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 can be used as an aid intuition.
3 Complex Social Systems
Some recent developments in the social sciences [40] claim for the usefulness of both experimentation (such the Experimental Economics research stream) and computer simulation as generative methods to analyse the complexity of social systems [87].
A complex social system consists of a collection of individuals that directly interact among them or through their social and technological environment. These individuals own a set of attributes that autonomously evolve, are motivated by their own beliefs and personal goals, and act under the specific circumstances of their social environment. This environment also contributes to shape their beliefs (values and knowledge about the world), can evolve in time, and has a complex structure so it is not easy to predict its net effects because of the different influence of each kind of interaction context [21]. It has also to be taken into account that social phenomena are contingent, so they are unpredictable and changing.
Universal laws that have been used in explanations for a general or average individual often prove to be inappropriate when modelling complex social systems. The problem is that no one behaves like this average person [46]. All these facts contribute to make social systems highly dynamic and complex. For this reason, abstracting them to functional mathematical models (by using, for instance, structural equation modelling, multivariate statistical analysis or statistical processing of temporal series) should be complemented by other techniques that consider how global and emergent behaviour can be derived from the real subjects’ behaviours, which are fundamental in any social system [44].
Yet another problem linked with the formalization of complex social systems is the selection of the basic elements to include; that is because formalization is a kind of reduction or simplification. Recent cognitive and decisional models of interacting social agents claim for the inclusion of a wider range of attributes than those used by early computational economics in the pioneer socio- economic simulations. So there is a strong challenge in the social science domain dealing with the theoretical justification of the minimum set of attributes that can be considered constitutive of a social agent.
The representative agent is not a realistic assumption to start with [46]. We have to deal with bounded rational agents, with finite processing capacity and without explicit utility functions. They adapt and settle for satisfaction under rules of thumb. They have emotions. And they are rather heterogeneous. Even if the resulting model with a representative full rational agent has high predictive capacity, it is still important to replicate the observed patterns from models with heterogeneous and bounded rational agents.
Multi Agent Systems
On the other hand, from a software engineering point of view, a multi-agent system (MAS) consists of a set of autonomous software entities (the agents) that interact among them and with their environment. Autonomy means that agents are active entities that can take their own decisions. In this sense, the agent paradigm assimilates quite well the individual in a social system. In fact,
4 there are numerous works in agent theory on organisational issues of MAS. Besides, theories from the field of Psychology have been incorporated to design agent behaviour, being the most extended the Believes-Desires- Intentions (BDI) model, on the work of Bratman [11].
With this interlinked perspective, agent-based simulation tools have been developed in the last years to explore the complexity of dynamics of complex social systems. An agent-based simulation executes several agents, which can be of different types, in an observable environment where agents’ behaviour can be monitored. Observations on agents can assist in the analysis of the evolution of their mental state (that is, individual values and reasons to act), the collective behaviour and the general trends of system evolution. This provides a platform for empirical studies of social systems evolution. As simulation is performed in a controlled environment, on one or several processors, this kind of tools allows the implementation of experiments and studies that would not be feasible otherwise [30][54].
There are, however, some limitations when trying to simulate real social systems. The main issue is that individuals, with regard to a software agent, are by nature complex systems, whose behaviour is unpredictable and less determined than for a software agent, whose behaviour and perception capabilities can be designed with relative simplicity. Moreover, it is not possible in practice to consider the simulation of countless nuances that can be found in a real social system with respect to agent interaction, characterization of the environment, etc. For this reason, it is impractical to intend the simulation of a social system in all dimensions. On the other hand, we should and can limit to simulate concrete social processes in a systemic and interactive context. Therefore, the simulation of social systems should be considered in terms of focus on a concrete process under research attention.
In spite of these limitations, the agent paradigm offers many advantages to express the nature and peculiarities of social phenomena, and to overcome limitations of statistical modelling [45]. However, social scientists that want to use this new methodology must confront a difficulty of practical order that should not be minimized. The use of existing agent based simulation tools is not simple because models have to be specified as programs, usually with an object-oriented programming language. This makes the definition of models a complex task for sociologists and other social scientists and professionals, as usually they have not developed the skills for computer programming.
