326 research outputs found
Contextualizing Patterns in Short-term Disaster Recoveries from the 2015 Nepal earthquakes: household vulnerabilities, adaptive capacities, and change
Disaster recovery is multidimensional and requires theoretical and methodological approaches from the interdisciplinary social sciences to illustrate short- and long-term recovery dynamics that can guide more informed and equitable policy and interventions. The 2015 Nepal earthquakes have had catastrophic impacts on historically marginalized ethnic groups and Indigenous households in rural locations, arising in the immediate aftermath and unfolding for years afterward. Analyzing factors that shape household recovery patterns can help identify vulnerabilities and adaptive capacities in addition to signaling potential future changes. We pursue this goal using survey data from 400 randomly selected households in 4 communities over 2 10-week intervals at 9 months and 1.5 years after the earthquakes. Building on previous research that used non-metric multidimensional scaling ordination to identify patterns among multiple indicators of recovery (Spoon et al. 2020a), we investigate associations among these patterns of recovery, hazard exposure, and four domains of household adaptive capacity: institutional participation, livelihood diversity, connectivity, and social memory. Our results suggest: (1) social inequality, high hazard exposure, and disrupted place-based livelihoods (especially for herders, farmers, and forest harvesters on the geographic margins) had strong associations with negative recovery outcomes and displacement; (2) inaccessibility and marginality appeared to stimulate ingenuity despite challenging circumstances through mutual aid and local knowledge; (3) recoveries were non-linear, differing for households displaced from their primary home and agropastoral practice and those displaced to camps; and (4) some households experienced rapid changes while others stagnated. We contribute a temporal dataset with a random sample collected following a disaster that uses a theoretically informed quantitative methodology to explore linear and non-linear relationships among multidimensional recovery, adaptive capacity and change and provide an example of how vulnerabilities interact with adaptive capacity
Indirect Reciprocity, Resource Sharing, and Environmental Risk: Evidence from Field Experiments in Siberia
Integrating information from existing research, qualitative ethnographic interviews, and participant observation, we designed a field experiment that introduces idiosyncratic environmental risk and a voluntary sharing decision into a standard public goods game. Conducted with subsistence resource users in rural villages on the Kamchatka Peninsula in Northeast Siberia, we find evidence consistent with a model of indirect reciprocity and local social norms of helping the needy. When participants are allowed to develop reputations in the experiments, as is the case in most small-scale societies, we find that sharing is increasingly directed toward individuals experiencing hardship, good reputations increase aid, and the pooling of resources through voluntary sharing becomes more effective. We also find high levels of voluntary sharing without a strong commitment device; however, this form of cooperation does not increase contributions to the public good. Our results are consistent with previous experiments and theoretical models, suggesting strategic risks tied to rewards, punishments, and reputations are important. However, unlike studies that focus solely on strategic risks, we find the effects of rewards, punishments, and reputations are altered by the presence of environmental factors. Unexpected changes in resource abundance increase interdependence and may alter the costs and benefits of cooperation, relative to defection. We suggest environmental factors that increase interdependence are critically important to consider when developing and testing theories of cooperatio
Skills, Division of Labor, and Economies of Scale Among Amazonian Hunters and South Indian Honey Collectors
In foraging and other productive activities, individuals make choices regarding whether and with whom to cooperate, and in what capacities. The size and composition of cooperative groups can be understood as a self-organized outcome of these choices, which are made under local ecological and social constraints. This article describes a theoretical framework for explaining the size and composition of foraging groups based on three principles: (1) the sexual division of labor; (2) the intergenerational division of labor; and (3) economies of scale in production. We test predictions from the theory with data from two field contexts: Tsimane\u27 game hunters of lowland Bolivia, and Jenu Kuruba honey collectors of South India. In each case, we estimate the impacts of group size and individual group membersā effort on group success. We characterize differences in the skill requirements of different foraging activities, and show that individuals participate more frequently in activities in which they are more efficient. We evaluate returns to scale across different resource types, and observe higher returns at larger group sizes in foraging activities (such as hunting large game) that benefit from coordinated and complementary roles. These results inform us that the foraging group size and composition are guided by the motivated choice of individuals on the basis of relative efficiency, benefits of cooperation, opportunity costs, and other social considerations
Pyro: A Python-based Versatile Programming Environment for Teaching Robotics
In this paper we describe a programming framework called Pyro which provides a set of abstractions that allows students to write platformĀindependent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a topĀdown approach. We describe the background of the project, novel abstractions created, its library of objects, and the many learning modules that have been created from which curricula for different types of courses can be drawn. Finally, we explore Pyro from the students\u27 perspective in a case study
Distributed Bees Algorithm Parameters Optimization for a Cost Efficient Target Allocation in Swarms of Robots
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBAās control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robotsā distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce
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Estimating the absolute wealth of households
The estimation of the economic status of individuals and their households is central to much work in epidemiology and the social sciences. Wealth is a key determinant of health and social achievement and an indicator of well-being in its own right. For this reason, the development and testing of novel measures of economic status is of interest. There is lively debate over the relative merits of the competing methods used to assess and compare the relative or absolute wealth of individuals and households.
