Evolutionary prisoner's dilemma game on a square lattice

A simplified prisoner's game is studied on a square lattice when the players interacting with their neighbors can follow only two strategies: to cooperate (C) or to defect (D) unconditionally. The players updated in a random sequence have a chance to adopt one of the neighboring strategies with a probability depending on the payoff difference. Using Monte Carlo simulations and dynamical cluster techniques we study the density $c$ of cooperators in the stationary state. This system exhibits a continuous transition between the two absorbing state when varying the value of temptation to defect. In the limits $c \to 0$ and 1 we have observed critical transitions belonging to the universality class of directed percolation.Comment: 6 pages including 6 figure

Kolmogorov Complexity and Solovay Functions

Solovay proved that there exists a computable upper bound f of the prefix-free Kolmogorov complexity function K such that f (x) = K(x) for infinitely many x. In this paper, we consider the class of computable functions f such that K(x) <= f (x)+O(1) for all x and f (x) <= K(x) + O(1) for infinitely many x, which we call Solovay functions. We show that Solovay functions present interesting connections with randomness notions such as Martin-L\"of randomness and K-triviality

Optimal linear reconstruction of dark matter from halo catalogs

We derive the weight function w(M) to apply to dark-matter halos that minimizes the stochasticity between the weighted halo distribution and its underlying mass density field. The optimal w(M) depends on the range of masses being used in the estimator. In N-body simulations, the Poisson estimator is up to 15 times noisier than the optimal. Implementation of the optimal weight yields significantly lower stochasticity than weighting halos by their mass, bias or equal. Optimal weighting could make cosmological tests based on the matter power spectrum or cross-correlations much more powerful and/or cost-effective. A volume-limited measurement of the mass power spectrum at k=0.2h/Mpc over the entire z<1 universe could ideally be done using only 6 million redshifts of halos with mass M>6\times10^{13}h^{-1}M_\odot (1\times10^{13}) at z=0 (z=1); this is 5 times fewer than the Poisson model predicts. Using halo occupancy distributions (HOD) we find that uniformly-weighted catalogs of luminous red galaxies require >3 times more redshifts than an optimally-weighted halo catalog to reconstruct the mass to the same accuracy. While the mean HODs of galaxies above a threshold luminosity are similar to the optimal w(M), the stochasticity of the halo occupation degrades the mass estimator. Blue or emission-line galaxies are about 100 times less efficient at reconstructing mass than an optimal weighting scheme. This suggests an efficient observational approach of identifying and weighting halos with a deep photo-z survey before conducting a spectroscopic survey. The optimal w(M) and mass-estimator stochasticity predicted by the standard halo model for M>10^{12}h^{-1}M_\odot are in reasonable agreement with our measurements, with the important exceptions that the halos must be assumed to be linearly biased samples of a "halo field" that is distinct from the mass field. (Abridged)Comment: Added Figure 3 to show the scatter between the weighted halo field vs the mass field, Accepted for publication in MNRA

The effects of heterogeneity on stochastic cycles in epidemics

Models of biological processes are often subject to different sources of noise. Developing an understanding of the combined effects of different types of uncertainty is an open challenge. In this paper, we study a variant of the susceptible-infective-recovered model of epidemic spread, which combines both agent-to-agent heterogeneity and intrinsic noise. We focus on epidemic cycles, driven by the stochasticity of infection and recovery events, and study in detail how heterogeneity in susceptibilities and propensities to pass on the disease affects these quasi-cycles. While the system can only be described by a large hierarchical set of equations in the transient regime, we derive a reduced closed set of equations for population-level quantities in the stationary regime. We analytically obtain the spectra of quasi-cycles in the linear-noise approximation. We find that the characteristic frequency of these cycles is typically determined by population averages of susceptibilities and infectivities, but that their amplitude depends on higher-order moments of the heterogeneity. We also investigate the synchronisation properties and phase lag between different groups of susceptible and infected individuals.Comment: Main text 16 pages, 9 figures. Supplement 5 page

Enhanced goal-oriented error assessment and computational strategies in adaptive reduced basis solver for stochastic problems

This work focuses on providing accurate low-cost approximations of stochastic ¿nite elements simulations in the framework of linear elasticity. In a previous work, an adaptive strategy was introduced as an improved Monte-Carlo method for multi-dimensional large stochastic problems. We provide here a complete analysis of the method including a new enhanced goal-oriented error estimator and estimates of CPU (computational processing unit) cost gain. Technical insights of these two topics are presented in details, and numerical examples show the interest of these new developments.Postprint (author's final draft

Economic Theory as Successive Approximations of Statistical Moments

This paper highlights the links between the descriptions of macroeconomic variables and statistical moments of market trade, price, and return. We consider economic transactions during the averaging time interval {\Delta} as the exclusive matter that determines the change of any economic variables. We regard the stochasticity of market trade values and volumes during {\Delta} as the only root of the random properties of price and return. We describe how the market-based n-th statistical moments of price and return during {\Delta} depend on the n-th statistical moments of trade values and volumes or equally on sums during {\Delta} of the n-th power of market trade values and volumes. We introduce the secondary averaging procedure that defines statistical moments of trade, price, and return during the averaging interval {\Delta}2>>{\Delta}. As well, the secondary averaging during {\Delta}2>>{\Delta} introduces statistical moments of macroeconomic variables, which were determined as sums of economic transactions during {\Delta}. In the coming years, predictions of the market-based probabilities of price and return will be limited by Gaussian-type distributions determined by the first two statistical moments. We discuss the roots of the internal weakness of the conventional hedging tool, Value-at-Risk, that could not be solved and thus remain the source of additional risks and losses. One should consider economic theory as a set of successive approximations, each of which describes the next array of the n-th statistical moments of market transactions and macroeconomic variables, which are repeatedly averaged during the sequence of increasing time intervals.Comment: 20 page