That is the main reason why some tools start to offer some graphical modelling capabilities. For instance, SeSam (www.simsesam.de) allows the graphical specification of state machines and provides a library of basic behaviours. In addition, Repast Py (repast.sourceforge.net/repastpy) facilitates the visual construction of simple simulations out of some component pieces, although at the end the user needs to write Python scripts. The problem with these solutions is that they also require some programming skills and the type of systems that can be modelled are quite simple (they are mainly rapid prototyping tools).
Agent-oriented software engineering, however, offers powerful modelling languages, at a more abstract level. Concepts in these languages are closer to those that a social scientist would use, and this makes them more appropriate to solve this usability issue [88].
5 The approach
With this working hypothesis, the goal of the project is to develop an agent- based modelling and simulation framework by extending a concrete agent- oriented methodology, INGENIAS [69][70]. This framework will allow the specification of social systems with a graphical modelling language, the simulation of the models of these systems by exploiting the capabilities of existing agent-based simulation tools/platforms, and the identification and analysis of social patterns (at a macroscopic, or aggregate, level) in terms of the atomic elements of the social system specification (at a microscopic, or individual/interaction, level). The advantages go further than usability. As it has been discussed in [82] this solution facilitates the replication of an experiment on different simulation engines, in order to contrast results. The availability of a graphical view of the system facilitates its understanding too and improves the identification of patterns in the system [67].
There are two main reasons for the choice of INGENIAS as starting point for this work. First, its modelling language [70] supports well the specification of organisation structure and dynamics, as well as agent intentional behaviour, characteristics that are present in social systems. This language is supported by the INGENIAS Development Kit (IDK) with a graphical editor, which can be extended to introduce new modelling concepts. Second, INGENIAS promotes a model-driven engineering approach [71] that facilitates the independence of the modelling language with respect to the implementation platform. This is especially important here in order to abstract away programming details and concentrate on modelling and analysis of social patterns. With this purpose the IDK supports the definition of transformations between models and code for a range of implementation platforms.
Objectives
Reasons and initial hypothesis
This proposal has emerged from the interaction between the applicant research groups at the First Int. Workshop on Social Simulation and Analysis of Artificial Societies (SSASA) organized by SSASA-UAB in Barcelona in may 2007. The debate and discussions resulted exciting and we found that we could exploit important synergies and mutually benefit coordinating our research programmes to gain important issues in the field of Agent Based Social Modelling and Simulation.
The main issue that arose at the SSASA workshop was that, although there is a wide range of software languages/shells/libraries to simulate agent-based models, all present two difficulties to be widely accepted by social scientists: the end-user should have certain programming skills, and these software frameworks have been implemented forgetting the social specifications. Theories about innovation and diffusion assign a relevant role to the user- friendliness of a new methodology or technology.
6 Our approach considers an alternative hypothesis, based on the main relevance of the specific reception community: in a social scientists community context, whose identity lays on some disciplinary domain fundamental assumptions, the diffusion of a new methodology or technology will be more influenced by the community-foundations-friendliness than by the plain user- friendliness. That is to say, innovations will have a high reception level, among other considerations, if they are embedded of some of the core assumptions of the disciplinary domain.
In this sense, it is necessary the collaboration of social scientists to identify the foundations on which the social simulation tools should be built. From the software engineering viewpoint, the use of meta-modelling techniques provides flexibility to define appropriate modelling languages, compliant with requirements from social scientists, and implement tools that facilitate models analysis and simulation. The feasibility of this assumption has been already validated with initial prototypes with INGENIAS, which have been assessed at international conferences and journals.
The proposal has received the interests of stakeholders: EPOs considering that it could be an opportunity for commercial exploitation in their sectors (providing consultancy services to public administration and private companies); international experts that endorse the scientific goals of the project; PhD students actually working in the field who increase their stock of knowledge; the applicant universities that increase their international visibility; the national groups doing research in complexity and social simulation that receive technological transfer; the Spanish Ministry of Science that gains a high return due the international impact of applied funds.
Antecedents and motivation
“Imagine how hard physics would be if electrons could think” Murray Gell-Mann (Physics Nobel Prize)
There is an increasing interest in Social Simulation as the new paradigm to study Complex Systems. While the physical world is considered constituted of systems that are linear or approximately linear, it is evident that human societies, institutions and organisations are complex systems, using ‘complex’ in the technical sense to mean that the behaviour of the system as a whole cannot be determined by partitioning it and understanding the behaviour of each of the parts separately (a classic strategy of physical sciences) [29].
