3. Houston’s Foresight Research Framework
Many firms buy industry foresight research from consultancies like Gartner, McKinsey, and IDG. Clients include C-level executives, technical innovators, strategists, planners, risk managers, leaders, journalists, and others. Market research is a lucrative business. A typical Gartner research subscription service covering the current conditions and future trends in a specialized industry with a reasonable market capitalization (for example, call center technology) might cost small to mid-sized firms $25K-40K annually.
Buying, finding, or creating such documents is part of the intelligence function (Skill 1) of a firm. A good foresight brief covers the present and gives advice on probable, possible, and preferable futures on any subject, with insights tailored to client need. There are many ways to organize foresight research for publication. A good foresight graduate program will introduce you to several.
During his thirty-year tenure as professor of strategic foresight at the University of Houston, Peter Bishop developed an excellent research framework for producing briefing papers on a foresight topic. For a full description, see Framework foresight, in the journal Futures (2013). Students of the Houston foresight master’s program apply the framework, and are assessed on their application of it. Below are its five main sections and subsections (titles adapted slightly for my own work):
- Introduction (Problem Definition, Executive Summary)
- Current Assessment (Current Conditions, Stakeholders, History, Constants)
- Forecast (Cycles, Trends, Plans, Investments, Basic Forecast)
- Alternate Futures (Potential Events, Issues, Ideas, Proposals, Uncertainties, Scenarios, Indicators)
- Information Sources (Experts, Texts, Periodicals, Articles, Organizations, Websites)
As one of my Houston master’s degree assignments I applied this framework to a long-term foresight problem, exploring the idea of underground automated highway systems in wealthy, high-density cities in coming decades (PDF of 2005 framework article here). I used the framework to argue that in the 2030s, our wealthiest cities may find it affordable and valuable to use automated excavation and construction systems to start moving large parking garages and even some freeways underground. They could do this by using urban robo-trucks to remove all the excavation tailings, largely at night when freeways are underused. This would allow cities to reclaim some of their expensive surface real estate for higher-value uses, including living and green space, and would further increase the tax base, living density, and commuting speeds in key urban corridors. As background trends, I noted the rise of zero emission vehicles, and that the global use of tunnel boring machines, one of the technologies necessary for such a future, is on a three year exponential doubling curve at present. The prediction is a speculative leap, so using a formal framework is a good way to try to produce foresight worth critiquing, at least. Feel free to skim it and let me know what you think.
Hello. I’m curious about how you calculated “the global use of tunnel boring machines…is on a three year exponential doubling curve at present.” I looked for language that might explain it in the UAHS pdf (http://accelerating.org/articles/uahsframework.pdf), and at http://www.accelerationwatch.com/articles/undergroundhighwaysystems.html, but I did not find any. I’m not doubting you, I’d just like to understand how you did it or where you found it. Thank you.
Hello David, I got that info from one of the TBM experts I interviewed for the research report. It was likely a seat of the pants estimate. As you may know, our in our current political and academic environment, no one has funded any programs that builds or qualifies these performance curve/experience curve datasets at present, in any country to my knowledge. I speculate on why that might be in this section of a recent paper: http://evodevouniverse.com/wiki/Evolutionary_development_(evo_devo,_ED)#Contingency_vs._inevitability:_The_two_extremes_of_scientific_and_societal_bias
A very nice, but highly incomplete, web page with some of these curves can be found at OWID: https://ourworldindata.org/technological-progress
Please let me know if you find any other great performance curve datasets, thanks! Warm regards, JS