Chapter 13. Visions and Challenges – Priorities for Professionals

Foresight is Becoming Open

As I argued with futurists Venessa Miemis and Alvis Brigis in Open Foresight (PDF), J. Futures Studies, 17(1):91-98, 2012, some of our best modern foresight is not only digital and collaborative, it’s being done increasingly in the open, with public access to the primary data as the foresight emerges. The EU’s Futurium project (2012-2014) which engaged 3500 Europeans in developing visions for the digital future of Europe in 2050, is a good recent example. Any kind of open, multi-stakeholder Delphi or group forecasting activity, as in the ELAC Action Plans for ICT development in Latin America, would be another.

We defined open foresight as any foresight initiative that is:

  • Collective and Participatory in Structure,
  • Open Access, both in the data collected and in its analysis and results,
  • Online, which brings access barriers for some, but can greatly lower participation barriers,
  • Input-Diverse, cognitively, ethnically, and in stakeholders, and
  • Designed with Incentives to Participate.

This last requirement is tricky, as it can devolve into pay-for-opinion if you don’t structure it right. But people don’t like filling out surveys, and surveys of their thoughts about the future can be particularly painful, as many don’t like thinking about it, and are rarely coached in how to do it. You need to give your group some kind of incentive, in reputation, opportunities, financial rewards, or otherwise to give their opinion and to think carefully about the future, adjusting for their own cognitive bias, and with no penalty for avoiding those areas where they feel unable to give an opinion. People need to feel like they are creating something valuable, like a Wikipedia page, and that there is value in struggling, together, toward better stories, visions, strategies, and plans for the future.

Even with all the above requirements satisfied, doing open foresight isn’t easy. In many cases, consultants and firms will prefer to keep most of their foresight work private, for competitive advantage. Controversial topics discussed openly can become flamewars, especially if anonymity is allowed (usually useful only for limited periods and purposes) and forums are not well moderated.

Furthermore, open doesn’t mean everyone’s opinion is equally valued in the final result. Expert groups may still be far more heavily weighted in your methodology. But by making your methodology and the exercise open, learning can happen faster than by any other method. Remember that full openness is usually demanded by good scientific method. Being open, your work becomes scientific, rather than voodoo. Others can examine your methods and results, and iterate their own version of what you’ve done, teaching you to be a better foresighter. Also, the participants themselves learn how they fit into the foresight generation process. They see the full messiness of how good foresight is created. Unlike laws and sausages, foresight production should be watched and critiqued by many “cooks”. With the right process, openness helps everyone becomes better collaborators, and more active users of foresight methods in their own lives and organizations.