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July 16, 2000


Precautionary Principle and Reductionism


AgBioView - http://www.agbioworld.org, http://agbioview.listbot.com

Date: Jul 16 2000 21:29:01 EDT
From: "C. S. Prakash"

Following was submitted by Dr. Mae-Wan Ho (Y.Royals@open.ac.uk)

ISIS submission to US Advisory Committee on International Economic Policy
(ACIEP) Biotech. Working Group, 13 July, 2000

The precautionary principle is accepted as the basis of the Cartegena
Biosafety Protocol agreed in Montreal in January 2000, already signed by
68 nations who attended the Convention on Biological Diversity
Conference in Nairobi in May, 2000. The principle is to be applied to all
GMOs whether used as food or as seeds for environmental release.

The precautionary principle states that when there is reasonable suspicion
of harm, lack of scientific certainty or consensus must not be used to
postpone preventative action. There is indeed sufficient direct and
indirect scientific evidence to suggest that GMOs are unsafe for use as
food or for release into the environment. And that is why more than 300
scientists from 38 countries are demanding a moratorium on all releases of
GMOs (World Scientists Statement and Open Letter to All Governments
<www.i-sis.org> ).

The precautionary principle is actually part and parcel of sound science.
Science is an active knowledge system in which new discoveries are made
almost every day. Scientific evidence is always incomplete and uncertain.
The responsible use of scientific evidence, therefore, is to set
precaution. This is all the more important for technologies, such as
genetic engineering, which can neither be controlled nor be recalled.

Dr. Peter Saunders, Professor of Applied Mathematics at King's College
London, co-Founder of ISIS, has written an article which shows how the
precautionary principle is just codified common sense that people have
accepted in courts of law and mathematicians have adopted in the proper
use of statistics. It begins to clarify how scientific evidence is to be
interpreted in a socially responsible way which is also in accord with
sound science.

Dr. Mae-Wan Ho
Institute of Science in Society
C/o Dept of Biological Sciences
Open University
Walton Hall Milton Keynes


Peter T. Saunders, Mathematics Department, King's College, London.

There has been a lot written and said about the precautionary principle
recently, much of it misleading. Some have stated that if the principle
were applied it would put an end to technological advance. Others claim to
be applying the principle when they are not. From all the confusion, it
is easy to mistake it for some deep philosophical idea that is
inordinately difficult to grasp (1).

In fact, the precautionary principle is very simple. All it actually
amounts to is this: if one is embarking on something new, one should think
very carefully about whether it is safe or not, and should not go ahead
until reasonably convinced it is. It is just common sense.

Too many of those who fail to understand or to accept the precautionary
principle are pushing forward with untested, inadequately researched
technologies, and insisting that it is up to the rest of us to prove them
dangerous before they can be stopped. The perpetrators also refuse to
accept liability; so if the technologies turn out to be hazardous, as in
many cases they have, someone else will have to pay the penalty

The precautionary principle hinges on concept of the burden of proof,
which ordinary people have been expected to understand and accept in the
law for many years. It is also the same reasoning that is used in most
statistical testing. Indeed, as a lot of work in biology depends on
statistics, misuse of the precautionary principle often rests on
misunderstanding and abuse of statistics. Both the accepted practice in
law and the proper use of statistics are in accord with the
common-sensible idea that it is incumbent
on those introducing a new technology to prove it safe, and not for the
rest of us to prove it harmful.

The Burden of Proof

The precautionary principle states that if there are reasonable scientific
grounds for believing that a new process or product may not be safe, it
should not be introduced until we have convincing evidence that the risks
are small and are outweighed by the benefits.

It can also be applied to existing technologies when new evidence appears
suggesting that they are more dangerous than we had thought (as in the
case of cigarettes, CFCs, greenhouse gasses and now GMOs). Then, it
requires that we undertake research to better assess the risk and that in
the meantime, we should not expand our use of the technology and should
put in train measures to reduce our dependence on it. If the dangers are
considered serious enough, then the principle may require us to withdraw
the products or impose a ban or a moratorium on further use.

The principle does not, as some critics claim, require industry to provide
absolute proof that something new is safe. That would be an impossible
demand and would indeed stop technology dead in its tracks, but I do not
know of anyone who is actually demanding it. The precautionary principle
does not deal with absolute certainty. On the contrary, it is specifically
intended for circumstances where there is no absolute certainty.

