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Risk attitude. We now turn to the following question: When a player is allowed to choose the odds themselves in a near-fair game, how would they balance the risk and potential return? Higher odds means a lower chance of winning and higher potential return, for example, setting odds of 10 means that the winning chance is only 1/10, but the potential winning payoff equals 9 times the original wager. In our analysis, we can examine such behaviors based on the gambling logs from Crash and Satoshi Dice games. For the Crash game only CSGOFAST.COM provides the player-selected odds even when players lose that round, whereas for the Satoshi Dice game only Coinroll accepts player-selected odds. We will therefore focus on the data collected on these two websites. For the Crash game on CSGOFAST.COM, the odds can only be set as multiples of 0.01, whereas for the Satoshi Dice game on Coinroll the odds can be set to 0.99·65536/ i where i is a positive integer less than 64000. To simplify our modeling work, we will convert the odds on Coinroll to be multiples of 0.01 (same as for the Crash game). It turns out that in both cases the odds can be modeled with a truncated shifted power-law distribution, where \(\zeta (\cdot ,\cdot )\) is the incomplete Zeta function, and m max is the upper truncation. Note that there is a jump at m max , meaning that the players are more likely to place bets on the maximum allowed odds than on a slightly smaller odds. The estimated parameters \(\alpha =1.881\) , \(\delta =0.849\) , and \(_ >>=1.15\) for csgofast-Jackpot on CSGOFAST.COM, whereas for Coinroll the parameters are found to be \(\alpha =1.423\) , \(\delta =2.217\) , and \(_ >>=2.58\) . From the comparison between the CCDFs of empirical data and fitting curves, as shown in Fig. 3, we can see that the truncated shifted power law can capture the overall decaying trends of odds distribution. The stepped behavior results from the gamblers’ preference of simple numbers. Figure 3. Odds distributions can be well-fitted by truncated shifted power-law distributions. A distribution that is close to a power law indicates that a gambler’s free choice of odds displays scaling characteristics (within the allowed range) in near-fair games. It also means that when gamblers are free to determine the risks of their games, although in most times they will stick to low risks, showing a risk-aversion attitude, they still present a non-negligible probability of accepting high risks in exchange for high potential returns. The scaling properties of risk attitude might not be unique to gamblers, but also may help to explain some of the risk-seeking behaviors in stock markets or financial trading. We now re-examine the distributions from the point of view of estimating the crash point m C (Satoshi Dice games can be explained with the same mechanism) The true distribution of m C generated by the websites follow a power-law decay with an exponent of 2 (with some small deviation due to the house edge). Meanwhile, a closer look at the fitted exponents listed above gives us two empirical exponents of 1.423 and 1.881, both of which are smaller than 2. The smaller exponents reveal that gamblers believe that they have a larger chance to win a high-odds game than they actually do. Or equivalently, it means the gamblers over-weight the winning chance of low-probability games. At the same time, the “shifted” characteristics here lead to more bets on small odds, which also indicates that the gamblers over-estimate the winning chance of high-probability games. As a result, they under-weight the winning chances of mild-probability games. These are clear empirical evidence of probability weighting among gamblers, which is believed to be one of the fundamental mechanisms in economics 6 ., roulette portomaso कैसीनो लाइव प्ले. Wealth distribution. In the previous study of skin gambling 8 , we pointed out that the wealth distribution of skin gamblers shows a pairwise power-law tail. This time, by considering the players’ deposits to their wallets on a gambling site as the wealth data, we find that the pairwise power-law tails are also observed for bitcoin gambling. We find that on the gambling website Coinroll, starting from 5660 cents, the players’ wealth distribution follows a pairwise power-law distribution, with the power of the first regime to be 1.585, and the power of the second regime to be 3.258, see Fig. 4. The crossover happens at 1.221 × 10 5 cents. 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