Traditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

To understand and manage complex organizations, we must develop tools capable of measuring human social interaction accurately and uniformly. Current technologies that measure face-to-face communication do not measure interaction in a unified manner and often ignore remote interaction, an increasingly common communication modality. In this paper we present Rhythm, a platform that combines wearable electronic badges and online applications to capture team-level and network-level interaction patterns in organizations. The platform measures conversation time, turn-taking behavior, and the physical proximity of both co-located and distributed members. Our goal is to empower organizations and researchers to measure formal and informal social interaction across teams, divisions, and locations. We describe two pilot studies that use this platform and discuss how measurement systems like Rhythm may further the fields of computational social science and organizational design.

A common technique to improve speed and robustness of learning in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel. A neglected component in the development of these algorithms has been how best to arrange the learning agents involved to better facilitate distributed search. Here we draw upon results from the networked optimization and collective intelligence literatures suggesting that arranging learning agents in less than fully connected topologies (the implicit way agents are commonly arranged in) can improve learning. We explore the relative performance of four popular families of graphs and observe that one such family (Erdos-Renyi random graphs) empirically outperforms the standard fully-connected communication topology across several DRL benchmark tasks. We observe that 1000 learning agents arranged in an Erdos-Renyi graph can perform as well as 3000 agents arranged in the standard fully-connected topology, showing the large learning improvement possible when carefully designing the topology over which agents communicate. We complement these empirical results with a preliminary theoretical investigation of why less than fully connected topologies can perform better. Overall, our work suggests that distributed machine learning algorithms could be made more efficient if the communication topology between learning agents was optimized.We focus on communication topology because it has been shown to result in increased exploration, higher overall maximum reward, and higher diversity of solutions in both simulated high-dimensional optimization problems (Lazer & Friedman, 2007) and human experiments (Barkoczi & Galesic, 2016), and because, to the best of our knowledge, almost no prior work has investigated how the topology of communication between agents affects learning performance in distributed Deep Reinforcement Learning (DRL). The two topologies that are almost always used are either a complete (fully-connected) network, in which all processors communicate with each other; or a star network-in which all processors communicate with a single hub server, which is, in effect, a more efficient, centralized implemen-

We present Open Badges, an open-source framework and toolkit for measuring and shaping face-to-face social interactions using either custom hardware devices or smart phones, and real-time web-based visualizations. Open Badges is a modular system that allows researchers to monitor and collect interaction data from people engaged in real-life social settings. In this paper we describe the technical aspects of the Open Badges project and the motivation for its creation.

The increasing prevalence of large-scale labor aggregation platforms, worker analytics, and algorithmic decision-making by management raises the question of whether workers can use similar technologies to advocate for their own goals. Yet, there are inherent challenges in building worker-centric tools that collect, aggregate, and share data in responsible and ethical ways. In this paper, we present the design and deployment of the Shipt Calculator, a tool developed in collaboration with non-profit worker groups that allows app-based delivery workers to track and share aggregate data about their pay, increasing wage transparency. We first discuss the design challenges inherent to building worker-centric technologies, particularly for informally organized workers, and ground our discussion in the history of worker inquiry and co-research. We then describe some principles from this history and our own lessons in designing the Calculator that can be applied by future researchers and advocates seeking to build technical tools for organizing campaigns. Finally, we share the results of using the Calculator to audit an app's shift to a black-box pay model using data contributed by 140 workers in the Summer of 2020, finding that although the average pay per-order increased under the new payment model, almost half of workers experienced an unannounced pay cut during the shift, and many workers worked shifts that paid under their state's minimum wage. Finally, we discuss how tools like the Calculator demonstrate the important role that aggregate worker data, and a new Digital Workerism, can serve in creating and maintaining a more balanced platform economy.