The crazy bookmaker and the Cult of probability

A Critique of the Dutch Book Argument

 

Many neutral observers concur into thinking we are assisting to the formation of a new religion among hopelessly nerdy people.

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy (Unabridged)

I’m thinking of course on what has been called hardcore Bayesianism, the epistemology according to which each proposition (“Tomorrow it’ll rain”, “String theory is the true description of the world”, “There is no god” etc.) has a probability which can and should be computed under almost every conceivable circumstance.

In a previous post I briefly explained the two main theories of probabilities, frequentism and Bayesianism. In another post, I laid out my own alternative view called “knowledge-dependent frequentism” which attempts at keeping the objectivity of frequentism while including the limited knowledge of the agent. An application to the Theory of Evolution can be found here.

It is not rare to hear Bayesians talk about their own view of probability as a life-saving truth you cannot live without, or a bit more modestly as THE “key to the universe“.

While trying to win new converts, they often put it as if it were all about accepting Bayes theorem whose truth is certain since it has been mathematically proven. This is a tactic I’ve seen Richard Carrier repeatedly employing.

http://www.utne.com/~/media/Images/UTR/Editorial/Articles/Online%20Articles/2012/07-01/Quest%20for%20the%20Historical%20Jesus/u-proving-history.jpg

 

I wrote this post as a reply for showing that frequentists accept Bayes theorem as well, and that the matter of the dispute isn’t about its mathematical demonstration but about whether or not one accepts that for every proposition, there exists a rational degree of belief behaving like a probability.

 

Establishing the necessity of probabilistic coherence

 

One very popular argument aiming at establishing this is the “Dutch Book Argument” (DBA). I think it is no exaggeration to state that many committed Bayesians venerate it with almost the same degree of devotion a Conservative Evangelical feels towards the doctrine of Biblical inerrancy.

FOBTs on Green Lanes

Put forward by Ramsey and De Finetti, it defines a very specific betting game whose participants are threatened by a sure loss (“being Dutch booked”) if the amounts of their odds do not fulfill the basic axioms of probabilities, the so-called Kolmogorov’s axioms (I hope my non-geeky readers will forgive me one day for becoming so shamelessly boring…):

1) the probability of an event is always a real positive number

2)  the probability of an event regrouping all possibilities is equal to 1

3) the probability of the sum of disjoint events is equal to the sum of the probability of each event

 

The betting game upon which the DBA lies is defined as follows: (You can skip this more technical green part whose comprehension isn’t necessary for following the basic thrust of my criticism of the DBA).

 

 

A not very wise wager

 

Let us consider an event E upon which it must be wagered.

The bookmaker determines a sum of money S (say 100 €) that a person R  (Receiver) will get from a person G (Giver) if E comes true. But the person R  has to give p*S to the person G beforehand.

The bookmaker determines himself who is going to be R and who is going to be G.

 

Holding fast to these rules, it’s possible to demonstrate that a clever bookmaker can set up things in such a way that any better not choosing p respecting the laws of probabilities will lose money regardless of the outcome of the event.

Let us consider for example that a better wagers upon the propositions 

1) “Tomorrow it will snow” with P1 = 0.65  and upon

2) “Tomorrow it will not snow” with P2 = 0.70.

P1 and P2 violate the laws of probability because the sum of the probabilities of these two mutually exclusive events should be 1 instead of 1.35

In this case, the bookmaker would choose to be G and first get P1*S + P2*S = 100*(1.135) = 135 €  from his better R. Afterwards, he wins in the two cases:

– It snows. He must give 100 € to R because of 1).  The bookmaker’s gain is  135 € – 100 = 35 €

– It doesn’t snow. He must give 100 € to R because of 2).  The bookmaker’s gain is also 135 € – 100 = 35 €

 

Let us consider the same example where this time the better comes up with P1 = 0.20 and P2 = 0.3 whose sum is largely inferior to 1.

The Bookmaker would choose to be R giving 0.20*100 = 20 € about the snow and 0.3*100 = 30 € about the absence of snow. Again, he wins in both cases:

– It snows. The better must give 100 € to R (the bookmaker) because of 1).  The bookmaker’s gain is -30 – 20 +100 = 50 €

– It does not snows. The better must give 100 € to R (the bookmaker) because of 2).  The bookmaker’s gain is  -30 – 20 +100 = 50 €

 

In both cases, P1 and P2 having fulfilled the probability axioms would have been BOTH a necessary and sufficient condition for keeping the sure loss from happening.

