Agent-Based Modeling of Social Complex Systems Candelaria Sansores and Juan Pavón∗ Universidad Complutense de Madrid, Dep. Sistemas Informáticos y Programación 28040 Madrid, Spain
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Abstract. This thesis proposal aims to provide a new approach to the study of complex adaptive systems in social sciences through a methodological framework for modeling and simulating these systems like artificial societies. Agent based modeling (ABM) is well fitted for the study of social systems as it focuses on how local interactions among agents generate emergent larger and global social structures and patterns of behavior. The issues addressed by our framework are presented as well as its most important components.
1 Motivation One of the most difficult challenges for understanding social phenomena is their intractably complex nature. The emergence of societies and the establishment of cooperative relationships among their individual components have facilitated the generation of an extremely rich repertoire of behavioral possibilities not present in solitary organisms. The collective action of social individuals produces hierarchical phenomena that extend in time and space well beyond the local environments of the participants. Large-scale processes generated by local-scale interactions without central control, so-called self-organization, may in turn affect the specification of how individuals behave, interact and respond in their most immediate spatial domains. This relationship between individual behavior and macroscopic properties is hard to understand and experiment with. Social scientists have attempted to understand this complexity with different methodologies based on mathematical equations. Although these methodologies have enhanced social science research, they have failed of capturing emergent behavior and self-organization, and had little success in expressing laws that regulate these phenomena. However, new approaches to the study of complex adaptive systems have emerged. One of the emerging methodologies is the use of Agent-Based Modeling (ABM) and computer simulation as a tool for analysis and explanations that are impossible or too difficult to formulate using mathematical functions only. In a computer simulation, the social system is represented with symbols of a programming language and the laws that regulate their behavior are formulated with the algorithms implemented by the simulation program. The system to be simulated is modeled with ∗ This work has been developed with support of the Consejo Nacional de Ciencia y Tecnología (CONACYT) from México and the project TIC2002-04516-C03-03, funded by the Spanish Council for Science and Technology. R. Marín et al. (Eds.): CAEPIA 2005, LNAI 4177, pp. 99 – 102, 2006. © Springer-Verlag Berlin Heidelberg 2006
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Agents. In this context, agents are autonomous software systems that are intended to describe the behavior of observed heterogeneous social entities (e.g. individuals and organizations). An advantage of ABM is the ability to estimate the plausibility of the behavior of agents, the way in which agents interact, and the consequences of that behavior and interaction [2]. ABM is well fitted for the study of social systems as it focuses on how local interactions among agents serve to create larger and global social structures and patterns of behavior. Thus, agent based modeling and simulation allow creating new social worlds, or artificial societies [5], by modifying various conditions and parameters, as new needs arise. Thus, in agent-based social simulations (ABSS) the model is a multi-agent system (MAS) which allows the exploration of the micro-tomacro relation [3].
2 Agent Based Modeling Open Issues As agent-based simulation has gained in popularity, software tools for modelers are emerging. There are various toolkits for developing agent-based simulation systems, like REPAST, ASCAPE and MASON. The design philosophy of these toolkits is to provide a model library to which an experienced programmer can easily add features for simple simulations. These libraries have great advantages for modelers over developing their own, but also have some limitations. They require modelers to have a good working knowledge of the programming language that they are aimed at. Thus, for inexperienced programmers specifying a simulation model in a high-level declarative language instead of a low-level programming language would be desirable. This was the purpose of SDML, which has been built on Smalltalk. Unlike ASCAPE and REPAST, it does not demand users to be fluent in the underlying programming language, but they have to learn a complex interface that can be as difficult to master as a full programming language, which finally limits its usability. Another kind of tools that have emerged for developing simulations is rapid development environments. These allow the building of simple models using visual programming, for instance, STARLOGO, NETLOGO, CORMAS and AGENTSHEETS. Although they are relatively easy to use, agents in this kind of systems are quite simple, usually with a poor or inexistent agents’ cognitive model, and without support to model direct interactions between agents. Simulations developed with both kinds of tools, either toolkits or rapiddevelopment libraries, are proprietary model descriptions. This makes impracticable to compare two different implementations of the same specification. Therefore, replication of models turns out complicated due to the diversity of specification languages and the lack of a common one that addresses conceptual modeling. One approach that seems to consider this requirement more formally is SESAM. This framework provides an environment for modeling, which is based on UML-like activity diagrams. Although it does a step forward in the modeling issue, it still lacks of certain functionalities which are prerequisites for complex negotiation abilities of complex goal-oriented agents. In fact, an open debate stays on the need for complex goal-oriented agents. From a computer science view, a complex adaptive system is a form of complex multi-agent
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system (MAS) with adaptive agents. Complex systems are dynamical systems composed of a high number of agents and complex interactions among them. Also, [6] requires that agents be themselves complex, which is certainly true for the actor of social systems, but in the case of some biology systems (like swarm systems) the actors use to be simple. Even social systems models usually contain relatively simple actors. Due to this simplicity in agents’ implementations, some authors (for instance [4]) question whether these are really computational agents, as those found on MAS or DAI, and arguments sustaining agents are not used to implement agent-based simulations, but only to design them. Simplicity is not a problem if we consider modeling is a term for simplification. However, regarding MAS discipline we question ourselves if MAS potential is not being leveraged, and the plausibility issue with regard to social simulation also arises, which, in principle, involves modeling human cognition a little more realistically.
