Growth, distribution and poverty in Madagascar

This paper presents an applied microsimulation model built on household data with explicit treatment of heterogeneity of skills, labor preferences and opportunities, and consumption preferences at the individual and/or household level, while allowing for an endogenous determination of relative prices between sectors. The model is primarily focused on labor markets and labor allocation at the household level, but consumption behavior is also modeled. Modeling choices are driven by a desire to make the best possible use of microeconomic information derived from household data. This framework supports analysis of the impact of different growth strategies on poverty and income distribution, without making use of the “representative agent” assumption. The model is built on household survey data and represents the behavior of 4,508 households. Household behavioral equations are estimated econometrically. Different sets of simulation are carried out to examine the comparative statics of the model and study the impact of different growth strategies on poverty and inequality. Simulation results show the potential usefulness of this class of models to derive both poverty and inequality measures and transition matrices without prior assumptions regarding the intra-group income distribution. Market clearing equations allow for the endogenous determination of relative prices between sectors. The impact of different growth strategies on poverty and inequality is complex given general equilibrium effects and the wide range of household positions in markets for factors and goods markets. Partial equilibrium analysis or the use of representative households would miss these effects.Microeconomics Madagascar. ,Madagascar. ,Labor market. ,Poverty. ,TMD ,

High performance genetic programming on GPU

The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when pro-grammed in the CUDA language. We compare two par-allelization schemes that evaluate several GP programs in parallel. We show that the fine grain distribution of compu-tations over the elementary processors greatly impacts per-formances. We also present memory and representation op-timizations that further enhance computation speed, up to 2.8 billion GP operations per second. The code has been developed with the well known ECJ library

Towards Human-Competitive Game Playing for Complex Board Games with Genetic Programming

International audienceRecent works have shown that Genetic Programming (GP) can be quite successful at evolving human-competitive strategies for games ranging from classic board games, such as chess, to action video games. However to our knowledge GP was never applied to modern complex board games, so-called eurogames, such as Settlers of Catan, i.e. board games that typically involve four characteristics: they are non zero-sum games, multiplayer, with hidden information and random elements. In this work we study how GP can evolve artificial players from low level attributes of a eurogame named " 7 Wonders " , that features all the characteristics of this category. We show that GP can evolve competitive artificial intelligence (AI) players against human-designed AI or against Monte Carlo Tree Search, a standard in automatic game playing

A Continuous Approach to Genetic Programming

Abstract. Differential Evolution (DE) is an evolutionary heuristic for continuous optimization problems. In DE, solutions are coded as vectors of floats that evolve by crossover with a combination of best and random individuals from the current generation. Experiments to apply DE to automatic programming were made recently by Veenhuis, coding full program trees as vectors of floats (Tree Based Differential Evolution or TreeDE). In this paper, we use DE to evolve linear sequences of imperative instructions, which we call Linear Differential Evolutionary Programming (LDEP). Unlike TreeDE, our heuristic provides constant management for regression problems and lessens the tree-depth constraint on the architecture of solutions. Comparisons with TreeDE and GP show that LDEP is appropriate to automatic programming