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PHILOSOPHY PATHWAYS electronic journal


P H I L O S O P H Y   P A T H W A Y S                   ISSN 2043-0728

Issue number 129
14th September 2007


I. 'Lessons Zimbabwe can learn from Voltaire's philosophy' by Francis Moyo

II. 'A View on Statistical Schools' by Jordi Vallverdu

III. 'Maverick' a short story by Amer Naveed



In this issue we have a second article from Zimbabwe, by Francis Moyo who holds
an MA from the University of Zimbabwe, following the article by BA student
Munamato Chemhuru in issue 126. Given the stream of bad economic news that has
recently come from that country, it strikes me as remarkable that anyone has
time to philosophise. In his brave call for universal tolerance, Francis Moyo
shows how important it is to keep alive the spirit of philosophy.

Whenever decisions need to be made under conditions of uncertainty, we rely on
our judgements of probability. It is a shock to the newcomer to philosopher
that the very same state of affairs can have different probabilities depending
on which theory of probability one subscribes to. But that is apparently how
things are with the Bayesian and Frequentist approaches. While Frequentists
derive judgements of probability from recording statistical frequencies,
Bayesians take into account how human beings actually make judgements on the
basis of limited evidence. Jordi Vallverdu from the Universitat Autonoma de
Barcelona shows how in practical science a blend of factors determines which of
the two approaches is adopted in a particular case.

For light relief -- or for those befuddled by formulae -- Amer Naveed's
character Behram is an object lesson in the vanity of those thinkers who never
consider the possibility that they might be wrong. His short story could also
be subtitled, 'A little knowledge is a dangerous thing.'

Geoffrey Klempner



According to Bernard of Chartres, a twelfth century philosopher of Neo-Platonic
tendencies: 'We are like dwarfs seated on the shoulders of giants; we see more
than the ancients and things more distant, but this is due neither to the
sharpness of our sight, nor the greatness of our stature but because we are
raised and born aloft on the giant mass'. In the collective quest to find an
efficacious and lasting solution to Zimbabwe's multi-faceted and complex
crisis, Zimbabweans ought to draw lessons from the ideas of past giants who
transmitted the syndrome of progressive thought. One such giant is Francois
Marie Aouet de Voltaire (1694-1778).

Voltaire was not a 'philosopher' in any sense approaching that of our modern
understanding of this term. He was through and through a literary man, whom we
are told, ardently wished and for the most part ardently worked for the
improvement of the human condition. In the course of his quest to better the
lives of fellow men, Voltaire made use of human disciplines that were relevant,
and among those philosophy took pride of place. Today, Voltaire is well-known
for the philosophical assertion he made to the effect that: 'I may disagree
with what you have to say but I will defend to death your right to say it.'

It is so unfortunate that in the present-day Zimbabwe the political, and even
the socio-economic climate is at variance with Voltaire's seminal insight. The
tremendous challenges of Mount Everest proportions faced by this small southern
African country are not political, social and even economic in nature. Well, of
course I must quickly admit that Zimbabwe's challenges manifest themselves in
economic and socio-political forms, but these are merely symptoms and they are
by no means the cause of the problem. Just like a disease shows itself through
a vast array of symptoms such as weight loss, nausea, skin rash, lack of
appetite etcetera; Zimbabwe's problems are seen through the record-breaking
runaway four-digit inflation (officially), very high unemployment rate of over
80%, the world's lowest life expectancy and grinding poverty. The tremendous
and serious challenge facing Zimbabwe is not any one of those symptoms; it is
the problem of intolerance.

Notice that I did not prefix intolerance with the term political, as many
analysts of the Zimbabwe crisis are wont to do. That again would be a plain and
simplistic way of analyzing the situation. Zimbabwe's problems in their richly
variegated form hinge on the question of intolerance, period! This intolerance
has engendered polarization, which in turn has brought about an eclipse of
peace. Ours is a country where people would not defend with death your right to
say something, especially when you disagree with them. Instead, you ought to
expect to find yourself at the receiving end of persecution for holding
different opinions. Someone once jokingly said that in Zimbabwe there is
freedom of speech but not freedom after speech. This is an unfortunate state of
affairs that highlights how low we have descended as a country. The tolerance
levels in the country are at their buck. Intolerance, sadly, creates
deep-seated division in people's minds, which in turn is likely to get them
polarized even physically. To illustrate the severity of the problem, I will by
means of a story highlight how dangerous intolerance can become.

