The structure and logic of interdisciplinary research in agent-based social simulation

WOS:000222772400002 (Nº de Acesso Web of Science)This article reports an exploratory survey of the structure of interdisciplinary research in Agent-Based Social Simulation. One hundred and ninety six researchers participated in the survey completing an on-line questionnaire. The questionnaire had three distinct sections, a classification of research domains, a classification of models, and an inquiry into software requirements for designing simulation platforms. The survey results allowed us to disambiguate the variety of scientific goals and modus operandi of researchers with a reasonable level of detail, and to identify a classification of agent-based models used in simulation. In particular, in the interdisciplinary context of social-scientific modelling, agent-based computational modelling and computer engineering, we analyse the extent to which these paradigmatic models seem to be mutually instrumental in the field. We expect that our proposal may improve the viability of submitting, explaining and comparing agent-based simulations in articles, which is an important methodological requirement to consolidate the field. We also expect that it will motivate other proposals that could further validate, extend or change ours, in order to refine the classification with more types of models

The Logic of the Method of Agent-Based Simulation in the Social Sciences: Empirical and Intentional Adequacy of Computer Programs

The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. We demonstrate that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences.Computer and Social Sciences, Agent-Based Simulation, Intentional Computation, Program Verification, Intentional Verification, Scientific Knowledge

What can we do with the Research Institute for Social Complexity Sciences in Indonesia?

The article discussed about the research opportunities in social complexity studies, especially in Indonesia. This issue is connected to the establishment a social research institute in Indonesia, how to establish and maintain it regarding the interdisciplinary research field. However a lot of localities are taken into the consideration to maintain the social complexity research institute, there would always things that can be learnt by any other similar research institute

Agent-based Computational Economics: a Methodological Appraisal

This paper is an overview of "Agent-based Computational Economics (ACE)", an emerging approach to the study of decentralized market economies, in methodological perspective. It summarizes similarities and differences with respect to conventional economic models, outlines the unique methodological characteristics of this approach, and discusses its implications for economic methodology as a whole. While ACE rejoins the reflection on the unintended social consequences of purposeful individual action which is constitutive of economics as a discipline, the paper shows that it complements state-of the-art research in experimental and behavioral economics. In particular, the methods and techniques of ACE have reinforced the laboratory finding that fundamental economic results rely less on rational choice theory than is usually assumed, and have provided insight into the importance of market structures and rules in addition to individual choice. In addition, ACE has enlarged the range of inter-individual interactions that are of interest for economists. In this perspective, ACE provides the economist‘s toolbox with valuable supplements to existing economic techniques rather than proposing a radical alternative. Despite some open methodological questions, it has potential for better integration into economics in the future.Agent-based Computational Economics, Economic Methodology, Experimental Economics.

What can we do with the Research Institute for Social Complexity Sciences in Indonesia?

The article discussed about the research opportunities in social complexity studies, especially in Indonesia. This issue is connected to the establishment a social research institute in Indonesia, how to establish and maintain it regarding the interdisciplinary research field. However a lot of localities are taken into the consideration to maintain the social complexity research institute, there would always things that can be learnt by any other similar research institute.research institute; social complexity studies; interdiscplinary research

Modelling Theory Communities in Science

This position paper presents a framework for modelling theory communities where theories interact as agents in a conceptual network. It starts with introducing the difficulties in integrating scientific theories by discussing some recent approaches, especially of structuralist theory of science. Theories might differ in reference, extension, scope, objectives, functions, architecture, language etc. To address these potential integration barriers, the paper employs a broad definition of "scientific theory", where a theory is a more or less complex description a describer puts forward in a context called science with the aim of making sense of the world. This definition opens up the agency dimension of theories: theories "do" something. They work on a - however ontologically interpreted - subject matter. They describe something, and most of them claim that their descriptions of this "something" are superior to those of others. For modelling purposes, the paper makes use of such description behaviour of scientific theories on two levels. The first is the level where theories describe the world in their terms. The second is a sub-case of the first: theories can of course describe the description behaviour of other theories concerning this world and compare with own description behaviour. From here, interaction and potential cooperation between theories could be potentially identified by each theory perspective individually. Generating inclusive theory communities and simulating their dynamics using an agent-based model means to implement theories as agents; to create an environment where the agents work as autonomous entities in a self-constituted universe of discourse; to observe what they do with this environment (they will try to apply their concepts, and instantiate their mechanisms of sense-making); and to let them mutually describe and analyse their behaviour and suggest areas for interaction. Some mechanisms for compatibility testing are discussed and the prototype of the model with preliminary applications is introduced.Simulating Science, Theory Interaction, Agent-Based Modelling, Theory Network

From Models to Simulations

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering