Denis Phan

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological tools so as to show to what precise extent each author is right when he focuses on some empirical, instrumental or conceptual significance of his model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity, section 2 gives conceptual tools to explain the rationale of the diverse epistemological positions presented in section 1. Finally, we claim that a careful attention to the real multiplicity of denotational powers of symbols at stake and then to the implicit routes of references operated by models and computer simulations is necessary to determine, in each case, the proper epistemic status and credibility of a given model and/or simulation.

The debates on the scientificity of social sciences in general, and sociology in particular, are recurring. From the original methodenstreitat the end the 19th Century to the contemporary controversy on the legitimacy of “regional epistemologies”, a same set of interrogations reappears. Are social sciences really scientific? And if so, are they sciences like other sciences? How should we conceive “research programs” Lakatos (1978) or “research traditions” for Laudan (1977) able to produce advancement of knowledge in the field of social and human phenomena? Is the progress of knowledge in social sciences similar to the one generally observed in natural sciences? Is it possible to evaluate the relative merits of each one of these research programs? These debates are important vectors of social and intellectual polarization. The historical divide between the positivist and the hermeneutics poles precedes the structure of the contemporary debate around the epistemic space of social sciences, It is not only a question of renewing the opposition between a monist view of sciences (e.g. McIntyre, 1996) and a dualistic one (e.g. Geertz, 1973) or even a trialist view of sciences (e.g. Lepenies, 1985). It is also a question of asserting dichotomies transformed into framework (including when it is a question of exceeding them): nature-culture, nomothetic-idiographic, models-narrative, structure-history, cause-reason, explanation-comprehension. In this short introduction, we provide, in section 2, a first overview of this epistemological debate in social science. Section 3 proposes a different standpoint on the same questions, by introducing both ontological and methodological aspects in this basic epistemological debate. Namely, following (Hollis, 1994) oppositions of the explanation - understanding, causes - meaning, etc., types discussed in the firsts ection are comparatively examined together with oppositions of the structure - action, holism - individualism types. This allows us to discus show multi-agent design, by integrating various dimensions and standpoints in the same framework (Phan, Amblard, 2007, Chapters 1, 5, 14) can help us to shift these boundaries, and to bypass these oppositions. As model building and ontology design are at the core of this process (Phan, Amblard, 2007, Chapter 12), Section4 discusses various issues of the art of modelling, starting both from economists’ and sociologists’ current standpoints.

In the Western tradition, at least since the 14th century, the philosophy of knowledge has been built around the idea of knowledge as a representation [BOU 99]. The question of the evaluation of knowledge refers at the same time (1) to the object represented (which one does one represent?), (2) to the process of knowledge formation, in particular with the role of the knowing subject (which one does one represent and how does one represent it?), and finally (3) to the relationship between the representation and the represented object. Criteria of evaluation such as “validity”, “adequacy” or “truth”, as mentioned in [AMB 07], make sense only with respect to these three dimensions. An evaluation can thus (1) depend on the ontological nature of the object of knowledge, (2) relate to the relationship between subject and object—including the structures (cognitive, social) which organize this relationship, or (3) relate to the relation of similarity between the object and its representation as well. The relevant criteria of evaluation thus depend on the points of view adopted on these questions. As there are indeed a plurality of points of view in this field, the goal of this appendix is to summarize, as briefly as possible, the various positions adopted by the philosophers and to refer to the relevant texts of reference for more information (for a first outline [CHA 76, SCH 01]). The first section 1 introduces useful discussions about the philosophy of theoretical knowledge and general epistemology, from a quasi-historical perspective. Section two 2 discusses the intermediary but central notion of models. Section three 3, more exploratory, introduces an approach to simulation as “concrete experiment”. It suggests that such a frequent claim in the literature, when precisely evaluated, can, to some extent, renew both the representational and the linguistic views on simulation.