It is said that through some very cruel hand of fate, four Zimbabweans of
diametrically opposed groupings and backgrounds, suddenly found themselves
stranded in one hut somewhere in the Antarctica, the coldest, highest,
windiest, driest, and iciest continent on earth. Typically, the temperature was
extremely low and the four Zimbabweans found the weather very inclement. To keep
themselves from freezing to death the four, who included a staunch ZANU-PF
supporter, an MDC supporter, a member of the majority Shona ethnic group and
finally, a member of the Ndebele minority ethnic group; all huddled together
around a fire, whose flames were ebbing low. 

This hut housing this foursome was disturbingly quiet - like a mortuary - as
these four had no common ground that would have enabled them to break the ice
that polarized them. The flames of the fire ebbed even lower and the fire
needed to be rekindled quickly in order for these four Zimbabweans to remain
warm and alive. But none in the room could find it in him to add more firewood
(which was in good supply in the very room itself!) to the fire brazier. Thirty
minutes passed and still no one had volunteered to regenerate the fire. One hour
went by. Another passed, and the fire had died out and so had the four men.

How did that happen, you must be asking yourself? It's very simple! The ZANU-PF
supporter must have said in his heart that he could not be seen kindling the
fire that would give warmth and life to the member of the opposition MDC, a
puppet party dancing to the tune of the evil West. Consequently, he decided to
sit on his laurels and watch the fire die down. The MDC supporter, in turn,
rationalized that it would be morally wrong for him to rekindle the fire that
would make the same ZANU-PF loyalist who had beaten him and his other comrades
up in the run-up to the previous Presidential and parliamentary polls. The
Shona gentleman in the room argued with himself that he could not keep the fire
alive so that the Ndebele man in the same room could receive warmth. After all,
was it not this same Ndebele guy's forefathers under King Lobengula who had
looted his grand grandparent's cattle and women? The Ndebele man philosophized
that Shonas were ruthless people who had butchered over 20,000 of his kinsmen
during the Gukurahundi massacres in the early 1980s.

The pathologist who did the autopsy on these four had this to write on each
death certificate as the cause of death: 'Died from the cold within'. There was
no apter way the doctor could have put it; the deceased men had died from the
cold within and not the cold without. The moral of this story is that
intolerance and polarization in any given society can be dangerous and even
fatal at times. Intolerance and subsequent polarization in any given society is
based on the logical fallacy known as tu quoque. This Latin name of this error
in reasoning can be translated to mean, 'You, also' or 'You're another'. Tu
quoque is a very widespread fallacy in which one tries to shield oneself or
another from criticism by turning the critique back against the accuser. This
is a classic red herring since whether the accuser is guilty of the same, or a
similar, wrong is unrelated to the truth of the original charge. However, as a
diversionary tactic, tu quoque can be very useful, since the accuser is put on
the defensive, and frequently feels compelled to defend against the accusation.

A case that suddenly comes to mind is the 3/11 brutal beatings of opposition
and civic society leadership who sought to be part of a prayer rally, whose
standpoint the ruling ZANU-PF government did not concur with. Voltaire's
exhortation to tolerance is a lesson that could have been heeded then. The
government led by President Robert Mugabe could have opted for peace and not
force, tolerance instead of violence. However, it is pointless to cry over
spilt milk, but lessons abound from that unfortunate piece of this country's
post-Independence history. As Zimbabwe inches towards harmonized Presidential
and Parliamentary elections to be held in March 2008, there is great need to
not only learn tolerance but also to practice it. Posterity will judge
Zimbabweans harshly if this important lesson is not learnt sooner rather than
later. As a country, Zimbabwe needs to learn the importance of tolerating
divergent viewpoints.

It is not only from the 3/11 beatings that Zimbabweans can draw lessons on the
importance of creating an environment conducive for tolerance.
Post-Independence Zimbabwean history is replete with incidences from which we
could draw more lessons. In the early 1980s, it was considered sinful, immoral
and indeed treasonous to the powers that be in the country to belong to an
ethnic minority Ndebele group and openly support the late and then opposition
PF-ZAPU party leader Dr. Joshua Mqabuko Nkomo. More than 20,000 people lost
their lives as a result of this intolerance. The status quo lasted until the
signing of the Unity Accord on 22 December 1987. Thereafter, 'the other' has
comprised of the opposition ZUM members, white commercial farmers, opposition
MDC members and leadership, student leaders, civic society, Catholic Bishops,
and lately, Archbishop Pius Ncube.

