Joseph A. E. Shaheen | Searcharxiv

arXiv2018

Challenges of Growing Social Media Networks From the Bottom-Up Through the Agent Perspective

We develop an agent-based model in order to understand agent/node behaviors that generate social media networks. We use simple rules to synthetically generate a backcloth (friend/follow) network collected using Twitter's API. The Twitter network was collected using seeds for known terrorist propaganda accounts in 2015. Model parameter adjustments were made to reproduce the collected network's summary statistics, stylized facts and general structural measures. We produced an approximate network in line with the general properties of our collected data. We present our findings with a focus on the challenging aspects of this reproduction. We find that while it is possible to generate a social media network utilizing a few simple rules, numerous challenges arise requiring departure from the agent viewpoint and the development of more useful methods. We present numerous weaknesses and challenges in our reproduction and propose potential solutions for future efforts.

cs.SI↗

arXiv2018

Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model

The ridesharing economy is experiencing rapid growth and innovation. Companies such as Uber and Lyft are continuing to grow at a considerable pace while providing their platform as an organizing medium for ridesharing services, increasing consumer utility as well as employing thousands in part-time positions. However, many challenges remain in the modeling of ridesharing services, many of which are not currently under wide consideration. In this paper, an agent-based model is developed to simulate a ridesharing service in the Washington D.C. metropolitan region. The model is used to examine levels of utility gained for both riders (customers) and drivers (service providers) of a generic ridesharing service. A description of the Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM) is provided, as well as a description of a typical simulation run. We investigate the financial gains of drivers for a 24-hour period under two scenarios and two spatial movement behaviors. The two spatial behaviors were random movement and Voronoi movement, which we describe. Both movement behaviors were tested under a stationary run conditions scenario and a variable run conditions scenario. We find that Voronoi movement increased drivers' utility gained but that emergence of this system property was only viable under variable scenario conditions. This result provides two important insights: The first is that driver movement decisions prior to passenger pickup can impact financial gain for the service and drivers, and consequently, rate of successful pickup for riders. The second is that this phenomenon is only evident under experimentation conditions where variability in passenger and driver arrival rates are administered.

cs.MA↗