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The referrer address for that link is 0x54daebc3ec3a693f876f90f52d11d3fd65c47498
There were a handful created after you, but you were one of the first, hence the low ID.
OK, thanks so much! I'm convinced. I want to confirm to anyone following this, that the referrer address is indeed correct. Good luck with the ICO. You have a working product, and you're solving a real problem. I like the bootstrapping aspect of it... activating the product to assist in the ICO itself. |
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The link you received is definitely unique to your Ethereum address (the one that you used to send the 0 ether transaction). Here is the affiliate link your system gave me: https://foreground.io?FDPAID=1&FPID=1 Can you see why I might question whether it's unique and tied to my ethereum address? Perhaps you could explain how this works. If you can tell me the ethereum address this link is tied to, that would convince me. |
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Hey m3ta, thanks for the comment. The best place to go is the DApp itself Smiley --> www.app.foreground.ioI tried this, and got an affiliate link with FPID=1 (the demo video had FPID=2). Somehow I don't think I got a unique link tied to my Ethereum address. |
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Very nice!
I also find the backgrounds distracting... I'd like to either browse the backgrounds without the site widgets, or see the site widgets without the backgrounds. Both at once is just too much.
As for market saturation, it is always possible to take market share away from the incumbents by out-innovating them. This site is on the way to doing that.
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It just that we are so far from what would be considered likely results that it is hard to reconcile. And when you are 35% down and getting constantly diluted, it is frustrating indeed. The house luck suggests a 0.51% historical edge which is perplexing to me after 150M bets. Anyway, I know I am merely vocalizing the concerns of many other investors and concerns Doog himself has brought up. The JD we have now is not the same JD as when I first invested. Investor behavior is different, for one thing. Nor is it the same JD that we will have whenever dooglus implements the variable max profit settings. I think it's reasonable to divest, stand aside, and see how things go with the new features. |
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This does not change the fact that trying to "ride the waves" or "predict the patterns" by investing/divesting multiple times while Nak is playing is just another form of gambler's fallacy. There are no predictable waves, no patterns, just random events. If this is true, then investing / divesting does no harm (other than the opportunity cost of not being invested part of the time). On the other hand, investing / divesting seems to work very well. If I'm not 100% convinced of the Gambler's Fallacy explanation, then it is reasonable to invest / divest. At best, I come out ahead. At worst, I come out about even. |
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I think you're reading my table wrong. I've deleted all but the relevent parts in the quote above.
On the first bet, the bankroll is 200 and the max bet is 1.25 You're right, my mistake. We are in agreement! I think it is inevitable that different max profit settings will lead to changes in the relative proportions of the bankroll, which will tend to amplify or dilute people's returns. That's not different in principle from what we have now, when the investments or divestments of other people affect my return. I don't see this as a problem, as long as the process is transparent and people have enough information to make decisions. |
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It's not as simple as that though.
If you both invest 100 BTC, and a whale constantly bets to win the max profit, and loses, then the 1% guy wins more than 4 times that of the 0.25% guy:
A ( p/l) B ( p/l) roll max ratio
-------- (------) -------- (------) -------- ------ --------
100.2500 (0.2500) 101.0000 (1.0000) 200.0000 1.2500 4.000000
100.5006 (0.5006) 102.0100 (2.0100) 201.2500 1.2606 4.014981
100.7519 (0.7519) 103.0301 (3.0301) 202.5106 1.2714 4.030050
101.0038 (1.0038) 104.0604 (4.0604) 203.7820 1.2822 4.045206
101.2563 (1.2563) 105.1010 (5.1010) 205.0642 1.2931 4.060451
101.5094 (1.5094) 106.1520 (6.1520) 206.3573 1.3042 4.075785
101.7632 (1.7632) 107.2135 (7.2135) 207.6614 1.3153 4.091208
102.0176 (2.0176) 108.2857 (8.2857) 208.9767 1.3265 4.106721
102.2726 (2.2726) 109.3685 (9.3685) 210.3033 1.3379 4.122325
...
127.4046 (27.4046) 262.5266 (162.5266) 387.0141 2.9170 5.930639
127.7231 (27.7231) 265.1518 (165.1518) 389.9311 2.9438 5.957197
128.0424 (28.0424) 267.8033 (167.8033) 392.8749 2.9708 5.983919
After 100 or so whale losses, the 1% guy's profit is almost 6 times that of the 0.25% guy. His share of the bankroll keeps increasing relative to the more timid guy.
The converse is also true. If the whale wins, then the 1% guy loses less than 4 times as much as the 0.25% guy, and after around 100 whale wins has lost around 3 times as much as the 0.25% guy. His share of the bankroll keeps decreasing relative to the more timid guy.
