Determinants of Foreign Direct Investment of OECD Countries 1991-2001

Using a fixed-effects panel data approach, FDI flows of 22 OECD countries are explained by gravity equations over the period 1991-2001. It is distinguished between all available observations, Intra-EU25 observations only, and observations not belonging to the EU25 area in order to control for EU-specific effects. Regressions are repeated with exports as dependent variable in order to capture diverging influences for trade flows. Changes in total market size and relative market size are important factors that lead both FDI and exports in the same direction. However, relative market size is only significant in the FDI equation when variation between the EU25 area and other investment is taken into account, thus indicating a concentration of FDI within Western and Central Europe. Stock market booms boost FDI but not exports. Differences in significance levels/signs of coefficients of political indicators and exchange rate changes indicate that exports are demand-driven while FDI is supply-driven. Year dummies interacted with country distance show that, overall, FDI and exports tended to flow less to distant countries over the period under consideration. However, this trend is reversed for exports within the EU25 area. --foreign direct investment and international trade ; multinational firms ; models with panel data

Lightweight Probabilistic Deep Networks

Even though probabilistic treatments of neural networks have a long history, they have not found widespread use in practice. Sampling approaches are often too slow already for simple networks. The size of the inputs and the depth of typical CNN architectures in computer vision only compound this problem. Uncertainty in neural networks has thus been largely ignored in practice, despite the fact that it may provide important information about the reliability of predictions and the inner workings of the network. In this paper, we introduce two lightweight approaches to making supervised learning with probabilistic deep networks practical: First, we suggest probabilistic output layers for classification and regression that require only minimal changes to existing networks. Second, we employ assumed density filtering and show that activation uncertainties can be propagated in a practical fashion through the entire network, again with minor changes. Both probabilistic networks retain the predictive power of the deterministic counterpart, but yield uncertainties that correlate well with the empirical error induced by their predictions. Moreover, the robustness to adversarial examples is significantly increased.Comment: To appear at CVPR 201

A Mean Field Approach for Optimization in Particles Systems and Applications

This paper investigates the limit behavior of Markov Decision Processes (MDPs) made of independent particles evolving in a common environment, when the number of particles goes to infinity. In the finite horizon case or with a discounted cost and an infinite horizon, we show that when the number of particles becomes large, the optimal cost of the system converges almost surely to the optimal cost of a discrete deterministic system (the ``optimal mean field''). Convergence also holds for optimal policies. We further provide insights on the speed of convergence by proving several central limits theorems for the cost and the state of the Markov decision process with explicit formulas for the variance of the limit Gaussian laws. Then, our framework is applied to a brokering problem in grid computing. The optimal policy for the limit deterministic system is computed explicitly. Several simulations with growing numbers of processors are reported. They compare the performance of the optimal policy of the limit system used in the finite case with classical policies (such as Join the Shortest Queue) by measuring its asymptotic gain as well as the threshold above which it starts outperforming classical policies

Incentives and Redistribution in Homogeneous Bike-Sharing Systems with Stations of Finite Capacity

Bike-sharing systems are becoming important for urban transportation. In such systems, users arrive at a station, take a bike and use it for a while, then return it to another station of their choice. Each station has a finite capacity: it cannot host more bikes than its capacity. We propose a stochastic model of an homogeneous bike-sharing system and study the effect of users random choices on the number of problematic stations, i.e., stations that, at a given time, have no bikes available or no available spots for bikes to be returned to. We quantify the influence of the station capacities, and we compute the fleet size that is optimal in terms of minimizing the proportion of problematic stations. Even in a homogeneous city, the system exhibits a poor performance: the minimal proportion of problematic stations is of the order of (but not lower than) the inverse of the capacity. We show that simple incentives, such as suggesting users to return to the least loaded station among two stations, improve the situation by an exponential factor. We also compute the rate at which bikes have to be redistributed by trucks to insure a given quality of service. This rate is of the order of the inverse of the station capacity. For all cases considered, the fleet size that corresponds to the best performance is half of the total number of spots plus a few more, the value of the few more can be computed in closed-form as a function of the system parameters. It corresponds to the average number of bikes in circulation

Distributing Labels on Infinite Trees

Sturmian words are infinite binary words with many equivalent definitions: They have a minimal factor complexity among all aperiodic sequences; they are balanced sequences (the labels 0 and 1 are as evenly distributed as possible) and they can be constructed using a mechanical definition. All this properties make them good candidates for being extremal points in scheduling problems over two processors. In this paper, we consider the problem of generalizing Sturmian words to trees. The problem is to evenly distribute labels 0 and 1 over infinite trees. We show that (strongly) balanced trees exist and can also be constructed using a mechanical process as long as the tree is irrational. Such trees also have a minimal factor complexity. Therefore they bring the hope that extremal scheduling properties of Sturmian words can be extended to such trees, as least partially. Such possible extensions are illustrated by one such example.Comment: 30 pages, use pgf/tik

Determinants of Foreign Direct Investment of OECD Countries 1991-2001

Using a fixed-effects panel data approach, FDI flows of 22 OECD countries are explained by gravity equations over the period 1991-2001. It is distinguished between all available observations, Intra-EU25 observations only, and observations not belonging to the EU25 area in order to control for EU-specific effects. Regressions are repeated with exports as dependent variable in order to capture diverging influences for trade flows. Changes in total market size and relative market size are important factors that lead both FDI and exports in the same direction. However, relative market size is only significant in the FDI equation when variation between the EU25 area and other investment is taken into account, thus indicating a concentration of FDI within Western and Central Europe. Stock market booms boost FDI but not exports. Differences in significance levels/signs of coefficients of political indicators and exchange rate changes indicate that exports are demand-driven while FDI is supply-driven. Year dummies interacted with country distance show that, overall, FDI and exports tended to flow less to distant countries over the period under consideration. However, this trend is reversed for exports within the EU25 area.foreign direct investment and international trade, multinational firms, model with panel data, Agricultural Finance, F21, F23, F14, C23,