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Sicossys PROJECT - Simulation of Complex Social Systems SiCoSSys PROJECT - Simulation of Complex Social Systems Summary ............................................................................................................................................ 1 Aims of the project ........................................................................................................................... 2 State of the Art .................................................................................................................................. 3 Agent-Based Simulation .........................................................................................................................3 Complex Social Systems ........................................................................................................................4 Multi Agent Systems................................................................................................................................4 The approach ...........................................................................................................................................6 Objectives .......................................................................................................................................... 6 Reasons and initial hypothesis ..............................................................................................................6 Antecedents and motivation...................................................................................................................7 UCM background .....................................................................................................................................8 UVA background......................................................................................................................................9 Goals........................................................................................................................................................10 References ....................................................................................................................................... 11 Summary The goal of this project is to provide a well-sound methodological framework for the treatment of complexity by policy makers and social scientists, endowed with an updated theoretical body of knowledge, a set of tools that enable scenario simulation, and a collection of case studies to guide and demonstrate the applicability of the framework. This framework will be based on agent- oriented modelling and simulation methods and tools. Agent oriented modelling provides a conceptual framework for analysis and simulation of complex social systems. This comes from the fact that agent related concepts allow the representation of organizational and behavioural aspects of individuals in a society and their interactions. This has motivated in the last years the development of a wide range of software languages/shells/libraries to simulate agent-based models. However, all present two difficulties for being widely accepted by social scientists: the end-user should have certain programming skills, and these software frameworks have been implemented forgetting the social specifications. This project addresses these difficulties by adopting multi-disciplinary viewpoints: The software engineering view: The UCM group will provide the infrastructure for agent-based modelling languages that allow the specification of complex 1 social systems, their simulation and analysis. These tools should be flexible to adapt to specific sociological problem domains and reuse existing agent-based simulation platforms when appropriate, while increasing usability taking into account the characteristics of the research object and the end users. This involves the methodology, from a software engineering viewpoint, and the provision of the software tools for its application. This work relies on its experience in INGENIAS agent-oriented methods and tools. The methods and applications approach: The UVA group will gather its wide experience in the application of agent-based simulation tools for the study of complex social systems in order to define the scientific method for the analysis of social phenomena and policy making. This will be accompanied by a library of mechanisms for social interaction ready to reuse, and a collection of case studies of interest for both social and computer scientists, which can benefit stakeholders in public administration and EPOs. Case studies will integrate the sociological foundations (developed by UAB) of artificial societies, with a methodology where a modelling framework provided by UCM will play a central role. Also, the project intends to promote the synergies between Spanish groups working in social simulation, as well as to reinforce their international relevance. For instance, note the compromise for participation of top researchers in the area such as N. Gilbert (U. Surrey), M.P. Gleizes (U. Toulouse), C. Sierra and P. Noriega (CSIC-IIIA), and L. Antunes (U. Lisbonne). Several versions of the framework will be produced along the project, and will be validated by case studies of the project and by EPOs, as it has been already done in previous projects (for instance, in the INGENIAS and SIMAGUA projects). This third party feedback, by both academics and industry, is quite useful to get further evaluation and to promote technology transfer. The tools will be distributed as open software in SourceForge.net, and the results of the project will be published in international journals and conferences, as well as standardization bodies and interest groups in the areas of agent technology and social simulation. Main efforts will be done to produce a set of resources (documentation, interactive presentations, tutorials, training workshops) to expand the knowledge and the use of the methodologies and tools into academic and professional communities. Aims of the project The project aims at providing a well-sound methodological framework for the treatment of complexity by policy makers and social scientists, endowed with an updated theoretical body of knowledge, a set of tools that enable scenario simulation, and a collection of case studies to guide and demonstrate the applicability of the framework. The main contributions are derived from its inter-disciplinary approach and expertise in complementary viewpoints: 1. The software engineering view: to provide the tools that will facilitate working with agent-based models and their simulation and analysis. These tools should be flexible to adapt to specific sociological problem domains and reuse existing agent-based simulation platforms. This is the subject of the SiCoSSys-Tools subproject by the Grasia research group (Grupo de investigación en Agentes 2 2. The methods and applications approach: from the experience in the application of agent-based simulation tools for the study of complex social systems it will be possible to define the scientific method for the analysis of social phenomena and policy making. This will be accompanied by a library of mechanisms for social interaction ready to reuse, and a collection of case studies of interest for both social and computer scientists, which can benefit stakeholders in public administration and EPOs. This is the subject of the SiCoSSys-MAS (Methods and ApplicationS) subproject by the INSISOC research group (Grupo de Ingeniería de los Sistemas Sociales) from the Universidad de Valladolid (UVA). State of the Art Agent-Based Simulation Simulation is a third way of doing science [3], and an important type of simulation in Social Sciences is agent-based modelling. This type of simulation is characterized by the existence of many agents that interact with each other with little or no central direction [29]. The emergent properties of an agent- based model are then the result of a bottom-up processes, rather than top- down direction [56]. A multi-agent model consists of a number of software entities, the agents, interacting within a virtual environment [19]. The agents are programmed to have a degree of autonomy, to react to and to act on their environment and on other agents, and to have goals that they aim to satisfy. In such models, the agents can have a one-to-one correspondence with the individuals, organisations, or other actors that exist in the real social world that is being modelled, while the interactions between the agents can likewise correspond to the interactions between the real world actors [45]. Agents are generally programmed in an object-oriented programming language and using some special-purpose simulation library or modelling environment, and are constructed using collections of condition-action rules to be able to perceive and react to their situation, to pursue the goals they are given, and to interact with other agents, for example by exchanging messages [46]. Many hundreds of multi-agent social simulation models have now been designed and built to examine a very wide range of social phenomena [28][49][64][74]. Like deduction, agent-based social simulation starts with a set of explicit assumptions. But unlike deduction, it does not prove theorems. Instead, a simulation generates data that can be inductively analyzed. Unlike typical induction, however, the simulated data comes from a rigorously specified set of rules rather than direct measurement of the real world. While induction can be used to find patterns in data, and deduction can be used to find consequences of assumptions, simulation modelling
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