The same demonstration can be generalized to all other basic axioms of probabilities.

 

The thrust of the argument and its shortcomings

https://i2.wp.com/i-love-my-life.com/wp-content/uploads/2011/08/Irrational.jpg

 

The Dutch Book Argument can be formulated as follows:

1) It is irrational to be involved in a bet where you’re bound to lose

2) One can make up a betting game such that for every proposition, you’re doomed to lose if the sums you set do not satisfy the rules of probabilities. In the contrary case you’re safe.

3) Thus you’d be irrational if the amounts you set broke the rules of probabilities.

4) The amounts you set are identical to your psychological degrees of belief

5) Hence you’d be irrational if your psychological degrees of beliefs do not behave like probabilities

 

Now I could bet any amount you wish there are demonstrably countless flaws in this reasoning.

 

I’m not wagering

One unmentioned premise of this purely pragmatic argument is that the agent is willing to wager in the first place. In the large majority of situations where there will be no opportunity for him to do so, he wouldn’t be irrational if his degrees of beliefs were non-probabilistic because there would be no monetary stakes whatsoever.

Moreover, a great number of human beings always refuse to bet by principle and would of course undergo no such threat of “sure loss”.

Since it is a thought experiment, one could of course modify it in such a way that:

“If you don’t agree to participate, I’ll bring you to Guatemala where you’ll be water-boarded until you’ve given up”. 

But to my eyes and that of many observers, this would make the argument look incredibly silly and convoluted.

 

I don’t care about money

Premise 1) is far from being airtight.

Let us suppose you’re a billionaire who happens to enjoy betting moderate amounts of money for various psychological reasons. Let us further assume your sums do not respect the axioms of probabilities and as a consequence you lose 300 €, that is 0.00003% of your wealth while enjoying the whole game. One must use an extraordinarily question-begging notion of rationality for calling you “irrational” in such a situation.

 

Degrees of belief and actions

It is absolutely not true that our betting amounts HAVE to be identical or even closely related to our psychological degree of beliefs.

Let us say that a lunatic bookie threatens to kill my children if I don’t accept to engage in a series of bets concerning insignificant political events in some Chinese provinces I had never heard of previously.

Being in a situation of total ignorance, my psychological degree of beliefs are undefined and keep fluctuating in my brain. But since I want to avoid a sure loss, I make up amounts behaving like probabilities which will prevent me from getting “Dutch-booked”, i.e. amounts having nothing to do with my psychology.

So I avoid sure loss even if my psychological states didn’t behave like probabilities at any moment.

 

Propositions whose truth we’ll never discover

There are countless things we will never know (at least assuming atheism is true, as do most Bayesians.)

Let us consider the proposition: “There exists an unreachable parallel universe which is fundamentally governed by a rotation between string-theory and loop-quantum gravity and many related assertions.

Let us suppose I ask to a Bayesian friend: “Why am I irrational if my corresponding degrees of belief in my brain do not fulfill the basic rules of probability?”

The best thing he could answer me (based on the DBA) would be:

“Imagine we NOW had to set odds about each of these propositions. It is true we’ll never know anything about that during our earthly life. But imagine my atheism was wrong: there is a hell, we are both stuck in it, and the devil DEMANDS us to abide by the sums we had set at that time.

You’re irrational because the non-probabilistic degrees of belief you’re having right now means you’ll get dutch-booked by me in hell in front of the malevolent laughters of fiery demons.”

Now I have no doubt this might be a good joke for impressing a geeky girl being not too picky (which is truly an extraordinarily unlikely combination).

But it is incredibly hard to take this as a serious philosophical argument, to say the least.

 

 

A more modest Bayesianism is probably required

 

To their credits, many more moderate Bayesians have started backing away from the alleged strength and scope of the DBA and state instead that:

“First of all, pretty much no serious Bayesian that I know of uses the Dutch book argument to justify probability. Things like the Savage axioms are much more popular, and much more realistic. Therefore, the scheme does not in any way rest on whether or not you find the Dutch book scenario reasonable. These days you should think of it as an easily digestible demonstration that simple operational decision making principles can lead to the axioms of probability rather than thinking of it as the final story. It is certainly easier to understand than Savage, and an important part of it, namely the “sure thing principle”, does survive in more sophisticated approaches.”