Social Simulation requires the construction of computer programs that simulate aspects of social behaviour. Table 3.1 presents a list that includes those preferred by the scientific community. There is a preference for Special Purpose Languages and Environments although they require some degree of programming skills. As it has been previously indicated, most of them present, however, two difficulties to be widely accepted by social scientists: the end-user should have certain programming skills, and these software frameworks have been implementing forgetting the social specifications.
7
Special Purpose Languages Generic agent AOSE and Environments platforms tools AScape ACT-RBOT AgentBuilde r EcoLab AgentSheets agentTool Glider Jack INGENIAS JAS JADE Islander LSD (Laboratory for Simulation Zeus Madkit Development) Magsy Passi MASON Prometheus Mimose Tropos Multi-Agent Simulation Suite (MASS) NetLogo POP-11 Powersim RePast SDML SeSAm SIM_AGENT Simile StarLogo Stella Swarm Vensim
Table 3.1. Computer software for social simulation.
UCM background
From the software engineering viewpoint, UCM has shown the flexibility of the model driven engineering (MDE) approach to provide abstract modelling tools that adapt well to different application domains. In concrete, MDE has been used in combination with the agent paradigm to propose the INGENIAS methodology for the development of MAS.
The main assumptions have been to consider models as the main artefacts of the software development process and agents and agent organizations as the foundation elements to build and work with models. By the application of transformations on models it is possible to generate implementation code on heterogeneous platforms and abstract from platform specific issues. It is also possible to transform MAS models in other forms that are more convenient to apply verification and validation tools. All this is supported by a tool framework, the INGENIAS Development Kit (IDK), which has been developed in the context of a current project [9].
This has been validated in different settings, by cooperation with EPOs in specific technology transfer projects (such as Boeing R&T, Telefónica
8 I+D, MindFields) as reported in [68]. The MDE approach is currently used in European project MOMOCS for modernization of complex systems. Other research groups are using the IDK as mentioned in section 6.
In this sense it is interesting to mention that IDK can be adapted to concrete application domains. For instance, at Univ. Politécnica Valencia, A. Giret has adapted INGENIAS meta-models to create a modelling language for holonic manufacturing systems [31], which shows that IDK can be customized by third parties. With respect to the current proposal, the feasibility of the approach for the simulation of social systems has been proved by the PhD thesis of C. Sansores at UCM, with some case studies that have been presented in main conferences in the area [79][80][82][83]. This is being continued by a case study in cooperation with the Fac. of Sociology at UCM on the evolution of values in Spanish society, which has been published in a relevant journal [67] and specific aspects in some conferences [38][39][43].
These works have shown that the agent paradigm can be appropriate for modelling social systems. However, it is still necessary to expand the number of case studies and the collaboration with experts in modelling societal problems in order to find abstractions that are closer to the application domain. In fact, it can be expected that there should be more than a single modelling language: A customizable framework for each social problem domain should be easier to apply by experts in such domain. This can be achieved with the flexibility that the MDE approach has shown with INGENIAS for the agent domain [31][72][71].
UVA background
In the last decade, UVA group has applied agent based modelling techniques to different domains: freshwater management, urban dynamics, design of market institutions, behavioural finance, yield management, industrial dynamics and evolutionary game theory, are the most relevant. All these applications have been developed in different projects and have benefited of international partners expertise that contributed in different ways to develop successful models and simulations. The group has a deep expertise in different software programming shells and environments: Swarm, SDML, Vensim, Stella, Powersim, Mason, Repast, Netlogo, AgentBuilder, Mathematica, Jade, AgentSheets and INGENIAS. This has given to the group a wide view of facilities and limitations of available software for social simulation. The INSISOC research on Agent-Based Modelling and Social Simulation has received financial support from: Spanish Ministry of Science and Education, Spanish Ministry of Science and Technology, Regional Government of Castilla y Leon, European Commission, and the Stockholm Environmental Institute Oxford Office, to fund the following works: . ISIA: Socio-Economics Research and Artificial Intelligence: Contributions in honour Herbert Simon.
9 . FIRMA: Freshwater Integrated Resource Management with Agents. . SocSimNet: Competence Network for Introduction of Modern ICTE Technologies in Vocational Learning in Social Systems Simulation and Research. . GIAVA: Integrative Water Management in the Metropolitan Area of Valladolid. . SIMAGUA: Agent Based Simulator of Water Management Policies in Metropolitan Areas.
