PHILOSOPHY PATHWAYS Issue 129

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.'

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 business.

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

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.

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 view).

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?

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:---). 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.

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 another.'

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.

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.

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 Bayesians'.

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 relationships.

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:---, 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.

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 reflections.

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 approach.

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 levels (Jasanoff 1994).

Considering the previous arguments, we must admit that the dilemma, understood as a choice between equally undesirable alternatives, is a false dilemma. We have enough judgment elements to decide rationally between one of two approaches, and so do scientists from diverse fields, whose words we have reproduced here. To understand these decisions better we have enumerated a new set of values that needs to be included in a richer and sounder scientific axiology.

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

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

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.

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