 

Given that Savage axioms rely heavily on risk assessment, they’re bound to be related to events very well treatable through my own knowledge-dependent frequentism, and I don’t see how they could justify the existence and probabilistic nature of degree of beliefs having no connection with our current concerns (such as the evolutionary path through which a small sub-species of dinosaurs evolved countless years ago).

 

To conclude, I think there is a gigantic gap between:

– the fragility of the arguments for radical Bayesianism, its serious problems such as magically turning utter ignorance into specific knowledge.

and

– the boldness, self-righteousness and terrible arrogance of its most ardent defenders.

 

I am myself not a typical old-school frequentist and do find valuable elements in Bayesian epistemology but I find it extremely unpleasant to discuss with disagreeable folks who are much more interested in winning an argument than in humbly improving human epistemology.

 

 

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On the probability of evolution

 In the following post, I won’t try to calculate specific values but rather to explicate my own Knowledge-dependent frequentist probabilities by using particular examples.

https://lotharlorraine.files.wordpress.com/2014/03/geek-zone.gif

I strongly encourage every reader new to this topic to first read my groundwork (Click here).

The great evolutionary biologist Stephen Jay Gould was famous for his view that Evolution follows utterly unpredictable paths so that the emergence of any species can be viewed as a “cosmic accident”.

 

 

Stephen Jay Gould

He wrote:

We are glorious accidents of an unpredictable process with no drive to complexity, not the expected results of evolutionary principles that yearn to produce a creature capable of understanding the mode of its own necessary construction.
 
“We are here because one odd group of fishes had a peculiar fin anatomy that could transform into legs for terrestrial creatures; because the earth never froze entirely during an ice age; because a small and tenuous species, arising in Africa a quarter of a million years ago, has managed, so far, to survive by hook and by crook. We may yearn for a ‘higher answer’– but none exists”

“Homo sapiens [are] a tiny twig on an improbable branch of a contingent limb on a fortunate tree.”

 

Dr. Stephen Jay Gould, the late Harvard paleontologist, crystallized the question in his book ”Wonderful Life.” What would happen, he asked, if the tape of the history of life were rewound and replayed? For many, including Dr. Gould, the answer was clear. He wrote that ”any replay of the tape would lead evolution down a pathway radically different from the road actually taken.”

 

You’re welcome to complement my list by adding other quotations. 🙂

 

Evolution of man

evolution

So, according to Stephen Jay Gould, the probability that human life would have evolved on our planet was extremely low, because countless other outcomes would have been possible as well.

Here, I’m interested to know what this probability p(Homo) means ontologically.

Bayesian interpretation

Image Of Thomas Bayes

 

 

For a Bayesian, p(Homo) means the degree of belief we should have that a young planet having exactly the same features as ours back then would harbor a complex evolution leading to our species.

Many Bayesians like to model their degrees of belief in terms of betting amount, but in that situation this seems rather awkward since none of them would still be alive when the outcome of the wager will be known.

 

Traditional frequentism

 

Let us consider (for the sake of the argument) an infinite space which also necessarily contain an infinite number of planets perfectly identical to our earth (according to the law of the large numbers.)

According to traditional frequentism, the probability p(Homo) that a planet identical to our world would produce mankind is given as the ratio of primitive earths having brought about humans divided by the total number of planets identical to ours for a large enough (actually endless) number of samples:

p(Homo)   ≈           f(Homo) = N(Homo) / N(Primitive_Earths).

 

Knowledge-dependent frequentism

 

According to my own version of frequentism, the planets considered in the definition of probability do not have to be identical to our earth but to ALL PAST characteristics of our earth we’re aware of.

Let PrimiEarths  be the name of such a planet back then.

The probability of the evolution of human life would be defined as the limit  p'(Homo) of

f'(Homo) = N'(Homo) / N(PrimiEarths‘)

whereby N(PrimiEarths‘)  are all primitive planets in our hypothetical endless universe encompassing all features we are aware of on our own planet back then and N'(Homo) is the number of such planets where human beings evolved.