3 Proposal The proposal of this thesis is to address the open issues just reviewed with the following contributions: 1. An easily customizable tool for describing social models with a visual language. This visual language is toolkit-independent for specifying MAS and should support agents that range from simple to complex ones, and consider organizational issues to manage other dimension of complexity (the society of agents and the system’s architecture). Simple agents like those already supported by existing agent-based simulation toolkits and complex agents that mimic real agents as much as necessary. 2. An agile methodology for developing simulations, starting on the conceptual modeling, supported by a visual model editor, and ending with the transformation of the specification model in an implementation for any target simulation toolkit, hiding computational complexity to modelers. Because each agent-based simulation toolkit has unique strengths and weaknesses, modelers should be given the opportunity to choose the one that best addresses each particular kind of problem. 3. Replication support, the need for a toolkit-independent visual modeling language is mainly supported by model replication requirements. For instance, [1] pointed out the relevance of model simplicity for replication issues, but we also consider fundamental, besides simplicity, to establish a common language to describe agentbased models applied to the social sciences, independently of the implementation platform. This common language could be also a mean for communication of models among users or tools. Presently, the way social scientists communicate their models for replication is by passing source code to each other, which demands high effort to understand and interpret the models, sometimes leading to misunderstandings. 4. Tools to adapt the visual language proposed to model phenomena in different social domains. We are aware of the fact that having a universal modeling and simulation language for the social domain is not feasible. Therefore, our aim is to provide an agent oriented language that can be customized to particular social
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domains, by specialization or addition of new elements, which can be defined by the modelers of such domains. The basis for achieving these goals is the application of the INGENIAS agentoriented methodology [7], as it provides methods and tools for multi-agent systems specification and code generation support. INGENIAS is based on the specification and management of meta-models that describe the agent-oriented modeling language and support the transformation towards implementation for multiple target platforms. Here we plan to adapt these meta-models for the simulation problem, and the methods and tools accordingly. The approach has been presented at ESSA 2004 and EUMAS 2004 conferences and the preliminary results have been presented to MABS 2005 [9] and MICAI 2005 [8] conferences.
References 1. Axelrod, R. Advancing the Art of Simulation in the Social Sciences. In: R. Conte, et al. (Eds.): Simulating Social Phenomena. Lecture Notes in Economics and Mathematical Systems, Vol. 456. Berlin Springer. (1997), 21-40. 2. Axtell, R., Why Agents? On the Varied Motivations for Agent Computing in the Social Sciences, in Working Paper No. 17. 2000, center on Social and Economic Dynamics, The Brookings Institution: Washington, DC. 3. Coleman, J.C. Foundations of Social Theory. ed. M.H.U.P. Cambridge. 1990, Cambridge, MA: Harvard University Press. 4. Drogoul, A., D. Vanbergue, T. Meurisse. Multi-agent Based Simulation: Where Are the Agents? In: J.S. Sichman, et al. (Eds.): Multi-Agent-Based Simulation II: Third International Workshop, MABS 2002. Lecture Notes in Computer Science, Vol. 2581. Springer. Bologna, Italy (2002), 1-15. 5. Epstein, J.M., R. Axtell. Growing artificial societies: social science from the bottom up. Complex adaptive systems. 1996, Washington, D.C.: Brookings Institution Press. 6. Gilbert, N., K.G. Troitzsch. Simulation for the Social Scientist. 1996, Buckingham, U.K.: Open University Press. 7. Pavón, J., J. Gómez-Sanz. Agent Oriented Software Engineering with INGENIAS. In: V. Marík, et al. (Eds.): Multi-Agent Systems and Applications III, 3rd International Central and Eastern European Conference on Multi-Agent Systems, CEEMAS 2003. Lecture Notes in Computer Science, Vol. 2691. Springer. Prague, Czech Republic (2003), 394-403. 8. Sansores, C., J. Pavón. Agent-Based Simulation Replication: a Model Driven Architecture Approach. In: A. Gelbukh, et al. (Eds.): Fourth Mexican International Conference on Artificial Intelligence, MICAI-2005. Lecture Notes in Artificial Intelligence, Vol. 3789. Springer-Verlag. Monterrey, México (2005), 244-256. 9. Sansores, C., J. Pavón, J. Gómez-Sanz. Visual Modeling for Complex Agent-Based Simulation Systems. In: J. Sichman, et al. (Eds.): Multi-Agent-Based Simulation, Sixth International Workshop on Multi-Agent-Based Simulation, MABS 2005. Lecture Notes in Computer Science, Vol. 3891. Springer-Verlag. Utrech, Netherlands (2005), 174-189.
