Abstract. A new approach is suggested under the slogan “Keep it Descriptive Stupid ” (KIDS) that encapsulates a trend in increasingly descriptive agentbased social simulation. The KIDS approach entails one starts with the simulation model that relates to the target phenomena in the most straightforward way possible, taking into account the widest possible range of evidence, including anecdotal accounts and expert opinion. Simplification is only applied if and when the model and evidence justify this. This contrasts sharply with the KISS approach where one starts with the simplest possible model and only moves to a more complex one if forced to. An example multiagent simulation of domestic water demand and social influence is described. 1.

cfpm.org/~bruce Abstract. The ‘engineering ’ and ‘adaptive ’ approaches to system production are distinguished. It is argued that producing reliable self-organised software systems (SOSS) will necessarily involve considerable use of adaptive approaches. A class of apparently simple multi-agent systems is defined, which however has all the power of a Turing machine, and hence is beyond formal specification and design methods (in general). It is then shown that such systems can be evolved to perform simple tasks. This highlights how we may be faced with systems whose workings we have not wholly designed and hence that we will have to treat them more as natural science treat the systems it encounters, namely using the classic experimental method. An example is briefly discussed. A system for annotating such systems with hypotheses, and conditions of application is proposed that would be a natural extension of current methods of open source code development. 1.

The goal of engineering self-organising emergent systems is to acquire a macroscopic system behaviour solely from autonomous local activity and interaction. Due to the non-deterministic nature of such systems, it is hard to guarantee that the required macroscopic behaviour is achieved and maintained. Before even considering a self-organising emergent system in an industrial context, e.g. for Automated Guided Vehicle (AGV) transportation systems, such guarantees are needed. An empirical analysis approach is proposed that combines realistic agent-based simulations with existing scientific numerical algorithms for analysing the macroscopic behaviour. The numerical algorithm itself obtains the analysis results on the fly by steering and accelerating the simulation process according to the algorithm’s goal. The approach is feasible, compared to formal proofs, and leads to more reliable and valuable results, compared to mere observation of simulation results. Also, the approach allows to systematically analyse the macroscopic behaviour to acquire macroscopic guarantees and feedback that can be used by an engineering process to iteratively shape a self-organising emergent solution.

This paper presents a conceptual framework for speaking about self-organizing systems. The aim is to provide a methodology useful for designing and controlling systems developed to solve complex problems. A brief introduction to complexity and selforganization is given before introducing the conceptual framework and the methodology. A case study on self-organizing traffic lights illustrates the ideas presented in the paper.

Abstract. Efficient discovery and resource allocation is one of the challenges of any large scale Application Layer Network (ALN) such as computational Grids, Content Distribution Networks or P2P applications. In centralized approaches, the user requests can easily matched to the most convenient resource. This approach, however, shows scalability limits. In this paper, we explore an architecture for incorporating fully decentralized economic mechanisms as an approach for resource allocation in ALNs. These mechanisms are implemented by a set of trading agents that operates on behalf of the clients and service providers, interacting over an overlay network and interfacing with the underlying platform’s resources. A prototype of the proposed architecture is presented and the practical implications of its implementation in a grid scenario are discussed. 1

Abstract. The utilization of self-organizing processes promises scalability, robustness and adaptivity in Multi-Agent Systems (MAS), solely based on decentralized coordination of individual actors. Bionic development approaches are being established, which reuse decentralized coordination mechanisms that are derived from natural self–organizing systems. In this paper, we address analysis activities in incremental MAS development, concerning with the derivation of system architectures that enable applications to meet system requirements. As the functional requirements to self–organizing MAS comprise recurring types of system wide dynamics, we propose a systemic approach to analysis and architectural design activities by the iterative refinement of macroscopic dynamics. Based on a catalog of dynamic models of currently applied environment–mediated design metaphors, we discuss how intended MAS dynamics can be modeled and refined to decentralized MAS designs. A systemic design procedure is proposed and exemplified in a case study that demands the combination of two established design metaphors to enable an projected level of MAS adaptivity. 1

The paper considers the problem of how a distributed system of agents (who communicate only via a localised network) might achieve consensus by copying beliefs (copy) from each other and doing some belief pruning themselves (drop). This is explored using a social simulation model, where beliefs interact with each other via a compatibility function, which assigns a level of compatibility (which is a sort of weak consistency) to a set of beliefs. The probability of copy and drop processes occurring is based on the increase in compatibility this process might result in. This allows for a process of collective consensus building whilst allowing for temporarily incompatible beliefs to be held by an agent. This is an example of socially-inspired computing (by analogy with biologically-inspired). The space of behaviours in a MAS where agents interact with each other at the same time as reasoning/learning themselves is so vast that a "structuring idea " is needed. Here we apply an analogy with human opiniondynamics as an analogy with which to design, manage and understand a subset of this huge space. Results suggest that a reasonable rate of copy and drop processes and a well connected network are required to achieve consensus, but given that, the approach is effective at producing consensuses for a sample of randomly defined compatibility functions. However, there are some belief structures where this is inherently difficult to achieve. The results are compared to those from the simple opinion dynamics models, and some tentative hypotheses about agent consensus in this model posited.

Abstract: The effective and reliable coordination of agent activities is a momentous problem for Multi–Agent System (MAS) developers. Particularly challenging is the decentralized coordination of agents that enables systems to exhibit self–organization. Natural phenomena typically serve as design metaphors and developers apply Decentralized Coordination Mechanisms (DCMs) that have been inferred from biological, physical or social systems. This paper addresses the utilization of DCMs as reusable software components.Current development practices give little guidance for DCM selection and force developers to manually design, implement and tune mechanism parameters ad hoc, leading to highly specialized algorithms. Here, we propose a layered software architecture that encapsulates DCMs in (multiple) coordination spaces. A generic, annotation–based interface allows to separate agent coordination from agent functionality, therefore enabling DCM reuse and facilitating application (re)designs, i.e. mechanisms exchange and parameter adjustments. Implications for development procedures are discussed and the application of the layered architecture is exemplified in a resource allocation case study.

Centre of the United Nations University (UNU). It is based in Macao, and was founded in 1991. It started operations in July 1992. UNU-IIST is jointly funded by the government of Macao and the governments of the People’s Republic of China and Portugal through a contribution to the UNU Endowment Fund. As well as providing twothirds of the endowment fund, the Macao authorities also supply UNU-IIST with its office premises and furniture and subsidise fellow accommodation. The mission of UNU-IIST is to assist developing countries in the application and development of software technology. UNU-IIST contributes through its programmatic activities: 1. Advanced development projects, in which software techniques supported by tools are applied, 2. Research projects, in which new techniques for software development are investigated, 3. Curriculum development projects, in which courses of software technology for universities in developing countries are developed, 4. University development projects, which complement the curriculum development projects by aiming to strengthen all aspects of computer science teaching in universities in developing countries, 5. Schools and Courses, which typically teach advanced software development techniques, 6. Events, in which conferences and workshops are organised or supported by UNU-IIST, and

Abstract — The context of this work is that of systems being able to produce a collective response from interaction between simple individuals. Such systems are qualified of self organized ones and can be modeled with reactive multi-agent systems. This article underlines why, from our point of view, the analysis of these systems requires an experimental approach and it details our proposition in terms of tools for experimentations. After commenting each main components of the platform, it provides some details about its current implementation in the case of two systems. I.