I’ve been in Singapore for two days at IRAHS and moderated one panel this morning. I’ll post on that panel and my summary tomorrow. I’m now sitting in the afternoon session after a lunch time keynote from Dave Sifry; a basic summary of the latest Technorati surveys and statistics which was useful for the audience, although less so the active bloggers in the group. I decided that conference blogging was the natural follow on from Dave, and I also enjoy the immediacy of real time blogging. Its different from reflection. The afternoon session is about challenges for Horizon Scanning and the first speaker is a “sociologist by training and a futurist by choice”, Peter Bishop from the University of Houston. He is introduced as a founding member of a new association of professional futurists
Says that we are not equipped in neurologically or physically for weak signal detection as our pleiolescene past was fairly simple. Good job he said we was not a neuoscienticst, he is obvioulsy not a palentologist or has much knowledge of evolutionary pschology if he can say that. I would argue we were really well equiped, but we have in part trained ourselves out of that natural abilities, or allowed technofetishism
States that the purpose of scanning is reduce the degree of surprise in respect of the future(surely its more the impact)
Says that there are three aspects of changing our image of the future.
Makes a good point, we should aim to be suprised in small ways not big ways. goes on to argue that horizon scanning is hard as weak signals are weak and early. Our neural system is a physical system of chunking which ends up with a percept with loss of data. We add more to the percept than the world does, which is good neuroscience, talking about it as internal machinery less good. There are a lot of mechanical metaphors in his talk: the instrumentalism of far too much sense-making sociology?
Direct quote from his bullet points:
See what I mean about the mechanical metaphor? Each of those points is riddled with it, and its the wrong metaphor; if you define the problem in terms of machines, then your solution is likely to be mechanical.Proceeds to say that we have Inherent tendencies to knowing rather than not knowing and certainty to uncertainty. We all have our own implications and he is using Myers-Briggs types to illustrate this as if that particular bit of pseudo-science has any factual basis. fair enough point about the issue of expert being paradigm bound compared with the novice, so less likely to see new things.
Now looking at all of this as a problem of signals. As they go up the probability goes up, but false positives go up, so he (argues) we are looking for the sweet spot, the right number of signals to balance discovery with minimal false positives. Now this is exactly what I argued against in my summary and he obviously realises this as he references my point about granularity (it will be in the next post)but excuses himself from its application.
Conclusions are that we should
And he says that will take ten years!
OK this is just what you don’t do. More tomorrow
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