The seed of reason

Vegetarianism and behaviorism

This is the first of a pair of posts.  In these posts I will consider major problems with the study of animal minds (and their implications for animal ethics) which are related to  Behaviorism.  To be clear, I shall be criticizing accounts of animal minds that are over reliant on direct observations of animal behavior.  I will not deal much with functionalism but the reader can assume I think the functionalist position to be much more robust to attack.

The Turing test appears to be a behavioral test.  The Turing test states that, if a computer can convince a judge (over a text only chat link) that it is a human, then that computer is intelligent.  This may seem to give me problems, as I agree that a computer passing the Turing test, would be intelligent.  However, I have no such problem, as I do not hold that a computer's failure to pass the test, shows it to be unintelligent.  Note that there are many other reasons a computer may fail.  It might be too poor an actor, not care about the test, or not understand the language used to perform the test, for examples.  Simply put, behavior can prove the presence of a mind but is not sufficient to prove its absence (and similarly for many functions of the mind as I discuss below).

So let us look at some problems with an over reliance on behavioral studies:

The motivations of animals may be anthropomorphized.  Historically, in testing chickens for their ability to solve mazes, food was used as a reward, and animals were partially starved, to ensure sufficient incentive for solving the maze.  However, later research showed, that far greater motivation could be achieved, by offering expectant hens a suitable nesting site as a reward.  It has also historically been hard, to ensure that the intended route to satisfying the motivation, is the only one available.  Animals have a tendency to think outside of our cultural boxes in their choice of behaviors.  Consider another experiment in which the experimenters attempted to teach a primate how to make stone tools (with which they could cut a string that would allow them access to a food reward).  The primate was shown how to knock rocks together to make a sophisticated stone tool.  The primate was shown how the tool could be used to cut the rope to access the treat.  Then the primate was given two stones, and the treat, string were reset.  The primate dropped the stone on the floor, retrieved a sharp shard and used it to get the treat.  The experimenters carpeted the floor to prevent this unintended solution to the problem.  The primate then located the edge of the carpet, lifted it up and dropped the stone onto the floor again.  The experimenters subsequently glued the carpet to the floor.  The primate just dropped one stone onto the other and the experimenters had to admit defeat.  The point here is that the primate was behaving intelligently but did not see the task in the same way as the experimenters.

It is common to say an animal is silly if it does something we imagine we wouldn't do n the situation.  But consider for a moment how silly some human behaviors would appear.  For example people cover themselves in mud at a holiday resorts; Climb up mountains; Drink so much alcohol that they are sick.  Of course I am not saying that all these behaviors are stupid, just that they can appear so to outsiders.  Some motivations are massively counter intuitive or irrational.  For instance consider the middle aged man who puts off having that lump checked due his fear of cancer.  A purely behavioral approach would lead us to believe this man did not fear cancer!  Many people  engage in short term thinking at the expense of their future selves.  But this alone does not imply they do not understand the consequences of their actions.  An experiment was performed on young children.  Under the gaze of a hidden camera, the children were offered a single marshmallow on a plate, and they were told that they could eat it but that they would be given the rest of the packet, if they did not eat the marshmallow until the experimenter came back.  Most children ate the marshmallow.  You might conclude, that these children did not have a concept, of future benefit.  However, the videos of the children undermine this claim.  The children exhibited stressed behavior, and many did not eat the marshmallow immediately, but held out for some time.  So the behavior here must be interpreted very carefully.  The children had no lack of understanding of the situation.  They just had little will power.

Education makes the human mind seem more capable.  Education, formal or informal, produces massive changes in behavior.  Almost all the skills of an adult human, are taught to them by other humans.  But we don't consider less educated people to be worth less morally, because they lack behavioral traits or skills.  We understand that a less educated person has the same inherent worth as a more educated person.  It is important to realize, that conceptual leaps have given the modern human, skills that our remote ancestors could not have dreamed of.  In mathematics, algebra and cartesian coordinates are crucial to presenting problems in a clear and unambiguous manner.  In our social lives, the stories that we read (and the soap operas that we watch) provide much virtual experience, of social situations.  The invention of the clock and the calendar, and their widespread adoption changed the concept of time with which we live our lives.  We must take into account the advantages that culture gives us, when judging the capacities of animals.  It is often claimed, that certain animal do not have a concept of the future.  This claim is generally based on studies where an animal fails to make decisions conducive to its future welfare.  Similar claims were until recently even made of small children on similar evidence!  I claim that animals and small children understand the concept of the future but do not necessarily act on this knowledge.  As an example consider the marshmallow experiment considered above.  This fact that children do not plan for the future does not mean that children should not be considered in our ethical discourse!  There is a danger, that we are failing to make the distinction, between an animals experience of the world, and that animals observed behavior.