Besides tolerance, Zimbabwe also badly needs healing. To quote an editorial in
the May 2007 edition of the Mukai-Vukani, Jesuit Journal for Zimbabwe: 'We need
to respond to our problem by healing human relationships, healing tortured
bodies and broken limbs, healing those dying of neglect, starved by the greedy
ones.' Tolerating each other's political viewpoints and working towards healing
are but a foundation to building a better Zimbabwe where safety and success
don't depend on political patronage. They also form the basis of for
peace-building and other mediation efforts, such as the one that South African
President Thabo Mbeki is trying to undertake.

The onus of building a better Zimbabwe does not rest on President Mbeki's
efforts alone, but on each and every Zimbabwean doing some soul-searching and
if necessary embarking on a behaviour and attitude change programme (BACP). A
better Zimbabwe will come about only if all work towards seeing the triumph of
love over oppression. The BACP philosophy that I am proposing will counter the
categorical fallacy that politics is the preserve of politicians alone, and
that they alone have the final say on all matters political. Its time we cut
through the mist and fog of this big lie. Politics, as someone once said, is
too serious a matter to be left to the politicians alone. It is everyone's

(c) Francis Moyo 2007

Email: live2think@yahoo.com

University of Zimbabwe
Department of Philosophy
P.P. Box MP 167
Mount Pleasant




There are two main opposing schools of statistical reasoning, 'Frequentist' and
'Bayesian' approaches. Until recent days, the Frequentist or classical approach
has dominated the scientific research, but Bayesianism has reappeared with a
strong impulse that is starting to change the situation. Recently the
controversy about the primacy of either of the two approaches seems to be
unfinished at a philosophical level, but scientific practice is giving an
increasingly important position to the Bayesian approach. This paper focuses on
the pragmatic point of view of scientists' day-to-day practices, in which
Bayesian methodology is very useful. Several facts and operational values are
described as the core-set for understanding the change.

I. What does 'Bayesian' or 'Frequentist' mean?

The Bayesian and Frequentist theories are the leading ways to understand the
various uses of statistics:

The Bayesian perspective on probabilities says that a probability is a measure
of a person's degree of belief in an event, given the information available
(Dale, 2003). Thus, probabilities refer to a state of knowledge held by an
individual, rather than to the properties of a sequence of events. The use of
subjective probability in calculations of the expected value of actions is
called subjective expected utility. There has been a renewal of interest for
Bayesianism since 1954, when L.J. Savage wrote Foundations of Statistics. There
are a large number of types of Bayesians (ironically, Good I.J. (1971) talked
about '46656 kinds of Bayesians', Amer. Statist., 25: 62-63.), depending on
their attitude towards subjectivity in postulating priors. Recent Bayesian
books are: Earman (1992), Howson & Urbach (1989), Bernardo & Smith (1996).

Frequentists understand probability as a long-run frequency of a 'repeatable'
event, and have developed a notion of confidence intervals. Probability would
be a measurable frequency of events determined from repeated experiments.
Reichenbach, Giere or Mayo have defended that approach from a philosophical
point of view, referred to by Mayo (1997) as the 'error statistical view' (as
opposed to the Bayesian or 'evidential-relation view'). As Giere (1988: 189)
asks: 'Are Scientists Bayesian Agents?... The overwhelming conclusion is that
humans are not Bayesian agents' (where Knill et al 1996 defend the opposite

How to choose?

One of the recurrent arguments against/ in favor of one of the two positions
(Frequentist or Bayesian) consists in saying that, 'A true scientist is always/
never a Frequentist/ Bayesian' (you can choose between the two possibilities,
Cousins, 1995, Efron 1986). It seems to be an epistemological law about
statistical practices: 'A true scientist never belongs to the opposite
statistical school.' What I can say is that this is a usual metatheoretical
thought about Frequentist/ Bayesian approaches and their ontological fitting
with reality, which is not useful for clarifying the closure of scientific
controversies, because they depend on another kind of values. We cannot know
what happens exactly in scientists' minds, but we can know how they act and,
therefore, infer from their actions how they think.

Obviously, we suppose and accept cognitive activity for scientists. The
question is: at what cost can we introduce cognitive arguments inside the
statistical techniques embedded in scientific practices? And when we talk about
'cognition' me must include not only rational aspects of cognition, but also
irrational ones (Thagard, 1992). In addition, Causality is a complex and
ancient problem: the Philosopher of Science Paul Thagard (1999) has offered a
very powerful conceptual framework to understand scientific causal explanations
(specially, of diseases) with his idea of 'causal network instantiation' (p.
114). According to him: 'causal networks are not simple schemas that are used
to provide single causes for effects, but they instead describe complex
mechanisms of multiple interacting factors', p. 115-116. But Thagard is no
Bayesian: he pursues another line of explanation which he considers better
suited to psychological reasoning: explanatory coherence. (Ibid. p. 65-66).

