Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit communities can maintain high levels of linguistic complexity and variability. However, the role of social network structure in the cultural evolution of languages has never been tested experimentally. Here, we present results from (...) a behavioral group communication study, in which we examined the formation of new languages created in the lab by micro‐societies that varied in their network structure. We contrasted three types of social networks: fully connected, small‐world, and scale‐free. We examined the artificial languages created by these different networks with respect to their linguistic structure, communicative success, stability, and convergence. Results did not reveal any effect of network structure for any measure, with all languages becoming similarly more systematic, more accurate, more stable, and more shared over time. At the same time, small‐world networks showed the greatest variation in their convergence, stabilization, and emerging structure patterns, indicating that network structure can influence the community's susceptibility to random linguistic changes (i.e., drift). (shrink)

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We provide a sound and complete algorithm for computing explanatory factors (...) with respect to a given context, and demonstrate its flexibility and competitive performance against state of the art alternatives on various tasks. (shrink)

In our study of U.S. counterterrorism programs, we found that anthropology needs a mode of analysis that considers security as a form distinct from insecurity, in order to capture the very heterogeneity of security objects, logics and forms of action. This article first presents a genealogy for the anthropology of security, and identifies four main approaches: violence and State terror; military, militarization, and militarism; para-state securitization; and what we submit as “security analytics.” Security analytics moves away from studying security formations, (...) and how much violence or insecurity they yield, to identifying security forms of action, whether or not they are part of the nation-state. As a framework for anthropological inquiry, it is oriented toward capturing how these forms of action work and what types of security they produce. We then illustrate this approach through our fieldwork on counterterrorism in the domains of law enforcement, biomedical research and federal-state counter extremism. In each of our cases, we use security analytics to arrive at a diagnosis of the form of action. The set of conceptual distinctions that we propose as an aid to approaching empirical situations and the study of security is, on another level, a proposal for an approach to anthropology today. We do not expect that the distinctions that aid us will suffice for every situation. Rather, we submit that this work presents a set of specific insights about contemporary U.S. security, and an example of a new approach to anthropological problems. (shrink)