Animal senses sometimes differ substantially from ours.  When evaluating an animal's behavior, it is easy to ignore the differences in the ways, that these animals sense the world.  Many animals have very poor vision, and many do not use their vision as a primary sense in the same way that humans do.  This may result in, an apparent stupidity, in the behavior of animals.  For instance cats have very poor color vision.  This means objects that are obvious to human eyes may not be to cat eyes.

Animals may not have the same motor functions under voluntary control. At least some primates have little voluntary control over their vocal utterances.  This does not preclude communication using these utterances (think of body language).  However, it does act as a strong barrier to the development of spoken language.  Successful attempts to teach primates a language normally involve a form of sign language (and these attempts have been remarkably successful).

Animals may not have the same mental setup as humans.  The variation of human mental setups include:

*People who hate being told what to do.

Animals may appear stupid if they are hard to train.  Some people regard cats as less intelligent than dogs, because cats are hard to train.  But when cats are placed in problem boxes, they solve them better than dogs.  We should also consider the possibility, that animals have mental attitudes/abilities, that would be considered mental illnesses in humans.  So non-social animals may well be autistic, from a human perspective.  This is actually highly plausible as autism is mainly problematic from a social perspective.

*People who couldn't care less about what they do.

Animals that do not recognize themselves in the mirror might just not care enough about their own appearance to react in the way we expect them to.  Animals that engage in posturing behavior in front of a mirror may be doing this because they enjoy practicing behaviors (people do similar things).

The point is, that we really don't know, why animals engage in the behaviors they do.  The lack of a common language, and the gaping gap between our sensory experiences of the world, should make us treat behavioral studies with great caution (I'll address alternatives in my next post).  Much thought on this matter (at least from the lay public and amongst ethicists) suffers from an anthropomorphic bias.  We also should not forget that most scientists and many ethicists are meat eaters and benefit in multiple ways from the exploitation of animals.  Sadly in the absence of good quality evidence people have a habit of favoring interpretations beneficial to their own interests.  Good science should take such biases into account.

That is the end of the first post.  In my next post I will address alternative ways to study animal minds and give my reasons for considering animals to be much closer in terms of the richness of their internal experience to humans than is often assumed.

What constitutes valid criticism of a scientific model?

The subject of this blog post is "What constitutes valid criticism of a scientific model?" (in relation to its predictions).  Certainly there seems to be much dubious criticism of scientific models particularly, but not only, in the mainstream media.  I'll give some examples in the form of thought experiments first and more concrete examples later.

By model I mean a simulation which is run in an attempt to derive predictions from a theory.  The first thing to note about such models, is that they are almost always inexact!  That is, the model is not completely accurate in its simulation of the scientific theory in question.  The reader might find it illuminating to search for counterexamples.

Models, however, can be more or less accurate, and certain models are closer to the underlying scientific theory.  Generally, models that are closer to the underlying scientific theory are better.  But as a practical matter we cannot demand that all models are completely exact because this currently requires (and probably always will) more computing power than we have available.

So the inexactness of a model on its own is not a practical way to criticize that model.  I give a few principles that I think any valid criticism of a model should obey.  Firstly though, I should point out that a model may be accurate in one area and woefully inaccurate elsewhere.  So we shall not be talking about criticizing a model itself but criticizing the model's use in making certain predictions.

Suppose that Alice has a model of some scientific theory.  Suppose in addition that Ben has found one or more inaccuracies in the way Alice's model simulates this scientific theory and Ben is claiming that this undermines certain predictions made by Alice from the theory with the help of the model.  Then my criterion (necessary but not sufficient) for Ben to have a valid criticism, are as follows:

1) The inaccuracy must affect the prediction in question.

