An approach to rollback recovery of collaborating mobile agents

Fault-tolerance is one of the main problems that must be resolved to improve the adoption of the agents' computing paradigm. In this paper, we analyse the execution model of agent platforms and the significance of the faults affecting their constituent components on the reliable execution of agent-based applications, in order to develop a pragmatic framework for agent systems fault-tolerance. The developed framework deploys a communication-pairs independent check pointing strategy to offer a low-cost, application-transparent model for reliable agent- based computing that covers all possible faults that might invalidate reliable agent execution, migration and communication and maintains the exactly-one execution property

Leveraging simulation practice in industry through use of desktop grid middleware

This chapter focuses on the collaborative use of computing resources to support decision making in industry. Through the use of middleware for desktop grid computing, the idle CPU cycles available on existing computing resources can be harvested and used for speeding-up the execution of applications that have “non-trivial” processing requirements. This chapter focuses on the desktop grid middleware BOINC and Condor, and discusses the integration of commercial simulation software together with free-to-download grid middleware so as to offer competitive advantage to organizations that opt for this technology. It is expected that the low-intervention integration approach presented in this chapter (meaning no changes to source code required) will appeal to both simulation practitioners (as simulations can be executed faster, which in turn would mean that more replications and optimization is possible in the same amount of time) and the management (as it can potentially increase the return on investment on existing resources)

DOING POLICY IN THE LAB! OPTIONS FOR THE FUTURE USE OF MODEL-BASED POLICY ANALYSIS FOR COMPLEX DECISION-MAKING

For models to have an impact on policy-making, they need to be used. Exploring the relationships between policy models, model uptake and policy dynamics is the core of this article. What particular role can policy models play in the analysis and design of policies? Which factors facilitate (inhibit) the uptake of models by policy-makers? What are possible pathways to further develop modelling approaches to better meet the challenges facing agriculture today? In this paper, we address these issues from three different points of view, each of which should shed some light on the subject. The first point of view discusses models in the framework of complex adaptive systems and uncertainty. The second point of view looks at the dynamic interplay between policies and models using the example of modelling in agricultural economics. The third point of view addresses conditions for a successful application of models in policy analysis.modelling, complexity, participatory modelling, policy analysis, model use, Agricultural and Food Policy, Research Methods/ Statistical Methods,

Environmental analysis for application layer networks

Die zunehmende Vernetzung von Rechnern über das Internet lies die Vision von Application Layer Netzwerken aufkommen. Sie umfassen Overlay Netzwerke wie beispielsweise Peer-to-Peer Netzwerke und Grid Infrastrukturen unter Verwendung des TCP/IP Protokolls. Ihre gemeinsame Eigenschaft ist die redundante, verteilte Bereitstellung und der Zugang zu Daten-, Rechen- und Anwendungsdiensten, während sie die Heterogenität der Infrastruktur vor dem Nutzer verbergen. In dieser Arbeit werden die Anforderungen, die diese Netzwerke an ökonomische Allokationsmechanismen stellen, untersucht. Die Analyse erfolgt anhand eines Marktanalyseprozesses für einen zentralen Auktionsmechanismus und einen katallaktischen Markt. --Grid Computing

PandoRust. Agent Based Modeling in Rust

L'aparicio de llenguatges de programació de nova generació com Rust, centrats en la concurrència i el paral·lelisme, poden millorar el rendiment de les plataformes de simulacio basades en agents (ABM) d'avui dia. En aquest estudi es redissenya un model existent, Pandora, aprofitant les característiques que ofereix Rustlang en materia de paral·lelisme per avaluar si es possible fer servir aquest llenguatge per a ABM. La implementacio d'aquestes polítiques permet millorar enormement el rendiment d'aquest tipus de softwares i reduir les línies de codi a mantenir, millorant l'experiencia de desenvolupament sense afectar les necessitats de rendiment per a l'execucio de models socials complexos.La aparicion de lenguajes de programación de nueva generación como Rust, centrados en la concurrencia y el paralelismo, puede mejorar el rendimiento de las plataformas de simulacion basadas en agentes actuales. En este estudio se rediseña un modelo existente, Pandora, aprovechando las características que ofrece Rustlang en materia de paralelismo para evaluar si es posible usar este lenguaje para ABM. La implementacion de estas políticas permite mejorar enormemente el rendimiento de este tipo de proyectos y reducir las líneas de codigo a mantener, mejorando la experiencia de desarrollo sin afectar las necesidades de rendimiento para la ejecucion de modelos sociales complejos.The emergence of next-generation programming languages such Rust, focused on concurrency and parallelism, can improve the performance of existing Agent Based Modeling platforms. In this study an existing ABM framework, Pandora, is redesign using Rustlang's capabilities for safe parallelism to assess whether is possible to use Rust in ABM. It was found that Rust ownership policies, if correctly implemented, can massively improve the performance of this type of parallel projects and reduce the lines of code to maintain, improving the development experience while maintaining the high performance needed for the execution of complex social models

An Introduction to Mechanized Reasoning

Mechanized reasoning uses computers to verify proofs and to help discover new theorems. Computer scientists have applied mechanized reasoning to economic problems but -- to date -- this work has not yet been properly presented in economics journals. We introduce mechanized reasoning to economists in three ways. First, we introduce mechanized reasoning in general, describing both the techniques and their successful applications. Second, we explain how mechanized reasoning has been applied to economic problems, concentrating on the two domains that have attracted the most attention: social choice theory and auction theory. Finally, we present a detailed example of mechanized reasoning in practice by means of a proof of Vickrey's familiar theorem on second-price auctions