It is my contention that if this quantity exists (that is the ratio converges to a fixed value whereas the size of the sample is enlarged), all Bayesians would adopt p'(Homo)  as their own degree of belief.

 

But what if there were no such convergence?  In other words, while one would consider more and more  N(PrimiEarths‘) f'(Homo) would keep fluctuating between 0 and 1 without zooming in to a fixed value.

If that is the case, this means that the phenomenon  “Human life evolving on a planet gathering the features we know” is completely unpredictable and cannot therefore be associated to a Bayesian degree of belief either, which would mean nothing more than a purely subjective psychological state.

 

Evolution of bird

I want to further illustrate the viability of my probabilistic ontology by considering another evolutionary event, namely the appearance of the first birds.

Let us define D as : “Dinosaurs were the forefathers of all modern birds”, a view which has apparently become mainstream over the last decades.

For a Bayesian, p(D) is the degree of belief about this event every rational agent ought to have.

Since this is an unique event of the past, many Bayesians keep arguing that it can’t be grasped by frequentism and can only be studied if one adopts a Bayesian epistemology.

 

It is my contention this can be avoided by resorting to my Knowledge-Dependent Frequentism (KDF).

Let us define N(Earths’) the number of planets encompassing all features we are aware of on our modern earth (including, of course, the countless birds crowding out the sky, and the numerous fossils found under the ground).

Let us define N(Dino’) as the number of these planets where all birds originated from dinosaurs.

According to my frequentism, f(D) = N(Dino’) / N(Earths’), and p(D) is the limit of f(D) as the sample is increasingly enlarged.

If p(D) is strong, this means that on most earth-like planets containing birds, the ancestors of birds were gruesome reptilians.

But if p(D) is weak (such as 0.05), it means than among the birds of 100 planets having exactly the known features of our earth, only 5 would descend from the grand dragons of Jurassic Park.

Dino

Again, what would occur if p(D) didn’t exist because f(d) doesn’t converge as the sample is increased?

This would mean that given our current knowledge,  bird evolution is an entirely unpredictable phenomenon for which there can be no objective degree of belief every rational agent ought to satisfy.

 

 

A physical probability dependent on one’s knowledge

 

In my whole post, my goal was to argue for an alternative view of probability which can combine both strengths  of traditional Frequentism and Bayesianism.

Like Frequentism, it is a physical or objective view of probability which isn’t defined in terms of the psychological or neurological state of the agent.

But like Bayesianism, it takes into account the fact that the knowledge of a real agent is always limited and include it into the definition of the probability.

 

To my mind, Knowledge-Dependent Frequentism (KDF) seems promising in that it allows one to handle the probabilities of single events while upholding a solid connection to the objectivity of the real world.

 

In future posts I’ll start out applying this concept to the probabilistic investigations of historical problems, as Dr. Richard Carrier is currently doing.

 

 

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Knowledge-dependent frequentist probabilities

 

This is going to be a (relatively) geeky post which I tried to make understandable for lay people.

Given the important role than epistemological assumptions play in debate between theists and atheists, I deemed it necessary to first write a groundwork upon which more interesting discussions (about the existence of God, the historicity of Jesus, miracles, the paranormal…) will lie.

Bayesianism, Degrees of belief

In other posts I explained why I am skeptical about the Bayesian interpretation of probabilities as degrees of belief. I see no need to adjust the intensity of our belief in string theory (which is a subjective feeling) in order to do good science or to avoid irrationality.

Many Bayesians complain that if we don’t consider subjective probabilities, a great number of fields  such as economy, biology, geography or even history would collapse.
This is a strong pragmatic ground for being a Bayesian I hear over and over again.

Central limit theorem and frequencies

I don’t think this is warranted for I believe that the incredible successes brought about by probabilistic calculations concern events which are (in principle) repeatable and therefore open to a frequentist interpretation of the related likelihoods.

According to a knowledge-dependent interpretation of frequentism I rely on the probability of an event is its frequency if the known circumstances were to be repeated an infinite number of times.

Let us consider an ideal dice which is thrown in a perfectly random way. Obviously we can only find approximations of this situation in the real world, but a computer can reasonably do the job.

In the following graphics, I plotted the results for five series of trials.