Suppose Alice has a model of celestial mechanics that places the earth about one thousand kilometers further from the sun than it actually is.  Assume that the model is otherwise reasonably accurate in the size, shape, motion and spin of the earth, sun and moon.  This model will then predict the existence of seasonal variation in the apparent intensity of the sun.

If Ben criticizes Alice's model on the basis of its error then he is not making a valid criticism.

2) The inaccuracy must be significant enough to change the prediction in question.

Suppose Alice uses the above model to predict the timing of the tides.  Now suppose that Alice's model is used to calculate the tides for a given day, a month in the future, to a claimed accuracy of 1 hour.  The model will give a correct prediction, despite this error in the model.  The error is simply not significant enough, to make a difference in the calculation (the sun is many millions of kilometer from the earth).

Now suppose in addition, that Ben's criticism is intended to show that an alternative prediction is in fact correct (or more weakly that the correct prediction lies in a certain direction from that originally calculated).  Then I give further criterion for Ben's criticism to be valid:

3) A modified model must be given that corrects the given inaccuracy and this model must make a prediction which supports the alternative prediction.

Let Alice have a model of photosynthesis which overestimates the weight of a molecule of carbon dioxide.  Suppose Alice's model predicts the efficiency with which some type of plant converts solar energy into sugar.  Suppose Ben criticizes Alice's model saying that it overestimates the actual predicted efficiency.  It is reasonable to ask Ben for an alternative simulation showing that the known inaccuracy actually affects the prediction in the manner asserted.

4) The relevant prediction of the modified model must itself be robust to criticism on the basis of any inaccuracies the modified model still has.

Consider again, Alice's model of photosynthesis.  Suppose that this model, was also inaccurate, in its estimate of the weight of a water molecule.  Then suppose Ben, gives an alternative model, that is more accurate in its assumption, regarding the weight of a carbon dioxide molecule.  I can get a third model by adjusting Ben's model so that the weight of a water molecule is more accurate.  Now suppose my model makes predictions that are closer to those made by Alice's model than those made by Ben's model.  Then it is not clear whether Alice's model or Ben's model is closer to calculating the correct predictions as far as the original theory is concerned.  There may of course be further inaccuracies present in my model.

The above four points can be summarized as "You should trust the predictions of a model (assuming you trust the underlying theory) if those predictions are robust under various improvements of that model".  However, in practice, it may often be easier to identify invalid criticism, by referring to the above four points, in the order in which I've given them.

Of course, this also cuts both ways.  When Alice proposes her model, then she should estimate the error in the models calculations, by considering various improvements to her model, and seeing how much these affect the result of calculations based on her model.

In weather forecasting, many simulations are performed in an attempt to estimate the uncertainty in any given prediction.  Similar techniques are used to estimate the uncertainty in our global climate models.

Now for some more concrete examples.

A) Global warming is unlikely because climate models are inaccurate in X,Y and Z.

Many assumption underlying climate models may be gross approximations but the global warming skeptic needs to show that it is likely that these inaccuracies actually affect the prediction that releasing CO2 will cause global warming.

I am informed, by those who know more about the climate than myself, that this is not the case.

B) Consider a simplified economic model, that assumes no collusion between companies.  Then the price of X, will not be reduced by a decreased cost in the production of X as this model would predict, because companies will collude to prevent this happening.

This depends on what X is! The argument needs to be supported with actual adjusted models, that include some details of the game theory involved in company interactions.  The accuracy of the models, is best tested empirically here (checking them directly from quantum mechanics is not an option).

Boltzman brains, progress and the second law of thermodynamics!

Its been a while since I've posted.  Apologies for that.  I have been busy and normal posts will resume soon.  In the mean time I thought I'd give you a discussion that I've been having with Dwielz Camauf Descartes over at The Meatyard.

It started with this post concerning Boltzmann brains.  A Boltzmann brain is a hypothetical observer who appears as a result of a quantum fluctuation (this is incredibly unlikely but not impossible according to some quantum theorists).  The argument about Boltzmann brains which is obliquely referred to is the following:

A random quantum fluctuation only creating a single observer is more likely than one creating a multitude of galaxies in which an multiple observer could evolve.  The universe will suffer a heat death followed by an infinite expanse of time afterwards (this infinite expanse of time is an assumption the argument is meant to be criticizing).  A vast expanse of time occurs after the heat death and though individually unlikely there will be infinitely many Boltzmann brains popping into existence during this time period.  So, you should expect to be a Boltzmann brain as there are infinitely many such observers and only finitely many occurring before the heat death.

