Although player performance in online games has been widely studied, few studies have considered the behavioral preferences of players and how they impact performance. In a competitive setting where players must cooperate with temporary teammates, it is even more crucial to understand how differences in playing style contribute to teamwork. Drawing on theories of individual behavior in teams, we describe a methodology to empirically profile players based on the diversity and conformity of their gameplay styles. Applying this approach to a League of Legends dataset, we find three distinct types of players that align with our theoretical framework: generalists, specialists, and mavericks. Importantly, the behavior of each player type remains stable despite players becoming more experienced. Additionally, we extensively investigate the benefits and drawbacks of each type of player by evaluating their individual performance, contribution to the team, and adaptation to changes in the game environment. We find that, overall, specialists tend to outperform others, while mavericks bear high risk but also potentially reap great rewards. Generalists are the most resilient to instability in the environment (game patches). We discuss the implications of these findings in terms of game design and community management, as well as team building in environments with varying levels of stability.

Collaborative crowdsourcing is a well-established model of work, especially in the case of open source software development. The structure and operation of these virtual and loosely-knit teams differ from traditional organizations. As such, little is known about how their behavior may change in response to an increase in external attention. To understand these dynamics, we analyze millions of actions of thousands of contributors in over 1100 open source software projects that topped the GitHub Trending Projects page and thus experienced a large increase in attention, in comparison to a control group of projects identified through propensity score matching. In carrying out our research, we use the lens of organizational change, which considers the challenges teams face during rapid growth and how they adapt their work routines, organizational structure, and management style. We show that trending results in an explosive growth in the effective team size. However, most newcomers make only shallow and transient contributions. In response, the original team transitions towards administrative roles, responding to requests and reviewing work done by newcomers. Projects evolve towards a more distributed coordination model with newcomers becoming more central, albeit in limited ways. Additionally, teams become more modular with subgroups specializing in different aspects of the project. We discuss broader implications for collaborative crowdsourcing teams that face attention shocks.

As a growing share of the world's population inhabits cities, a central focus of current development policy has been on building urban infrastructure to support increasing population density. An important component of such policy has been the construction of major roads and highways, which in principle can reduce congestion, promote development of peri-urban areas, and increase labor mobility. However, the actual impacts of such investments have been difficult to evaluate empirically. Here, we use a rich dataset capturing the mobility patterns of roughly 9 million individuals to study the impact of a new super-highway on travel patterns in and around Colombo, the capital of Sri Lanka. Our results indicate that this road had an immediate and pronounced impact on travel patterns: people changed their primary routes of travel, which reduced overall congestion, increased average travel speeds, and reduced the amount of time spent in transit. We further find that the super-highway led to a modest, but statistically significant, increase in the total amount of travel in and around Colombo. We discuss how such insights can inform future policymaking, and point to several promising areas for future research.