title: 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.
authors.affiliation: Distinguished Teaching Professor in Philosophy at Stony Brook and Visiting Professor with the Center for Study of Complex Systems at the University of Michigan. Grim's current work centers on computational modeling and philosophical logic.
authors.forename: Patrick
authors.initials: P
authors.lastname: Grim
authors.affiliation: Assistant Professor of Philosophy at the University of Pennsylvania. He primarily researches the nature of epistemic normativity and uses computational models to explore the social structure of inquiry.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Singer
authors.affiliation: graduate of the University of Michigan in Complex Systems. He is currently working with early stage startups to optimize technology strategies.
authors.forename: Steven
authors.initials: S
authors.lastname: Fisher
authors.affiliation: visiting scientist in the economics department of Gent University and at the Riken Brain Science Institute. His research focuses on complex systems methodology: developing new tools for building and analyzing agent-based, network, and mathematical models.
authors.forename: Aaron
authors.initials: A
authors.lastname: Bramson
authors.affiliation: doctoral candidate in the department of political science and NSF IGERT Fellow at the University of Michigan where he studies the institutional aspects of affective trust.
authors.forename: William J
authors.initials: WJ
authors.lastname: Berger
authors.affiliation: consultant at ThoughtWorks Inc. and a graduate of the Gerald R. Ford School of Public Policy at the University of Michigan.
authors.forename: Christopher
authors.initials: C
authors.lastname: Reade
authors.affiliation: studies Complex Systems at the University of Michigan. As a research assistant at the Santa Fe Institute, she explores the evolution of social network topology using computational methodology.
authors.forename: Carissa
authors.initials: C
authors.lastname: Flocken
authors.affiliation: postdoctoral researcher at Carnegie Mellon University. He studies statistical methods for evaluating educational programs using observational data and likes hanging around philosophers.
authors.forename: Adam
authors.initials: A
authors.lastname: Sales