On the strategy frequency problem in batch Minority Games

Ergodic stationary states of Minority Games with S strategies per agent can be characterised in terms of the asymptotic probabilities $\phi_a$ with which an agent uses $a$ of his strategies. We propose here a simple and general method to calculate these quantities in batch canonical and grand-canonical models. Known analytic theories are easily recovered as limiting cases and, as a further application, the strategy frequency problem for the batch grand-canonical Minority Game with S=2 is solved. The generalization of these ideas to multi-asset models is also presented. Though similarly based on response function techniques, our approach is alternative to the one recently employed by Shayeghi and Coolen for canonical batch Minority Games with arbitrary number of strategies.Comment: 17 page

On the transition to efficiency in Minority Games

The existence of a phase transition with diverging susceptibility in batch Minority Games (MGs) is the mark of informationally efficient regimes and is linked to the specifics of the agents' learning rules. Here we study how the standard scenario is affected in a mixed population game in which agents with the `optimal' learning rule (i.e. the one leading to efficiency) coexist with ones whose adaptive dynamics is sub-optimal. Our generic finding is that any non-vanishing intensive fraction of optimal agents guarantees the existence of an efficient phase. Specifically, we calculate the dependence of the critical point on the fraction $q$ of `optimal' agents focusing our analysis on three cases: MGs with market impact correction, grand-canonical MGs and MGs with heterogeneous comfort levels.Comment: 12 pages, 3 figures; contribution to the special issue "Viewing the World through Spin Glasses" in honour of David Sherrington on the occasion of his 65th birthda

Satellite Luminosities in Galaxy Groups

Halo model interpretations of the luminosity dependence of galaxy clustering assume that there is a central galaxy in every sufficiently massive halo, and that this central galaxy is very different from all the others in the halo. The halo model decomposition makes the remarkable prediction that the mean luminosity of the non-central galaxies in a halo should be almost independent of halo mass: the predicted increase is about 20% while the halo mass increases by a factor of more than 20. In contrast, the luminosity of the central object is predicted to increase approximately linearly with halo mass at low to intermediate masses, and logarithmically at high masses. We show that this weak, almost non-existent mass-dependence of the satellites is in excellent agreement with the satellite population in group catalogs constructed by two different collaborations. This is remarkable, because the halo model prediction was made without ever identifying groups and clusters. The halo model also predicts that the number of satellites in a halo is drawn from a Poisson distribution with mean which depends on halo mass. This, combined with the weak dependence of satellite luminosity on halo mass, suggests that the Scott effect, such that the luminosities of very bright galaxies are merely the statistically extreme values of a general luminosity distribution, may better apply to the most luminous satellite galaxy in a halo than to BCGs. If galaxies are identified with halo substructure at the present time, then central galaxies should be about 4 times more massive than satellite galaxies of the same luminosity, whereas the differences between the stellar M/L ratios should be smaller. Therefore, a comparison of the weak lensing signal from central and satellite galaxies should provide useful constraints. [abridged]Comment: 8 pages, 3 figures. Matches version accepted by MNRA

Von Neumann's expanding model on random graphs

Within the framework of Von Neumann's expanding model, we study the maximum growth rate r achievable by an autocatalytic reaction network in which reactions involve a finite (fixed or fluctuating) number D of reagents. r is calculated numerically using a variant of the Minover algorithm, and analytically via the cavity method for disordered systems. As the ratio between the number of reactions and that of reagents increases the system passes from a contracting (r1). These results extend the scenario derived in the fully connected model (D\to\infinity), with the important difference that, generically, larger growth rates are achievable in the expanding phase for finite D and in more diluted networks. Moreover, the range of attainable values of r shrinks as the connectivity increases.Comment: 20 page

Constrained Allocation Flux Balance Analysis

New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an "ensemble averaging" procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws.Comment: 21 pages, 6 figures (main) + 33 pages, various figures and tables (supporting); for the supplementary MatLab code, see http://tinyurl.com/h763es