Optimal and Myopic Information Acquisition

We consider the problem of optimal dynamic information acquisition from many correlated information sources. Each period, the decision-maker jointly takes an action and allocates a fixed number of observations across the available sources. His payoff depends on the actions taken and on an unknown state. In the canonical setting of jointly normal information sources, we show that the optimal dynamic information acquisition rule proceeds myopically after finitely many periods. If signals are acquired in large blocks each period, then the optimal rule turns out to be myopic from period 1. These results demonstrate the possibility of robust and "simple" optimal information acquisition, and simplify the analysis of dynamic information acquisition in a widely used informational environment

Optimal control and optimal sensor activation for Markov decision problems with costly observations

This paper considers partial observation Markov decision processes. Besides the classical control decisions influencing the transition probabilities of the Markov process, we also consider control actions that can activate the sensors to provide more or less accurate information about the system state, explicitly including the cost of activating sensors. We synthesize control laws that minimize a discounted operating cost of the system over an infinite interval of time, where the instantaneous cost function depends on the current state, the control influencing the transition probabilities, and the control actions activating the sensors. A general computationally efficient optimal solution for this problem is not known. Hence we design supoptimal controllers that only use knowledge of the value function for the full state information Markov decision problem. Our solution guarantees that the discounted cost of operating the plant increases only by a bounded amount with respect to the minimal cost for the full state information problem. A new concept of pinned conditional distributions of the state given the observed history of the plant is required in order to implement these control laws online

Quantization as Histogram Segmentation: Optimal Scalar Quantizer Design in Network Systems

An algorithm for scalar quantizer design on discrete-alphabet sources is proposed. The proposed algorithm can be used to design fixed-rate and entropy-constrained conventional scalar quantizers, multiresolution scalar quantizers, multiple description scalar quantizers, and Wyner–Ziv scalar quantizers. The algorithm guarantees globally optimal solutions for conventional fixed-rate scalar quantizers and entropy-constrained scalar quantizers. For the other coding scenarios, the algorithm yields the best code among all codes that meet a given convexity constraint. In all cases, the algorithm run-time is polynomial in the size of the source alphabet. The algorithm derivation arises from a demonstration of the connection between scalar quantization, histogram segmentation, and the shortest path problem in a certain directed acyclic graph

Growth and the pollution convergence hypothesis: A nonparametric approach

The pollution-convergence hypothesis is formalized in a neoclassical growth model with optimal emissions reduction: pollution growth rates are positively correlated with output growth (scale effect) but negatively correlated with emission levels (defensive effect). This dynamic law is empirically tested for two major and regulated air pollutants - nitrogen oxides (NOX) and sulfur oxides (SOX) - with a panel of 25 European countries spanning over years 1980-2005. Traditional parametric models are rejected by the data. However, more flexible regression techniques - semiparametric additive specifications and fully nonparametric regressions with discrete and continuous factors - confirm the existence of the predicted positive and defensive effects. By analyzing the spatial distributions of per capita emissions, we also show that cross-country pollution gaps have decreased over the period for both pollutants and within the Eastern as well as the Western European areas. A Markov modeling approach predicts further cross-country absolute convergence, in particular for SOX. The latter results hold in the presence of spatial non-convergence in per capita income levels within both regions.Air pollution, convergence, economic growth, mixed nonparametric regressions, distribution dynamics.

Growth and the pollution convergence hypothesis: a nonparametric approach

The pollution-convergence hypothesis is formalized in a neoclassical growth model with optimal emissions reduction: pollution growth rates are positively correlated with output growth (scale effect) but negatively correlated with emission levels (defensive effect). This dynamic law is empirically tested for two major and regulated air pollutants - nitrogen oxides (NOX) and sulfur oxides (SOX) - with a panel of 25 European countries spanning over years 1980-2005. Traditional parametric models are rejected by the data. However, more flexible regression techniques - semiparametric additive specifications and fully nonparametric regressions with discrete and continuous factors - confirm the existence of the predicted positive and defensive effects. By analyzing the spatial distributions of per capita emissions, we also show that cross-country pollution gaps have decreased over the period for both pollutants and within the Eastern as well as the Western European areas. A Markov modeling approach predicts further cross-country absolute convergence, in particular for SOX. The latter results hold in the presence of spatial non-convergence in per capita income levels within both regions.Air pollution, convergence, economic growth, mixed nonparametric regressions, distribution dynamics

Weighted-Lasso for Structured Network Inference from Time Course Data

We present a weighted-Lasso method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own a prior internal structure of connectivity which drives the inference method. This prior structure can be either derived from prior biological knowledge or inferred by the method itself. We illustrate the performance of this structure-based penalization both on synthetic data and on two canonical regulatory networks, first yeast cell cycle regulation network by analyzing Spellman et al's dataset and second E. coli S.O.S. DNA repair network by analysing U. Alon's lab data

Alternative Perspectives on Optimal Public Debt Adjustment

We compare alternative optimal public debt adjustment strategies in a New Keynesian economy. We find that the unconditionally optimal policy is consistent with a gradual adjustment in public debt towards its mean value at a speed determined by the rate of time preference of agents. To a second-order approximation in a stochastic setting, debt follows a unit root process with a negative drift under the 'timeless-perspective' approach but converges to an unconditional mean different from the non-stochastic steady state in the unconditionally optimal economy. Overall, increases in public debt are shown to be optimally reduced by half only after approximately two decades at best.Optimal Monetary and Fiscal Policy, Unconditionally Optimal Policy, Timeless Perspective, Public Debt Dynamics, Second-Order Approximation.

Modelling of Agricultural Behavior under the CAP Regime: Assessment of Environmental Impacts and Policy Effectiveness

The structure of farming activity under the provisions of the generalized regime of the Common Agricultural Policy involving both the first and second pillar elements is modelled. Independently of whether regulated agents exhibit unbounded or bounded rationality, the impact of the different type of CAP measures, as prescribed by Agenda 2000, in the decision making - and thus on the environmental performance of a homogeneous population of farmers - are discussed. The problem of a representative farmer is used for this purpose. After assessing the environmental effectiveness of the various CAP regimes, the mechanism that provides the type of CAP instruments that safeguard the collective attainment of a social environmental target, along with the type of interdependence characterizing them, is defined under the analytical framework of unboundedly and boundedly rational agents respectively. The problem of the optimal regulation of an unboundedly rational population of farmers is discussed in both a static and a dynamic context. The long-run viability of the Agenda 2000 CAP reform is also examined under the assumption of bounded rationality by employing the evolutionary framework of replicator dynamics.Environmental impacts, coupling, decoupling, production subsidy, direct payment, cross-compliance principle, rural development subsidy.