Patterns of Individual Shopping Behavior

Much of economic theory is built on observations of aggregate, rather than individual, behavior. Here, we present novel findings on human shopping patterns at the resolution of a single purchase. Our results suggest that much of our seemingly elective activity is actually driven by simple routines. While the interleaving of shopping events creates randomness at the small scale, on the whole consumer behavior is largely predictable. We also examine income-dependent differences in how people shop, and find that wealthy individuals are more likely to bundle shopping trips. These results validate previous work on mobility from cell phone data, while describing the unpredictability of behavior at higher resolution.Comment: 4 pages, 5 figure

PhD forum: extracting similar patterns of behavior with a network of binary sensors

The aging population is continuously growing and this results in increasing the demands for using technologies to help to manage the rapidly growing sector of the elderly population. To contribute in this effort, we propose a method that can find similar patterns of behavior for extended durations. Our method uses motion sensors as a privacy-aware alternative to cameras. We compute three initial parameters to extract similar patterns of behavior: (1) movement in spot; (2) movement between rooms; and (3) movement within rooms. The three parameters demonstrate good similarity indicators for finding patterns of behavior between each pair of days

Are Muslims the New Catholics? Europe’s Headscarf Laws in Comparative Historical Perspective

In this paper a biologically-inspired model for partly occluded patterns is proposed. The model is based on the hypothesis that in human visual system occluding patterns play a key role in recognition as well as in reconstructing internal representation for a pattern’s occluding parts. The proposed model is realized with a bidirectional hierarchical neural network. In this network top-down cues, generated by direct connections from the lower to higher levels of hierarchy, interact with the bottom-up information, generated from the un-occluded parts, to recognize occluded patterns. Moreover, positional cues of the occluded as well as occluding patterns, that are computed separately but in the same network, modulate the top-down and bottom-up processing to reconstruct the occluded patterns. Simulation results support the presented hypothesis as well as effectiveness of the model in providing a solution to recognition of occluded patterns. The behavior of the model is in accordance to the known human behavior on the occluded patterns

Transfer Learning for Content-Based Recommender Systems using Tree Matching

In this paper we present a new approach to content-based transfer learning for solving the data sparsity problem in cases when the users' preferences in the target domain are either scarce or unavailable, but the necessary information on the preferences exists in another domain. We show that training a system to use such information across domains can produce better performance. Specifically, we represent users' behavior patterns based on topological graph structures. Each behavior pattern represents the behavior of a set of users, when the users' behavior is defined as the items they rated and the items' rating values. In the next step we find a correlation between behavior patterns in the source domain and behavior patterns in the target domain. This mapping is considered a bridge between the two domains. Based on the correlation and content-attributes of the items, we train a machine learning model to predict users' ratings in the target domain. When we compare our approach to the popularity approach and KNN-cross-domain on a real world dataset, the results show that on an average of 83$$ of the cases our approach outperforms both methods

Social Behavior and Personality Patterns of Captive African Elephants

Through the Hamel Center for Undergraduate Research, I received an International Research Opportunities Program (IROP) grant to study the social behaviors of African elephants. My research took place in South Africa with the African Elephant ResearchUnit at Knysna ElephantPark. Elephants live in herds and have very strong social bonds. The social interactions and dominance hierarchy between individuals of a herd depend upon many factors, including maternal lineage, age, sex, and personality traits of the elephants.I studied how social behaviors among captive elephants vary throughout the day on an hourly time scale, if those behavior patterns are related to age, and if handler perceptions of elephant personality are an accurate predictor of those social behaviors.To answer these questions, I spent about six hours in the field, four days a week, observing a herd of seven elephants and recording every time that any of the elephants interacted with each other.I also surveyed the elephant handlers regarding their perceptions of the elephants’ social behaviors and personality.The results of this study aim to give insight into the best management practices for African elephants in captivity, with special consideration for their patterns of social behavior

Global Patterns of Synchronization in Human Communications

Social media are transforming global communication and coordination. The data derived from social media can reveal patterns of human behavior at all levels and scales of society. Using geolocated Twitter data, we have quantified collective behaviors across multiple scales, ranging from the commutes of individuals, to the daily pulse of 50 major urban areas and global patterns of human coordination. Human activity and mobility patterns manifest the synchrony required for contingency of actions between individuals. Urban areas show regular cycles of contraction and expansion that resembles heartbeats linked primarily to social rather than natural cycles. Business hours and circadian rhythms influence daily cycles of work, recreation, and sleep. Different urban areas have characteristic signatures of daily collective activities. The differences are consistent with a new emergent global synchrony that couples behavior in distant regions across the world. A globally synchronized peak that includes exchange of ideas and information across Europe, Africa, Asia and Australasia. We propose a dynamical model to explain the emergence of global synchrony in the context of increasing global communication and reproduce the observed behavior. The collective patterns we observe show how social interactions lead to interdependence of behavior manifest in the synchronization of communication. The creation and maintenance of temporally sensitive social relationships results in the emergence of complexity of the larger scale behavior of the social system.Comment: 20 pages, 12 figures. arXiv admin note: substantial text overlap with arXiv:1602.0621