Rage Against the Machines: How Subjects Learn to Play Against Computers

We use an experiment to explore how subjects learn to play against computers which are programmed to follow one of a number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious play, imitation, reinforcement learning, and a trial & error process. We test whether subjects try to influence those algorithms to their advantage in a forward-looking way (strategic teaching). We find that strategic teaching occurs frequently and that all learning algorithms are subject to exploitation with the notable exception of imitation. The experiment was conducted, both, on the internet and in the usual laboratory setting. We find some systematic differences, which however can be traced to the different incentives structures rather than the experimental environment

Conservation Laws in Smooth Particle Hydrodynamics: the DEVA Code

We describe DEVA, a multistep AP3M-like-SPH code particularly designed to study galaxy formation and evolution in connection with the global cosmological model. This code uses a formulation of SPH equations which ensures both energy and entropy conservation by including the so-called \bn h terms. Particular attention has also been paid to angular momentum conservation and to the accuracy of our code. We find that, in order to avoid unphysical solutions, our code requires that cooling processes must be implemented in a non-multistep way. We detail various cosmological simulations which have been performed to test our code and also to study the influence of the \bn h terms. Our results indicate that such correction terms have a non-negligible effect on some cosmological simulations, especially on high density regions associated either to shock fronts or central cores of collapsed objects. Moreover, they suggest that codes paying a particular attention to the implementation of conservation laws of physics at the scales of interest, can attain good accuracy levels in conservation laws with limited computational resources.Comment: 36 pages, 10 figures. Accepted for publication in The Astrophysical Journa

Two-Stage Metric Learning

In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of fixed anchor points. Then, we define the distance in the input data space as the Fisher information distance on the associated statistical manifold. This induces in the input data space a new family of distance metric with unique properties. Unlike kernelized metric learning, we do not require the similarity measure to be positive semi-definite. Moreover, it can also be interpreted as a local metric learning algorithm with well defined distance approximation. We evaluate its performance on a number of datasets. It outperforms significantly other metric learning methods and SVM.Comment: Accepted for publication in ICML 201

Testing of quantum phase in matter wave optics

Various phase concepts may be treated as special cases of the maximum likelihood estimation. For example the discrete Fourier estimation that actually coincides with the operational phase of Noh, Fouge`res and Mandel is obtained for continuous Gaussian signals with phase modulated mean.Since signals in quantum theory are discrete, a prediction different from that given by the Gaussian hypothesis should be obtained as the best fit assuming a discrete Poissonian statistics of the signal. Although the Gaussian estimation gives a satisfactory approximation for fitting the phase distribution of almost any state the optimal phase estimation offers in certain cases a measurable better performance. This has been demonstrated in neutron--optical experiment.Comment: 8 pages, 4 figure

Rage Against the Machines: How Subjects Learn to Play Against Computers

We use an experiment to explore how subjects learn to play against computers which are programmed to follow one of a number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious play, imitation, reinforcement learning, and a trial & error process. We test whether subjects try to influence those algorithms to their advantage in a forward-looking way (strategic teaching). We find that strategic teaching occurs frequently and that all learning algorithms are subject to exploitation with the notable exception of imitation. The experiment was conducted, both, on the internet and in the usual laboratory setting. We find some systematic differences, which however can be traced to the different incentives structures rather than the experimental environment.learning; fictitious play; imitation; reinforcement; trial & error; strategic teaching; Cournot duopoly; experiments; internet.