someone takes the time to share a link to an article, they consider that article to be worth their attention. More links, especially links over time, indicate the emergence of a group consensus that a link points to a trend worth watching.

This kind of filtering could be done automatically, and improved by users. People may be able to identify associations between articles that automated systems don't. They could group together content from the data stream by adding tags to specific pieces of content; and they can either tags or identify synonymous terms (e.g., ubiquitous computing, ubicomp, and ubic, and ubiq all mean the same thing, for example). My experience with Delta Scan and Signtific suggests, however, that this system needs to be kept as simple as possible. People generally don't classify things unless there are clear incentives and immediate rewards. Even then there are huge variations in the use of hash tags, keywords, etc. between users and across systems, and little chance that people can be induced to adopt standard taxonomies or ontologies. However, when you're working with high social knowledge, and information that by nature exists at the boundaries of the human corpus, it's important to maintain a high degree of ontological flexibility.

Rewarding people for doing this kind of tagging and associating would send the important signal that community-oriented work deserves to be recognized and encouraged. This kind of work has traditionally been essential for high-quality scholarly and professional activity (think of the legal profession's vast corpus of precedents and codes, the medical profession's reference works, the scientific world's gigantic structures for sharing everything from raw data to polished research) but has either been done largely by professionals– librarians, catalogers, and others– with little professional visibility, or by organizations that extract high rents for their work. By rewarding users for improving the system and contributing to the professional good, we can harvest some of the benefits of that organizational work without incurring its costs.

[This is extracted from a longer essay on social scanning. A PDF of the entire piece is available.]

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