What’s Behind MDMA, Rebill, and Marriage Vows?

Free access to scriptures religious leaders try to censor

Okay we can go on and on about MDMA. When it was legal MILLIONS are using it in 1980. So MDMA is tested on human subjects already. So far, those millions are mostly doing fine. They’re not brain damaged or anything. May even have higher income than the rest. I’ve read that alcoholic has higher income.

When we see government record, we need to understand that government tries to scare us out of drug. So whatever government put must be THE BEST government can come up with. Government is not scientist. More like sellers, or prosecutors.

Let’s ignore the unclear statements. Saying that something is very dangerous is not exactly a clear scientific statement. So let’s examine the clear ones. Now what’s left? If the best government can come up with is “INCONCLUSIVE evidence that MDMA can interfere with memory and cognition in LONG TERM” without even describing how long and how much, that’s not very convincing.

I read about marijuana legalization issue. The best government can come up is that marijuana legalization doesn’t work because it increases use of marijuana. Think about it. If marijuana legalization actually increase murder rate, for example, government would have put it there. This is not a straw man argument. This is the best man argument. It’s the best government can put up without lying. If that’s the best you can do, well, tough.

Now go back to rebill vs marriage.

I think we have several issues here:

1. is limitation of rationality. Not simply because you know something you know anything logically implied by it. As the saying goes, read the small print. However, in reality no body would read every TOS of every website. The same way, no body read the whole marriage rules and contract that can be changed all the time anyway. So a way to persuade people is to hide negative material aspects away. Few people can reason that “By the power of the state of bla bla I declare you” mean “You got to keep paying this hoes after she keep fucking others’ cock.” The logic goes that the power of the state means you agree to be governed by the whim of the states’ legislator influenced by the votes of those other cocks. But who would have thought that far?
2. Thought experiment. Say the “I agree to keep paying you cash even though you already openly ride other’s cock” is part of the marital vow. After all, that’s what men actually effectively agree to right? It should be part of the “I do” thingy. Will that drop marital rate? The fact that it will shows that it’s actually a material part of marriage. Which is an important lesson why it should be part of the vow.
3. Outside legal framework, there are ways to maintain contract. One popular way is splitting the contract into small pieces. If people can enforce contract without government then they are free to decide for themselves. Marriage, for example, can be split apart into smaller pieces. You do me one time, I pay $100 first for example. You stop sucking. I stop paying. Wait a minute…. Why is it illegal again?
4. And finally the will of the consenting parties vs the will of those who write contracts. Let’s face it. Marriage may be a mistake for Beatty Chadwick. It’s a mistake so many swing voters want Beatty to make. Imagine if Beatty don’t make that mistake. Then he has 2.5 million dollars and can sleep with 10 beauties. For every Beatty, there will at least be 10 lonely males that want to vote against that under democracy. So yea, the fuck up part of marital deal will not be on the vow. What about if Beatty “import” women from Rusia? Then for every 1 Beatty there will be 10 ugly beldams that’ll never get laid. Not that someone with 2.5 million dollars will aim for ugly beldams. But women target can shift “up” and down when polygamy or women trafficking are allowed. Needless to say, I bet in US, those who opposes polygamy are usually middle class male and those who opposes women trafficking are women.
5. The point is, when you get married, everybody’s bigotic interest suddenly come into the marriage deal. It’s like asking Hitler to be Israel President.

Marriage is Consensual

[quote=FerrisHilton;1048296]A true capitalistic economy thrives on the concept of buyer discretion. Morons get hosed and those who are diligent in their research and purchases get good products and a generally efficient lifestyle due to their smart purchases.[/quote]

Great. Buyers’ discretion. What about if buyers decide that they want short term marriage, or polyandry, or polygamy, or same sex marriage, or prostitution and they want to practice that openly? I thought buyers and sellers usually write their own contract. So what the fuck is legislators doing writing the contract for them.

Imagine if someone’s put gun in your head and you give your wallet. Is that consensual?

There is a fate worse than death, called extinction. A robbers can say, “Don’t life till I say it’s okay.” There is a power greater than those who can kill you, namely the power that says, “Don’t get laid till I say it’s okay.” That power is weaken, but still societies can greatly restrict many form of relationship that directly competes with marriage.

