Social networks where the actors occupy geospatial locations are prevalent in military, intelligence, and policing operations such as counter-terrorism, counter-insurgency, and combating organized crime. These networks are often derived from a variety of intelligence sources. The discovery of communities that are geographically disperse stems from the requirement to identify higher-level organizational structures, such as a logistics group that provides support to various geographically disperse terrorist cells. We apply a variant of Newman-Girvan modularity to this problem known as distance modularity. To address the problem of finding geographically disperse communities, we modify the well-known Louvain algorithm to find partitions of networks that provide near-optimal solutions to this quantity. We apply this algorithm to numerous samples from two real-world social networks and a terrorism network data set whose nodes have associated geospatial locations. Our experiments show this to be an effective approach and highlight various practical considerations when applying the algorithm to distance modularity maximization. Several military, intelligence, and law-enforcement organizations are working with us to further test and field software for this emerging application.

Researchers have invested much effort in constructing models of the state-dependent (sometimes risk-averse and sometimes risk-prone) nature of human decision making. An important open question is how state-dependent risk behavior can arise and remain prominent in populations. We believe that one part of the answer is the interplay between risk-taking and sequentiality of choices in populations subject to evolutionary population dynamics. To support this hypothesis, we provide simulation and analytical results for evolutionary lottery games, including results on evolutionary stability. We consider a parameterized class of imitation dynamics in which the parameter 0 ≤ α ≤ 1 yields the replicator dynamic with α = 1 and the imitate-the-better dynamic with α = 0. Our results demonstrate that for every population dynamic in this class except for the replicator dynamic, the interplay between risk-taking and sequentiality of choices allows state-dependent risk behavior to have an evolutionary advantage over expected-value maximization.

We present a music information retrieval approach based on power laws. Research in cognitive science and neuroscience reveals connections between power laws, human cognition, and human physiology. Empirical studies also demonstrate connections between power laws and human aesthetics. We utilize 250+ power-law metrics to extract statistical proportions of music-theoretic and other attributes of music pieces. We discuss an experiment where artificial neural networks classify 2,000 music pieces, based on aesthetic preferences of human listeners, with 90. 70% accuracy. Also, we present audio results from a music information retrieval experiment, in which a music search engine prototype retrieves music based on "aesthetic" similarity from a corpus of 15,200+ pieces. These results suggest that power-law metrics are a promising model of music aesthetics, as they may be capturing statistical properties of the human hearing apparatus.