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It is difficult to face up to the risk and uncertainty
around innovation. All actions, including inaction, may
have benefits and harms, but we don’t know, nor can we
even imagine, everything that might happen. We cannot be
confident about the valuations people might attach to the
possible consequences. Nor can we confidently say how
likely things are to happen — in fact, the whole meaning of
such a statement can be contested.
There are also multiple players who have a stake in
decisions about innovations — the public (which should not
be treated as some homogenous mass, but as a diversity of
‘publics’); the regulators and policy-makers; the innovators
themselves; non-governmental organizations (NGOs) and
so on. There may be substantial problems of trust between
these actors.
Communication between these stakeholders, especially
when channelled through media outlets that may not
be concerned with balanced reporting, is fraught with
difficulties. Any progress that can be made in improving that
communication should be beneficial, and hence the search
for a ‘common language’.
What policymakers might like in an ideal world
The theory of rational decision-making in the face of
uncertainty comprises four basic stages:
• Structuring the list of actions, and the possible
consequences of those actions.
• Giving a financial or other numerical utility to those
possible future outcomes.
• Assigning a probability for each possible consequence,
given each action.
• Establishing a rational decision that maximizes the
expected benefit.
The real world is a messy place, however. The theory
described above only holds in rarefied situations of perfect
contextual knowledge, such as gambling on roulette when
we know the probabilities of possible gains or losses.
These pre-chance problems are the only type in which
we might talk about the risk: and even then we need to
make assumptions about the fairness of the wheel and the
integrity of the casino.
What policy-makers actually get
In reasonably well-understood situations, numerical risk
assessments can enable appropriate decisions for prioritising
actions. The Health and Safety Executive’s Tolerability of Risk
framework provides appropriate guidance: in this chapter,
the case study Nuclear: The Submariner’s Perspective shows
how societal valuations of potential consequences can be
incorporated, while Adapting regulation to changing evidence
on risks: delivering changes to pig inspection illustrates that risk
assessment can form a basis for evidence-based regulation.
But in some areas of innovation there are likely to be
different groups making claims about the risks, raising
different issues with different values, and competing scientific
claims based on different evidence. Even within a single
group there will generally be a range of possible analyses
based on different assumptions, while any predictions
about how people will react to innovations must be fairly
speculative.
Thus a policymaker will be faced with plural analyses
that are both contingent on assumptions and inevitably
inadequate, whether self-professed as such or not. The
resulting uncertainty is far more than not being able to
predict the future — it is as much about ‘don’t know’ as
‘can’t know’.
The problems of language
1. Definition of terms
The crucial distinction between ‘hazard’ — the potential
for harm if mitigation is not put in place — and ‘risk’ is
well-known. But frank discussion about risk and uncertainty
is not helped by the variety of language used by people in
different domains, not least in the meanings of the terms
‘risk’ and ‘uncertainty’. For example, many scientists would
use the term ‘uncertainty’ for everything that was not
certain, including a single coin flip, and only distinguish the
extent to which the uncertainty was quantifiable. In contrast,
those with an economics and social science background will
often adopt the distinction made by Frank Knight between
‘risk’ — in which extensive data and good understanding of
a controlled environment leads to agreed quantification —
and ‘uncertainty’, for when this quantification is not feasible.
The term ‘ambiguity’ is also, ironically, ambiguous: some use
it to refer to situations in which outcomes and values are
contested or unknown (see Chapter 4), while in behavioural
economics it refers to uncertainty about probabilities.
2. Communicating using numbers
Even when numbers are agreed, there is a wide variety
of ways in which probabilities may be expressed: as
percentages, odds, frequencies, in graphics and so on. For
example, the Safety Cases for high-hazard installations
provided to the Health and Safety Executive might mention
that the individual risk per annum (IRPA) = 4 x 10 -4 : it means
that each worker has a 4 in 10,000, or a 1 in 2,500 risk of
being killed each year, but seems almost designed to prevent
comprehension.
Research has shown that the way numbers are framed
influences our perceptions. The 99% Campaign, an initiative
that aims to dispel negative stereotypes about young people,
offered one example of a positively-framed message when
it issued advertisements proclaiming that “99% of young
Londoners do not commit serious youth violence”. This, of
The important lesson
from numerical risk
communication is that one
size does not fit all.