If some people can prevent you from getting laid unless you get married, does that make marriage consensual?

Would you give your wallet to robbers if the alternative, namely living without surrendering your wallet, is easy? I don’t think so.

Would people get married if all alternatives are legal and can be practiced openly for all to do? I don’t think so. Well, at least there are very good reason why 50% of babies are born outside marriage in US, compared to in Arab. The alternatives are the market choice. Will be even more so when government interfere less. I actually write another topic for this.

Basically “fairer more explicit” deals will attract better objects. Porn, for example, in contrast to burqha, will attract the pretty. However, as better objects move to better market, buyers will find those on the original market “worse”. So buyers too will move to better market. At the end, everybody moves.

This process called adverse selection explains why insurance companies, and eventually ALL/MOST insurance companies, want to discriminate you based on your genes. Discriminating insurance companies is a “fairer more explicit” market. Governments of course prohibits that.

It’s the same reason why religious ideas get worst and worst. With internet and science, all the better ideas already fly to those better market where ideas quality can be more easily measured.

The same way marriage will be replaced by better market. It just that for that to happen, the alternative market will have to be legal AND seen. This is something government prevent with criminalization and censorship. But their power is cracking out.

You think marriage is consensual. Well many think David Cooperfield eliminated liberty statues. There is a trick on things. Yes you can have cakes and eat it too.

Marriage is like an operating system. There is a network effect. We think everyone use windows so we use windows. Imagine of all mac users must use mac only secretly? Imagine if Microsoft censors all Mac ads. Can you consensually say that people buy microsoft’s product consensually?

We don’t have national operating system. Why do we have national language or national fucking agreement? Why government endorse one consensual deals over the other?

Well porn is censored for minors. The same way, escort, while legal, can only be done secretly.That’s promotion of one system over the others.

As I said, it’s not about the interest of the consenting parties. It’s the interest of those who wants to control the party. Why alimony is so expensive?

Imagine if it’s cheap. Then Beatty Chadwick can just marry strings of women and divorcing them latter. The women may not mind. The other males would mind.

Religious bigots just want to dominate the operating system market. No I am not blaming them. I am blaming everyone now, especially my self, for not trying to be as evil as them.

Why Power?

And why power? Some might ask? Libertarian do not like power over others. Why power?

Well, what does the Nazi had that the Jews didn’t? What did the Turks had that the Armenian didn’t? What did the Mongols had that the Baghdad citizens didn’t?

Money? Think again.

Money without power is like putting a wanted sign. Cash for whoever can come up with justification to kill me, do it, and get away with it.

Power, is then true wealth. It’s stupid to work all our life just for money. Do it, and you’ll end up like Beatty Chadwick.

Love of money is root of looking evil and get fucked over. Love of power kicks ass.

I am gonna be a dictator. Then I’ll fuck over everyone else. Because that’s the only thing worth pursuing. That’s the kind of attitude everyone must have. Of course, to trick others into giving me power I got to pretend I am angel.

Capitalists do not have to convince others that they’re unselfish. Capitalists need only to explain (wrongfully, though honestly) that their interests are in line with everyone else anyway.

Those who go the extra mile of trying to look like angels are usually then the vilest demons. They said they’re an angel. Which mean they lied. Why would they lie? Because their interests are not aligned with us. What will they pursue? Their own interests. What will they effectively become? Demons.

That’s the point of true enlightenment. Becoming devils yourself.

Fuck one of my server is down. Got to fix that. Back to the real world, embracing my job as a lowly techie in my own biz . No one else that worked for me can do that . Must admit Suharto did have far better leadership skills.

Make Marriage Terms More Explicit

Look let’s shorten this one at a time.

Google money tree is bad (at least for many). People do not agree to pay $80 per month. There is no way they would agree to pay $80 per month. But somehow they are presumed to agree anyway based on statements not readily available buried deep within TOS.