What the precautionary principle does is to put the burden of proof onto
the innovator or perpetrator, but not in an unreasonable or impossible
way. It is up to the perpetrator to demonstrate beyond reasonable doubt
that it is safe, and not for the rest of society to prove that it is not.

No one should have any difficulty understanding that because precisely the
same sort of argument is used in the criminal law. The prosecution and
the defence are not equal in the courtroom. The members of the jury are
not asked to decide whether they think it is more or less likely that the
defendant has committed the crime he or she is charged with. Instead, the
prosecution is supposed to prove beyond reasonable doubt that the
defendant is guilty. Members of the jury do not have to be absolutely
certain that the defendant is guilty before they convict, but they do have
to be confident they are right.

There is a good reason for adopting a burden of proof that assumes
innocence until proven guilty. The defendant may be guilty or not, and may
be found guilty or not. If the defendant is guilty and convicted, justice
has been done, as is the case if innocent and found not guilty. But
suppose the jury reaches the wrong verdict, what then?

That depends on which of the two possible errors was made. If the
defendant actually committed the crime, but found not guilty, then a crime
goes unpunished. The other possibility is that the defendant is wrongly
convicted of a crime, in which case an innocent life is ruined. Neither of
these outcomes is satisfactory, but society has decided that the second is
so much worse than the first that we should do as much as we reasonably
can to avoid it. It is better, so the saying goes, that "a hundred guilty
men should go free than that one innocent man be convicted". In any
situation in which there is uncertainty, mistakes will be made. Our aim is
to minimise the damage that results when mistakes are made.

Just as society does not require the defendant to prove innocence, so it
should not require objectors to prove that a technology is harmful. It is
for those who want to introduce something new to prove, not with
certainty, but beyond reasonable doubt, that it is safe. Society balances
the trial in favour of the defendant because we believe that convicting an
innocent person is far worse than failing to convict someone who is
guilty. In the same way, we should balance the decision on hazards and
risks in favour of safety, especially in those cases where the damage,
should it occur, is serious and irredeemable.

The objectors must bring forward evidence that stands up to scrutiny, but
they do not have to prove that there are serious dangers. It is for the
innovators to establish beyond reasonable doubt that what they are
proposing is safe. The burden of proof is on them.

The Misuse of Statistics

You have an antique coin that you want to use for deciding who will go
first at a game, but you are worried it might be biased in favour of
heads. You toss it three times, and it comes down heads all three times.
Naturally, that does not do anything to reassure you, until someone who
claims to know something about statistics comes along, and informs you
that as the "p-value" is 0.125, you have nothing to worry about. The coin
is not biased.

Does that not sound like arrant nonsense? Surely if a coin comes down
heads three times in a row, that cannot prove it is unbiased. No, of
course it cannot. But this sort of reasoning is too often being used to
prove that GM technology is safe.

The fallacy, and it is a fallacy, comes about either through a
misunderstanding of statistics or a total neglect of the precautionary
principle - or, more likely, both. In brief, people are claiming that
they have proven that something is safe, when what they have actually done
is to fail to prove that it is unsafe. It's the mathematical way of
claiming that absence of evidence is the same as evidence of absence.

To see how this comes about, we have to appreciate the difference between
biological and other kinds of scientific evidence. Most experiments in
physics and chemistry are relatively clear cut. If you want to know what
will happen if you mix, say, copper and sulphuric acid, you really only
have to try it once. If you want to be sure, you will repeat the
experiment, but you expect to get the same result, even to the amount of
hydrogen that is produced from a given amount of copper and acid.

In biology, however, we are dealing with organisms which vary a lot and
never behave in predictable, mechanical ways. If we spread fertiliser on a
field, not every plant will increase in size by the same amount, and if
you cross two lines of corn not all the resulting seeds will be the same.
So we almost always have to use some statistical argument to tell us
whether what we observe is merely due to chance or reflects some real

The details of the argument will vary depending upon exactly what it is we
want to establish, but the standard ones follow a similar pattern.
Suppose, that plant breeders have come up with a new strain of maize, and
we want to know if it gives a better yield than the old one. We plant each
of them in a
field, and in August, we harvest more from the new than from the old. That
is encouraging, but it might simply be a chance fluctuation. After all,
even if we had planted both fields with the old strain, we would not
expect to have obtained exactly the same yield in both fields.

