Payoff Mechanism Design for Coordination in Multi-Agent Task Allocation Games

We investigate a multi-agent decision-making problem where a large population of agents are responsible for carrying out a set of assigned tasks. The amount of jobs in each task varies over time governed by a dynamical system model. Each agent needs to select one of available strategies to take on one or more tasks. Since each strategy allows an agent to perform multiple tasks at a time, possibly at distinct rates, the strategy selection of the agents need to be coordinated. The main objective of this work is to design a decentralized decision-making model that coordinates the agents in selecting strategies and allows them to asymptotically adopt the optimal strategies, e.g., the strategies that minimize remaining jobs in all assigned tasks. We formulate the problem using the population game formalism and refer to it as the task allocation game. We discuss the design of a decision-making model that incentivizes the agents to coordinate in the strategy selection process. As key contributions, we propose a method to find a payoff-driven decision-making model, and discuss how the model allows the strategy selection of the agents to be responsive to the amount of remaining jobs in each task while asymptotically attaining the optimal strategies. Leveraging analytical tools from feedback control theory, we derive technical conditions that the model needs to satisfy, which are used to construct a numerical approach to compute the model. We validate our solution through simulations to highlight how the proposed approach coordinates the agents in task allocation games

Recurrent Neural Network ODE Output for Classification Problems Follows the Replicator Dynamics

This letter establishes a novel relationship between a class of recurrent neural networks and certain evolutionary dynamics that emerge in the context of population games. Specifically, it is shown that the output of a recurrent neural network, in the context of classification problems, coincides with the evolution of the population state in a population game. This connection is established with dynamic payoffs and under replicator evolutionary dynamics. The connection provides insights into the neural network's behavior from both dynamical systems and game-theoretical perspectives, aligning with recent literature that suggests that neural network outputs may resemble the Nash equilibria of suitable games. It also uncovers potential connections between the neural network classification problem and mechanism design. To illustrate our results, we present different numerical experiments in the context of classification problems.Comment: Extended Manuscrip

Energy efficiency of hybrid-power hetnets: a population-like games approach

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a distributed control scheme based on population games is proposed. The controller is in charge of dealing with the energy consumption problem in a Heterogeneous Cellular Network (HetNet) powered by hybrid energy sources (grid and renewable energy) while guaranteeing appropriate quality of service (QoS) level at the same time. Unlike the conventional approach in population games, it considers both atomicity and non-anonymity. Simulation results show that the proposed population-games approach reduces grid consumption by up to about 12% compared to the traditional best-signal level association policy. © 2018 AACC.Peer ReviewedPostprint (author's final draft

Fire-mediated germination syndromes in Leucadendron (Proteaceae) and their functional correlates

A mechanistic understanding of fire-driven seedling recruitment is essential for effective conservation management of fire-prone vegetation, such as South African fynbos, especially with rare and threatened taxa. The genus Leucadendron (Proteaceae) is an ideal candidate for comparative germination studies, comprising 85 species with a mixture of contrasting life-history traits (killed by fire vs able to resprout; serotinous vs geosporous) and seed morphologies (nutlets vs winged achenes). Individual and combined effects of heat and smoke on seed germination of 40 species were quantified in the laboratory, and Bayesian inference applied to distinguish biologically meaningful treatment effects from non-zero, but biologically trivial, effects. Three germination syndromes were identified based on whether germination was dependent on, enhanced by, or independent of direct fire cues (heat and smoke). Seed storage location was the most reliable predictor of germination syndromes, with soil-stored seeds c. 80% more likely to respond to direct fire cues (primarily smoke) than canopy-stored seeds. Notable exceptions were L. linifolium, with an absolute requirement for smoke to germinate (the third serotinous species so reported), and two other serotinous species with smoke-enhanced germination. Nutlet-bearing species, whether serotinous or geosporous, were c. 70% more likely to respond to fire cues than winged seeds, but there was no evidence for an effect of phylogeny or persistence strategy on germination. This comprehensive account of seed germination characteristics and identification of germination syndromes and their predictors, supports propagation, conservation and restoration initiatives in this iconic fynbos genus and other fire-prone shrubs with canopy or soil-stored seeds

Distributionally Robust Optimization

This chapter presents a class of distributionally robust optimization problems in which a decision-maker has to choose an action in an uncertain environment. The decision-maker has a continuous action space and aims to learn her optimal strategy. The true distribution of the uncertainty is unknown to the decision-maker. This chapter provides alternative ways to select a distribution based on empirical observations of the decision-maker. This leads to a distributionally robust optimization problem. Simple algorithms, whose dynamics are inspired from the gradient flows, are proposed to find local optima. The method is extended to a class of optimization problems with orthogonal constraints and coupled constraints over the simplex set and polytopes. The designed dynamics do not use the projection operator and are able to satisfy both upper- and lower-bound constraints. The convergence rate of the algorithm to generalized evolutionarily stable strategy is derived using a mean regret estimate. Illustrative examples are provided

Trade impacts of external border measures under the European Union's plant health legislation

[EN] Background: This article assesses whether the European Union's (EU's) plant health regulations have had an impact on imports. A dynamic modelling approach was applied, using a two-step generalized method of moments estimator for panel data, and covering an 8-year period (2013-2020). The estimated equation includes volumes of trade, economic drivers, the trading partner, and variables capturing categories of import requirements (phytosanitary certificates, exemptions, restrictions) with regards to external border measures for enhanced biosecurity. Results: From the analysis we can conclude that the import regime and its recent changes have had a limited impact, if any, on trade flows of the affected products. The most significant impact is found for products classified as high-risk plants, while the extension of the phytosanitary certificate requirement to new products seems to have had negligible effects on trade. Conclusion: Therefore, the plant protection regime for extra-EU trade seems to be not trade distorting while supplying a framework to enhance plant health in the EU.Barreiro-Hurle, J.; García Alvarez-Coque, JM.; Martinez Gomez, VD.; Martí Selva, ML. (2024). Trade impacts of external border measures under the European Union's plant health legislation. Pest Management Science. 80(3):1607-1614. https://doi.org/10.1002/ps.78931607161480

Unveiling the Dynamics of the Universe

We explore the dynamics and evolution of the Universe at early and late times, focusing on both dark energy and extended gravity models and their astrophysical and cosmological consequences. Modified theories of gravity not only provide an alternative explanation for the recent expansion history of the universe, but they also offer a paradigm fundamentally distinct from the simplest dark energy models of cosmic acceleration. In this review, we perform a detailed theoretical and phenomenological analysis of different modified gravity models and investigate their consistency. We also consider the cosmological implications of well motivated physical models of the early universe with a particular emphasis on inflation and topological defects. Astrophysical and cosmological tests over a wide range of scales, from the solar system to the observable horizon, severely restrict the allowed models of the Universe. Here, we review several observational probes -- including gravitational lensing, galaxy clusters, cosmic microwave background temperature and polarization, supernova and baryon acoustic oscillations measurements -- and their relevance in constraining our cosmological description of the Universe.Comment: 94 pages, 14 figures. Review paper accepted for publication in a Special Issue of Symmetry. "Symmetry: Feature Papers 2016". V2: Matches published version, now 79 pages (new format