Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda

A large literature describes how local risk sharing networks can help individuals smooth consumption in the face of idiosyncratic economic shocks. However, when an entire community faces a large covariate shock, and when the transaction costs of transfers are high, these risk sharing networks are likely to be less effective. In this paper, we document how a new technology – mobile phones – reduces transaction costs and enables Rwandans to share risk quickly over long distances. We examine a comprehensive database of person-to-person transfers of mobile airtime and find that individuals send this rudimentary form of “mobile money” to friends and family affected by natural disasters. Using the Lake Kivu earthquake of 2008 to identify the effect of a large covariate shock on interpersonal transfers, we estimate that a current-day earthquake would result in the transfer of between $22,000 and$30,000 to individuals living near the epicenter. We further show that the pattern of transfers is most consistent with a model of reciprocal risk sharing, where transfers are determined by past reciprocity and geographical proximity, rather than one of pure charity or altruism, in which transfers would be expected to be increasing in the wealth of the sender and decreasing in the wealth of the recipient.Risk Sharing; Mobile Phones; Mobile Money; Information and communications technologies; Development; Earthquakes; Rwanda; Africa.

Eigenbehaviors: Identifying Structure in Routine

Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research

Machine perception and learning of complex social systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 125-136).The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months has generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify self- report survey data, and study social network dynamics. By combining theoretical models with rich and systematic measurements, we show it is possible to gain insight into the underlying behavior of complex social systems.by Nathan Norfleet Eagle.Ph.D

Behavior based adaptive call predictor

Predicting future calls can be the next advanced feature of the next-generation telecommunication networks as the service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedules, avoiding unwanted communications (e.g. voice spam), and resource planning in call centers. Predicting calls is a very challenging task. We believe that this is an emerging area of research in ambient intelligence where the electronic devices are sensitive and responsive to people’s needs and behavior. In particular, we believe that the results of this research will lead to higher productivity and quality of life. In this article, we present a Call Predictor (CP) that offers two new advanced features for the next-generation phones namely “Incoming Call Forecast” and “Intelligent Address Book.” For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller’s behavior and reciprocity. For the Intelligent Address Book, the CP generates a list of most likely contacts/numbers to be dialed at any given time based on the user’s behavior and reciprocity. The CP consists of two major components: Probability Estimator (PE) and Trend Detector (TD). The PE computes the probability of receiving/initiating a call based on the caller/user’s calling behavior and reciprocity. We show that the recent trend of the caller/user’s calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. The TD detects the recent trend of the caller/user’s calling pattern and computes the adequacy of historical data in terms of reversed time (time that runs towards the past) based on a trace distance. The recent behavior detection mechanism allows CP to adapt its computation in response to the new calling behaviors. Therefore, CP is adaptive to the recent behavior. For our analysis, we use the real-life call logs of 94 mobile phone users over nine months, which were collected by the Reality Mining Project group at MIT. The performance of the CP is validated for two months based on seven months of training data. The experimental results show that the CP performs reasonably well as an incoming call predictor (Incoming Call Forecast) with false positive rate of 8%, false negative rate of 1%, and error rate of 9%, and as an outgoing call predictor (Intelligent Address Book) with the accuracy of 70% when the list has five entries. The functionality of the CP can be useful in assisting its user in carrying out everyday life activities such as scheduling daily plans by using the Incoming Call Forecast, and saving time from searching for the phone number in a typically lengthy contact book by using the Intelligent Address Book. Furthermore, we describe other useful applications of CP besides its own aforementioned features including Call Firewall and Call Reminder

Limits of Predictability in Commuting Flows in the Absence of Data for Calibration

The estimation of commuting flows at different spatial scales is a fundamental problem for different areas of study. Many current methods rely on parameters requiring calibration from empirical trip volumes. Their values are often not generalizable to cases without calibration data. To solve this problem we develop a statistical expression to calculate commuting trips with a quantitative functional form to estimate the model parameter when empirical trip data is not available. We calculate commuting trip volumes at scales from within a city to an entire country, introducing a scaling parameter α to the recently proposed parameter free radiation model. The model requires only widely available population and facility density distributions. The parameter can be interpreted as the influence of the region scale and the degree of heterogeneity in the facility distribution. We explore in detail the scaling limitations of this problem, namely under which conditions the proposed model can be applied without trip data for calibration. On the other hand, when empirical trip data is available, we show that the proposed model's estimation accuracy is as good as other existing models. We validated the model in different regions in the U.S., then successfully applied it in three different countries