In the Western tradition, at least since the 14th century, the philosophy of knowledge has been built around the idea of knowledge as a representation [Boulnois 1999]. The question of the evaluation of knowledge refers at the same time (1) to the object represented (which one does one represent?), (2) to the process of knowledge formation, in particular with the role of the knowing subject (which one does one represent and how does one represent it?), and finally (3) to the relationship between the representation and the represented object. Criteria of evaluation such as “validity”, “adequacy” or “truth”, as mentioned in chapter 4, make sense only with respect to these three dimensions. An evaluation can thus (1) depend on the ontological nature of the object of knowledge, (2) relate to the relationship between subject and object—including the structures (cognitive, social) which organize this relationship, or (3) relate to the relation of similarity between the object and its representation as well. The relevant criteria of evaluation thus depend on the points of view adopted on these questions. As there are indeed a plurality of points of view in this field, the goal of this appendix is to summarize, as briefly as possible, the various positions adopted by the philosophers and to refer to the relevant texts of reference for more information. The first section introduces useful discussions about the philosophy of theoretical knowledge and general epistemology, from a quasi-historical perspective. Section two discusses the intermediary but central notion of models. Section three, more exploratory, intro-duces an approach to simulation as “concrete experiment”. It suggests that such a frequent claim in the literature, when precisely evaluated, can, to some extent, renew both the representational and the linguistic views on simulation.

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through a simulation, section 2 gives the possibility to understand more precisely - and then to justify - the diversity of the epistemological positions presented in section 1. Our final claim is that careful attention to the multiplicity of the denotational powers of symbols at stake in complex models and computer simulations is necessary to determine, in each case, their proper epistemic status and credibility.

The explanatory power of structures in analytical sociologists’ agent-based models brings into question methodological individualism. We defend that from an explanatory point of view, the syntactic properties of models require semantic conditions of interpretation drawn from a conceptual research framework; in such a framework, social/relational structures have only partial, explanatory power ; and taking the explanation further through generative mechanism modeling necessitates calling upon methodological individualism’s generic framework of interpretation that relies on social actors’ rational capacity. According to this interpretive framework, forces in action in society are governed by the subjective meaning of/the reasons for individual actions.

Les modèles basés sur des agents en interactions, constituent des systèmes sociaux complexes, qui peuvent être simulés par informatiques. Ils se répandent dans les sciences économiques et sociales - comme dans la plupart des sciences des systèmes complexes. Des énigmes épistémologiques (ré)apparaissent. On a souvent opposé modèles et investigations empiriques : d’un côté, on considère les sciences empiriques fondées sur une observation méthodique (enquêtes, expériences) tandis que de l’autre, on conçoit les approches théoriques et la modélisation comme s’appuyant sur une approche conceptuelle ou hypothético-déductive. la simulation a de son coté souvent été comparée à une forme d’expérience ou à une méthode intermédiaire entre la théorie et l’expérience. Tesfatsion a parlé de « laboratoire computationnel » comme d’une manière d’étudier « les comportements des systèmes complexes au moyen d’expériences sous contrôle qu’on peut répéter (réplicable)» et Axelrod a proclamé que la simulation serait « une troisième manière de pratiquer la science dans les sciences sociales », entre l’induction et la déduction. Nous introduisons de nouveaux concepts épistémologiques afin de montrer dans quelle mesure les auteurs ont raison lorsqu'ils se concentrent sur une signification empirique, instrumentale ou conceptuelle de leur modèle ou simulation. La première partie du chapitre discute plus particulièrement de l'épistémologie des modèles économiques à partir des travaux d’Hausman, Guala, Mäki, Sugden, Schelling etc. pour expliquer les divergences entre modèles et simulations. En distinguant entre modèles et simulations, comme entre entre types de modèles, types de simulations informatiques et types d'empiricité obtenue par une simulation, la seconde partie donne la possibilité de comprendre plus précisément - et ensuite de justifier - la diversité des positions épistémologiques présentées dans la première partie. En utilisant les notion de sous-symbolisation (due à Smolensky) et, de hiérarchie dénotationnelle (due à Goodman), une attention particulière est accordée à la multiplicité des pouvoirs dénotationnels des symboles en jeu dans les modèles complexes et les simulations informatiques. Selon cette grille d'analyse, on peut déterminer, dans chaque cas particulier son statut épistémique et la crédibilité qui peut y être associée. Ce chapitre est une traduction (par Gilles Campagnolo) réviséee et augmentée par les auteurs de : Agent-Based Models and Simulations in Economics and Social Sciences: From Conceptual Exploration to Distinct Ways of Experimenting, Journal of Artificial Societies and Social Simulation 13 (1) 5 DOI: 10.18564/jasss.1532