But we must go to the core set of the question: what are the new values, beyond
scientific values from Merton, implied in the choice between the two schools?

2.1. Formational values

We will use the words of Bland & Altman (1998): 1160, to illustrate these kinds
of values:

'Most statisticians have become Bayesians or Frequentists as a result of their
choice of university. They did not know that Bayesians and Frequentists existed
until it was too late and the choice had been made.' There have been subsequent
conversions. Some who were taught the Bayesian way discovered that when they
had huge quantities of medical data to analyze the Frequentist approach was
much quicker and more practical, although they remain Bayesian at heart. The
same idea is repeated in a different way by a High Energy physicist, D'Agostini
(1998:1): 'The intuitive reasoning of physicists in conditions of uncertainty is
closer to the Bayesian approach than to the Frequentist ideas taught at
university and which are considered the reference framework for handling
statistical problems.' -- One thing is the theory taught at university, and
another one is the true scientific practice.

Some Frequentists have had Damascus road conversions to the Bayesian view (like
Harrell, Frank E. Jr, 2000 Practical Bayesian data Analysis from a Former
Frequentist, downloadable PDF document at
http://hesweb1.ed.virginia.edu/biostat/teaching/bayes.short.course.pdf). Many
practicing statisticians, however, are fairly ignorant of the methods used by
the rival camp and too busy to have time to find out. As the epidemiologist
Berger says (2003): 'Practising epidemiologists are given little guidance in
choosing between these approaches apart from the ideological adherence of
mentors, colleagues and editors.' Giles (2002), talking about members of the
Intergovernmental Panel on Climatic Change (IPCC), says that those researchers
were suspicious of Bayesian statistics because 'these attitudes also stem from
the authors' backgrounds', p. 477.

So, the arguments go beyond the ethereal philosophical arena and closer to
practical ones. Better opportunities to find a good job is an important
argument, and the value of a Bayesian academic training is now accepted: 'Where
once graduate students doing Bayesian dissertations were advised to try not to
look too Bayesian when they went on the job market, now great numbers of
graduate students try to include some Bayesian flavor in their dissertations to
increase their marketability', Wilson (2003): 372.

2.2. Metaphysical values

By their writings, we can extract some information about scientist's thoughts.
Knowledge is framed by feelings, emotions, facts, and even faiths. How to
consider, then, classical and continuous disputes among the full range of
possible positions between realists and subjectivists? (Savage, 1954; Suppes,
1970; Weatherford, 1982).
All scientists believe for different reasons, that the constituents of the
world have certain dispositions that can be discovered under certain
investigative conditions. As expressed by Hacking (1972): 133: 'Euler at once
retorted that this advice is metaphysical, not mathematical. Quite so! The
choice of primitive concepts for inference is a matter of 'metaphysics'. The
orthodox statistician has made one metaphysical choice and the Bayesian

2.3. Simplicity and cheapness: computerizing statistical thought

One of the arguments against Bayesian methods says that the Bayesian approach
is too complex to apply in day-to-day research. And simplicity is one of the
best values for scientific activity. But during the past few years a large
amount of Bayesian software programs have appeared which have changed the
situation: now it is easy, fast and cheap to implement the Bayesian approach in
experimental practices (Escoto 2003; Gigerenzer 1998). Programs like BACC, [B/
D], BOA, BUGS (Bayesian inference using Gibbs sampling, and WinBUGS), MINITAB,
EPIDAT, FIRST BAYES, HYDRA, STATA, SAS, S-Plus and others, some of them
available as freeware, make possible an efficient use of Bayesian methods in
several scientific fields. Their flexibility helps to incorporate multiple
sources of data and of uncertainty within a single coherent composite model.

Until the 1980's, the potential for the application of Bayesian methods was
limited by the technical demands placed on the investigator. Over the past
fifteen years these limitations have been substantially reduced by innovations
in scientific computing (faster computer processors, according to NAS, 1991)
and drastic drops in the cost of computing (Editorial BMJ, 1996). These changes
and an increase in the number of statisticians trained in Bayesian methodology
are encouraging the new status of Bayesianism (Tan, 2001).

Medicine is, perhaps, the scientific field in which Bayesian analysis is being
more intensively applied (Szolovits, 1995; Grunkemeir & Payne, 2002). Two
trends, evidence-based medicine and Bayesian statistics are changing the
practice of contemporary medicine. As Ashby & Smith (2000) tells us: 'Typically
the analysis from such observational studies [those of epidemiology] is complex,
largely because of the number of covariates. Probably for this reason, Bayesian
applications in epidemiology had to wait for the recent explosion in computer
power, but are now appearing in growing numbers', p. 3299 (see also Breslow,
1990 and Ashby & Hutton, 1996.