Heterogeneous Mobile Phone Ownership and Usage Patterns in Kenya

The rapid adoption of mobile phone technologies in Africa is offering exciting opportunities for engaging with high-risk populations through mHealth programs, and the vast volumes of behavioral data being generated as people use their phones provide valuable data about human behavioral dynamics in these regions. Taking advantage of these opportunities requires an understanding of the penetration of mobile phones and phone usage patterns across the continent, but very little is known about the social and geographical heterogeneities in mobile phone ownership among African populations. Here, we analyze a survey of mobile phone ownership and usage across Kenya in 2009 and show that distinct regional, gender-related, and socioeconomic variations exist, with particularly low ownership among rural communities and poor people. We also examine patterns of phone sharing and highlight the contrasting relationships between ownership and sharing in different parts of the country. This heterogeneous penetration of mobile phones has important implications for the use of mobile technologies as a source of population data and as a public health tool in sub-Saharan Africa

Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones.

Human travel impacts the spread of infectious diseases across spatial and temporal scales, with broad implications for the biological and social sciences. Individual data on travel patterns have been difficult to obtain, particularly in low-income countries. Travel survey data provide detailed demographic information, but sample sizes are often small and travel histories are hard to validate. Mobile phone records can provide vast quantities of spatio-temporal travel data but vary in spatial resolution and explicitly do not include individual information in order to protect the privacy of subscribers. Here we compare and contrast both sources of data over the same time period in a rural area of Kenya. Although both data sets are able to quantify broad travel patterns and distinguish regional differences in travel, each provides different insights that can be combined to form a more detailed picture of travel in low-income settings to understand the spread of infectious diseases

Africa 2060: good news from Africa, April 16, 2010

This repository item contains a single issue of the Pardee Conference Series, As the keystone event of a research program called “Africa 2060,” the Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University convened a conference on April 16, 2010 called Africa 2060: Good News from Africa. The program featured more than a dozen expert panelists from Boston University and across the world, and the approximately 100 participants included many African scholars and citizens from the continent who contributed to lively and well-informed discussion. The Pardee Center conference was co-sponsored by Boston University’s Africa Studies Center (ASC), the African Presidential Archives & Research Center (APARC), and the Global Health & Development Center (GHDC).This report provides commentary reflecting upon and information pertaining to the substance of the conference. An introductory overview looks at the major issues discussed at the event, which are placed within the larger literature on Africa’s future. Four short essays prepared by Boston University graduate students provide readers with more specific reflections and highlights of each conference session and the main issues discussed by panelists. The final section presents analyses of key trends and projections related to societal, economic, and governance issues for Africa and a commentary on what this information tells us about the drivers that will determine the continent’s future

Corneal Edema Associated With Degenerating Soemmering Ring Cataract: Clinical-Pathologic Correlation

Purpose: To report three patients with an uncommon delayed complication of cataract extraction: corneal edema following dispersion of calcific lens particles from a degenerating Soemmering ring cataract. Observations: We report three patients, 75-92 years old, presenting with corneal edema and dispersed, degenerated calcific lens material in the anterior chamber and vitreous 20-30 years after cataract surgery. In all patients, calcific particles studded the posterior surface of the cornea in a gravity-dependent distribution without apparent inflammation and were associated with localized corneal edema. In one patient, calcific particles were also associated with secondary open angle glaucoma. Deposits originated from the calcified Soemmering ring cataract. Histopathological examination demonstrated extracellular calcific deposits compatible with cataractous lens material on the posterior surface of stripped Descemet membrane of two patients. The deposits were associated with prominent localized loss of corneal endothelium and were not associated with inflammation. Morphologically similar acellular material was identified in the biopsied aqueous and vitreous fluid of one patient. Management included endothelial keratoplasty, anterior chamber lavage, pars plana vitrectomy, aspiration/removal of a portion of Soemmering ring cataract without intraocular lens implant explantation, and the removal of the entire capsular bag/implant complex. Cornea cleared and visual acuity improved in both patients who underwent endothelial keratoplasty. Persistent elevated intraocular pressure led to visual deterioration in one patient with secondary glaucoma. Conclusions and importance: Dispersion of calcific Soemmering ring cataract can occur decades following cataract surgery leading to corneal edema, secondary glaucoma, and vitreous opacities. Timely recognition of this phenomenon may prevent ocular morbidity, including corneal edema and glaucoma