Learning and Pooling, Pooling and Learning (with Rush Stewart) [Preprint] [Erkenntnis, 2018]
Radical Pooling and Imprecise Probabilities [Preprint] [Erkenntnis, 2022]
Social Media and Data Science
Polarization and trust in the evolution of vaccine discourse on Twitter during COVID-19 (first author) [Preprint] [TBU, 2022]. Automated Clustering of COVID'19 anti-vaccine discourse on Twitter (first author) [Arxiv, 2022].
The affiliative use of emoji and hashtags in the Black Lives Matter movement: A Twitter case study (co-authored) [Preprint] [2021].
The structural collapse approach reconsidered [Preprint] Análisis Filosófico, 2012] (a response to Roy T. Cook)
Works in Progress Altruistic and Spiteful Utilities This essay bridges Harsanyi's Aggregation Theorem (1955, 1977) with Adam Smith moral sentiments, and makes use of the formalism to characterize altruism, spite and self-interest in line with contemporary work by Kitcher (2010). This is a departure from the traditional understanding of Harsanyi's results as defining a utilitarian social welfare function. Instead, they here provide a formalization of Smith's tripartite distinction of other-directed attitudes. Furthermore, I will emphasize the importance of the recognition of unsocial passions like spite, and how this aspect of Smith's account makes Das Adam Smith Problem even harder to solve. Segregation on Networks I define a network dynamics analogous to Thomas Schelling's model for segregation and show that even minor homophily can leads to polarization. Yet, the interaction between homopily and heterophily shows encouraging results, since heterophily has an integration effect in unequal populations, even in the presence of homophily.
(Mis)Information Contagion on Networks (In Progress) David Hume's first develop a contagion account of opinions and moral passions. This can be formalized by making use of epidemiological diffusion models on networks. The question guiding the investigation is how does the social structure, represented as a network, affects the spread of opinions. For example, more hierarchical structures like trees and stars make it harder for emotions to spread than more "democratic" ones like random networks. More precisely, using epidemiological models of contagion I show the effect that networks variables like centrality, clustering, homophily, and others have on the contagion of moral sentiments.