So what we do is the following. We suppose that the new strain is the same
as the old one. (This is called the "null hypothesis", because we assume
that nothing has changed.) We then work out the probability that the new
strain would yield as well as it did simply on account of chance. We call
this probability the "p-value". Clearly the smaller the p-value, the more
likely it is that the new strain really is better - though we can never be
absolutely certain. What counts as 'small' is arbitrary, but over the
years, statisticians have adopted the convention that if the p-value is
less than 5% we should reject the null hypothesis, i.e. we can infer that
the new strain really is better. Another way of saying the same thing is
that the difference in yields is 'significant'.

Note that the p-value is neither the probability that the new strain is
better nor the probability that it is not. When we say that the increase
is significant, what we are saying is that if the new strain were no better
than the old, the probability of such a large increase happening by chance
would be less than 5%. Consequently, we are willing to accept that the new
strain is better.

Why have statisticians fastened on such a small value? Wouldn't it seem
reasonable that if there is less than a 50-50 chance of such a large
increase we should infer that the new strain is better, whereas if the
chance is greater than 50-50 - in racing terms if it is "odds on" -
then we should infer that it is not.

No, and the reason why not is simple: it's a question of the burden of
proof. Remember that statistics is about taking decisions in the face of
uncertainty. It is serious business recommending that a company changes
the variety of seed it produces and that farmers should switch to planting
the new one. There could be a lot of money to be lost if we are wrong. We
want to be sure beyond reasonable doubt, and that's usually taken to mean
a p-value of .05 or less.

Suppose that we obtain a p-value greater than .05, what then? We have
failed to prove that the new strain is better. We have not, however,
proved that it is no better, any more than by finding a defendant not
guilty we have proved him innocent.

In the example of the antique coin coming up three heads in a row, the
null hypothesis was that the coin was fair. If so, then the probability of
a head on any one toss would be 1/2, so the probability of three in a row
would be (1/2)3=0.125. This is greater than .05, so we cannot reject the
null hypothesis, i.e. we cannot claim that our experiment has shown the
coin to be biased. Up to that point, the reasoning was correct. Where it
went wrong was in claiming that the experiment has shown the coin to be

Yet that is precisely the sort of argument we see in scientific papers
defending genetic engineering. A recent report, "Absence of toxicity of
Bacillus thuringiensis pollen to black swallowtails under field
conditions" (2) is claiming by its title to have shown that there is no
harmful effect. Only in the discussion, however, do they state correctly
that there is "no significant weight differences among larvae as a
function of distance from the corn field or pollen level".

A second paper claims to show that transgenes in wheat are stably
inherited. The evidence for that is the "transmission ratios were shown to
be Mendelian in 8 out of 12 lines". In the accompanying table, however,
six of the p-values are less that 0.5 and one of them is 0.1. That is not
sufficient to prove that the genes are unstable, or inherited in a
non-Mendelian way. But it certainly does not prove that they are, which is
what is claimed.

The way to decide if the antique coin is biased is to toss it more times
and record the outcome; and in the case of the safety and stability of GM
crops, more and better experiments should be done.

The Anti-Precautionary Principle

The precautionary principle is such good common sense that one would
expect it to be universally adopted. Naturally, there can be disagreement
on how big a risk we are prepared to tolerate and on how great the
benefits are likely to be, especially when those who stand to gain and
those who will bear the costs if things go wrong are not the same. It is
significant that the corporations are rejecting proposals that they should
be held liable for any damage caused by the products of GM technology.
They are demanding a one-way bet: they pocket any gains and someone else
pays for any losses. It's also an indication of exactly how confident they
are that the technology is really safe.

What is baffling is why our regulators have failed and continue to fail to
act on the precautionary principle. They tend to rely instead on what we
might call the anti-precautionary principle. When a new technology is
being proposed, it must be permitted unless it can be shown beyond
reasonable doubt that it is dangerous. The burden of proof is not on the
innovator; it is on the rest of us.

The most enthusiastic supporter of the anti-precautionary principle is the
World Trade Organisation (WTO), the international body whose task it is to
prevent countries from setting up artificial barriers to trade. A country
that wants to restrict or prohibit imports on grounds of safety has to
provide definitive proof of hazard, or else be accused of erecting false
barriers to free trade. A recent example is WTO's judgement that the EU
ban on US growth-hormone injected beef is illegal.

