Coordination and Critical Mass in a Network Market: An Experimental Investigation

A network market is a market in which the benefit each consumer derives from a good is an increasing function of the number of consumers who own the same or similar goods. A major obstacle that plagues the introduction of a network good is the ability to reach critical mass, namely, the minimum number of buyers required to render purchase worthwhile. This can be likened to a coordination game with multiple Pareto-ranked equilibria. We introduce an experimental paradigm to study consumers' ability to coordinate on purchasing the network good. Our results highlight the central importance of the level of the critical mass.Experimental economics, network goods, coordination game, critical mass

Coordination and Critical Mass in a Network Market: An Experimental Investigation

A network market is a market in which the benefit each consumer derives from a good is an increasing function of the number of consumers who own the same or similar goods. A major obstacle that plagues the introduction of a network good is the ability to reach critical mass, namely, the minimum number of buyers required to render purchase worthwhile. This can be likened to a coordination game with multiple Pareto-ranked equilibria. We introduce an experimental paradigm to study consumers' ability to coordinate on purchasing the network good. Our results highlight the central importance of the level of the critical mass.experimental economics, network goods, coordination game, critical mass

Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals

An asymptotically optimal blind calibration scheme of uniform linear arrays for narrowband Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) approach for joint estimation of all the unknown model parameters, which leads to a multi-dimensional optimization problem with no closed-form solution, we revisit Paulraj and Kailath's (P-K's) classical approach in exploiting the special (Toeplitz) structure of the observations' covariance. However, we offer a substantial improvement over P-K's ordinary Least Squares (LS) estimates by using asymptotic approximations in order to obtain simple, non-iterative, (quasi-)linear Optimally-Weighted LS (OWLS) estimates of the sensors gains and phases offsets with asymptotically optimal weighting, based only on the empirical covariance matrix of the measurements. Moreover, we prove that our resulting estimates are also asymptotically optimal w.r.t. the raw data, and can therefore be deemed equivalent to the ML Estimates (MLE), which are otherwise obtained by joint ML estimation of all the unknown model parameters. After deriving computationally convenient expressions of the respective Cram\'er-Rao lower bounds, we also show that our estimates offer improved performance when applied to non-Gaussian signals (and/or noise) as quasi-MLE in a similar setting. The optimal performance of our estimates is demonstrated in simulation experiments, with a considerable improvement (reaching an order of magnitude and more) in the resulting mean squared errors w.r.t. P-K's ordinary LS estimates. We also demonstrate the improved accuracy in a multiple-sources directions-of-arrivals estimation task.Comment: in IEEE Transactions on Signal Processin

On the Origins and Control of Community Types in the Human Microbiome

Microbiome-based stratification of healthy individuals into compositional categories, referred to as "community types", holds promise for drastically improving personalized medicine. Despite this potential, the existence of community types and the degree of their distinctness have been highly debated. Here we adopted a dynamic systems approach and found that heterogeneity in the interspecific interactions or the presence of strongly interacting species is sufficient to explain community types, independent of the topology of the underlying ecological network. By controlling the presence or absence of these strongly interacting species we can steer the microbial ecosystem to any desired community type. This open-loop control strategy still holds even when the community types are not distinct but appear as dense regions within a continuous gradient. This finding can be used to develop viable therapeutic strategies for shifting the microbial composition to a healthy configurationComment: Main Text, Figures, Methods, Supplementary Figures, and Supplementary Tex