2018/07/04 by Daniel Frey, Dunja Šešelja · 4 citations
Physics and Astronomy · Economics, Econometrics and Finance · Social Sciences · #Opinion Dynamics and Social Influence #Complex Systems and Time Series Analysis #Evolutionary Game Theory and Cooperation
paper · doi:10.1093/bjps/axy039
The article presents an agent-based model (ABM) of scientific interaction aimed at examining how different degrees of connectedness of scientists impact their efficiency in knowledge acquisition. The model is built on the basis of Zollman’s ([2010]) ABM by changing some of its idealizing assumptions that concern the representation of the central notions underlying the model: epistemic success of the rivalling scientific theories, scientific interaction and the assessment in view of which scientists choose theories to work on. Our results suggest that whether and to what extent the degree of connectedness of a scientific community impacts its efficiency is a highly context-dependent matter since different conditions deem strikingly different results. More generally, we argue that simplicity of ABMs may come at a price: the requirement to run extensive robustness analysis before we can specify the adequate target phenomenon of the model.1 1. Introduction 2. Zollman's 2010 Model 3. Static versus Dynamic Epistemic Success 3.1. Introducing the notion of dynamic epistemic success 3.2. Implementation and results for the basic setup 4. Critical Interaction 4.1. Introducing critique 4.2. Implementation and results 5. Inertia of Inquiry 5.1. Introducing rational inertia 5.2. Implementation and results 6. Threshold Below Which Theories Are Equally Promising 6.1. An inquiry that is even more difficult 6.2. Implementation and results 7. Discussion 8. Conclusion