This book brings together contributions from leading researchers in the field of agent-based modelling and simulation. This approach has grown out of some recent and innovative ideas in the social sciences, computer sciences, life sciences, physics and game theory. It is proving helpful in understanding complexity in many domains. The opportunities it offers to explore the experimental approach to social and human behaviour is proving of theoretical and empirical value across a wide range of fields. With contributions from researchers whose work has served to define this new field such as Nigel Gilbert, Robert Axtell (in economics and social science) and Jacques Ferber (in multi-agent systems), as well as practitioners who are working at the cutting edge of the new domain, this collection of essays has been assembled by two of its leading exponents: Frédéric Amblard and Denis Phan. The research, case studies and theoretical approaches discussed in this book are designed to introduce beginners and experts alike to the current state of play in this new and exciting field of social science.

This volume brings together contributions from leading researchers in the field of agent-based modelling and simulation. This approach has grown out of some recent and innovative ideas in the social sciences, computer sciences, life sciences, physics and game theory. It is proving helpful in understanding complexity in many domains. The opportunities it offers to explore the experimental approach to social and human behaviour is proving of theoretical and empirical value across a wide range of fields. With contributions from researchers whose work has served to define this new field such as Nigel Gilbert, Robert Axtell (in economics and social science) and Jacques Ferber (in multi-agent systems), as well as practitioners who are working at the cutting edge of the new domain, this collection of essays has been assembled by two of its leading exponents: Frédéric Amblard and Denis Phan. The research, case studies and theoretical approaches discussed in this book are designed to introduce beginners and experts alike to the current state of play in this new and exciting field of social science.

En philosophie, l'ontologie étudie ce qui pourrait exister : le type et la structure des objets, les propriétés, évènements, processus et relations. En ingénierie des connaissances, c'est la spécification de la conceptualisation d'un domaine de savoir. Ce domaine concerne ici la modélisation à base d'agents (ABM) pour les sciences de l'homme et de la société (SHS) en vue de la simulation par systèmes multi-agents (SMA). La modélisation SMA en SHS propose la formalisation d'une pluralité de points de vue dans un cadre général qui permet de comparer et de combiner différents angles de réflexion. Agrémenté d'exemples issus de trois domaines (la géographie, l'économie et la sociologie), cet ouvrage utilise des formalismes principalement basés sur UML. L'exercice de description d'une ontologie est présenté comme un dialogue conceptuel interdisciplinaire et l'ontologie comme le médiateur de ce dialogue.

Agent-Based Models are useful to describe and understand social, economic and spatial systems' dynamics. But, beside the facilities which this methodology offers, evaluation and comparison of simulation models are sometimes problematic. A rigorous conceptual frame needs to be developed. This is in order to ensure the coherence in the chain linking at the one extreme the scientist's hypotheses about the modeled phenomenon and at the other the structure of rules in the computer program. This also systematizes the model design from the thematician conceptual framework as well. The aim is to reflect upon the role that a well defined ontology, based on the crossing of the philosophical and the computer science insights, can play to solve such questions and help the model building. We analyze different conceptions of ontology, introduce the 'ontological test' and show its usefulness to compare models. Then we focus on the model building and show the place of a systematic ABM ontology. The latter process is situated within a larger framework called the 'knowledge framework' in which not only the ontologies but also the notions of theory, model and empirical data take place. At last the relation between emergence and ontology is discussed.

The aim of this paper is to stress some ontological and methodological issues for Agent-Based Model (ABM) building, exploration, and evaluation in the Social and Human Sciences. Two particular domain of interest are to compare ABM and simulations (Model To Model) within a given academic field or across different disciplines and to use ontology for to discuss about the epistemic and methodological consequences of modeling choices. The paper starts with some definitions of ontology in philosophy and computer sciences. The implicit and different ontology which underlies the approach of a same object of interest are discussed in the case of spatial economists and geographers. Finally, using the case of Shelling’s model, we discuss the concept of “ontological test,” and raise the question of the ontological compatibility between the “model world” and the “real world.”