Results of these research activities have been published in (see more details in section 6. Background of the group) principal journals in social simulation (Journal of Artificial Societies and Social Simulation, Simulation; Cybernetics, and Advances in Complex Systems), relevant journals in sociology and economics (Journal of Socioeconomics; the Journal of Business Research and Games and Economic Behaviour) and international conferences in the field (European Social Simulation Conference, World Conference on Social Simulation and Artificial Economics).
Goals
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 can be refined in a set of concrete objectives:
1. To develop a modelling framework based on the application of multi-agent based modelling and simulation to enable social scientists and decision makers to model, play simulations, test and analyse social (complex) systems. 2. To develop sound theoretical and architectural foundations for this framework, based on existing agent-oriented methodologies and the INGENIAS model-driven approach. 3. To apply the framework to a number of challenging case studies in order to refine the framework, and to get policy assessment in the selected case studies. 4. To demonstrate to social scientists and policy makers that the framework can be used to conduct validation of social hypothesis as well as realistic policy design, deployment, and assessment, so fostering increasingly trusted outcomes. 5. To design and test a suite of training materials for social scientists, policy-makers, advisers, and postgraduate students.
10 References
[1] Aguilera, A. y López, A. (2001). Modelado Multiagente de Sistemas Socioeconómicos. Una introducción al uso de la inteligencia artificial en la investigación social. Ediciones COLSAN. San Luis de Potosí (México). ISBN: 968- 7727-55-1 [2] Aguilera, A.; López, A. y Martínez, J.R. Eds. (2004). Sociedades Artificiales: Aplicación de la simulación en la teoría social. El Colegio de San Luis. Páginas, 178. ISBN: 970-93886-3-0. San Luis de Potosí (México) [3] Axelrod, R. (1997). Advancing the Art of Simulation in the Social Sciences. in Rosario Conte, Rainer Hegselmann and Pietro Terna (eds.), Simulating Social Phenomena (Berlin: Springer, 1997), pp. 21-40. [4] Bellifemini, F., Poggi, A., Y Rimassa, G. (1999). JADE - A FIPA-compliant Agent Framework. Proc. of the 4th International Conference and Exhibition on The Practical Application of Intelligent Agents and Multi-Agents. [5] Bergenti, F., Gleizes, M.P., Zambonelli, F. (2004). Methodologies and Software Engineering for Agent Systems. The Agent-Oriented Software Engineering Handbook. Kluwer Series on Multiagent Systems, Artificial Societies, and Simulated Organizations, Vol. 11. [6] Bernon, C., Cossentino, M., Pavón, J. (2005). Agent Oriented Software Engineering. Knowledge Engineering Review 20(2), pp. 99-116. [7] BOE num. 287 (30 nov. 2007). RESOLUCIÓN de 26 de noviembre de 2007, de la Secretaría de Estado de Universidades e Investigación, por la que se convocan ayudas para la realización de proyectos de investigación, programas de actividad investigadora y acciones complementarias dentro del Programa Nacional de Proyectos de Investigación Fundamental, en el marco del VI Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica 2008-2011. [8] Botía, J., Gómez-Sanz, J., Pavón, J. (2006). Intelligent Data Analysis for the Verification of Multi-Agent Systems Interactions. 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006). Intelligent Data Engineering and Automated Learning – IDEAL 2006, LNCS 4224 , Springer- Verlag, pp. 1207-1214. [9] Botía, J., González Moreno, J.C., Gómez-Sanz, J., Pavón, J. (2007). The INGENIAS project. 6th Int. Workshop on Practical Applications of Multi-Agent Systems (IWPAAMS’07) pp. 339-348. [10] Boudon, R. (2006) “Homo sociologicus: Neither a Rational nor an Irrational Idiot”, in PAPERS 80, 2006, pp. 149-169. [11] Bratman, M.E. , 1987. Intentions, Plans and Practical Reason. Harvard University Press. [12] Caire, G. et al. (2002). Agent Oriented Analysis using MESSAGE/UML. Second International Workshop, AOSE 2001, Montreal, Canada, May 29, 2001. Revised Papers and Invited Contributions. LNCS 2222. Springer-Verlag. 119-135. [13] Consentino, M., Guessoum Z., Pavón, J. (2004). Roadmap of Agent-Oriented Software Engineering, en Methodologies and Software Engineering for Agent Systems, Kluwer (capítulo libro), 431-450. [14] Corchado, J.M., Pavón, J., Corchado, E., Castillo, L. (2004). Development of CBR- BDI Agents: A Tourist Guide Application, 7th European Conference, ECCBR 2004, Lecture Notes in Artificial Intelligence 3155, Springer Verlag, 547-559.
