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Classical results of Bennett and Gill (1981) show that with probability 1, $P^A \neq NP^A$ relative to a random oracle $A$, and with probability 1, $P^\pi \neq NP^\pi \cap coNP^\pi$ relative to a random permutation $\pi$. Whether $P^A =…

Computational Complexity · Computer Science 2025-11-13 John M. Hitchcock , Adewale Sekoni , Hadi Shafei

In 1973 Fraenkel discovered interesting sequences which split the positive integers. These sequences became famous, because of a related unsolved conjecture. Here we construct combinatorial games, with `playable' rulesets, with these…

Combinatorics · Mathematics 2017-05-24 Aviezri S. Fraenkel , Urban Larsson

Strassen's classical martingale coupling theorem states that two real-valued random variables are ordered in the convex (resp.\ increasing convex) stochastic order if and only if they admit a martingale (resp.\ submartingale) coupling. By…

Probability · Mathematics 2017-05-11 Lasse Leskelä , Matti Vihola

We study randomness beyond $\Pi^1_1$-randomness and its Martin-L\"of type variant, introduced in \cite{MR2340241} and further studied in \cite{Continuous-higher-randomness}. The class given by the infinite time Turing machines (\ITTM s),…

Logic · Mathematics 2026-05-19 Merlin Carl , Philipp Schlicht

Classify simple games into sixteen "types" in terms of the four conventional axioms: monotonicity, properness, strongness, and nonweakness. Further classify them into sixty-four classes in terms of finiteness (existence of a finite carrier)…

Computer Science and Game Theory · Computer Science 2011-07-05 Masahiro Kumabe , H. Reiju Mihara

The general use of subjective probabilities to model belief has been justified using many axiomatic schemes. For example, ?consistent betting behavior' arguments are well-known. To those not already convinced of the unique fitness and…

Artificial Intelligence · Computer Science 2013-03-25 Paul Snow

In this article we demonstrate how algorithmic probability theory is applied to situations that involve uncertainty. When people are unsure of their model of reality, then the outcome they observe will cause them to update their beliefs. We…

Artificial Intelligence · Computer Science 2014-05-26 Phil Maguire , Philippe Moser , Rebecca Maguire , Mark Keane

Given a random sample from a random variable $T$ which is bounded from above, $T\le\tau$ a.s., we define processes that are positive supermartingales if $E(T)\ge\mu$. Such processes are called test martingales. Tests of the supermartingale…

Methodology · Statistics 2018-02-20 Harrie Hendriks

In algorithmic randomness, when one wants to define a randomness notion with respect to some non-computable measure $\lambda $, a choice needs to be made. One approach is to allow randomness tests to access the measure $\lambda $ as an…

Logic · Mathematics 2014-08-14 Bjørn Kjos-Hanssen , Antoine Taveneaux , Neil Thapen

Continuous first-order logic is used to apply model-theoretic analysis to analytic structures (e.g. Hilbert spaces, Banach spaces, probability spaces, etc.). Classical computable model theory is used to examine the algorithmic structure of…

Logic · Mathematics 2008-06-04 Wesley Calvert

The notion of Schnorr randomness refers to computable reals or computable functions. We propose a version of Schnorr randomness for subcomputable classes and characterize it in different ways: by Martin L\"of tests, martingales or measure…

Logic in Computer Science · Computer Science 2019-03-14 Claude Sureson

Positive $T$-martingales were developed as a general framework that extends the positive measure-valued martingales and are meant to model intermittent turbulence. We extend their scope by allowing the martingale to take complex values. We…

Probability · Mathematics 2016-08-14 Julien Barral , Xiong Jin , Benoît Mandelbrot

Gambles are random variables that model possible changes in monetary wealth. Classic decision theory transforms money into utility through a utility function and defines the value of a gamble as the expectation value of utility changes.…

Economics · Quantitative Finance 2016-02-03 Ole Peters , Murray Gell-Mann

In the context of Risk Neutral Pricing theory, we consider the classic problem of calibrating a martingale over $\mathbb{R}^n$ to a finite number of marginals thereof, or more practically, to prices of an arbitrary finite set of (joint)…

Probability · Mathematics 2025-12-19 Michael M. Kay

Programs with randomization constructs is an active research topic, especially after the recent introduction of martingale-based analysis methods for their termination and runtimes. Unlike most of the existing works that focus on proving…

Logic in Computer Science · Computer Science 2019-02-18 Satoshi Kura , Natsuki Urabe , Ichiro Hasuo

Chances of a gambler are always lower than chances of a casino in the case of an ideal, mathematically perfect roulette, if the capital of the gambler is limited and the minimum and maximum allowed bets are limited by the casino. However, a…

General Finance · Quantitative Finance 2016-02-23 A. V. Kavokin , A. S. Sheremet , M. Yu. Petrov

Sequential Monte Carlo methods which involve sequential importance sampling and resampling are shown to provide a versatile approach to computing probabilities of rare events. By making use of martingale representations of the sequential…

Probability · Mathematics 2012-02-22 Hock Peng Chan , Tze Leung Lai

Consider a universal Turing machine that produces a partial or total function (or a binary stream), based on the answers to the binary queries that it makes during the computation. We study the probability that the machine will produce a…

Computational Complexity · Computer Science 2017-04-28 George Barmpalias , Douglas Cenzer , Christopher P. Porter

We study cautious reasoning in finite sequential games played by agents with perfect recall. Our contribution lies in formulating a definition of prudent rationalizability (Heifetz et al. 2021, BEJTE) as an iterative reduction procedure of…

Computer Science and Game Theory · Computer Science 2025-12-01 Nicodemo De Vito

We derive computationally tractable methods to select a small subset of experiment settings from a large pool of given design points. The primary focus is on linear regression models, while the technique extends to generalized linear models…

Machine Learning · Statistics 2017-12-21 Yining Wang , Adams Wei Yu , Aarti Singh