My response follows:

"There is no uniform probability distribution of the whole real line. Any distribution you use must give greater weight to earlier brains than later ones (in the limit). So there may be infinitely many Boltzmann brains and yet your probability of being one of them could still be negligible.

Also can I recommend you read my post on why the heat death isn't so obvious."

Now I made a mistake here.  I missed Dwielz Camauf Descarte's assumption "if they believed that the universe arose in a random fluctuation.".  This does make things different.  As I'm not committed to that assumption though it doesn't effect my argument against the above version of the Boltzmann brain problem.  It also doesn't effect the discussion regarding scientific progress and the second law of thermodynamics that follow.

Never the less the resulting discussion was interesting.  Dwielz Camauf Descartes responded with this post.

My responses follow:

Firstly you say:

"If there is no uniform probability distribution of the whole real line, then there surely isn't a skewed distribution that gives "greater weight to earlier brains than later ones", if we don't have information about it we generally assume a uniform distribution; he needs to provide evidence for his assertion that a skewed distribution is the default."

No there are skewed distributions just no uniform ones. I can prove it rigorously as follows (I am a mathematician so it would be embarrassing to be wrong on this!):

Probability distributions integrate to 1. A uniform distribution on the positive real line has the same integral over any unit interval. So the probability of being in the interval n to n+1 is the same as being in the interval m to m+1. Call that probability 'q'. Then we get the following infinite sum:

1= P(x between 0 and 1)+P(x between 1 and 2)+P(x between 2 and 3)+...

which simplifies to

1 = q+q+q+q...

But this equation cannot be defined for any value of q. Therefore there is no uniform distribution on the positive real line.

Indeed it is clear that the limit as n tends to infinity of P(x between n and n+1) must be 0 because the equation could not be satisfied otherwise.

This proves that a probability distribution on the positive integers will always be skewed to the origin (at some scale). Of course one does not know the appropriate scale but this does not help your argument I'm afraid.

Again regarding my probabilistic argument you suggest that it wouldn't work in a metaverse.

If the metaverse is expressible as a subset of some finite dimensional space then the same reasoning applies (with unit blocks of the appropriate dimension taking the place of unit intervals). If not then you would need to specify some meaning to the term "probability measure" and then make your argument. I very much doubt though that this would save the argument.

Finally regarding your response to my arguments regarding the heat death:

The problem with using induction here is that we just don't have enough information to even begin guessing. We have less than a millennium (10^3) of proper scientific investigation of physics. But in predicting a heat death we are making predictions about events 10^40 years in the future.

Using induction here would be like picking up a single grain of sand at a beach and concluding that the entire solar system was made of silicon dioxide!

Don't get me wrong I'm all for induction but I'm just pointing out that we're effectively guessing in the dark when it comes to questions of this scale.

Finally (oops I thought I was finished) you accuse me of not trusting in scientific progress and ask what I mean by progress.

But what I've said isn't really a criticism of science. I'm only saying science cannot yet answer certain questions including the plausibility of a heat death. Given the scale of the question that's no real surprise.

I provisionally define progress to include (but not be limited to) increasing knowledge of strategies that allow multiple parties to achieve mutual benefit from non-zero sum games. That is part of what moral progress means in my view. This form of progress is certainly worth having and I think most people would have a great interest in it occurring.

It occurs to me that it would have been better to post these comments together as a blog post on my site. I shall do that and I suggest you delete the comments here for clarity. I'll post a link to the page once its up.

Cheers for an interesting discussion!

Delay

Just a quick note to say that the next blog post will be delayed somewhat (maybe a couple of weeks).  I'm hugely busy presently.  The topic of the post will probably be criticising predictions based on faulty models.

A few TED talks I like

Today I will be writing about an online conference called TED.  TED stands for Technology, Engineering, Design.  However, this conference rarely sticks to these topics.  Typical speakers include entrepreneurs, charity workers, scientists, artists, musicians, politicians and inventors.  Most talks are about cutting edge developments or new work in business, science, art and politics.  There's a particular emphasis on information technology, green energy.