11 [15] Cuesta Morales, P., González Moreno, J.C., Zahia Guessoum and Pavón, J. (2004). Las Tecnologías de Agentes, Novática, (170):3-5. [Versión en inglés publicada en la revista Upgrade V (4), A World of Agents] [16] Davidson, P. (2002). Agent Based Social Simulation: A Computer Science View. Journal of Artificial Societies and Social Simulation (5) 1. [17] Edmonds, B., Hernandez, C., Troitzsch, K. (Eds) (2007). “Social Simulation: Technologies, Advances and New Discoveries”. IGI Global. ISBN-10: 1599045222. [18] Elster, J. (1999) Alquimias de la Mente: La racionalidad y las emociones. Barcelona, Paidós, 2002. [19] Ferber, J. (1999), Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999 [20] FIPA: Foundation for Intelligent Physical Agents. htt://www.fipa.org [21] Fiske, A. P. (1991) Structures of Social Life: The Four Elementary Forms of Human Relations. New York, The Free Press, 1991. [22] Fuentes, R. (2004). Teoría de la Actividad para el Desarrollo de Sistemas Multiagente. Tesis doctoral. Dep. Sistemas Informáticos y Programación, Universidad Complutense Madrid. [23] Fuentes, R., Gómez-Sanz, J., Pavón, J. (2003). Social Análisis of MultiAgent Systems with Activity Theory, 10th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2003), LNCS 3040, Springer-Verlag, 526-535. [24] Fuentes, R., Gómez-Sanz J., Pavón J. (2003). Activity Theory for the Analysis and Design of Multi-Agent Systems. En: Agent Oriented Software Engineering 2003 (AOSE 2003), LNCS 2935, Springer-Verlag, 110-122. [25] Fuentes, R., Gómez-Sanz J., Pavón J. (2006). Discovering Patterns in the Behaviour of Open Multi-Agent Systems. 18th European Meeting on Cybernetics and Systems Research. Cybernetics and Systems 2006, Vol. II, pp. 533-538. [26] Fuentes, R., Gómez-Sanz J., Pavón J. (2007). Managing Contradictions in Multi- Agent Systems. IEICE Transactions on Information and Systems, E90-D, pp. 1243-1250. [27] Fuentes, R., Gómez-Sanz, J., Pavón, J., Uden, L. (2004). Activity Theory applied to Requirements Elicitation of Multi-Agent Systems, First International Workshop on Activity Theory Based Practical Methods for IT Design, ATIT 2004. [28] Galán J.M., Izquierdo L.R. (2005). “Appearances Can Be Deceiving: Lessons Learned Re-Implementing Axelrod's 'Evolutionary Approach to Norms' ”. Journal of Artificial Societies and Social Simulation vol. 8, no. 3. SIMSOC Consortium. ISSN: 1460-7425. [29] Gilbert, Nigel. (2005b). Agent-based social simulation: Dealing with complexity. http://www.complexityscience.org/NoE/ABSS-dealing%20with%20complexity-1- 1.pdf [30] Gilbert, N. & Troitzsch, K. (2005) Simulación para las Ciencias Sociales: Una guía práctica para explorar cuestiones sociales mediante el uso de simulaciones informáticas (2ª edición), Madrid, McGraw-Hill, 2006. [31] Giret, A., Botti, V. y Valero, S (2005). MAS Methodology for HMS. In: Holonic and Multi-Agent Systems for Manufacturing, HoloMAS 2005. Lecture Notes in Artificial Intelligence, 3593. Springer-Verlag, pp. 39-49. [32] Gómez-Sanz J., Pavón J. (2004). Methodologies for developing Multi-Agent Systems. Journal of Universal Computer Science 10 (4), 359-374. [33] Gómez-Sanz J., Pavón J. (2007). Ingenias Development Kit (IDK): Tutorial and Manual.