The development of Markov Chain Monte Carlo (MCMC) computation algorithms, now
permit fitting models with incredible realistic complexity. When we study
models for multiple comparisons, we can see that Frequentists adjust Multiple
Comparison Procedures (MCP) considering intersection of multiple null
hypotheses. They also advocate for a control of the familywise error-rate
(FWE). So, 'Bayesians will come closer to a Frequentist per-comparison or to a
FEW approach depending on the credibility they attach to the family of (null)
hypotheses being tested... the Bayesian is closer to the per-comparisonist',
Berry & Hochberg (1999): 216. The Bayesian approach has received a great
impulse from MCMC models (Dunson, 2001; Carlin & Louis, 2000; Gelman et al
1996). MCMC procedures are also extremely flexible and constitute the primary
factor responsible for the increased use and visibility of Bayesian methods in
recent years.

2.4. Ethical values

We can find an appeal to ethical values as parts of arguments about both
schools. Wilson (2003) affirms that Bayesian methods are a more ethical
approach to clinical trials and other problems. On the contrary, Fisher (1996)
affirms that 'Ethical difficulties may arise because of the differing types of
belief', especially during Randomized Clinical Trials (the Phase III Trials in
the FDA model).

From the history of standard literature on ethics in medical research, one can
infer the great value of prior beliefs in clinical trials. And the key concept
is 'uncertainty': 'Subjective opinions are typically not included in the
background material in a clinical trial protocol, but as they are often a
driving force behind the existence of a protocol, and as uncertainty is deemed
to be ethically important, documentation will be useful. Without documentation
it may be difficult to determine whether uncertainty exists... There are
compelling ethical reasons that uncertainty should be present before a clinical
trial is undertaken' (Chaloner & Rhame, 2001: 591 and 596). When uncertainty is
introduced in the reasoning procedures, the quantification of prior beliefs
and, therefore, the use of Bayesian methodologies, seems to be an operationally
and ethically better decision.

2.5. Better fitting for results

Berger (2003), proposes using both models and studying case by case their
possibilities: 'Based on the philosophical foundations of the approaches,
Bayesian models are best suited to addressing hypotheses, conjectures, or
public-policy goals, while the Frequentist approach is best suited to those
epidemiological studies which can be considered 'experiments', i.e. testing
constructed sets of data.' Usually, we find no such equitable position. But
this is not a theoretical question but a practical one: Bayesian methods work
better than Frequentist. Therefore, Bayesian methods are increasing their
application range, although it does not always mean that there are more 'true

As Wilson (2003) explains: 'their methodological successes [from Bayesian] have
indeed impressed many within the field and without, but those who have adopted
the Bayesian methods have often done so without adopting the Bayesian
philosophy'. As the Editorial from British Medical Journal (1996) states, 'most
people find Bayesian probability much more akin to their own thought
processes... The areas in which there is most resistance to Bayesian methods
are those were the Frequentist paradigm took root in the 1940s to 1960s, namely
clinical trials and epidemiology. Resistance is less strong in areas where
formal inference is not so important, for example during phase I and II trials,
which are concerned mainly with safety and dose finding.'

Therefore, Popper or Lakatos could say: 'Bayesian methods solve problems better
than Frequentist ones'. And practical success usually means the theory's
success. Look to the history of science: Copernicus astronomical tables were
better than those of Ptolomeus and if at first, were accepted as an instrument,
in a later they were considered as a true representation of reality.

The Scientific Information and Computing Center at CIBA-GEIGY's Swiss
headquarters in Basle moved towards the systematic use of Bayesian methods not
so much as a result of theoretical conviction derived from philosophical
debates, but rather as a pragmatic response to the often experienced inadequacy
of traditional approaches to deal with the problems with which CIBA-GEIGY
statisticians were routinely confronted (Racine et al, 1986). An example:
clinical trials made by pharmaceutical industries are usually Bayesian (Estey &
Thall, 2003) although such methods are not easily implemented (Wang et al, 2002).

Bayesian methods are ideally suited to dealing with multiples sources of
uncertainty, and risk assessment must include a lot of them: one experiment can
be affected by several terms like sex, age, occupation, skill of technician,
number of specimens, time of sampling, genetic background, source of intake.
So, according to an epidemiologist, Dunson (1991): 1225: 'Bayesian approaches
to the analysis of epidemiological data represent a powerful tool for
interpretation of study results and evaluation of hypotheses about
exposure-disease relations. These tools allow one to consider a much broader
class of conceptual and mathematical models than would have been possible using
non-Bayesian approaches'.