I watch the TED conference videos regularly and I thought I would start a series of posts on particular talks that I like and why I liked them.

In the first talk Hans Rosling talks about myths regarding the 'third world'.  He demonstrates some quite impressive data visualization tools in the process.

Hans Rosling

The second talk is by the comedian Julia Sweeney.  Her talk is insightful and hilarious.  She talks about her atheism.

Julia Sweeney

The third talk is a demonstration of a new multi-touch technology.  I'm impressed by the potential but a little skeptical that people won't get too tired using this screen full long lengths of time.  Still I'd love to have one.

Jeff Hans

The fourth topic concerns Wikipedia.  Wikipedia has been a hobby of mine on and off for many years now.  At some stage I will write a series of articles about the inner world of Wikipedia.  In the mean time you can here a short introduction from someone much better qualified to speak on the matter.

Jimmy Wales

Do take a look at these and tell me what you think!

Finally apologies for the lack of recent blogging.  I am incredibly busy right now.  Things should be more regular by December.

Problems with WHO's Swine flu numbers

Anyone who has talked to me recently will know that I'm going on a lot about swine flu right now.  That is because I think its important in a way that most of what hits the headlines is not.  I'll be addressing issues with infection and death figures in this post.  I will probably come back to the topic in subsequent posts.

How many people are/were infected?

The WHO collates figures on numbers infected right? No! The WHO collates the number of tests for swine flu that have come back positive.  Wikipedia also collates these figures (and does so faster than the WHO).  At the time I'm writing this the figure stands at 80,633.

I don't know of any credible estimate of world wide infections at all.  However, the CDC recently estimated that 1,000,000 people have been made ill in the USA.  The difference between these two figures is large but it can be explained as follows:  Once the number of cases rises beyond about 1,000 countries stop testing people they believe have the disease because the testing facilities are insufficient.  The more people get infected the worse a guide these numbers will become (and they're probably poor to start with).  The USA stopped testing most people for swine flu after a few thousand cases.  But they can still estimate how many people have become ill by how many people turn up with flu like symptoms adjusted for how many of them on average test positive (or other similar statistical techniques).

The WHO numbers (and those reported by national governments) have been endlessly reported, picked over and analyzed.  However, these figures are a poor way to track the disease (except for most other ways that are currently available).  I will explain two of the problems with the figures so you get the idea:

1) The reporting procedures of countries may differ.  Some countries will test almost no one (such as many African countries) and some may test everyone they can in the first few weeks and then start monitoring the disease in other ways (like the USA and Australia).

2) The chance of a case being noticed will be higher in a person for whom the disease is worse so more infections may be detected amongst groups who are worse affected.

At the end of the day these figures are really lower bounds and probably only give a very rough guide to the early stages of the disease in a country.  Better estimates of the numbers infected should begin to become available but the best estimates may well emerge many months or years after the first wave of the pandemic is over.

Who is getting infected?

This is relatively easy to answer.  Most people for are infected are young, in the Southern hemisphere or in North America.  The geographic distribution will change and the age distribution could change too.  Unlike the seasonal flu few old people are getting infected (and very few are dying)

Who is dying?

The highest deaths are between the ages 15 and 50.  The USA has (as of now) a mean age of death of 37.  Mexico had a mean age of death in the 20s towards the end of April (I don't have more up to date figures).  I have found good information on who is at greater risk: Pregnant women, asthmatics, diabetics and those with heart disease.  All increase the risk significantly.  However, between 1/3 and 1/2 of all deaths are in fit healthy people.

How many people have died from swine flu?

The WHO is keeping a running tally of essentially all deaths from swine flu, right? No, wrong again!  The WHOs confirmed death figures cannot even be used as an estimate of this number.  This might seem absurd (surely its easy to notice that someone has died?) but I'll explain why this is:

People die all the time.  There are many many deaths every day and a significant number die in a way that looks superficially similar to a flu caused death.  Many people who have seasonal flu die from opportunistic secondary infections or complications that are essentially caused by the flu itself.  By the time these people die the flu symptoms and the virus may have gone.  Because we are in the early stages of this pandemic the deaths are swamped by deaths that look superficially similar.  This makes it hard to detect deaths in people not already known to have swine flu.

