Crowdsourcing systems aggregate decisions of many people to help users quickly identify high-quality options, such as the best answers to questions or interesting news stories. A long-standing issue in crowdsourcing is how option quality and human judgement heuristics interact to affect collective outcomes, such as the perceived popularity of options. We address this limitation by conducting a controlled experiment where subjects choose between two ranked options whose quality can be independently varied. We use this data to construct a model that quantifies how judgement heuristics and option quality combine when deciding between two options. The model reveals popularity-ranking can be unstable: unless the quality difference between the two options is sufficiently high, the higher quality option is not guaranteed to be eventually ranked on top. To rectify this instability, we create an algorithm that accounts for judgement heuristics to infer the best option and rank it first. This algorithm is guaranteed to be optimal if data matches the model. When the data does not match the model, however, simulations show that in practice this algorithm performs better or at least as well as popularity-based and recency-based ranking for any two-choice question. Our work suggests that algorithms relying on inference of mathematical models of user behavior can substantially improve outcomes in crowdsourcing systems.

The articles in this special issue focus on social network services in digital environments.

Work practices vary among software developers. Some are highly focused on a few artifacts; others make wideranging contributions. Similarly, some artifacts are mostly authored, or “owned”, by one or few developers; others have very wide ownership. Focus and ownership are related but different phenomena, both with strong effect on software quality. Prior studies have mostly targeted ownership; the measures of ownership used have generally been based on either simple counts, information-theoretic views of ownership, or social-network views of contribution patterns. We argue for a more general conceptual view that unifies developer focus and artifact ownership. We analogize the developer-artifact contribution network to a predator-prey food web, and draw upon ideas from ecology to produce a novel, and conceptually unified view of measuring focus and ownership. These measures relate to both cross-entropy and Kullback-Liebler divergence, and simultaneously provide two normalized measures of focus from both the developer and artifact perspectives. We argue that these measures are theoretically well-founded, and yield novel predictive, conceptual, and actionable value in software projects. We find that more focused developers introduce fewer defects than defocused developers. In contrast, files that receive narrowly focused activity are more likely to contain defects than other files.

This paper introduces a model of strategic network formation grounded in two disparate modes of acquiring social capital - brokerage and closure - through the unification of a dual-level view of interactions between individuals and between groups of individuals referred to as structural autonomy. After motivating and introducing the model, we establish the existence of equilibrium and propose interesting open questions and extensions to the basic model for future research.

We employ user activity data from three highly popular gifting applications on Facebook to study the evolution of user activity on applications through the most commonly-used growth mechanism, namely Application Requests. We find user activity graphs differ from friendship graphs in large part due to the inherent directionality of user activity, and node transience. Our results show that, unlike degree distributions in friendship graphs, activity graphs exhibit strong asymmetry in in- and out-degree distributions, and that out-degrees are not accurately described by currently known parametric distributions. As such, user activity graphs cannot be simulated through existing intent- and feature-driven algorithms that can model friendship graphs.

Commercial software project managers design project organizational structure carefully, mindful of available skills, division of labour, geographical boundaries, etc. These organizational "cathedrals" are to be contrasted with the "bazaar-like" nature of Open Source Software (OSS) Projects, which have no pre-designed organizational structure. Any structure that exists is dynamic, self-organizing, latent, and usually not explicitly stated. Still, in large, complex, successful, OSS projects, we do expect that subcommunities will form spontaneously within the developer teams. Studying these subcommunities, and their behavior can shed light on how successful OSS projects self-organize. This phenomenon could well hold important lessons for how commercial software teams might be organized. Building on known well-established techniques for detecting community structure in complex networks, we extract and study latent subcommunities from the email social network of several projects: Apache HTTPD, Python, PostgresSQL, Perl, and Apache ANT. We then validate them with software development activity history. Our results show that subcommunities do indeed spontaneously arise within these projects as the projects evolve. These subcommunities manifest most strongly in technical discussions, and are significantly connected with collaboration behaviour.

Adaptive power topology control (APTC) is a local algorithm for constructing a one-parameter family of θ-graphs, where each node increases power until it has a neighbor in every θ sector around it.We show it is possible to use such a local geometric θ-constraint to ensure full network connectivity, and consider tradeoffs between assumptions about the wireless footprint and constraints on the boundary nodes. In particular, we show that if the boundary nodes can communicate with neighboring boundary nodes and all interior nodes satisfy a θI π constraint, we can guarantee connectivity for any arbitrary wireless footprint. If we relax the boundary assumption and instead impose a θB < 3π/2 constraint on the boundary nodes, together with the θI < π constraint on interior nodes, we can guarantee full network connectivity using only a "weak-monotonicity" footprint assumption. The weak-monotonicity model, introduced herein, is much less restrictive than the disk model of coverage and captures aspects of the spatial correlations inherent in signal propagation and noise. We show that under the idealized disk model of coverage, APTC constructs graphs that are sparse. Finally, we show that if the wireless footprint has sufficiently small "eccentricity", then there is some θ for which greedy geometric routing always succeeds.

This paper defines the metrics to characterize the performance of ad hoc networks based on timescales for information flow, power consumption and interference. The statistical distribution of timescales has not been previously considered. Yet, it is important for understanding the feasibility of communicating over such networks, for comparing different algorithms for building up network topology and for distinguishing regimes of routing. We quantify the longest timescale for information flow and estimate its distribution. We also introduce a decentralized adaptive power algorithm, that uses only information local to each device, for building ad hoc networks. This algorithm is shown to perform significantly better by all our metrics when compared with a standard, constant power, algorithm.