Grunkmeier & Payne (2002: 1901), talking about surgery enumerate several
advantages of Bayesian statistics applied to it: '(1) providing direct
probability statements -- which are what most people wrongly assume they are
getting from conventional statistics; (2) formally incorporating previous
information in statistical inference of a data set, a natural approach which
follows everyday reasoning; and (3) flexible, adaptive research designs
allowing multiple examination of accumulating study data.' The Bayesian
approach is more efficient at unifying and calculating multilevel causal

2.6. Diffusion of science: guidelines

At the core of science lies information communication. By the process of
writing and communicating his/ her results, a scientist is at the same time
evaluated (through peer review) and judged (by his/ her colleagues). All the
norms implied in the guidelines, define a trend in 'good' scientific practices.
And those groups who control the communication channels can make sure that
special kinds of ideas are never allowed. Therefore, design and control of
communication channels is something crucial for the interest of a community.

The Frequentist approach has dominated statistics journals all through 20th
Century but, recently, Bayesians are gaining more and more power. As Wilson
(2003): 372, says: 'Bayesians have successfully and extensively published in
JASA and other prominent journals, bringing their methods into the spotlight
where they cannot be ignored'. It is not only a question of general perception
but also of radical changes in the bases of the epistemic frame

The International Committee of Medical Journal Editors, wrote the Uniform
Requirements for Manuscripts Submitted to Biomedical Journals, available at
http://www.icmje.org, where they specified for statistical norms: 'Avoid
relying solely on statistical hypothesis testing, such as the use of P values,
which fail to convey important quantitative information.' Nevertheless, we must
also recognize that the use of statistical methodologies in medical research is
highly controversial, beyond the Bayesian-Frequentist dilemma (Altman et al,
2002). Spiegelhalter (1999) reflects that: 'Current international guidelines
for statistical submissions to drug regulatory authorities state that 'the use
of Bayesian and other approaches may be considered when the reasons for their
use are clear and when the resulting conclusions are sufficiently robust.'

So, these new trends 'accepted' as the new axiological frame for statistical
research have changed the weight of both schools: while Frequentist models are
decreasing their expansion, Bayesian ones are being employed in an increasing
number of situations. Basanez (2004) has explained the reasons for this gradual
shift: practical, theoretical and philosophical.

3. Framing values: conclusions about theories and uses

My old Webster's Dictionary has its own definition of 'dilemma': '1. a
situation requiring a choice between equally undesirable alternatives. 2. any
difficult or perplexing situation or problem. 3. Logic. a form of syllogism in
which the major premise is formed of two or more hypothetical propositions and
the minor premise is an exhaustive disjunctive proposition, as: If A, then B;
if C then D. Either A or C. Therefore, either B or D.'

It seems clear that we have not been talking about logic relationships inside
statistical controversies. Therefore, the third definition is not of interest.
The second one seems to be closer to the aims of this paper: the analysis of a
complex problem for which there is no obvious solution. However, the first
definition is the core of this paper: Does the Bayesian vs. Frequentist dilemma
constitute a difficult choice 'between equally undesirable alternatives'? Are we
forced to die for our rational criteria like Buridan's ass?

At a metatheoretical level, that is philosophy, the debate is still open and
more and more complex. But that is not the level of analysis we have considered
as crucial for the solution of the debate. We have talked about scientific
practices in which are involved both statistical approaches. And when
scientists work, they take decisions continuously.

We have shown a new range of values that constitute part of the statistical
axiology. These are non-epistemic values, but shape the underlying framework of
research epistemology. Academic training, ease of use, powerful infrastructures,
cognitive fitting, ethics, metaphysical options, cheapness, and better results,
are the arguments to decide in favor of either one of the two approaches.
Perhaps these are not the values which theoreticians would have chosen, but are
the real values which appear when we look at scientists' practices and

We don't know if the prediction made by Bruno de Finetti (1937), that it would
take until the year 2020 for the Bayesian view of statistics to completely
prevail will be accurate. This is another question, far from our interests and
methodology. I have indicated several values that make it possible to choose
between both approaches.