Marriage is bad (at least for many). Men do not agree to keep paying alimony to girls that are already riding someone else’ cock. Men do not agree to support someone else’ kids. Beatty Chadwick do not agree to pay $2.5 million dollar and prefer to go to jail for 14 years instead of it. But somehow they are presumed to agree anyway based on some statements not readily available buried deep within marital laws.

FTC demands that those terms must be explicitly stated near the OK button.

So why not demand all marriage couple to explicitly agree to support bastard child, keep paying huge alimony, etc. during marital vow?

Not a rhetorical question.

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Originally Posted by nickster View Post
Although not exactly eloquent the OP is actually very right. Why should there be clear instructions next to the buy button that make sure that suckers and morons don’t purchase the product without all the pertinent facts. Surely the fact that they are in the terms and conditions should be enough in the same way that when you get married you are not being made aware that if you decide to “return” you will not be getting a refund but a shakedown. and the main difference is the money is not $80 when it comes to divorce, more like 1000s or 10,000s times that amount. More of a reason to make full disclosure at the alter a legal requirement.

Although the above is rather tongue in cheek I do agree with the sentiment.

That is exactly my point .

It’s not a rethorical question. The answer lies deep in the very structure of humans’ political structure.

I’d say it’s the following. Laws are not there to protect justice. Laws are there to protect the interests of whoever makes the law, which may include maintaining illusion or perception of justice, sometimes truthfully. I got the idea from an interview of PETA (indonesian’s troops under japanese occupation) members. The guy said that when Japs rule, the japs said that they ain’t care if people are stealing and robbing. They care about anti japs sentiments.

The same way US governments don’t really care whether a trade is consensual or not. Maybe they do a little bit. They care for their own interests, namely maximizing controls of the everyone. Google money tree don’t pay up enough to congress. That’s the issue.

Also humans have many zero sum games aspects of life. We’re actually by nature, enemies or competitors. We just don’t talk about it. After all not everyone is “THEM”. There have to be enough “US” to fight “THEM” whoever that US and THEM is. Many divisions are indeed Nash equlibrium and certain people prefer certain division more than another. KKK, for example, prefer race. Libertarian, for example, prefer non victimless criminals as “Them”. Muslims prefers faith as divisor. It’s only till recently the dividing line is ideology. Free market is winning.

So we don’t talk about the zero sum game aspect among “US”. We don’t question why marriage must be monogamy (women acquisition is zero sum game). We don’t question why MDMA is illegal despite being saver than cigarette (power is zero sum game). Also why Government screw e-gold, rather than aiming for the actual ponzy’s (well, dollar is not backed by gold, again control of economy is zero sum game).

What we don’t talk honestly, is what we don’t understand. All of us are lying to each other. It’s the realm of religion, superstition, and government. If I am an emperor, I would say God made me an emperor, to confuse other emperor wannabe. I wouldn’t say a more realistic theory that I become an emperor because I stab the former one in the back and can bullshit my way out to the population after ling chi -ing a few that disagree.

It never is about the interest of the consenting parties. It’s the interest of those that want to control the parties. It never is about honesty. It’s about confusing and lying to everyone else. Unfortunately by embracing lies and faith, we ended up lying to our self too.

Perhaps, as Budha says, enlightment is indeed a way out of this stupid cycle. Hail Adam Smith, for enlightening us that humans are selfish.

Faith is Bayesian Anomaly

Hmm… This is very interesting about Bayesian. I’ll check that out.

So let’s see. P(A|B) is P(A^B)/P(B)

According to bayesian rule, P(B|A)=P(A^B)/P(A)=P(A^B)/P(B)*P(B)/P(A)=P(A|B)*P(B)/P(A).

So probability of P(B|A) is just the probability of P(A|B) times Probability of B divided by Probability of A. That’s because now we’re dividing by A rather than B. Probability of (terrorist|muslims) is probably 80%. Probability of (muslim|terrorists) is less than 1%. That’s simply because there are way more muslims than terrorists most of which have less violent job. If P(A|B)=1 we have what we call logically B->A

Actually that’s not quite correct. In bayesian theory, P(A|B) means “The probability (degree of confidence) that A is true GIVEN that B is assumed to be true” — not “the probability that B implies A,” or even far worse, Popper’s self-inconsistent “propensity” interpretation that it means the “the probability that B causes A.”

