Current Projects

"What does a neural network model?"
(Philosophy of Science)

Supervised deep neural networks figure prominently in scientific contexts that are heavily mediated by rich background theory. I argue that, in theory mediated contexts, there are currently two main theoretical interpreations (ontological and psychological) of which target systems are modeled by neural networks. I show that, while compelling, neither is empirically nor conceptually tenable. The positive aim of this paper is to propose a third interpreation which charts, as it were, a middle course between the ontological and the psychological.
Eamon Duede (UChicago)

"Abduction Through Social Syllogisms and the Logic of Scientific Advance"
(Philosophy of Science)

Here we argue that the production of novel scientific claims through the process of abduction, the historical province of philosophy, represents a critical scientific practice, central to scientific success and failure, and increasingly available to science studies through large-scale data. We re-scale abduction to show how it necessarily occurs through conversation between those who understand a particular scientific system and its anomalies—its priests—and those exposed to alien and disruptive patterns, theories, and findings that could resolve them—its prophets. We argue that these complex, extended conversations between those with divergent backgrounds define a social logic of discovery that yield speculative hypotheses with outsized impact across science.
Eamon Duede (UChicago) and James Evans (UChicago)

"Distance Matters in Science and Scholarship"
(Science of Science)

The effect of spatial distance on scientific discovery and innovation is a highly important policy issue. Yet, there is still significant debate concerning how space affects science. Ubiquitous digitization has led many to declare a “death of distance” in science. This implies that, while search and discovery are still necessary, what influences us is not, itself, a function of its location. Consequently, citations, a primary policy metric, are assumed to be “placeless” such that, when scientists cite work, they simply acknowledge the contributions that influenced them. Nevertheless, a growing body of research argues that spatial distribution of science still matters, with distance affecting the probability of citation. However, this work relies on indirect measures of influence. Here, we present and deploy a direct measure of influence, derived from a survey of ~12,000 authors. Combining direct reporting of how authors were influenced by their references, authors’ institutional data, and geographic locations from the Google Maps API, we are able to measure the relationship between spatial distance and influence much more directly than previously possible.
Eamon Duede (UChicago), Ashwin Aggarwal (Berkeley), Misha Teplitskiy (Michigan), and Karim Lakhani (Harvard)

"Do Citations Measure Influence?"
(Science of Science)

Citations and metrics derived from them increasingly dictate all aspects of science, from what gets funded to who gets hired. But what do citations counts actually measure? Authors may cite for a variety of reasons, including purely rhetorical ones, but how these motives affect aggregate citation counts is poorly understood. Here, we use a large-scale survey of authors of scientific papers, asking them about specific citation decisions. Using responses from nearly 10K authors from all areas of science, we find that most citations are rhetorical in nature, and many are only skimmed. Authors "invest" reading effort primarily into famous papers, and end up being influenced primarily by them, whereas obscure papers are cited more rhetorically. An experiment embedded in the survey shows that low citation counts cause scientists to perceive those papers as being of lower quality. Taken together, the results suggest a model of reading and citing in which famous papers matter even more than their already high citation counts indicate.
Misha Teplitskiy (Michigan), Eamon Duede (UChicago), Michael Menietti (Harvard), and Karim Lakhani (Harvard)

When Technology Transforms Society: Considering the Societal and Ethical Impacts of Quantum Computing and AI (Upcoming Workshop).

Quantum computing and artificial intelligence are currently making significant technical progress, with commensurate interest from the public, media outlets, funding agencies, and corporate partners. Stakeholders frequently point to the potential of these technologies to “transform society,” but what does this mean, practically? Should we, as researchers, anticipate the social, political, and ethical consequences of our work and steer our research programs accordingly? Can we draw from scholarship in the social sciences and the humanities to inform understanding of the distributional impacts of our programs? This workshop will explore these questions and develop collaborations across disciplines, institutions, and key stakeholders who may be able to help responsibly steer the evolution of these revolutionary technologies in ethical and socially beneficial ways.

Co-Organized by Daniel Bowring (Fermilab), Chihway Chang (UChicago), Eamon Duede (UChicago), and Brian Nord (Fermilab). Funding provided by the Center for Data and Computing at the University of Chicago and the Kavli Foundation