104 research outputs found
Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science
The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.Agent-Based Models, Empirical Calibration and Validation, Taxanomy of Models
The Impact of Agent-Based Models in the Social Sciences after 15 Years of Incursions
This paper provides an overview on the impact of agent-based models in the social
sciences. It focuses on the reasons why agent-based models are seen as important
innovations in the recent decades. It is aimed to evaluate the impact of this innovation on
various disciplines, such as economics, sociology, anthropology, and behavioural sciences.
It discusses the advances it contributed to achieve and illustrates some comparatively new
fields to which it gave rise. Finally, it emphasizes some research issues that need to be
addressed in the future
Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science
The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies
The Agent-Based Modeling Approach through Some Foundational Monographs
L’article analyse quelques monographies fondamentales qui mettent en évidence la pertinence de la simulation multi-agents pour l’analyse sociologique. Ces ouvrages ont été sélectionnés au sein de travaux qui portent sur la coopération, les dynamiques sociales et les normes. Ils montrent l’importance de modéliser les comportements complexes des acteurs et leurs interactions pour comprendre les régularités sociales ainsi que les raisons pour lesquelles la modélisation et l’abstraction sont importantes pour l’analyse sociologique. La modélisation multi-agents peut nous aider à produire des théories des phénomènes sociaux plus cohérentes et vérifiables et nous permet de mieux organiser les théories avant de les tester et en vue de les répliquer. Enfin, dans l’esprit d’une approche collaborative, cet article argumente en faveur du besoin de liens plus étroits entre les approches expérimentales et la sociologie
Micro Behavioural Attitudes and Macro Technological Adaptation in Industrial Districts. An Agent-Based Prototype
Industrial Districts (IDs) are complex productive systems based on an evolutionary network of heterogeneous, functionally integrated and complementary firms, which are within the same market and geographical space. Setting up a prototype, able to reproduce an idealised ID, we model cognitive processes underlying the behaviour of ID firms. ID firms are bounded rationality agents, able to process information coming from technology and market environment and from their relational contexts. They are able to evaluate such information and to transform it into courses of action, routinising their choices, monitoring the environment, categorising, typifying and comparing information. But they have bounded cognitive resources: attention, time and memory. We test two different settings: the first one shows ID firms behaving according to a self-centred attitude, while the second one shows ID firms behaving according to a social centred attitude. We study how such a strong difference at micro-level can affect at macro-level the technological adaptation of IDs
Does Empirical Embeddedness Matter? : Methodological Issues on Agent-Based Models for Analytical Social Science
The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies
Is three better than one? simulating the effect of reviewer selection and behavior on the quality and efficiency of peer review
This paper looks at the effect of multiple reviewers and their behavior on the quality and efficiency of peer review. By extending a previous model, we tested various reviewer behavior, fair, random and strategic, and examined the impact of selecting multiple reviewers for the same author submission. We found that, when reviewer reliability is random or reviewers behave strategically, involving more than one reviewer per submission reduces evaluation bias. However, if scientists review scrupulously, multiple reviewers require an abnormal resource drain at the system level from research activities towards reviewing. This implies that reviewer selection mechanisms that protect the quality of the process against reviewer misbehavior might be economically unsustainable
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