Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Agent-Based Computational Economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.

Seed-div: an abstract role-playing game for discussing collective management of agrobiodiversity

L'objectif principal de cette recherche est d'accompagner les paysans dans l'élaboration d'un cadre de gestion collective de la diversité de leurs variétés de céréales vivrières. La gestion semencière et son impact sur la dynamique de la biodiversité sont soumis aux choix individuels des paysans, leurs stratégies d'une part, et au fonctionnement général du système semencier d'autre part. Nous postulons que la compréhension partagée des interactions au sein de ce système complexe est un prérequis pour travailler ensemble à la construction de règles de gestion collective qui participent à la durabilité de l'agriculture via un accès à un large choix de semences. La modélisation participative conceptuelle et les jeux de rôle ont été utilisés durant différents ateliers réunissant chercheurs, ONG, organisations paysannes et agriculteurs. Le résultat de cette série d'ateliers correspond à un modèle, un système multi-agents, représentant un archétype de village malien permettant de simuler la diversité de stratégies individuelles de gestion de semences qui sont ensuite disponibles gratuitement pour la communauté villageoise. Les agents du modèle sont des agriculteurs qui choisissent les variétés à semer ce qui provoque des échanges dans la communauté en fonction de leurs stratégies individuelles ou de facteurs externes. Les paramètres utilisés ont une valeur qualitative c'est pourquoi le modèle sert de support de discussion entre paysans de différentes régions. Le modèle a été construit et validé au travers de ces ateliers avec un impact évident sur les acteurs locaux pour la construction de nouveaux scénarios de gestion de la diversité variétale. Ainsi, le modèle a pu être utilisé en termes de prospective pour simuler des scénarios à partir de nouvelles formes d'action collective. (Résumé d'auteur

Formalization of the partnering structure for networked businesses

Rapidly changing market demands and increasing competitive pressure cause many businesses implement changes to the way they conduct business. One of these changes is the decision to collaborate with other businesses, forming what we call a 'networked business'. Networked businesses are formed by different organizations working together to reach a common goal. For the participating organizations in a networked business to be able to promptly react to their customers' needs, they must set up as cornerstone a well-defined collaborative partnering structure. In this report we discuss the partnering structure of networked businesses and present a framework for its formalization. Using a case study, we illustrate that existing approaches for value modeling, roles specification, and responsibilities definition can be used successfully if employed in a unifying way to address this structure concept