Politicians should constantly be reminded of the effects of applying the
anti-precautionary principle over the past fifty years, and consider their
responsibility for allowing corporations to damage our health and the
environment, which could have been prevented. I mention just a few: mad
cow disease and new variant CJD, the tens of millions dead from cigarette
smoking, intolerable levels of toxic and radioactive wastes in the
environment that include hormone disrupters, carcinogens and mutagens.


There is nothing difficult or arcane about the precautionary principle. It
is the same sort of reasoning that is used in the courts and in
statistics. More than that, it is just common sense. If we have genuine
doubts about
whether something is safe, then we should not use it until we are
convinced it is all right. And how convinced we have to be depends on how
much we need it.

As far as GM crops are concerned, the situation is straightforward. The
world is not short of food; where people are going hungry, it is because
of poverty. There is both direct and indirect evidence to indicate that
the technology may not be safe for health and biodiversity, while the
benefits of GM agriculture remain illusory and hypothetical. We can easily
afford a five-year moratorium to support further research on how to
improve the safety of the technology, and into better methods of
sustainable, organic farming, which do not have the same unknown and
possibly serious risks.

Notes and references

1. See, for example Holm & Harris (Nature 29 July, 1999).
2. Wraight, A.R. et al, (2000). Proceedings of the National Academy of
Sciences (early edition). Quite apart from the use of statistics, it
generally requires considerable skill and experience to design and carry
out an experiment that will be sufficiently informative. It is all too
easy to fail to find something even when it is there. Our failure to
observe it may simply reflect a poor experiment or insufficient data or
3. Cannell, M.E. et al (1999). Theoretical and Applied Genetics 99
(1999) 772-784.

< Policy-14.doc>>

Mrs Y. Royals
Biology Dept
Open University
Milton keynes
Tel: +44 1908 653318
Fax: +44 1908 654167
e-mail: y.royals@open.ac.uk

Date: Jul 17 2000 03:07:15 EDT
From: Phil Larkin
Subject: reductionism

What is reductionist thinking anyway? Why is it used in a pejorative sense
of biotech crop breeding?

Is it reductionist to pull a hat on when its cold? Yes, it ignores the
complexities of global weather patterns and doesn't make it warmer for
small furry animals. But it does keep your ears warm.

Is it reductionist to eat a salad for lunch? Yes, it is only a short-term
solution; you will be hungry again by 6 pm and it was inadequate for daily
protein intake. However it tasted good and kept you going for a few hours.

Is it reductionist to spray copper salts on organic cabbages? Yes. But it
kills and deters some insects and fungi. And it is only reductionist in a
na´ve and dangerous sense if other risks and precautions are not
considered. Copper sprays have caused so-called Wilson's disease in French
vineyard workers; they build up in soils and are toxic to fish and bees.

Is it reductionist to backcross disease resistance from a grass into
wheat? Yes. But such breeding has protected many crops. Wheat breeders
never rest on their laurels; they anticipate breakdown of the resistance
and their breeding programs are continuous and responsive to what is
happening in the

Is it reductionist to deploy a known and cloned gene to protect cotton
from lepidopteran pests? Yes. But it is also effective and uses a protein
which can be expected to be more benign to agricultural workers,
non-target species and the environment in general. Again it is important
to be wise in its deployment, recognising the possibilities of complex
pest biology developing resistance.

All human actions are reductionist because we are finite creatures - we do
what we can. Plant breeders who are naively reductionist are not very
successful and probably will not keep their jobs for very long.

On the other hand holistic is a term sometimes used as a smokescreen for
actions which are poorly understood but pursued because they fit into a
particular world view. The actions described as holistic upon analysis may
be just as reductionist (finite and limited in their effects) but
surrounded by wholesome sounding rhetoric. I would submit that this
reductionist/holistic dichotomy is not very useful in our current debate.
The wisdom or otherwise of our actions must be determined on their merits
and their realised effects.

Phil Larkin

Dr. Phil Larkin
CSIRO Plant Industry
P.O. Box 1600 Canberra, 2601
ph 61 2 6246 5060
fax 61 2 6246 5000