The logical relation B->A has the somewhat counterintuitive boolean representation (not(B and (not A))), which can also be written as ((not B) or A). That is because B->A only demands that when B is true, A must also be true, so ((B=True) and (A=False)) means B->A must be False, whereas if B is false, the implication relationship does not say anything about whether or not A must be true.

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Say B is the probability that a guy is guilty say for mutilating hot babes to pieces with tooth pics. Say A is an evidence that would be true if B is true. Say A is that defendant clothes will be filled with blood. So P(A|B)=1.

Then P(B|A)=P(A|B)*P(B)/P(A)=1 *P(B)/P(A) . Wait a minute. If P(A) is very small than yea P(B|A) should go up significantly. If P(A) is common then it’s circumstantial.

Where does it say that P(B) stuck at 1 once our prior is 1 again? I got to take a look.

That’s not the clearest way of looking at it. Try the example below the following background paragraphs

In bayesian probability theory, all probabilities are conditional on your background information, which consists of the things you assume to be a priori true (your axioms), and whatever empirical data that you have acquired by experience; for short, I’ll write this as the logical predicate “Exp,” for “Experience plus A Priori Assumptions,” or just “E” for short.

Bayesian theory takes it as axiomatic that the probability of a statement that is always False (i.e., a logical contradiction) is zero independent of any condition X, P(False|X) = 0, and likewise the probability of a statement that is always True (a tautology) is unity independent of any condition X, P(True|X) = 1.

Also, you must explicitly specify your “Universe of Discourse” up front, i.e., the set of alternative hypotheses {H1,H2,…,Hn} that you intend to consider. The hypotheses defining the Universe of Discourse are usually taken to be mutually exclusive, i.e., if one hypothesis is true, then all the other hypotheses must be false (this can always be arranged by the logical equivalent of “orthogonalization”), and exhaustive, i.e., no other explanation will be considered. (This latter assumption is not a restriction, since one can always tack on the “catch-all” hypothesis “There is some other explanation that I haven’t thought of yet” — which depending on your degree of humility or arrogance can have an a priori probability that may be quite significant to quite small, as long as it is less than 1 but more than 0.)

Since {H1,H2,…,Hn} are assumed exhaustive and mutually exclusive, exactly one hypothesis must always be true, so it’s taken as an axiom that the logical conjunction H1+H2+…+Hn (“+” means “logical OR”) must be true with certainty, implying that P(H1+H2+…+Hn|X) == 1. Also, since by mutual exclusivity exactly one of the hypotheses can be true while the others must be false, we take it as axiomatic that P(H1+H2+…+Hn|X) = P(H1|X) + P(H2|X) + … + P(Hn|x) == 1.

Since by the first axiom of bayesian probability, P(A + (not A)|X) = 1 for all X, and since only one of A or (not A) can be true, it immediately follows that P(not A|X) = 1 – P(A|X) for all X.

Finally, there is the “chain rule” for factoring joint probabilities into conditionals: P(A&B|X) == P(A|X&B) * P(B|X) == P(B|X&A) * P(A|X). (For readability reasons, this is more often written as P(A,B|X) == P(A|X,B) * P(B|X) == P(B|X,A) * P(A|X), and even to drop the “AND commas” if it won;t result in ambiguity.)

It turns out that the above axioms completely define all of bayesian probability theory, and that from them it’s possible to compute the probability of any statement that can be expressed in terms of the set of hypothesis {H1,H2,…,Hn} and the “background predicate” E representing your axioms and experience. Furthermore, a careful analysis shows that they represent the unique extension of boolean logic to truth-values intermediate between 0 and 1, and that any other set of rules will fail to be consistent with logic. (I’m leaving out some technical details here, as the proof of this theorem turns out to be remarkable subtle.)

Ah I see. So we make P(Something|X) as a new probability universe. Wow I forgot that part of probability when I was in school.