A ( p/l) B ( p/l) roll max ratio
-------- (------) -------- (------) -------- ------ --------
99.7500 (-0.2500) 99.0000 (-1.0000) 200.0000 1.2500 4.000000
99.5006 (-0.4994) 98.0100 (-1.9900) 198.7500 1.2394 3.984981
99.2519 (-0.7481) 97.0299 (-2.9701) 197.5106 1.2289 3.970050
99.0037 (-0.9963) 96.0596 (-3.9404) 196.2818 1.2184 3.955206
98.7562 (-1.2438) 95.0990 (-4.9010) 195.0633 1.2081 3.940449
98.5093 (-1.4907) 94.1480 (-5.8520) 193.8552 1.1979 3.925778
98.2631 (-1.7369) 93.2065 (-6.7935) 192.6574 1.1878 3.911192
98.0174 (-1.9826) 92.2745 (-7.7255) 191.4696 1.1777 3.896691
97.7724 (-2.2276) 91.3517 (-8.6483) 190.2919 1.1678 3.882275
97.5279 (-2.4721) 90.4382 (-9.5618) 189.1241 1.1579 3.867943
...
78.4426 (-21.5574) 37.7237 (-62.2763) 116.7439 0.5776 2.888855
78.2464 (-21.7536) 37.3464 (-62.6536) 116.1662 0.5733 2.880154
78.0508 (-21.9492) 36.9730 (-63.0270) 115.5929 0.5691 2.871500 In this example, you're setting the max bet to 1% of the total bankroll (20,000 on the first roll). But this is wrong! The .25% people are willing to risk .25% of their 10,000, or 25. The 1% people are willing to risk 1% of their 10,000, or 100. The max bet on the first roll is 125, not 200. |
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If you pick 0.25% and I pick 1%, then I make (or lose) 4 times as much as you. Simple as that. This sounds like one of those problems that you don't truly understand until you try to code it. Then you throw away the first attempt and start over. Maybe it really is that simple... but it has to be done in a way that scales so it doesn't drag down performance. I hope dooglus will share some details after he's wrestled with the implementation. |
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My mind is open and I think a lot of us here [lurkers included  ] are trying to learn too, so please continue to share why you believe 0.5% or 1% is too high. I think by now it's clear that the discussion will not converge to a consensus. I'm looking forward to when investors can specify their max bet percentage individually. Maybe the 1% people will end up with the lion's share of the profits over the long run. Maybe they'll go bust, leaving the .25% people the winners. Who knows? (We all know, we just disagree!) I hope that dooglus will build in some kind of statistics so we can gain some insight from other people's experience. (Maybe the equivalent of creating a 1 BTC investment in each tier and reporting the balance once a day. I'd love to be able to load that kind of data into a spreadsheet.) |
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I'm challenged because I don't know how to explain it. I'm trying to say that while the pattern fits and looks random, it is predictably random. Here's an example of "predictably random": the decimal expansion of Pi is statistically random (the digits pass every known test for randomness), but Pi is obviously not random. |
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What will happen is that smart investors with bots will take a majority of the winnings when small fish are playing, and then will drop down to 0.25% when the whales play. Fucking the low-risk investors like mechs who will be stuck at 0.25%.
This is exactly the bot that I will be coding. This is no different in principle from divesting when the whales play. It's not a new problem. And really, switching between 1% and .25% is less of a problem than switching between 1% and 0%. |
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PROBLEM 2: Are we 100% confident that 1% max bet does in fact optimize profits over the long term given fair dice rolls (e.g., does the Kelly criteria consider the dynamic nature of the max bet size and the size of the gambler's bankroll)? There is some disagreement on this. I've argued that the JD situation is different enough from the Kelly model that the 1% max bet is not necessarily optimal. And I think that Nakowa is evidence of this. Others think the Kelly Criterion is applicable, and all we need to do is wait for Nakowa to go bust. It's a difficult question... probabilities are notoriously counter-intuitive. Since we have people who believe in the Kelly Criterion despite our recent experience, maybe the best solution all around is to let investors set their max bet percentage individually. If the Kelly supporters are right, they'll prosper in the long run. |
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It would be cool. Big work for Doog however.
Only way for the house to limit min/max spreads is to put each user in a "time out" in whatever min/max bracket they're in. But then they'd just open a 2nd account and both each bracket with a different bot.
