An argumentative agent-based model of scientific inquiry
This work addresses issues in scientific methodology and policy for researchers and policymakers, but it is incremental as it builds on existing agent-based models with a novel argumentation-based approach.
The paper tackles the problem of understanding how social networks affect scientists' knowledge acquisition efficiency by developing an agent-based model that incorporates argumentative dynamics, aiming to avoid idealizations found in existing models.
In this paper we present an agent-based model (ABM) of scientific inquiry aimed at investigating how different social networks impact the efficiency of scientists in acquiring knowledge. As such, the ABM is a computational tool for tackling issues in the domain of scientific methodology and science policy. In contrast to existing ABMs of science, our model aims to represent the argumentative dynamics that underlies scientific practice. To this end we employ abstract argumentation theory as the core design feature of the model. This helps to avoid a number of problematic idealizations which are present in other ABMs of science and which impede their relevance for actual scientific practice.