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A number of useful corollaries can be proved from the above axioms, for propositions A, B, and X:

  • P(A|X,A) == 1, since it’s given that A is assumed to be true, and by definition P(True|X) = 1;
  • P(A,A|X) = P(A|X), since logically A&A == A
  • P(B|X,A,A) = P(B|X,A), since logically A&A == A;
  • P(A|X,B) = P(A|X), since if A and B are logically independent, knowing B tells us nothing about A;
  • P(A,B|X) = P(A|X)  * P(B|X), if A and B are logically independent (follows from chain-ruloe plus above);
  • P(A+B|X) = P(A|X) + P(B|X) – P(A,B|X), which allows us to treat correlations;

Bayes’ Theorem follows directly from the chain-rule axiom: P(A|BX) = P(B|AX) * P(A|X) / P(B|X). However, this is not the most useful form for reasoning about how to update the a priori probabilities of your hypotheses given new information. Denote your empirical data or new information by the logical predicate “D.” Assume that you also have some “statistical model” that predicts the probability P(D|Hi,E) (your degree of confidence or how “unsurprised” you would be) that you would see data D given your past experience E and assuming that hypothesis “Hi” is true; P(D|Hi,E) is often called the “data likelihood” of hypothesis “Hi.” Bayes’ Theorem allows you to invert P(D|Hi,E)  to give the updated or “a posteriori” probability of hypothesis “Hi,” P(Hi|D,E) = P(D|Hi,E) * P(Hi|E) / P(D|E) in terms of the “data likelihood” for “Hi,” the a priori probability P(Hi|E), and a quantity we don’t seem to have, P(D|E), the probability one would observe the data “D” given only our experience, sometimes called the “evidence” provided by the data. However, there is a clever trick: since by hypothesis H1+H2+…+Hn = True, and since P(D&True|X) = P(True|D,X) * P(D|X) = 1*P(D|X) = P(D|X) for all D and X, it follows that:

Code:
P(D|E) = P(D(H1+H2+...+Hn)|E) = P(D&H1 + D&H2 + ... + D&Hn|E) = P(D,H1|E) | P(D,H2|E) + ... P(D,Hn|E)

= P(D|H1,E) * P(H1|E) + P(D|H2,E) * P(H2|E) + ... + P(D|Hn,E) * P(Hn|E)

and now we have expressed P(D|E) entirely in terms of things we know. Hence, bayesian theory allows one to revise one’s a priori probabilities P(Hi|E) to include new data “D” into one set of assumptions and empirical experience “E” if one has a statistical model for estimating the likelihood of observing data “D:”

Great. I see. So P(D|E) will be the probability of D given our natural experience. To know that, we need some a priori (except for E) understanding of what’s likely and what’s not. I get that.

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Code:
P(Hi|D,E) = P(D|Hi,E) * P(Hi|E) / (Sum(k=1..n) P(D|Hk,E) * P(Hk|E))

Note that if the a priori probability P(Hi|E) is zero for some specified “i” (i.e., Hi is a priori false), no amount of data can ever budge it from zero (i.e. false), and that if it’s one (i.e. a priori true), no amount of data can ever budge it from one (i.e. true), since if one P(Hi|E) is one, then all the others must be zero, by the axiom Sum(i=1..n) P(Hi|E) == 1. Hence, one must take an “agnostic” attitude to learn from experience, because if one dogmatically rejects a given hypothesis (or blindly accepts it on faith), no amount of experimental evidence to the contrary can ever alter that a priori probability.

Now for the example: Suppose that you are walking down a street in an arid town, and you notice that the sidewalk in front of a house is wet. From prior experience you know that people tend to sprinkle their lawns about three days a week, whereas it only rains once a week, so a priori you expect that P(Sprinkler|Exp) > P(Rain|Exp), with a priori odds of about 3 to 1. Let’s assume for the moment that you can’t think of any third explanation, so your Universe of Discourse will consist of the two propositions “It was raining earlier,” and “The sprinkler was on earlier.” From experience, you know a priori that P(Wet|Sprinkler,Exp) and P(Wet|Rain,Exp) are both close to unity, i.e., if the sprinkler was on, the sidewalk will probably get wet, and if it was raining, the sidewalk will also probably get wet, but if all the information you have is that one given sidewalk in front of one given house is wet, one can’t say much more than P(Sprinkler|Wet,Exp) > P(Rain|Wet,Exp), since people sprinkle more often than it rains.