12 [34] Gómez-Sanz, J., Gervais M.P., Weiss, G. (2004). A Survey on Agent-Oriented Software Engineering Research, en Methodologies and Software Engineering for Agent Systems, Kluwer (capítulo libro), 33-64. [35] Gómez-Sanz, J., Pavón., J., Garijo, F. (2002). Meta-models for Building Multi- Agent Systems, The 2002 ACM Symposium on Applied Computing (SAC 2002), ACM Press, 37-41. [36] Gómez-Skarmeta, A., Botía, J., López, A. (2004) Aclanalyser: a tool for debugging multi-agent systems. In European Conference on Artificial Intelligence (ECAI 2004). [37] Gonzalez, S. (2004) “¿Sociedades artificiales?: una introducción a la simulación social”, Revista Internacional de Sociología REIS, 39, 2004, pp. 199-222. [38] Hassan, S., Garmendia, L., Pavón, J. (2007). Agent-Based Social Modeling and Simulation with Fuzzy Sets. Hybrid Artificial Intelligence Systems (HAIS 2007), in Hybrid Intelligent Systems, Advances in Soft-Computing 44, Springer-Verlag, pp. 40-47. [39] Hassan, S., Pavón, J., Arroyo, M., León, C. (2007). Agent Based Simulation Framework for Quantitative and Qualitative Social Research: Statistics and Natural Language Generation. 4th Conference of the European Social Simulation Association (ESSA’07), pp. 697-707. [40] Hedström, P. (2005) Dissecting the Social: On the Principles of Analytical Sociology. Cambridge University Press, 2005. [41] Izquierdo, S.S. and Izquierdo, L.R. (2007). “The impact of quality uncertainty without asymmetric information on market efficiency”. Journal of Business Research, Volume 60, Issue 8, pp. 858-867. [42] Kinny, D., Georgeff, M., Rao, A. (1996). A Methodology and Modelling Technique for Systems of BDI Agents. En W. Van de Velde and J. W. Perram, editors, Agents Breaking Away: Proceedings of the Seventh European Workshop on Modelling Autonomous Agents in a Multi-Agent World, (LNAI 1038), 56–71. [43] León, C., Hassan, S., Gervás, P., Pavón, J. (2007). Mixed Narrative and Dialog Content Planning Based on BDI Agents. XII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2007). LNAI 4788, Springer- Verlag, pp. 150-159. [44] Lizón, A. (2007) La otra sociologia: Una saga de empíricos y analíticos, Ed. Montesinos, 2007. [45] López-Paredes, A. (2001). Análisis e Ingeniería de las Instituciones Económicas. Una metodología Basada en Agentes. Servicio de Publicaciones de la Universidad del País Vasco. Bilbao. ISBN: 84-8373-347-1. [46] López-Paredes, A. (2004). Ingeniería de Sistemas Sociales. Diseño, Modelado y Simulación de Sociedades Artificiales de Agentes. Servicio de Publicaciones de la Universidad de Valladolid. Páginas, 205. ISBN: 8484482766 [47] López-Paredes, A. and Hernández, C. (2007). Agent Based Modelling in Natural Resource Management. Pearson. [48] López-Paredes, A.; Hernández, C, y Pajares, J. (2002). Towards a New Experimental Socio-economics. Complex Behaviour in Bargaining. Journal of Socioeconomics. Vol.: 31, pp. 423 – 429. Elsevier. ISSN: 1053-5357 [49] López-Paredes, A., Saurí D., Galán J.M. (2005). Urban Water Management with Artificial Societies of Agents. The FIRMABAR Simulator. Simulation-Transactions of the Society of Modelling and Simulation International. Vol. 81 (3), pp. 189-199. [50] Lozares, C. (2004) “La simulación social, ¿una nueva manera de investigar en ciencia social?”, PAPERS, 72, 2004, pp. 165-188.
13 [51] Luck, M., McBurney, P. and Preist, C. (2003). Agent Technology: Enabling Next Generation Computing. A Roadmap for Agent Based Computing. Agentlink, January 2003. [52] Mas, Ana (2005). Agentes Software y Sistemas Multia-Agente. Conceptos, Arquitecturas y Aplicaciones. Pearson-Prentice Hall. [53] Miguel, F. (1996) “¿Deberían interesarse los sociológos por el marxismo analítico?”, PAPERS, 50, 1996, pp. 155-163. [54] Miguel, F.J. (2006). Gilbert & Troitzsch, 2005: Simulation for the Social Scientist (2nd edition).
