Topic: Just-Dice.com : Invest in 1% House Edge Dice Game - page 123. (Read 435353 times)

I disagree, The raise in cap edge would be for bets beyond the max bet.  This way variance could be controlled for higher risk (+ for investors).  And it allows gamblers to bet over the max bet (for a bit of house edge).  But, they still get to bet more which is a plus.  Its like a feature.

I think its okay to allow people to leverage.  They're exposing themselves to more risk.  But, there should be a limit.  And I'll discuss this later tonight but I think leveraging will bring some new problems.

Yeah, just like in the stock market :-P

Right.

Maybe it would have been better if they were called 'Bankrollers' instead of 'Investors'. 

It seems like people here think the term 'Investor' somehow means that no risk is being taken and they should have guaranteed profits.

The edge should under no circumstances be changed.  The 1% edge is why just-dice has a market advantage and 2 million BTC wagered.

If he raises it, gamblers will instantly go play at a competitor with 1%.   Adjust the max bet if you'd like, but changing the edge would be a terrible business decision.

TOTALLY not the same.

If you place a 10 bet, or two 5 bets, on an nfl game, the result will be the same.

If you roll dice at 10, or roll dice twice at 5, the result is much different.

Higher house edge on big rolls compensates variance, variance is bad for investors, so investors want to be paid more.
Easy.

You missed the point - I wasn't disagreeing. My point is that CLT != normality.

I was the one saying we should "cuddle" our whales, but I just mean to be fair with them by not changing how the casino works when they are playing precisely to "stop" them from winning. Honestly, that's lame, I assume that everybody should understand what 1% edge + 1% max profit means (high variance). I don't think we should adulate our whales (you can see how in other threads I tell Nakowa very clearly I think he is delusional and he will eventually go busto), I just think we should just let them play and follow "their strategy" without messing with them and the reason why is veyr clear for me: the fundamentals of the casino (1% edge, Kelly criterion) are sound and solid, and gambler's mentality is definitely our ally.

As it was obvious, now Nakowa justifies his losses by the change in the rules, and it's possible he won't play as much as before because he feels his "strategy" has been cheated. IMO he is delusional, I don't think he is as smart as you seem to think (otherwise he wouldn't believe he found "a flaw in sha256" and that he can "spot patterns"), and I strongly believe he would have played over and over until he lost everything. You say he might win and "never come back", that is a possibility but IMO very small, I've seen many gamblers in my life and Nakowa is just the prototypical one (big rush when gambling lots of money, denies he is a gambler, believes he has a strategy that beats the house, etc. etc. etc.) They just come back, and they come back more when they are losing, that is what happens 99% of the times. Now that we "changed the rules" while he was playing, precisely to stop him (this is very obvious Doog, and hardly justifiable), we just gave him an excuse to feel cheated, and we just gave him a reason to never play again. In his mind is not his "strategy" not working any more, is US that messed up with it.

I still think he will eventually play again (gamblers are gamblers), but nevertheless as I said many times before I think changing the max profit while he was playing was a very bad, rushed move.

Finally: 1/2 Kelly is much better than 1/4 Kelly, and probably this situation will just make that important changes (like variable risk) will be implemented sooner. I for one fully support you, I just increased my investment, and probably will continue to do so.

clt applies in our case as the distribution is independent and identical.

Fullquote and BIG +1

You would have no investors, only gamblers.
Makes no sense, not even a bit.
(unless you were being ironic, of course)

Whoever said we should cuddle our whale...  it sounds like he needs a big hug right about now:

https://bitcointalksearch.org/topic/m.3246012

In what I am sure is a totally unrelated incident, the Just-Dice chat has recently been spammed with LD ads:


Ho hum...

This. To me, increasing the edge is worse than not going full Kelly.

Indeed.  We have no evidence that Nakowa ever cheated.  

Okay so let's assume for a second that it is normal; in that case we're talking about a slightly >12% event to this point, which while lucky isn't THAT lucky. Certainly not unlikely enough to start screaming cheater...

I think it is normal.  Maybe Bugpowder can superimpose a normal distribution on his histogram:

I don't really like the idea of charging big players a higher house edge.  For one we should be encouraging big play, not punishing it.  And for two it's nice to be able to advertise "1% house edge" without having to put in small print "* unless you're a serious player, in which case it's up to double that, determined by some complex formula or other"

Don't forget that the CLT doesn't necessarily mean that sums of random variables eventually become normally distributed. It just means that the sums of iid RVs tend toward a stable distribution.

For example, sums of Pareto distributed RVs for example emphatically do not tend to a normal distribution (as I found out to my dismay while working on Ozcoin's PoT reward method last year).

I have no idea if that's the case here, and probably not. I just thought it a good idea to point out that the CLT doesn't necessarily mean sums of iid RVs tend to normality.

The number of plays doesn't have to be very high for a discrete distribution to approximate a Gaussian, something like n=8 if I remember correctly, though it's been at least 11 years since I've studied it. I'm probably in over my head in saying this; so take it with a grain of salt; but the central limit theorem is about the distribution of the means of samples, and holds regardless of the underlying distribution. I think that you can basically consider this data to be a mean of many n=1 samples.