I think having a flawed (human) agent using this technique is too susceptible to the agent convincing themselves that they tried hard enough, and so we've just pushed the problem one step back: How do you know when you've tried hard enough.

Slight nitpick on your summary of the story:

Gur cebgntbavfg qbrf abg rzcgl uvf tha vagb gur eboore zreryl orpnhfr crbcyr ner zrng naq qba'g znggre. Vafgrnq, gur cebgntbavfg unq vagraqrq gb yrg gur eboore yvir orpnhfr gur cebgntbavfg ernyvmrq gur jubyr fvghngvba jnf nofheq naq abguvat znggrerq nalzber, abg rira uvf tveysevraq'f qrngu (vg'f nzovthbhf nf gb jurgure gur qeht jnf gur cevznel pnhfr bs uvz pbzvat gb guvf pbapyhfvba). Gur cebgntbavfg ghearq gb jnyx njnl, naq gung'f jura gur eboore ehfurq uvz. Va ernpgvba/frys qrsrafr, gur cebgntbavfg fubg gur eboore. Nsgre frrvat gung ur jnf qrnq, gur cebgntbavfg sryg ab erzbefr naq yrsg.

I think there are two distinct concepts here: One of them is Bayesian reasoning, and the other is Solomonoff induction (which is basically Occam's Razor taken to its logical extreme).

Bayesian reasoning is applicable when you have some prior beliefs, usually formalized as probabilities for various theories being true, (e.g 50% chance God did it, 50% amino acids did it), and then you encounter some evidence (e.g. observe angels descend from the sky), and you now want to update your beliefs to be consistent with the evidence you encountered (e.g. 90% chance God did it, 10% amino acids did it). To emphasize, Bayesian reasoning is simply not applicable unless some prior belief to update.

However, I have other data at hand that seems to lend credence to the "God exists" theory;

Sounds like you're referring to Bayesian reasoning here. You're saying without that "other data", you have some probabilities for your various theories, but then when you add in that data, you're inclined to update your probabilities such that "God did it" becomes more probable.

In contrast, Occam's Razor and Solomonoff induction do not work with "prior beliefs" (in fact, Solomonoff is often used, in theory, to bootstrap the Bayesian process, providing the "initial belief", from which you can start using Bayesian to update from). When using Solomonoff, you enumerate all conceivable theories, and then for each theory, you check whether it is compatible with the the data you currently have. You don't think in terms of "this theory is more probable given data set 1, but that theory is more probable given data set 2". You simply mark each theory as "compatible" or "not compatible". Once you've done that, you eliminate all theories which are "not compatible" (or equivalently, assign them a probability of 0). Now all that remains is to assign probabilities to the theories the remain (i.e. the ones which are compatible with the data you have). One naive way to do that is to just assign uniform probability to all remaining theories. Solomonoff induction actually states that you should assign probabilities based on the complexity of the theory.

If I did, then by that same logic, we would always have to assume that a coin once flipped would be 100% biased toward the side upon which is landed.

That's actually not true. Coincidentally, I wrote a web app which illustrates a similar point: http://nebupookins.github.io/binary-bayesian-update/

Mentally relabel the button "Witnessed Failure" with "Saw a coin come up tails" and "Witnessed Success" with "Saw a coin come up heads", then click the "Witnessed Success"/"Saw a coin come up heads" button.

Note that the results is not "You should assume that a coin is 100% biased towards head."

Instead, the results are "There's a 0% chance that the coin is 100% biased towards tail, a tiny chance that the coin is 99% biased towards tail, a slightly larger chance that the coin is 98% biased towards tail" and so on until you reach "about a 2% chance the coin is 100% biased towards head", which is currently your most probable theory. But note that while "100% biased towards head" is your most probable theory, you're extremely non-confident in that theory (only a 2% chance that the theory is true). You need to witness a lot more coin flips to increase you confidence levels (go ahead and click on the buttons a few more times).

Disclaimer: This web app actually uses the naive solution of initially assigning uniform probability to all possible theories, rather than the Solomonoff solution of assigning probability according to complexity.

The first idea to come to my mind, on reading this article, is that companies do not produce jobs and attempt to sell them to potential employees. Rather, potential employees produce labour, which they attempt to sell to companies (or, in the case of entrepreneurs, which they use to produce goods or services for sale).

What kind of predictive power does this belief have? I.e. why does this inversion mean that the labour market is "different", but performing the inversion "stores don't sell goods to consumers for money, consumers sell money to stores for goods" does not make the goods market similarly "different"?

Okay, thanks.

So it sounds to me like this is not iterated prisoner's dilemma, because if my country gets nuked, I do not get to elect another military leader for the next round.

What's the difference between "Survival" and "having at least one survivor"?

The way I see it:

• If I'm dead, 0 points.
• If I'm alive, but my city got nuked, so it's like a nuclear wasteland, 1 point.
• If I'm alive, and living via normal north american standards, 2 points.

We're assuming a conflict is about to happen, I guess, or else the hypothetical scenario is boring and there are no important choices for me to make.

The question is not "Do I elect a crazy leader or a non-crazy leader?", but rather, "Do I elect a leader that's believes 'all's fair in love and war?' or a leader that believes in 'always keep your word and die with honor'?"

I.e. if you think "ethical vs unethical" means "will retaliate-but-not-initiate vs will not retaliate-but-not-intiiate", then it's no wonder why we're having communication problems.

I'm still not following you.

So first of all, you'll need to convince me that the payoff matrix for an individual civilian within a nation deciding who their military leader should be is similar to one of the prisoners in PD. In particular, we'll need to look at what "cooperate" and "defect" even mean for the individual citizen. E.g. does "cooperate" mean "elect an ethical military leader"?

Second, asuming you do convince me that the payoff matrices are similar, you'll have to clarify whether you think warfare is iterated for an individual civilian, especially when the "other" nation defects. I suspect if my leader is ethical, and their leader is not, then I will be dead, and hence no iteration for me.

Thirdly, you may wish to clarify whether all the sentences after your first are intended to be new assertions, or if they are supposed to be supporting arguments for the first sentence.

I'm afraid I don't see the relevance.

No, but I'm not sure military conflicts are necessarily iterated, especially from the perspective of me, an individual civilian within a nation.

Of course, if your goal is to learn Python but you find Zed's book too easy, "Read a book on Lisp" is probably not suitable advice.