Improving Distributed Intelligence in Complex Innovation Systems

Abstract

It is the year 2005 and the Managing Director of Biomat has a problem. Her spin- off firm builds replacement human organs using metagenic technology, but after two years of success Biomat is at a crossroads. Should it stick to metagenics, which is costly and prone to production problems, or should it use the latest ultragenic approaches - still unproven but likely to yield great cost reductions? She switched on her videophone and traced the local head of RIB, the Regional In- novation Bureau of ENDBITS, the European Network of Distributed Bureaux of Intelligence for Technology Strategies. RIB helped her to prepare a videonote on technology options. It ran a standard search on the European Foresight Bank, an electronic tool which logged all of the world's foresight outputs and used AI algo- rithms to cluster the results and build scenarios. Recent expert assessments all looked good for ultragenics, but RIB advised her not to rely solely upon foresight results – however positive. Social and regulatory problems were also possible, and the Bureau had heard of some problems in Austria. RIB used the Technology Assessment directory to identify the main Austrian experts in the field and confirmed that ultragenics had been subject to ethical challenges from a local religious foundation. RIB then called for more information and scanned the recordings of the Consensus Conference. Relief! The objections were based on a misunderstanding of the procedures for ultragenics (which unlike earlier approaches did not depend upon foetal cells) and the citizens’ jury had come out in favour of the technology. Biomat was ready to launch its ultragenics research programme, but was worried about the cost. RIB pointed out that all three of the European Research Fra