Now, suppose you look up and down the sidewalk, and notice that the sidewalks in front of all the houses are wet. From experience, you know that rainstorms seldom rain on only one house while avoiding others, so you suspect that it probably rained — but how confident can you be of that conclusion?

We can estimate the relative data likelihoods using the chain-rule for conditional probabilities:

Code:
P(Wet_1 & Wet_2 & ... & Wet_N | X & E) = P(Wet_1 | X & E, Wet_2 & ... & Wet_N) * P(Wet_2 | X & E & Wet_3 & ... & Wet_N) * ... * P(Wet_N | X & E)

where “X” is either “Rain” or “Sprinkler,” and “E” is your experience and assumptions.

First, suppose that it rained — then you know from experience that Wet_1 = Wet_2 = … Wet_N; hence, since P(A&A|X) = P(A|X), P(Wet_1 & Wet_2 & … & Wet_N | Rain, Exp) will not be appreciably different from any individual P(Wet_i | Rain, Exp), which is furthermore close to unity; hence, the data likelihood that if it rained, all the sidewalks will be wet is close to unit, in agreement with commons sense.

By contrast, you know from experience that people decide to water their lawns more or less independently, so P(Wet_i|Sprinkler,Exp,Wet_j) = P(Wet_i|Sprinkler,Exp) for all i != j; hence

Code:
P(Wet_1, Wet_2, ..., Wet_N | Sprinkler, Exp) = P(Wet_1 | Sprinkler, Exp, Wet_2, ..., Wet_N) * P(Wet_2 | Sprinkler, Exp, Wet_3, ..., Wet_N) * ... * P(Wet_N | Sprinkler, Exp)

= P(Wet_1 | Sprinkler, Exp) * P(Wet_2 |  Sprinkler, Exp) * ... * P(Wet_N | Sprinkler, Exp)

~= ( P(Wet | Sprinkler, Exp) )**N

where the last step assumes that most people water their lawns with about the same frequency. It thus follows that, even if  P(Wet | Sprinkler, Exp) is close to unity, it will not take a very large number of houses N before the data likelihood becomes very small — which is consistent with both experience and common sense that it’s unlikely that every resident on the block will water their lawn on the same day (unless it’s extremely hot!).

Plugging these and similar estimates of data-likelihoods for the two hypotheses into Bayes’ Theorem, it’s fairly straightforward to show that, if all the sidewalks are wet, then the a posterior probability for rain becomes quite large, even though the a priori probability of rain was much smaller than for sprinkling.

Conversely, if only one sidewalk is wet and all the others are dry, then sprinkling becomes even likely than rain — although a more careful analysis will convinces you that something odd must be going on, since it’s far more likely that about 3 sidewalks out of 7 would be wet than just one sidewalk out of N.

Finally, if we had included the “catch all” hypothesis that something we haven’t thought of has happened, then in the case that only 1 sidewalk out of N was wet, it would be the “catch-all” that would have gotten the highest posterior probability — even if one had assumed that its a priori probability was small — suggesting that it’s time to re-think your set of hypotheses.

For an elementary introduction to bayesian probability theory, I recommend “Data Analysis: A Bayesian Tutorial,” by D.S. Sivia. for a detailed discussion of both the philosophy and practice of bayesian probabilistic reasoning, I recommend “Probability Theory: The Logic of Science, by E.T. Jaynes. For free repositories of many papers and tutorials online, see http://bayes.wustl.edu/ (which contains the first several chapters of Jaynes’ book and a complete but unpublished draft of an earlier book), and http://www.astro.cornell.edu/staff/loredo/bayes/ which contains tutorials and links to other Bayesian websites.

This is very enlightening. Now I start seeing where “faith” kicks in. Once people are convinced that something is true, nothing will shake that believe.

Okay so we have 2 hypothesis. Hr (for rain) and Hs for sprinkler. Say I see that a lawn is wet. Say I come from middle east where rain comes once a year. So I would believe that sprinkler must be on. Now this is close to “faith”. I already believe, with great prejudice that it ain’t rain.

