I’m honored to have two chapters included in the recent new book, “The Future of Futures“, published by the Association of Professional Futurists. The book explores emerging issues in scenario planning and foresight. My first chapter, reproduced below, is about the effect of crowdsourcing, big data and the web on scenario planning and foresight.   You can also download a formatted PDF of the chapter. The second chapter is about design fiction, experiential futures and the role of visual media.  You can read that chapter here.


Crowdsourced Futures

by Noah Raford, PhD Jeff Howe defined crowdsourcing as, “the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call.” Under this definition, many parts of the futures process could be “outsourced to the crowd,” at least in theory. In practice, however, most crowdsourcing efforts related to futures and scenario work address only the earliest stages of the process, those related to environmental scanning and the collection of drivers. While there are exceptions, the bulk of futures’ examples that have engaged crowdsourcing techniques focus on this early stage. There are excellent examples ranging from trend databases such as Shaping Tomorrow to weak signal databases such as TrendWiki. Many advertising and creative services agencies practice similar forms of environmental scanning as well, more commonly known as ‘cool hunting.’ This kind of approach is an important one. Evidence suggests that the Web can enhance both the breadth and depth of our horizon scanning activities by, for example, providing ‘always-on’ monitoring and ‘at your fingertips’ evidence for almost any weak signal or emerging trend.

Pitfalls of Crowdsourcing

My own experience testing these approaches, however, suggests that the use of the Web in this way has several pitfalls. On the upside, crowdsourcing the ‘drivers’ process can provide an order of magnitude increase in speed, depth and breadth over a traditional scanning exercise. It also allows more people to be involved, over a shorter period of time, with demonstrably more disciplines, groups and geographies represented. On the other hand, as any first year statistics student will tell you, “data does not equal meaning.” Unfortunately, more participation does not necessarily mean better participation, enhanced learning or increased understanding. The paradoxical effect of more and faster data collection can also, as Jaron Lanier suggests, be less understanding and a greater analytical burden on the practitioner. Whereas past processes may have been slow and cumbersome by today’s standards, the difficult process of discovery often allowed time for inductive synthesis and integration of opposing viewpoints, creating meaning even as the trends and drivers are still being uncovered and understood. From a social perspective, many crowdsourcing contributions are essentially one–way, in which the contributor invites the futurist to pay attention to something they have found interesting. Whereas participation in a workshop or scanning exercise often meant engagement in a dialogue, with the potential for pedagogical outcomes, simply adding data to a system through a series of clicks does not offer the same potential.

Participation Problems

Next generation futures systems will therefore have to address the synthesis and interpretation of results in a way that is more substantial and useful than most crowdsourcing solutions today. At the same time, they will also need to engage the social dynamics of participation more directly; why people contribute, what they get out of it, and how it factors into the final product (which will most likely be for a very different, paying, audience). Many interesting experiments are currently under way that point towards promising opportunities. Yet while it is fairly certain that the use of Web-based participation and content creation in futures work is here to stay, the form it will take is still open. In the meantime, the promise of speed and efficiency gains is likely to produce continued demand for the development of such systems, especially as ‘big data’ and algorithmic clustering of content becomes more common.

Speeding Up and Dumbing Down

The result will be, at least in the short-term, both a speeding up and a dumbing down of the process, with certain kinds of analytical exercises yielding to the pressures of commodification and automation before others. This can be seen already in the field, where many ‘non-futurist’ companies provide similar trend tracking and monitoring services, delivered by non-specialists, far more cheaply, with reasonably good results. Similar things have happened right across the service sector, from graphic design to accountancy. Specialists have responded by becoming more adept at more complex projects and more difficult problems. It seems naïve to imagine that futures will somehow escape such a powerful driver of change.

Both Simpler and Richer

If the futures field does follow the same path as other service sectors, we can expect simpler tasks to become quicker, cheaper, and less profitable, while futurists need to demonstrate greater capability to earn the trust of clients. Parts of the product will be less ‘original’ or ‘insightful’ by today’s handmade standards, but this should free resources for richer analysis of depth and complexity. It is therefore possible that the ‘future of futures’ may resemble something akin to modern day psychotherapy; anyone will be able to get free (and possibly even accurate) advice from their horoscopes at the back of the newspaper. But professional, personalized service will still come from a cadre of expensive, highly trained, personally trusted advisors; even if the empirical validity of both may still be open to question.


UPDATE: There is an excellent summary of the entire book by Andrew Curry, the main editor of the book, on his website.  You can also buy the complete book here.]]>

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