Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence

Abstract

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts-Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient.

Robustness and Idealizations in Agent-Based Models of Scientific Interaction

“…To this end, we have paradigmatically focused on Zollman's model as the most prominent ABM of scientific interaction. Examining to which extent our changes in assumptions may affect results of other highly idealized ABMs of scientific interaction (such as those by Grim et al, 2013;Holman and Bruner, 2015, etc. ) remains a task for future research.…”

“…Recent studies into social aspects of scientific inquiry have been increasingly employing agent-based models (ABMs). A number of articles presenting results of such computer-based simulations have suggested that increased information flow among scientists can be epistemically harmful (Zollman, 2007, Grim, 2009, Grim et al, 2013, Kummerfeld and Zollman, 2016. A specific feature of these models is that they are highly idealized, based on simplified representation of scientific inquiry.…”

An Argumentative Agent-Based Model of Scientific Inquiry

“…In this section we will first compare our results with those obtained by other ABMs of science, and then we will turn to a critical analysis of some idealizations present in our model. Our finding that increased communication tends to be epistemically beneficial (or at least, not epistemically harmful) undermines the robustness of conclusions drawn from ABMs in [27,28,8,9], under different modeling choices. As we argue below, there is no reason to assume that any of these ABMs represent scientific interaction more adequately than the presented ABM does.…”

Germs, Genes, and Memes: Function and Fitness Dynamics on Information Networks

“…Alternatives have been proposed, often with attention to parameters of polarization rather than convergence, though these are most easily seen as refinements or variations on the simple form of the central model we use here (Friedkin and Johnsen 1990;Krause 2000;Hegselmann and Krause 2002;Bindel, Kleinberg, and Oren 2011;Acemoglu et al 2012;Grim et al 2012a;Su et al 2014). For simplicity's sake, we concentrate on the flow of beliefs within a communication network, independent of any external input or information, though important work has been done there as well (Zollman 2007(Zollman , 2013Grim 2009;Grim et al 2013).…”

“…How information moves is one of the primary questions of the philosophy of information (Floridi 2003(Floridi , 2011Adriaans and van Benthem 2008), but the flow of information is also important to social epistemologists, political philosophers, and researchers on the evolution of norms (Alexander and Skyrms 1999;Goldman 1999;Bendor and Swistak 2001;Bicchieri 2006;Alexander 2007). Additionally, philosophers of science ask how the social structure of science and the transfer of information affect the knowledge of the community at large (Grim 2009;Weisberg and Muldoon 2009;Zollman 2010Zollman , 2013Alexander 2013;Grim et al 2013).…”