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.]