Re: Low exposure risk estimates

["Joe Alvarez" <jalvarez@auxier.com>]

A comment on Stephen Brown's Post
on Low Exposure Risk Estimates
by Joe Alvarez and Fritz Seiler

Dear Steve,

We have a problem with your post, and essentially it boils down to what we 
call expectation values and what we call knowledge.  Together with others 
who say that they 'know that the signal is there' you do not separate 
knowledge from expectation.  If a series of measurements is performed on a 
serial dilution and the results follow a given dilution/concentration 
model, then we can expect that the next dilution will also follow the 
model, even if we cannot measure the result.  Indeed, as long as the next 
dilution is on the order of what has been used thus far, we can usually 
quote an expectation value of the concentration and its confidence 
interval.  The assumption here is that no obstacle such as chemical 
potential or a quantum mechanical limit is likely to interfere. 
  Nevertheless, it would not be scientific to claim we know the 
concentration within those confidence limits.  Knowledge is based on 
measurements, and extrapolations give at most an expectation value but no 
proof.
Let us turn to the radio reception analogy: When the signal to noise is so 
low that no effort or device can extract the signal any more, then it is 
still possible, as you correctly assert, to predict an expectation value. 
 The predicted value is unscientific only if the investigator then claims 
that this value contradicts the finding of no value.  You seem to be saying 
that because you made measurements at successive dilutions (distances in 
this case) and modeled them (By the way, it is not 1 / r**2, but more like 
1 / r approximately at sufficient distances from the antenna), you know 
what the expectation value is and it, therefore, contradicts the finding of 
no signal.  We fear that this is not correct.   The prior measurements do 
not guarantee the expectation value or even ensure that it is likely. 
 Another effect may superpose its contribution and make the extrapolation 
invalid.  This is precisely what is happening at low doses in the radon / 
lung cancer relationship.  If you cannot measure it, you do not know what 
is going on down at low doses!
The characterization of the expectation value is a scientific 
responsibility and, as you note, it must be kept separate from policy.  If 
the manager of the policy chooses to assume that the expectation value is a 
'known' value and must be acted upon, then this is a choice which is made 
independently from any scientifically valid characterization and is purely 
political in nature.
Your introduction of the SOS analogy is very useful in respect to 
discussing science versus policy.  The measurements assert that a signal is 
not available because at that low a signal to noise ratio the presence of a 
signal is not discernible.  The empirical model asserts there is a signal, 
but that it is, given the level of noise, less than can be measured by any 
expenditure of effort.  Sometimes, however, the policy maker insists that 
something must be done based on the modeling alone. We must ask ourselves 
why?  So, to carry the analogy further, even if the presence of a signal is 
established in a receiver on the emergency channel, but the signal is not 
intelligible, it would be foolhardy to take any action unless more 
information is not available.  To assume that it is an SOS is reasonable, 
but taking any action is not.  Why?  Because there is no advantage to be 
gained from the modeling or the policy decision, if there is no reasonable 
indication that additional lives may be saved.


*************************
Joe Alvarez
Auxier & Associates, Inc
10317 Technology Dr, Suite 1
Knoxville, TN 37932
Email: jalvarez@auxier.com
Tel: 423-675-3669
FAX: 423-675-3677


Fritz A. Seiler, Ph.D.
Principal
Sigma Five Associates
P.O. Box 14006
Albuquerque, NM 87191-4006
Tel.    505-323-7848
Fax.    505-293-3911
e-mail: faseiler@nmia.com

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