I think this can be done on the investor side, with each investor specifying a max bet percentage between 0% to 1%. That's not really so different from what we have now... investors are either at 0% (divested) or .25%. From the gambler's point of view, nothing has changed. He sees a max bet (max profit) and bets accordingly. The profit or loss is distributed over those investors who were willing to cover the bet. For small bets, it's distributed over all the investors (non-0%), since they were all willing to cover small bets. For a large bet, the profit or loss is distributed over those investors willing to cover large bets. The details need to be worked out for a couple of things. One is the calculation of the max bet, the other is exactly how the profit or loss is distributed. For example, if someone bets 100 BTC, maybe 10 BTC of the profit or loss goes to all investors willing to cover 10 BTC bets, the next 10 BTC goes to those investors willing to cover 10 BTC but not 20 BTC, and so forth. But I think if the distribution algorithm is set, the max bet follows. |
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Doog proposed a solution: make max bet a market. You choose what your tolerance is and the site will adjust. Mech could have set his to 0.25%, I would have kept it at 1%, every investor votes with their wallet. From there you get the max bet and the proportion to be paid out to each investor.
No controversy, no arbitrarily choosing numbers. House edge can remain fixed so that from the player's point of view, nothing changes or is unfair.
The more I think about this, the more I like it. This is doable. |
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No gamblers fallacy here. Nakowa was lucky, very lucky, unlikely lucky - if he had played long enough, he would have ended up losing. That's no fallacy, that's math, because you do realize that the house has an edge - right?
The point is that he doesn't have to play long enough to lose. He's demonstrated over and over again that his bankroll is big enough that he can let variance take him into positive territory, and walk away. The Kelly Criterion applies to situations where the player with the edge can keep betting as long as he likes. With JD, the house has the edge, but can't initiate bets. |
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This is a matter of math and logic. Some people argued that the max bet was too high and the variance allowed a whale with a large enough bankroll to win despite the house edge. There is plenty of evidence lately that this is the case. Nakowa himself agrees with this and has said so. Others argued that the max bet had to kept at 1% to give them a chance to recoup their losses. But this sounds like gambler's fallacy... what's lost is lost, and there is no reversion to the mean to make things right again.
My point is that math is not a democracy. Voting to set pi equal to 3 does not make it so.
JD is an experiment. Let's see what happens, analyze some more data and reconsider the max bet.
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I think lowering the max bet temporarily was a good move.
If this works, it undermines the conspiracy theories. (That is, if JD profit starts rising again.) It shows that the whale had no magical powers, and there was no mole at the data center.
Going forward:
The sliding house edge is a bad idea... it violates people's sense of fairness.
There are some very interesting ideas on letting the investors choose how much variance they're willing to accept. These need some thinking through, but this may be the best of both worlds... it would be a way to bring the max bet back up.
I'd like to suggest a design principle: it's OK to make things more complicated for the investors, but keep things simple for the gamblers.
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If you look at Nakowa's actual bet data, he makes a bunch of small bets mixed in with his large bets. If you take his actual data, but just shift one colum so that he wages what he actually wagered 2 rolls earlier, then most the losses line up with the small bets and most of the profits line up with the large bets. This should be statistically impossible. So either I made a mistake, there is an error in the file, or the seed was known by Nakowa and he left a clue for us to find.
You may have found Nakowa's strategy. If he is looking 2 rolls back to determine his current bet, then shifting the data by 2 rolls might result in something like this. |
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Here what I found elsewhere: "Betting half the Kelly amount, for example, reduces bankroll volatility by 50%, but growth by only 25%."
The big question is, is volatility the same as risk? If yes, then you are right, reducing max bet reduces risk more than the loss in growth. But if volatility does not equal risk, then you just lost growth in return for nothing.
I would say, volatility does not equal risk. If I have a statistical certain win, then volatility is not risk, since loss is always temporary with statistical certainty. Wrong. By your logic, the risk is always zero, regardless of the max bet, therefore there is no reduction in risk when the max bet is reduced. "Risk" and "volatility" usually refer to the standard deviation of the return, and I think in your quote "bankroll volatility" is the standard deviation of the bankroll, which would be the same. In this thread we also use the term "variance", which is technically the square of the standard deviation. Not to get all pedantic on everyone, but we use "risk", "variance" and "volatility" imprecisely, to capture the idea that the investor's balance can be up one day and down the next. And that's OK. Now, for something like "reduces bankroll volatility by 50%", then we need to get specific and ask whether we're talking about a standard deviation or the square of a standard deviation, because that 50% number can't be correct for both of those at the same time. Even if you persist in your colloquial use of "risk", we can talk about a volatility/growth ratio, and see that an investor might reasonably prefer a situation with half the volatility and three-fourths the growth. |
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