Institutional designs to face the dark side of total allowable catches

Setting total allowable catches (TACs) is an endogenous process in which different agents and institutions, often with conflicting interests and opportunistic behaviour, try to influence policy-makers. Such policy-makers, far from being the benevolent social planners many would wish them to be, may also pursue self-interest when making final decisions. Although restricted knowledge of stock abundance and population dynamics, and weakness in enforcement, have effects, these other factors may explain the reason why TAC management has failed to guarantee sustainable exploitation of fish resources. Rejecting the exogeneity of the TAC and taking advantage of fruitful debate on economic policy (i.e. the rules vs. discretion debate, and that surrounding the independence of central banks), two institutional developments are analysed as potential mechanisms to face up to misconceptions about TACs: long-term harvest control rules, and a central bank of fish.

Les jeunes face au marché du travail en France: entre difficultés d´insertion et stratégies de pénétration

En une génération, entre 1975 et 1995, le paysage du marché du travail auquel les jeunes font face a radicalement changé.

Fe−TPP Coordination Network with Metalloporphyrinic Neutral Radicals and Face-to-Face and Edge-to-Face π−π Stacking

Artículo científico Inorg. Chem. 2013, 52, 8074−8081

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

PGPRs and nitrogen-fixing legumes: a perfect team for efficient Cd phytoremediation?

Cadmium (Cd) is a toxic, biologically non-essential and highly mobile metal that has become an increasingly important environmental hazard to both wildlife and humans. In contrast to conventional remediation technologies, phytoremediation based on legume rhizobia symbiosis has emerged as an inexpensive decontamination alternative which also revitalize contaminated soils due to the role of legumes in nitrogen cycling. In recent years, there is a growing interest in understanding symbiotic legume rhizobia relationship and its interactions with Cd. The aim of the present review is to provide a comprehensive picture of the main effects of Cd in N-2-fixing leguminous plants and the benefits of exploiting this symbiosis together with plant growth promoting rhizobacteria to boost an efficient reclamation of Cd-contaminated soils.