The Structural Collapse Approach Reconsidered

En este trabajo argumentaré que la reformulación que roy Cook (forthcoming) hacede la paradoja de yablo en el sistema infinitario D es una genuina paradoja no circular,pero por motivos distintos a los defendidos por ese autor. la primera parte del trabajoconsiste en mostrar que la ausencia de puntos fijos en la construcción es insuficientepara demostrar su no circularidad, a lo sumo prueba su no autorreferencialidad. lasegunda parte consiste en volver a considerar el enfoque del colapso estructuralqueCook rechaza, y argumentar que una correcta comprensión del mismo revela que laparadoja es genuinamente no circular.I will argue that Roy Cook’s (2013) reformulation of Yablo’s Paradox in the infinitary system D is a genuinely non-circular paradox, but for different reasons than the ones he sustained. In fact, the first part of the job will be to show that his argument regarding the absence of fixed points in the construction is insufficient to prove the non-circularity of it; at much it proves its non-self referentiality. The second is to reconsider the structural collapse approach Cook rejects, and argue that a correct understanding of it leads us to the claim that the infinitary paradox is actually non-circular.Fil: Ojea Quintana, Ignacio María. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Instituto de Filosofía "Dr. Alejandro Korn"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Opinions and Preferences as Socially Distributed Attitudes

The dissertation focuses on how to best represent the consensus and attitude dynamic of a group given the attitudes of its individuals. This is done in the Bayesian epistemology framework using pooling with imprecise probabilities, and in utility theory extending Harsanyi's aggregation theorem to characterize other directed attitudes like spite and altruism. The final part of the dissertation considers attitudes within social networks and provides explanations and simulation models for online segregation and tribalism as well as the spread of rumors through contagion. The dissertation hopes to contribute to foundational issues like that of epistemic consensus, but also to new emerging phenomena in social epistemology

Probabilistic Opinion Pooling with Imprecise Probabilities

The question of how the probabilistic opinions of different individuals should be aggregated to form a group opinion is controversial. But one assumption seems to be pretty much common ground: for a group of Bayesians, the representation of group opinion should itself be a unique probability distribution (Madansky 44; Lehrer and Wagner 34; McConway Journal of the American Statistical Association, 76(374), 410--414, 45; Bordley Management Science, 28(10), 1137--1148, 5; Genest et al. The Annals of Statistics, 487--501, 21; Genest and Zidek Statistical Science, 114--135, 23; Mongin Journal of Economic Theory, 66(2), 313--351, 46; Clemen and Winkler Risk Analysis, 19(2), 187--203, 7; Dietrich and List 14; Herzberg Theory and Decision, 1--19, 28). We argue that this assumption is not always in order. We show how to extend the canonical mathematical framework for pooling to cover pooling with imprecise probabilities (IP) by employing set-valued pooling functions and generalizing common pooling axioms accordingly. As a proof of concept, we then show that one IP construction satisfies a number of central pooling axioms that are not jointly satisfied by any of the standard pooling recipes on pain of triviality. Following Levi (Synthese, 62(1), 3--11, 39), we also argue that IP models admit of a much better philosophical motivation as a model of rational consensus

Learning and Pooling, Pooling and Learning

We explore which types of probabilistic updating commute with convex IP pooling (Stewart and Ojea Quintana 2017). Positive results are stated for Bayesian conditionalization (and a mild generalization of it), imaging, and a certain parameterization of Jeffrey conditioning. This last observation is obtained with the help of a slight generalization of a characterization of (precise) externally Bayesian pooling operators due to Wagner (Log J IGPL 18(2):336--345, 2009). These results strengthen the case that pooling should go by imprecise probabilities since no precise pooling method is as versatile

Are generics and negativity about social groups common on social media? A comparative analysis of Twitter (X) data

Many philosophers hold that generics (i.e., unquantified generalizations) are pervasive in communication and that when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to 1.1 million tweets about people, and analyzed the tweets. While it is often suggested that generics are ubiquitous in everyday communication, we found that most tweets (78%) about people contained no generics. However, tweets with generics received more “likes” and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter

Learning and Pooling, Pooling and Learning

We explore which types of probabilistic updating commute with convex IP pooling (Stewart and Ojea Quintana 2017). Positive results are stated for Bayesian conditionalization (and a mild generalization of it), imaging, and a certain parameterization of Jeffrey conditioning. This last observation is obtained with the help of a slight generalization of a characterization of (precise) externally Bayesian pooling operators due to Wagner (Log J IGPL 18(2):336--345, 2009). These results strengthen the case that pooling should go by imprecise probabilities since no precise pooling method is as versatile

The Coordination Dilemma For Epidemiological Modelers

Epidemiological models directly shape policy responses to public health crises. We argue that they also play a less obvious but important role in solving certain coordination problems and social dilemmas that arise during pandemics. This role is both ethically and epistemically valuable. However, it also gives rise to an underappreciated dilemma, as the features that make models good at solving coordination problems are often at odds with the features that make for a good scientific model. We examine and develop this dilemma in the context of the COVID-19 pandemic, and suggest extensions to other domains

Attention and counter-framing in the Black Lives Matter movement on Twitter

The social media platform Twitter platform has played a crucial role in the Black Lives Matter (BLM) movement. The immediate, flexible nature of tweets plays a crucial role both in spreading information about the movement’s aims and in organizing individual protests. Twitter has also played an important role in the right-wing reaction to BLM, providing a means to reframe and recontextualize activists’ claims in a more sinister light. The ability to bring about social change depends on the balance of these two forces, and in particular which side can capture and maintain sustained attention. The present study examines 2 years worth of tweets about BLM (about 118 million in total). Timeseries analysis reveals that activists are better at mobilizing rapid attention, whereas right-wing accounts show a pattern of moderate but more sustained activity driven by reaction to political opponents. Topic modeling reveals differences in how different political groups talk about BLM. Most notably, the murder of George Floyd appears to have solidified a right-wing counter-framing of protests as arising from dangerous “terrorist” actors. The study thus sheds light on the complex network and rhetorical effects that drive the struggle for online attention to the BLM movement

Polarizing language on social media: The distribution and impact of generics on Twitter

The polarization between people within society and social media may be significantly affected by the way individuals talk to and generalize claims about each other. This project focuses on the distribution and impact of a particular kind of linguistic expression on Twitter that may play a key role in this process, namely generics. Generics are generalizing sentences with a noun phrase that refers to a whole category of individuals (‘mosquitos’, ‘people’, ‘liberals’, etc.) without any quantificational determiner (e.g., ‘most’, ‘66%’, etc.), describing the members of a social category as such. Generics may fuel polarization on social media in so far unnoticed ways, as they gloss over exceptions and variability (e.g., one can say ‘mosquitoes carry malaria’ even though only 1% of mosquitos have this feature) and are resistant to counter evidence (e.g., ‘mosquitoes carry malaria’ remains true even 99% of mosquitos don’t have this feature). Crucially, studies suggest that the use of generics increases when people have limited space to express themselves. Since on Twitter, people have only limited space for content, this may cause them to frequently use generics. Space constraints on social media may thus indirectly, via increasing the use of generics, exacerbate social polarization. However, this has not yet been investigated. More generally, it is unclear how frequently generics are used on social media, how they are related to impact on social media, and how they may affect polarization. The goal of this project is to change this. A core part of the project is to develop an AI model for automatically classifying social generics in Twitter data