Although several methods to estimate absolute household wealth have already been developed or proposed, each has its limitations, including sensitivity to the sample of countries as well as to the country selected as baseline. Most also rely on arbitrary wealth indicators, cut-offs to anchor comparisons and/or a common set of assets. Such approaches often exclude countries using different assets in surveys, ignore assets that may be important in a specific country setting and assume that an asset in one country provides the same measure of wealth as it does in another country. In an attempt to address these limitations, we have developed a method for estimating the absolute household wealth per capita ā called the absolute wealth estimate ā in units that permit meaningful comparisons across countries and years. We used the method to evaluate the prevalence of poverty and indicators of nutritional status and compared these results to common benchmarks
Towards reliable and scalable robot communication
The Robot Operating System (ROS) is the de facto standard platform
for modern robots. However, communication between ROS nodes
has scalability and reliability issues in practice. In this paper, we
investigate whether Erlangās lightweight concurrency and reliability
mechanisms have the potential to address these issues. The basis
of the investigation is a pair of simple but typical robotic control
applications, namely two face-trackers: one using ROS publish/subscribe
messaging, and the other a bespoke Erlang communication
framework.
We report experiments that compare five key aspects of the
ROS and Erlang face trackers. We find that Erlang communication
scales better, supporting at least 3.5 times more active processes
(700 processes) than its ROS-based counterpart (200 nodes) while
consuming half of the memory. However, while both face tracking
prototypes exhibit similar detection accuracy and transmission
latencies with 10 or fewer workers, Erlang exhibits a continuous
increase in the total time taken to process a frame as more agents
are added, and we identify the cause. A reliability study shows
that while both ROS and Erlang restart failed computations, the
Erlang processes restart 1000ā1500 times faster than ROS nodes,
reducing robot component downtime and mitigating the impact of
the failures
Team-level programming of drone sensor networks
Autonomous drones are a powerful new breed of mobile sensing platform that can greatly extend the capabilities of traditional sensing systems. Unfortunately, it is still non-trivial to coordinate multiple drones to perform a task collaboratively. We present a novel programming model called team-level programming that can express collaborative sensing tasks without exposing the complexity of managing multiple drones, such as concurrent programming, parallel execution, scaling, and failure recovering. We create the Voltron programming system to explore the concept of team-level programming in active sensing applications. Voltron offers programming constructs to create the illusion of a simple sequential execution model while still maximizing opportunities to dynamically re-task the drones as needed. We implement Voltron by targeting a popular aerial drone platform, and evaluate the resulting system using a combination of real deployments, user studies, and emulation. Our results indicate that Voltron enables simpler code and produces marginal overhead in terms of CPU, memory, and network utilization. In addition, it greatly facilitates implementing correct and complete collaborative drone applications, compared to existing drone programming systems
Coordination of Mobile Mules via Facility Location Strategies
In this paper, we study the problem of wireless sensor network (WSN)
maintenance using mobile entities called mules. The mules are deployed in the
area of the WSN in such a way that would minimize the time it takes them to
reach a failed sensor and fix it. The mules must constantly optimize their
collective deployment to account for occupied mules. The objective is to define
the optimal deployment and task allocation strategy for the mules, so that the
sensors' downtime and the mules' traveling distance are minimized. Our
solutions are inspired by research in the field of computational geometry and
the design of our algorithms is based on state of the art approximation
algorithms for the classical problem of facility location. Our empirical
results demonstrate how cooperation enhances the team's performance, and
indicate that a combination of k-Median based deployment with closest-available
task allocation provides the best results in terms of minimizing the sensors'
downtime but is inefficient in terms of the mules' travel distance. A
k-Centroid based deployment produces good results in both criteria.Comment: 12 pages, 6 figures, conferenc
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