A clear fact is that Bayesian analysis is widely used in a variety of fields,
from the pioneering field of medicine to engineering, image processing, expert
systems, decision analysis, psychological diagnoses (Meehl & Rosen, 1955),
criminal investigations (Sullivan & Delaney, 1982), for presenting evidence in
court (Feinberg & Schervish, 1986; Matthew, 1994; Mossman, 2000), gene
sequencing, financial predictions, neural networks or epidemiological studies.
If we return to the classic paper of Winkler (1974) 'Why are experimental
psychologists (and others) reluctant to use Bayesian inferential procedures in
practice?', we read: 'This state of affairs appears to be due to a combination
of factors including philosophical conviction, tradition, statistical training,
lack of 'availability', computational difficulties, reporting difficulties, and
perceived resistance by journal editors.' Well, all these factors
(non-epistemic values) are now not against but in favor of the Bayesian

Is the solution to unify as a synthesis both approaches (Berger et al, 1997),
like a synthesis solution to a dualistic problem? Could a hybrid method of
inference satisfy both camps? Is the 'Likelihood' approach a third alternative?
(Senn, 2003). But this is, once more, a philosophical question.

Finally, we must consider the existence of a really fundamental question: how
to make decisions based on evidence. And we find a basic problem: there are
several decision levels with their own individual exigencies regarding what is
considered as evidence. If we talk about decision making in health
controversies, we should consider several levels like: decision making for
patients (diagnosis), decision making for individual patients (interventions),
decision making about studies (start from prior beliefs and data monitoring),
decision making for pharmaceutical companies and public policy decision making
(Ashby & Smith, 2000). But these multi-criteria analyses can be found in other
scientific fields, such as forestry (Kangas & Kangas, 2004). And we find
another set of problems present in both approaches when they are applied to
controversial scientific practices, such as those of risk assessment:
difficulties in establishing clear relationships, the significant sample, data
interpretation, cognitive paradoxes (Simpson, Ellsberg, St. Petersbourg,
Base-Rate Fallacy, Kahneman & Tversky, 1982), the idea of evidence at multiple
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(c) Jordi Vallverdu 2007

E-mail: Jordi.vallverdu@uab.es

Jordi Vallverdu, Ph.D.
Philosophy Department
Universitat Autonoma de Barcelona
E-08193 Bellaterra (BCN)



Life has always been strange as well as colorful. Sorrow, when it is strange
and joy, when it is colorful. Life is just life; it is the mental state of the
individual that determines the aspect of joy or sorrow. Life is all about
people. All other things in it are relative to man, the interpreter. There are
various kinds of people in it. Behram was also a special kind. When he used to
speak he talked incessantly and when he was mute it also lasted for long. He
was a maven of human psyche. He was always different; perhaps, he wanted to be.
This trait makes people hold different opinions about him. Some consider him a
scoundrel and for a few others he was a genius. Well, people and their
opinions, this also is a matter for different opinions.

Anyhow, we are discussing the personality of Behram. Ah! Gossiping, it goes to
anywhere. They are as long as Satan's entrails. We will narrate some stories of
his life. You are the judge to square up what he was. Again, you are the people
and it's all about interpretation.

You have all studied mathematics in your educational career. Generally, when we
solve any problem we say that Left Hand Side is equal to Right Hand Side. In
mathematical terminology it is stated as: L.H.S = R.H.S. While Behram always
insisted on stating it as: R.H.S = L.H.S. And to our amazement he always proved
it. He has his own reasoning for this assertion; as he says, the majority of us
are right handed, and according to the democratic principle the majority is
always right. He further accused academia and government for promoting the
false view.

Another argument of his was that, 'We are rightist against the leftist,
communist and socialist. Therefore, it is our moral duty to promote right-ism
in all spheres of life.' Then, he said that, 'Linguistically right means true
and left means abandoned, so logically it is more valid and correct to say,
R.H.S = L.H.S.' 

But what to do of School Master Nazar sahib, who was always annoyed about this
claim? Some people tried to patch up the difference. 'Whether one can touch
one's ear from here or from there, what difference does it make? The answer
remains the same, so both are correct.' Nonetheless, both Nazar sahib and
Behram stuck to their point. Nazar sahib got so fed up that he stated to the
Principal sahib that, 'Either you expel him or me.' The Principal resolved the
issue by transferring Behram to the other section. In this way, Behram came to
section B from section A.

Here, Nizami sahib like an affectionate teacher accepted Behram's R.H.S = L.H.S
view. However, now Behram took the issue of the circumference of a circle and
asserted that in this case R.H.S = L.H.S can't hold true. The circle just keeps
on going around and around and around. It follows, then, that what happened has
to happen. Nizami sahib also came to share the views of Nazar sahib.