But then I see all the other houses are wet too.

Now let’s see how things work.

Look I will edit this much latter. I need time to think.

I think for simplicity sake, let’s call the first neighbor Wet0

That way we consider only 2 possibilities, rain, or sprinkler (sprinkler 0)

Also for simplicity sake lets’ call

P(A|B W0E) as Pwe (A|B). Where Pwe is the probability measure when E and W is part of the assumption. That should leave all the clutters out.

I think there should be an easier way to see Pwe(R | W1 W2 W3 W4… WN). I’ll come back to this one.

…. Genepool goes back home thinking about it:

There is an easier way to compute this.

Say all the houses are wet.

So what is the probability of raining?

What is the probability that all the houses are wet given rain?

Well here I mean P () to mean Pwe just to make things shorter. And Pwe is actually P(|WE)

P(W1 W2 W3 … WN|R)=1. Tadaaa. If it’s raining then obviously all the grass will be wet.

What is the probability that all the houses are wet given that it’s not raining? Well that’s the probability that all the houses turn their sprinkler at the same time. Say the probability is the same with P(S)

Independence means

P(W1|S)=P(W1) because W1 do not depend on S. It’s also the same with P(S) for simplicity sake. So everybody has the same probability of running a sprinkler.

So P(W1 W2 W3 … WN|S) = P(W1 W2 W3 … WN)=P(S)^N . This get SOOOOO small as N goes large.

Now, what is P(R|W1 W2 W3 … WN)?

Well, it’s P(W1 W2 W3 … WN|R) * P(R)/P(W1 W2 W3 … WN)

Now here is the trick.

P(W1 W2 W3 … WN) is following gdp formula

P(W1 W2 W3 … WN | R) P(R) +  P(W1 W2 W3 … WN | S) P(S)

It’s actually a weighted average formula. Now P(W1 W2 W3 … WN | R) is 1.

So that becomes  P(W1 W2 W3 … WN|R) * P(R)/(P(R)+ P(W1 W2 W3 … WN | S) P(S))

P(W1 W2 W3 … WN | S) is a small number. Let’s call it E. I mean if P(S) is 10 and N is 1000, E is like 10^(-1000). That’s how small it is.

So

P(W1 W2 W3 … WN|R)=P(R)/(P(R)+e)

Simplifying we get

P(W1 W2 W3 … WN|R)=P(R)(1/(1+e/P(R))

What does it mean?

If P(R) is small, say 1 thousandth. Given that e is very small P(W1 W2 W3 … WN|R) will still be close to 1.

That depends on the ratio of (1+e/P(R))

However, if P(R) is exactly \0, then P(R)/(P(R)+e) is 0. The small e, even though is close to 0 is still bigger than 0. So faith becomes some form of bayesian anomaly.

Basically as P(R) began to be equal to e, then P(W1 W2 W3 … WN|R) would go to .5. A few more N and it goes back up to 1 again.

That means if you have a doubt, a little doubt, that P(R) is a possibility, your believe will jump to the normal one as enough evidence shows up. As N grows big, and every houses is wet, quite obviously it’s raining.

However, when you believe that there is no rain, no amount of wet houses will convince you that it’s raining. The small probabilities that all the houses run their sprinkler becomes your “belief”.

Now that explains a lot.

There are thousands of proof that morality comes from the interest of whoever makes morality rather than God. Yet, people that are of faith will simply think that their morality comes from God or some higher reasoning besides profit (including libertarians). The small probabilities that explain that away, then becomes their belief. That explains why Christian believes that the bible is divinely inspired. Some even go all the way believing that the king james translation of the bible is divinely inspired. Then some believe that they are guided by Holy Spirit straight despite the fact that the Holy Spirit do not help them to correctly predict stocks or anything verifiable. Also the fact that most people have different faith and hence can’t all be correct doesn’t deter them from believing that somehow they’re luckier. That’s because that’s the only way their faith can be true.

So is faith useful? For who? If you want to know the truth, then always have some doubt. If  you want to convince people, then teach them to have faith.