Rafael Ventura

As the content of propositional attitudes, propositions are usually taken to help explain the behavior of rational agents. However, a closer look at signaling games suggests otherwise: rational agents often acquire partial beliefs, and many of their signals are ambiguous. Signaling games also suggest that it is rational for agents to mix their behavior in response to partial beliefs and ambiguous signals. But as I show in this paper, propositions cannot help explain the mixing behavior of rational agents: to explain mixing behavior, we need a probabilistic notion of content. I also show that a probabilistic notion of content renders propositions explanatorily idle in the case of unambiguous signals and full beliefs as well. My suggestion is thus that we should abandon propositions in explanations of rational behavior and adopt instead a probabilistic notion of content. The notion of probabilistic content ultimately provides a simpler framework for explanations of rational behavior than the notion of propositional content.

Recent attention to complex group-level behavior amongst bacteria has led some to conceive of multicellular clusters of bacteria as individuals. In this article, I assess these recent claims by first drawing a distinction between two concepts of individuality: physiological and evolutionary. I then survey cases that are representative of three different modes of growth: myxobacteria (surface-attached agglomerative growth), Bacillus subtilis (agglomerative growth not attached to a surface), and cyanobacteria (filamentous growth). A closer look at these cases indicates that multicellular individuality among bacteria is remarkably complex. Physiologically, the three cases of multicellular clusters do not form physiological individuals. But matters are different when it comes to evolutionary individuality; although multicellular clusters that grow by agglomeration are not highly individuated, filament-forming groups achieve a relatively high degree of individuality. I also suggest that debates about bacterial multicellular individuality may have been obscured by a failure to see that selection on highly individuated groups is by no means the only mechanism to bring about the complex group-level behaviors that have led some to view bacteria as multicellular individuals.

This paper proposes a sender-receiver model to explain two large-scale patterns observed in natural languages: Zipf’s inverse power law relating the frequency of word use and word rank, and the negative correlation between the frequency of word use and rate of lexical change. Computer simulations show that the model recreates Zipf’s inverse power law and the negative correlation between signal frequency and rate of change, provided that agents balance the rates with which they invent new signals and forget old ones. Results are robust across a wide range of parameter values and structural assumptions, such as different forgetting rules and forgetting rates. Analysis of the model further suggests that Zipf’s law relating word frequency and rank arises because of language-external factors and that frequent signals change less because frequent signals are less subject to drift than rare ones. The paper concludes with some brief considerations on model-based and data-driven approaches in philosophy.

In this paper, I survey and defend the use of quantitative methods in philosophy of language. Quantitative methods in philosophy of language include a wide variety of methods, ranging from model‐based techniques (computer simulations and mathematical models) to data‐driven approaches (experimental philosophy and corpus‐based studies). After offering a few case studies of these methodologies in action, I single out some debates in philosophy of language that are especially well served by their use. These are cases in which quantitative methods increase precision, improve the accuracy and reliability of results, and allow philosophers of language to borrow from other fields the best epistemic practices available. I conclude with some far‐reaching considerations about the role of qualitative and quantitative methods in debates about philosophical methodology.

In a series of papers, Forber and Smead :151–166, 2014, Biol Philos 30:405–421, 2015) and Smead and Forber :698–707, 2013) make a valuable contribution to the study of cooperation in finite populations by analyzing an understudied model: the prisoner’s delight. It always pays to cooperate in the one-shot prisoner’s delight, so this model presents a best-case scenario for the evolution of cooperation. Yet, what Forber and Smead find is highly counterintuitive. In finite populations playing the prisoner’s delight, increasing the benefit of cooperation causes selection to favor defection. Here, I extend their model by considering the effects of non-linear payoffs. In particular, I show that interesting subtleties arise when payoffs are synergistic. Indeed, analysis reveals that increasing the benefit of cooperation does not always favor the spread of defection if payoffs are synergistic. I conclude by drawing some general considerations about robustness analysis in evolutionary models.

Cultural phylogenetics has made remarkable progress by relying on methods originally developed in biology. But biological and cultural evolution do not always proceed according to the same principles. So what, if anything, could justify the use of phylogenetic methods to reconstruct the evolutionary history of culture? In this paper, we describe models used to assess the reliability of inference methods and show how these models play an underappreciated role in addressing that question. The notion of reliability is of course central to these models. As we explain, a common way of understanding reliability is in terms of low error rates. A careful look at case studies in cultural phylogenetics suggests that reliability models partly corroborate this understanding of reliability but also raises points of tension. We conclude by hinting at a few ways forward.

Recent models of scientific collaboration show that minorities can end up at a disadvantage in bargaining scenarios. However, these models presuppose the existence of social categories. Here, we present a model of scientific collaboration in which inequality arises in the absence of social categories. We assume that all agents are identical except for the position that they occupy in the collaboration network. We show that inequality arises in the absence of social categories. We also show that this is due to the structure of the collaboration network and that similar patterns arise in two real-world collaboration networks.

This paper proposes a sender-receiver model to explain two large-scale patterns observed in natural languages: Zipf’s inverse power law relating the frequency of word use and word rank, and the negative correlation between the frequency of word use and rate of lexical change. Computer simulations show that the model recreates Zipf’s inverse power law and the negative correlation between signal frequency and rate of change, provided that agents balance the rates with which they invent new signals and forget old ones. Results are robust across a wide range of parameter values and structural assumptions, such as different forgetting rules and forgetting rates. Analysis of the model further suggests that Zipf’s law relating word frequency and rank arises because of language-external factors and that frequent signals change less because frequent signals are less subject to drift than rare ones. The paper concludes with some brief considerations on model-based and data-driven approaches in philosophy.

Over half a century ago, George Zipf observed that more frequent words tend to be older. Corpus studies since then have confirmed this pattern, with more frequent words being replaced and regularized less often than less frequent words. Two main hypotheses have been proposed to explain this: that frequent words change less because selection against innovation is stronger at higher frequencies, or that they change less because stochastic drift is stronger at lower frequencies. Here, we report the first experimental test of these hypotheses. Participants were tasked with learning a miniature language consisting of two nouns and two plural markers. Nouns occurred at different frequencies and were subjected to treatments that varied drift and selection. Using a model that accounts for participant heterogeneity, we measured the rate of noun regularization, the strength of selection, and the strength of drift in participant responses. Results suggest that drift alone is sufficient to generate the elevated rate of regularization we observed in low‐frequency nouns, adding to a growing body of evidence that drift may be a major driver of language change.