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Register a new userCapital process and optimality properties of a Bayesian Skeptic in coin-tossing games
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We study capital process behavior in the fair-coin game and biased-coin games in the framework of the game-theoretic probability of Shafer and Vovk (2001). We show that if Skeptic uses a Bayesian strategy with a beta prior, the capital process is lucidly expressed in terms of the past average of Realitys moves. From this it is proved that the Skeptics Bayesian strategy weakly forces the strong law of large numbers (SLLN) with the convergence rate of O(sqrt{log n/n})$ and if Reality violates SLLN then the exponential growth rate of the capital process is very accurately described in terms of the Kullback divergence between the average of Realitys moves when she violates SLLN and the average when she observes SLLN. We also investigate optimality properties associated with Bayesian strategy.
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We study multistep Bayesian betting strategies in coin-tossing games in the framework of game-theoretic probability of Shafer and Vovk (2001). We show that by a countable mixture of these strategies, a gambler or an investor can exploit arbitrary patterns of deviations of natures moves from independent Bernoulli trials. We then apply our scheme to asset trading games in continuous time and derive the exponential growth rate of the investors capital when the variation exponent of the asset price path deviates from two.
In this letter we present the first implementation of a quantum coin tossing protocol. This protocol belongs to a class of ``two-party cryptographic problems, where the communication partners distrust each other. As with a number of such two-party protocols, the best implementation of the quantum coin tossing requires qutrits. In this way, we have also performed the first complete quantum communication protocol with qutrits. In our experiment the two partners succeeded to remotely toss a row of coins using photons entangled in the orbital angular momentum. We also show the experimental bounds of a possible cheater and the ways of detecting him.
A significant aspect of the study of quantum strategies is the exploration of the game-theoretic solution concept of the Nash equilibrium in relation to the quantization of a game. Pareto optimality is a refinement on the set of Nash equilibria. A refinement on the set of Pareto optimal outcomes is known as social optimality in which the sum of players payoffs are maximized. This paper analyzes social optimality in a Bayesian game that uses the setting of generalized Einstein-Podolsky-Rosen experiments for its physical implementation. We show that for the quantum Bayesian game a direct connection appears between the violation of Bells inequality and the social optimal outcome of the game and that it attains a superior socially optimal outcome.
We propose strongly consistent estimators of the $ell_1$ norm of the sequence of $alpha$-mixing (respectively $beta$-mixing) coefficients of a stationary ergodic process. We further provide strongly consistent estimators of individual $alpha$-mixing (respectively $beta$-mixing) coefficients for a subclass of stationary $alpha$-mixing (respectively $beta$-mixing) processes with summable sequences of mixing coefficients. The estimators are in turn used to develop strongly consistent goodness-of-fit hypothesis tests. In particular, we develop hypothesis tests to determine whether, under the same summability assumption, the $alpha$-mixing (respectively $beta$-mixing) coefficients of a process are upper bounded by a given rate function. Moreover, given a sample generated by a (not necessarily mixing) stationary ergodic process, we provide a consistent test to discern the null hypothesis that the $ell_1$ norm of the sequence $boldsymbol{alpha}$ of $alpha$-mixing coefficients of the process is bounded by a given threshold $gamma in [0,infty)$ from the alternative hypothesis that $leftlVert boldsymbol{alpha} rightrVert> gamma$. An analogous goodness-of-fit test is proposed for the $ell_1$ norm of the sequence of $beta$-mixing coefficients of a stationary ergodic process. Moreover, the procedure gives rise to an asymptotically consistent test for independence.
Given a sequence of numbers ${p_n}$ in $[0,1]$, consider the following experiment. First, we flip a fair coin and then, at step $n$, we turn the coin over to the other side with probability $p_n$, $nge 2$. What can we say about the distribution of the empirical frequency of heads as $ntoinfty$? We show that a number of phase transitions take place as the turning gets slower (i.e. $p_n$ is getting smaller), leading first to the breakdown of the Central Limit Theorem and then to that of the Law of Large Numbers. It turns out that the critical regime is $p_n=text{const}/n$. Among the scaling limits, we obtain Uniform, Gaussian, Semicircle and Arcsine laws.
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