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of innovations that were systematically marginalized —
sometimes actively suppressed — by incumbent interests in
science, government and industry 165 .
It is of course important not to become too romantic
about the dynamics of social movements and their favoured
innovations 162 . These too warrant exactly the same kinds of
healthy scepticism appropriate to other actors in innovation
debates. But history does reveal the origins of many of the
ostensibly driving environmental and social justice concerns,
which currently play such prominent roles in justifications
of current innovation policy. Without decades of
struggle by social movements dedicated to humanitarianism,
environmentalism and social justice, it is doubtful that highlevel
global agenda-setting developments like the Stockholm
Environment Conference or the Brundtland Commission or
the Millenium Development Goals would ever have become
as formative as they have 166–170 . And this pattern is arguably
reinforced by the history of continuing crucial roles played
by civil society in other emancipatory transformations
around colonialism, racism, women’s and gay rights 171–177 .
Just as the famous dark matter in cosmology stabilizes the
visible structures of galaxies, so these apparently intangible
distributed social forces help condition the gradients
that ultimately help forge and steer new directions for
innovation 172 . The greater the critical interest in the most
progressive orientations for innovation — rather than those
that preserve the status quo — the more this is generally
true.
Risk, Uncertainty, Ambiguity and Ignorance
These policymaking challenges are compounded because the
pros and cons of different innovation pathways are — under
all views — subject to seriously incomplete and problematic
knowledge. As discussed further in Chapter 6, the normal
way to address these dilemmas is by means of regulatory
risk assessment 173, 174 . Although often not implemented in
full, this prevailing approach invokes the apparent authority
of probabilistic analysis 175, 176 to assert a notionally single
‘sound scientific‘ or ‘evidence-based’ picture 177, 178 . This task
can be approached in many variously complex ways 179, 180 . But
at root, it involves alternative possible positive and negative
outcomes being weighted by their respective likelihoods to
aggregate a single overall ‘expected value’ for the balance of
future benefits and harms 181, 182 .
In conventional innovation policy and regulation, it is
simply assumed that whatever products or technologies are
most energetically advanced for risk assessment are in some
way self-evidently beneficial 183, 184 . Questions then typically
focus on whether any associated risks will be tolerable 185,
186
. It is rare for the claimed benefits themselves to be
rigorously scrutinized 57, 187 , let alone compared in a balanced
way with other potential benefits of alternative innovation
pathways 58, 188 . Therefore existing forms of risk regulation
do little to address the wider issues in innovation politics
discussed above. Innovation pathways backed by the most
powerful interests typically prevail.
Further challenges arise in the reliance of risk-based
regulation on the methods provided by probability theory 189,
190
. Probabilistic tools can be useful in tackling familiar, highfrequency,
relatively unchanging challenges, as found in the
risk regulation of many urban transport or public health
systems 191, 192 , for example. Where empirical evidence arising
from past experience is held to be a reliable guide to the
future, these tools can be very powerful — as in established
responses to familiar safety risks 193 . But where an innovation
pathway (or its context) is novel, complex or rapidly
changing, uncertainties cannot confidently be reduced to
single definite probabilities 194 . Such inabilities to justify a
single picture of probabilities can arise, for instance, in the
regulation of nanotechnologies 195 (see the case study in this
chapter), endocrine disrupting chemicals 196 , or novel living
organisms 197 . Under these conditions, it can be irrational
to assert a single definitive ‘evidence-based’ picture 198 . In
these fields (as more widely), policy making must often
contend with contrasting — but equally reasonable —
interpretations of uncertainty 65, 78 . These cannot reliably or
rationally be reduced to simple numerical probabilities.
These are not the only limits to risk assessment. Beyond
uncertainty in the sense discussed above 199–200 , there exists
a further array of challenges 201, 202 . These involve not the
relative likelihoods of different outcomes, but the meanings
of the possibilities themselves. For instance, divergent views
may exist over how to categorize or partition different
kinds of benefit or harm. Or there may be questions over
exactly how to frame the various dimensions under which
these are defined 203 . What are the appropriate imaginations,
understandings, values, or interests according to which they
should be interpreted or prioritized 204 ? There may also be
differences over which innovation pathways to include or
exclude from scrutiny, or how to allocate attention 76 .
These are challenges of ambiguity — contradictory
certainties 205 — rather than strict uncertainty 206, 218 . And risk
assessment is no more able to resolve these disagreements
over meanings as over likelihoods 207 . Indeed, Nobel Prizes
In conventional innovation
policy and regulation, it
is simply assumed that
whatever products or
technologies are most
energetically advanced
for risk assessment are in
some way self-evidently
beneficial.