FrequencyNormal

FrequencyLog

The frequentist probability of the event is defined as

formel1

,

that is the limit of the frequency of “3” when the number of trials becomes close to infinity.

This is a mathematical abstraction which never exists in the real world, but from the 6000-th trial onward the frequency is a very good approximation of the probability which will converge to the probability according to the central limit theorem.

Actually my knowledge-dependent frequentist interpretation allows me to consider the probability of unique events which have not yet occurred.

For example, a Bayesian wrote that “the advantage of this view over the frequency interpretation is that it can deal with cases where there is no relative frequency to draw on: for example, Gigerenzer mentions the first ever heart transplant patient who was given a 70% chance of survival by the surgeon. Under the frequency interpretation that statement made no sense, because there had never actually been any similar operations by then.“

HeartTransplant

I think there are many confusions going on here.
Let us call K the total knowledge of the physician which might include the different bodily features of the patient, the state of his organs and the hazard of the novel procedure.

The frequentist probability would be defined as the ratio of surviving patients divided by the total number of patients undergoing the operation if the known circumstances underlying K were to be repeated a very great (actually infinite) number of times.formel2Granted, for many people this does not seem as intuitive as the previous example with the dice.
And it is obvious there existed for the physician no frequency he could have used to directly approximate the probability.
Nevertheless, this frequentist interpretation is by no means absurd.

The physician could very well have used Bayes’s theorem to approximate the probability while having only used other frequentist probabilities, such as the probability that the body reacting in a certain way would be followed by death or the probability that introducing a device in some organs could have lethal consequences.

Another example is the estimation of the probability it is going to rain tomorrow morning as you will wake up.

Wetter
While the situation you are confronted with might very well be unique in the whole history of mankind, the probability is well defined by the frequency of rain if all the circumstances you know of were to be repeated an extremely high number of times.

Given this extended, knowledge-dependent variant of frequentism, the probabilities of single events are meaningful and many fields considered as Bayesian (such as economical simulations, history or evolutionary biology) could be as well interpreted according to this version of frequentism.

It has a great advantage: it allows us to bypass completely subjective degrees of belief and to focus on an objective concept of probability.

Now, some Bayesians could come up and tell me that it is possible that the frequentist probabilities of the survival of the first heart transplant patient or of the weather does not exist: in other words, if the known circumstances were to be repeated an infinite number of times, the frequency would keep oscillating instead of converging to a fixed value (such as 1/6 for the dice).

Fluctuations

This is a fair objection, but such a situation would not only show that the frequentist probability does not exist but that the Bayesian interpretation is meaningless as well.

It seems utterly nonsensical to my mind to say that every rational agent ought to have a degree of belief of (say) 0.45 or 0.87 if the frequency of the event (given all known circumstances) would keep fluctuating between 0.01 and 0.99.
For in this case the event is completely unpredictable and it seems entirely misguided to associate a probability to it.

Another related problem is that in such a situation a degree of belief could be no nothing more than a pure mind state with no relation to the objective world whatsoever.

As professor Jon Williamson wrote:
Since Bayesian methods for estimating physical probabilities depend on a given prior probability function, and it is precisely the prior that is in question here, this leaves classical (frequentist) estimation methods—in particular confidence interval estimation methods—as the natural candidate for determining physical probabilities. Hence the Bayesian needs the frequentist for calibration.”

But if this frequentist probability does not exist, the Bayesian has absolutely no way to relate his degree of  belief to reality since no prior can be defined and evaluated.

Fortunately, the incredible success of the mathematical treatment of uncertain phenomenons (in biology, evolution, geology, history, economics and politics to name only a few) show that we are justified in believing in the meaningfulness of the probability of the underlying events, even if they might be quite unique.

In this way, I believe that many examples Bayesians use to argue for the indispensability of their subjectivist probabilistic concept ultimately fail because the same cases could have been handled using the frequentist concept I have outlined here.

However this still leaves out an important aspect: what are we to do about theories such as the universal gravitation, string theory or the existence of a multiverse?
It is obvious no frequentist interpretation of their truth can be given.
Does that mean that without Bayesianism we would have no way to evaluate the relative merits of such competing models in these situations?
Fortunately no, but this will be the topic of a future post.
At the moment I would hate to kill the suspense 🙂