Behram then got interested in physics and was so much impressed by the law of
inertia that he passed four years in ninth class. At that time he was
eulogizing the virtues of inertia. He was highly critical of the Newtonian laws
of motion. It was because of Newton's incapacity to cope with the ultimate
phenomenon of metaphysics that he promoted these laws; Behram reckoned so. He
called inertia, Nirvana, a state of bliss, a complete peace and a way of life.

He was not seen happier than in those days. Perhaps, those were the Golden days
of his life with a Golden truth of inertia. He might have adhered to this
principle for life but one of his uncles got appointed Chairman of the school
committee and on the basis of his influence he was promoted to the next class.
This was a shock of a lifetime to him. Whenever one's beliefs are broken that
gives a feeling of bewilderment. The pathos and suffering that results are
heart rending. This was an inexpressible state of mind. One is lost. One's
cherished beliefs are at stake.

Biology came to Behram's rescue. He got interested in Darwin's ideas and was
inspired by the principles of survival of the fittest and adaptability.
However, he learned that despite all his claims Darwin is also dead. He
meditated on death and then realized the falsity of the Darwinian principle
that no one can survive death. He abandoned these views but this time he was
more experienced. The shock was not so shocking to him. It's always the first
shock that is shockingly unbearable. Time passes by. He finished his schooling
and joined college.

Poverty, inflation, unemployment and money have always been hot topics. This
induced Behram's interest in Economics. Again he formulated his own theories.
He was of the opinion that basically it is thought that is the basic reason for
these problems. Therefore, these problems would be solved permanently if people
stop thinking. And his views about poverty are very notable: 'The reason for
poverty is that the number of poor are much greater than the number of rich,
therefore in order to maintain balance and for greater welfare of the people
the government should exterminate the poor.'

One way or other, Behram kept on passing examinations while exhibiting his
tenuity in school and college. Nowadays, higher education is extremely sought
after. It is considered must for progress. This was the impetus for his doing a
PhD. As you know for PhD, it is a prerequisite to do research and submit thesis.
He chose the topic: 'Which things induce intoxication.'

To this purpose, Behram diligently articulated a comprehensive research
methodology, consisting of primary and secondary research. He then chose a
special population, i.e. a group of people who are characterized as boozers. As
part of his secondary research he studied extensively on alcohol and related
matters. Anyhow, regardless of the good or evil nature of the job, one has to
work hard. Then he chose the population and divided them into five groups of
twenty persons each:

     Vodka Drinkers (V)
     Gin Drinkers (G)
     Whiskey Drinkers (Wh)
     Rum Drinkers (R)
     John Barleycorn Drinkers (J)

He adopted the observation, questionnaire and experimentation method (he tried
all these things himself). The gist of his research was:

     (a) V + W  = Iv
     (b) G + W  = Ig
     (c) Wh+ W  = Iwh
     (d) R + W  = Ir
     (e) J + W  = Ij

By combining the above equations:

     (f) Iv * Ig * Iwh * Ir * Ij = In

Putting the values of (a), (b), (c), (d) and (e) in equation (f), we get,

     (g) (V + W) (G + W) (Wh+ W) (R + W) (J+W) = In

Taking the common factor:

     (h) W (V + G + Wh + R + J) = In

W, is a symbol of water, which is a common factor.

After all the hard work and efforts the last stage was to submit the research
paper. Behram's findings were as follows:

It is obvious from the equation (h), that W is the factor which is common in
all the equations (a), (b), (c), (d) and (e). Hence, W is that factor which
causes intoxication.

Behram submitted his research report; there was lot of commotion and exchange
of hot arguments. We don't know what was the reason. When he was leaving the
academic block he gave this historical remark similar to that of Einstein: 'If
my theory will be proved Indians would say that I am citizen of the world and
Pakistanis would say that I am Pakistani; and if my theory will be disproved
then Indians would say that I am Muslim and Pakistanis would say that I am an
Indian agent.'

I never saw him again. What was the outcome? And what happened to him? These
are the questions that are a mystery till today. Some, who were positive that
he was a brainiac, still believe that it was a monumental work deserving high
academic credence and nomination for a Nobel Prize. Some people are of the
opinion that they have seen him in pubs, where he was still fully convinced
that 'W' is the factor, which causes intoxication. Therefore in order to avoid
inebriety, he only uses the other elements. While some others believe that a
scoundrel he was. Life is indeed enigmatic. Do you know if this mystery can
ever be solved? Anyway, as all others have their opinions, it is up to you to
judge what he was.

(c) Amer Naveed 2007

E-mail: giramer@hotmail.com

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