As an example of this difficulty consider the deaths from flu in the USA.  The number of death certificates issued each year which list flu as a contributing factor is about 1,800.  However, it is estimated that flu is a significant factor in the deaths of 36,000 people in the USA each year.  That is a difference of about 20 times.  The second figure can be calculated by varying methods (and the numbers change but not enough to invalidate my point) which rely on statistical sampling or modeling.

So the number of deaths from the swine flu could easily be 20 times as great as the WHO confirmed figure.  However, we shouldn't assume that the problems with estimation are the same for seasonal flu as for swine flu.  We may:

*Miss fewer deaths because more young people are dying from swine flu.  Younger people are less likely to randomly die so they are more likely to be detected above the background noise.  The strong media attention has surely increased the chance that people who are in a serious condition will be tested for swine flu.  On the other hand the availability of tests is much more limited for swine flu than for seasonal flu.

*Miss more deaths because at this early stage its easier for deaths to be drowned out by the noise and economic pressures may cause countries to mislead the international community about the deaths they've had.  Sadly this second point is probably significant.

Finally it should be noted that you cannot calculate a fatality rate by dividing the confirmed deaths by the estimated infections (as many news sites have).  That will just give you a hopelessly inaccurate figure.

Its not just me saying this either.  For instance the CDC is on record saying "Only counting deaths where influenza was included on a death certificate would be a gross underestimation of influenza’s true impact.".

Apology

There will be another post along soon.  Please check back at the weekend!

Is there a lowest level of reality?

I want to take my idea of a coding invariance principle further (see my earlier post defining the philosophical principle).

What do we mean when we say "The law of gravity is true"? We could mean that the law of gravity matches all the available data.  We could even require that it matches all true statements about the universe (or does so to within some degree of approximation).  But more might be required.  We might ask that there is no other simpler theory that matches the world as well or better.

What if there are two theories explaining different physics (like quantum mechanics and relativity) and a third simpler theory comes along that explains both areas (the very small and the very large) better than either theory.  This third theory might be simpler than the combination of quantum mechanics and relativity.  But in this case would it make sense to say quantum physics is true or even the best theory for the very small anymore?

Clearly its conceivable that a theory explains a narrow domain well but is superseaded by a theory which is little better on this domain but explains a much wider domain and therefore is a better theory overall.  A theory could also be better at explaining that narrow domain than our original theory in which case you would also say that it was a better theory.

A significant question is then "Is there a best theory to explain any domain of our universe?".  It is consistent that the answer is no and that for any theory there is another theory which is either more elegant (explains more with little additional complexity) or explains the same data with a higher degree of accuracy.

This isn't to say that in such a universe science could not progress and make better predictions over time.  However, it does draw into question whether it is meaningful to talk about a theory giving a truthful description of the universe.

There are various bizarre possibilities in such a universe:
For instance the best available theory may keep changing its opinions on matters such as:

1) Whether the universe had a beginning
2) What exactly happened in the past
3) Whether the universe is infinite
4) The number of dimensions the universe has

A universe that has many partial descriptions matching 1,2,3,4 would be a strange beast indeed but it would certainly be an interesting place to reside!

Three versions of Occam's razor

In today's post I will be discussing differing interpretations of Occam's razor.

Wikipedia defines it as recommending that "the explanation of any phenomenon should make as few assumptions as possible, eliminating those that make no difference in the observable predictions of the explanatory hypothesis or theory"

I shall give three common interpretations of how the razor should be applied.  I have come across each of these versions in use but they are not equivalent to each other.

1) One should always assume the existence of the fewest objects necessary to achieve an explanation.

This version has major problems.  It depends on what you consider your basic level of reality to be and how you measure quantity.  So for example  if we consider matter to be the basic level of reality and quantity to be mass then we should assume only those galaxies exist we have direct measurements from.  However, our best cosmological theories imply the existence of a multitude of galaxies for which we have no other evidence.

