When should labor contracts be nominal?

This paper proposes a theory of when labor contract should be nominal or, instead, indexed. We find that, contracts should be indexed if prices are difficult to forecast and nominal otherwise. We use a principal-agent model developed by Jovanovic and Ueda (1997), with moral hazard, renegotiation, and where a signal (the nominal value of the sales of the agent) is observed before renegotiation takes place. We show that their result, that the optimal contract is nominal when agents must choose pure strategies, is robust to the case where agents can choose mixed strategies in the sense that, for certain parameters, the optimal contract is still nominal. For other parameters, however, we show that the optimal contract is indexed. Our findings are consistent with two empirical regularities. First prices are more volatile with higher inflation and, second, countries with high inflation tend to have indexed contracts. Our theory suggests that it is because prices are difficult to forecast in high inflation countries that contracts are indexed.Labor contract

The Role of Normalization in the Belief Propagation Algorithm

An important part of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov Random Field. The belief propagation algorithm, which is an exact procedure to compute these marginals when the underlying graph is a tree, has gained its popularity as an efficient way to approximate them in the more general case. In this paper, we focus on an aspect of the algorithm that did not get that much attention in the literature, which is the effect of the normalization of the messages. We show in particular that, for a large class of normalization strategies, it is possible to focus only on belief convergence. Following this, we express the necessary and sufficient conditions for local stability of a fixed point in terms of the graph structure and the beliefs values at the fixed point. We also explicit some connexion between the normalization constants and the underlying Bethe Free Energy

Local stability of Belief Propagation algorithm with multiple fixed points

A number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov random field. Belief propagation, an iterative message-passing algorithm, computes exactly such marginals when the underlying graph is a tree. But it has gained its popularity as an efficient way to approximate them in the more general case, even if it can exhibits multiple fixed points and is not guaranteed to converge. In this paper, we express a new sufficient condition for local stability of a belief propagation fixed point in terms of the graph structure and the beliefs values at the fixed point. This gives credence to the usual understanding that Belief Propagation performs better on sparse graphs.Comment: arXiv admin note: substantial text overlap with arXiv:1101.417

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Pairwise MRF Calibration by Perturbation of the Bethe Reference Point

We investigate different ways of generating approximate solutions to the pairwise Markov random field (MRF) selection problem. We focus mainly on the inverse Ising problem, but discuss also the somewhat related inverse Gaussian problem because both types of MRF are suitable for inference tasks with the belief propagation algorithm (BP) under certain conditions. Our approach consists in to take a Bethe mean-field solution obtained with a maximum spanning tree (MST) of pairwise mutual information, referred to as the \emph{Bethe reference point}, for further perturbation procedures. We consider three different ways following this idea: in the first one, we select and calibrate iteratively the optimal links to be added starting from the Bethe reference point; the second one is based on the observation that the natural gradient can be computed analytically at the Bethe point; in the third one, assuming no local field and using low temperature expansion we develop a dual loop joint model based on a well chosen fundamental cycle basis. We indeed identify a subclass of planar models, which we refer to as \emph{Bethe-dual graph models}, having possibly many loops, but characterized by a singly connected dual factor graph, for which the partition function and the linear response can be computed exactly in respectively O(N) and $O(N^2)$ operations, thanks to a dual weight propagation (DWP) message passing procedure that we set up. When restricted to this subclass of models, the inverse Ising problem being convex, becomes tractable at any temperature. Experimental tests on various datasets with refined $L_0$ or $L_1$ regularization procedures indicate that these approaches may be competitive and useful alternatives to existing ones.Comment: 54 pages, 8 figure. section 5 and refs added in V

Une famille de classes polynomiales de CSP basée sur la microstructure

International audienceL’étude des classes polynomiales constitue une question importante en intelligence artificielle, en particulier au niveau des problèmes de satisfaction de contraintes. Dans ce contexte, la propriété BTP fournit une classe importante de l’état de l’art. Dans cet article, nous proposons d’étendre et de généraliser cette classe en introduisant la propriété k-BTP (et la classe des instances satisfaisant cette propriété) où le paramètre k est une constante donnée. Ainsi, nous avons 2-BTP = BTP, et pour k > 2, k-BTP est une relaxation de BTP au sens où k-BTP ( (k + 1)-BTP. En outre, nous montrons que si k-TW est la classe d’instances ayant une largeur arborescente bornée par une constante k, alors k-TW ((k+1)-BTP. Au niveau de la complexité, nous montrons que les instances satisfaisant k-BTP et qui vérifient la k-cohérence-forte sont reconnaissables et résolubles en temps polynomial. Nous étudions aussi la relation entre k-BTP et l’approche de W. Naanaa qui a proposé un outil théorique connu sous le vocable directional rank afin d’´étendre les classes polynomiales de manière paramétrée. Enfin, nous proposons une étude expérimentale de 3-BTP qui montre l’intérêt pratique de cette classe