14 Model Driven Architecture – Foundations and Application, LNCS 4066, Springer- Verlag (2006) 284-298. [72] Pavón, J., Gómez-Sanz, J.J., Valencia-Jiménez, J., Fernández-Caballero, A. (2007). Development of intelligent multi-sensor surveillance systems with agents. Robotics and Autonomous Systems 55, pp. 892-903. [73] Pavón, J., Pérez de la Cruz, J.L. (2005). Agentes Software y Sistemas Multi- Agente: Conceptos, Arquitecturas y Aplicaciones. Pearson-Prentice Hall. [74] Posada, M & López-Paredes, A. (2008). How to Choose the Bidding Strategy in Continuous Double Auctions: Imitation Versus Take-The-Best Heuristics. Journal of Artificial Societies and Social Simulation; vol. 11, nº1, 6. SIMSOC Consortium. ISSN: 1460-7425. [75] Repast 3.0 http://repast.sourceforge.net/ [76] Richiardi, M. & Saam, N.J. (2005). A Common Protocol for Agent-Based Social Simulation (Closing plenary session of the third annual conference of the European Social Simulation Association ESSA, September 9, 2005). [77] Rodríguez, O., Vizcaíno, A., Martínez, A. I., Piattini, M., & Favela, J. (2004). Using a Multi-Agent Architecture to Manage Knowledge in the Software Maintenance Process. Proc. Int. Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES'2004), Wellington, New Zealand, 1181-1188. [78] Troitszch, Klaus G. (2007) “Theory and Practice of Multi-Agent Methodology in Microsimulation: Presentation of a New JAVA-BasedTool”, contribution to “Jornada sobre Simulación Fiscal, UAB2007”, GSADI and SSASA-UAB, http://www.uni-koblenz.de/~kgt/Pub/Barcelona.pdf, (22/11/2007). [79] Sansores, C., Pavón, J., López-Paredes, A. (2004). A Framework for ABSS on the Grid. Proc. Second Conference of the European Social Simulation Association (ESSA 2004). [80] Sansores, C., Pavón, J. (2004). A framework for Agent Based Social Simulation. Proc. Second European Workshop on Multi-Agent Systems (EUMAS 2004). [81] Sansores, C., Pavón, J. (2004). Simulación social basada en agentes. Revista Iberoamericana de Inteligencia Artificial 25, pp. 71-78. [82] Sansores, C., Pavón, J., 2005. Agent-Based Simulation Replication: A Model Driven Architecture Approach. In: 4th Mexican International Conference on Artificial Intelligence (MICAI 2005). Lecture Notes in Artificial Intelligence, 3789. Springer-Verlag, pp. 244-253. [83] Sansores, C., Pavón, J., Gómez-Sanz, 2006. Visual Modeling for Complex Agent- Based Simulation Systems. In: Int. Workshop on Multi-Agent-Based Simulation 2005, MABS 2005. Lecture Notes in Artificial Intelligence, 3891, Springer-Verlag, pp. 174—189. [84] Selic, B.: The Pragmatics of Model-Driven Development. IEEE Software 20, 5 (2003), 19-25 [85] Simon, H. (1996) Las ciencias de lo artificial (3ª edición), Granada, Ed. Comares, 2006. [86] Swarm (2004). Swarm Wiki, the agent-based modelling resource. http://wiki.swarm.org [87] Squazzoni, F. (2005) Agent-Based Models as Tools for Analytical Sociology, in Italian Sociology Association, Young Sociologists 2004, Franco Angeli, Milan, pp. 192-206. [88] Squazzoni, F. & Boero, R. (2005) Towards an Agent-Based Computational Sociology. Good Reasons to Strengthen a Cross-Fertilization Between Complexity and Sociology, in (ed) Stoneham P. M., Advances in Sociology Research II, Nova Science Publishers Inc., New York, USA, pp. 103-133.
15 [89] TISA Master Programme: http://www.colcpis.org/tisa (december, 2007) [90] Troitszch, Klaus G. (2007) “Theory and Practice of Multi-Agent Methodology in Microsimulation: Presentation of a New JAVA-BasedTool”, contribution to “Jornada sobre Simulación Fiscal, UAB2007”, GSADI and SSASA-UAB, http://www.uni-koblenz.de/~kgt/Pub/Barcelona.pdf, (22/11/2007
16