But had we applied such a principle with stars or planets in the past then we would have been proved wrong.  Indeed its hard to think of an instance where it could have been correctly implied.  Occam's razor is often justified by a claim that "its worked in the past!".  Although this is circular (similar to the sentence "I think I make accurate claims") its still the case that if it hadn't worked in the past this would be evidence against it (similar to the sentence "I think I make innacurate claims").

I have a further criticism of this version using the coding equivalence principle.  The problem is that universes with differing ontologies (accounts of what exists) are equivalent under this principle.  So this is going to cause inconsistencies.  There are ways to get around this but they're messy and not easy to justify.

2) Of two theories the simplest is more likely to be true.

Note that this version makes a strong truth claim for the simplest theory (it doesn't just say that the simplest theory is closest to the truth).

In particular according to this version one should always assume any theory that matches reality is true if there are no simpler theories that do.  But this is problematic because almost every theory of physics so far  proposed has been proven false including ones which at some point had no evidence against them.  Again as it hasn't worked in the past we shouldn't trust it in the future.

Note also that this version is supportive of the idea of a general unified theory in that it will always assign a non-zero probability to such a theory existing.

3) The simplest theory is the best choice of theory.  If two theories produce identical predictions choose the simpler presentation.

This version I am happier with.  But why is it good?  Is it because it is easier to have a theory that is simple for making calculations?  I would argue no as complex theories are often easier to compute with in practice.

I think that this version of Occam produces theories that are better because they produce better predictions.

One example would be continental drift as opposed to the various ad hoc explanations previously accepted (land bridges and the like).

A second example would be how Newtonian dynamics was better than previous theories of planatery motion.

Note that Einstein's theory of relativity does not harm this version of Occam's razor as it makes no truth claims concerning simpler theories.  Simple theories are often replaced by better theories that are more complicated.  Never the less one cannot necessarily tell which more complicated theory will end up replacing the current theory so this does not invalidate this version of Occam as a heuristic.

This is the only version of the razor of the three which has worked well in the past and I argue that it is the version that we should use in the future.

There are some interesting applications of the first two versions that do not follow from the third:

A) The 2nd version is used to argue that the 2nd law of thermodynamics is probably absolutely true (not just approximately true).

The prediction of the heat death of universe needs the 2nd law of thermodynamics to be absolute.

B) The first version supports the assumption that the universe is finite and the second the assumption that a general unified theory exists.

Neither of these assumptions are supported by the third version.  Indeed it it difficult to see how one could ever have positive evidence of either of these claims. 

The third version of the razor also gives us the advantage of being able to ignore silly questions such as are photons particles or waves.  For the third version What matters is the mathematical form of the theories and whether these forms make accurate predictions or not.

The data flood

Over the last decade western societies have begun to generate vast quantities of data.  Examples include:

1) Sales statistics
2) Medical records
3) Web traffic data
4) Media usage data (scrobbling, play counts etc.)
5) User generated data (facebook pages, text messages, emails etc.)
6) Online gaming data (second life, online board game archives etc.)

We have also begun to mine this data for useful patterns.

I give two major problems with the way in which we are doing this. 
A) The privacy of this data is often undermined by the careless manner in which it is stored.  Whilst the data has great economic value (mostly to companies and governments but also to the consumer/citizen) it can also be used for new and worrying kinds of abuse.

B) We depend too much on people to analyze this data.  In my opinion the main limiting factor in gaining economic advantage from this data is our ability to process it.

I predict two trends over the next decade with regard to data mining. 
Firstly there will be significant abuses of people's personal data resulting in a backlash against the mass storage of such data.  This will cause more data to be stored in a distributed or properly encrypted form.  Computation on this data may be performed in a distributed manner too with the programs that perform the computations being more likely to be under the control of the users.

Secondly simple artificial intelligence agents (not much more intelligent than insects) will proliferate as the cost of the necessary hardware to run them approaches zero.  These agents will make be called upon to dramatically reduce the burden of decision making for users.

Various developments have been hampered in the past by the unwillingness of people to make large numbers of trivial choices.  Some examples include:

1) Highly efficient payment structures (for text, music and video) which require large numbers of individual payment choices.

2) Consumer boycotts based on the complexities of the supply chain and company financing.

So I think that we'll deal with the flood of information by finding better ways to control the movement of that information and by finding better ways to utilize the value in that information.