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Register a new userCrowdsourcing, Attention and Productivity
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The tragedy of the digital commons does not prevent the copious voluntary production of content that one witnesses in the web. We show through an analysis of a massive data set from texttt{YouTube} that the productivity exhibited in crowdsourcing exhibits a strong positive dependence on attention, measured by the number of downloads. Conversely, a lack of attention leads to a decrease in the number of videos uploaded and the consequent drop in productivity, which in many cases asymptotes to no uploads whatsoever. Moreover, uploaders compare themselves to others when having low productivity and to themselves when exceeding a threshold.
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We present a study of the group purchasing behavior of daily deals in Groupon and LivingSocial and introduce a predictive dynamic model of collective attention for group buying behavior. In our model, the aggregate number of purchases at a given time comprises two types of processes: random discovery and social propagation. We find that these processes are very clearly separated by an inflection point. Using large data sets from both Groupon and LivingSocial we show how the model is able to predict the success of group deals as a function of time. We find that Groupon deals are easier to predict accurately earlier in the deal lifecycle than LivingSocial deals due to the final number of deal purchases saturating quicker. One possible explanation for this is that the incentive to socially propagate a deal is based on an individual threshold in LivingSocial, whereas in Groupon it is based on a collective threshold, which is reached very early. Furthermore, the personal benefit of propagating a deal is also greater in LivingSocial.
We discuss superstatistics theory of labour productivity. Productivity distribution across workers, firms and industrial sectors are studied empirically and found to obey power-distributions, in sharp contrast to the equilibrium theories of mainstream economics. The Pareto index is found to decrease with the level of aggregation, {it i.e.}, from workers to firms and to industrial sectors. In order to explain these phenomenological laws, we propose a superstatistics framework, where the role of the fluctuating temperature is played by the fluctuating demand.
The efficacy of sensor data in modern bridge condition evaluations has been undermined by inaccessible technologies. While the links between vibrational properties and structural health have been well established, high costs associated with specialized sensor networks have prevented the integration of such data with bridge management systems. In the last decade, researchers predicted that crowd-sourced mobile sensor data, collected ubiquitously and cheaply, will revolutionize our ability to maintain existing infrastructure; yet no such applications have successfully overcome the challenge of extracting useful information in the field with sufficient precision. Here we fill this knowledge gap by showing that critical physical properties of a real bridge can be determined accurately from everyday vehicle trip data. We collected smartphone data from controlled field experiments and UBER rides on the Golden Gate Bridge and developed an analytical method to recover modal properties, which paves the way for scalable, cost-effective structural health monitoring based on this abundant data class. Our results are consistent with a comprehensive study on the Golden Gate Bridge. We assess the benefit of continuous monitoring with reliability models and show that the inclusion of crowd-sourced data in a bridge maintenance plan can add over fourteen years of service (30% increase) to a bridge without additional costs. These results certify the immediate value of large-scale data sources for studying the health of existing infrastructure, whether the data are crowdsensed or generated by organized vehicle fleets such as ridesourcing companies or municipalities.
The association between productivity and impact of scientific production is a long-standing debate in science that remains controversial and poorly understood. Here we present a large-scale analysis of the association between yearly publication numbers and average journal-impact metrics for the Brazilian scientific elite. We find this association to be discipline-specific, career-age dependent, and similar among researchers with outlier and non-outlier performance. Outlier researchers either outperform in productivity or journal prestige, but they rarely do so in both categories. Non-outliers also follow this trend and display negative correlations between productivity and journal prestige but with discipline-dependent intensity. Our research indicates that academics are averse to simultaneous changes in their productivity and journal-prestige levels over consecutive career years. We also find that career patterns concerning productivity and journal prestige are discipline-specific, having in common a raise of productivity with career age for most disciplines and a higher chance of outperforming in journal impact during early career stages.
Throughout history, a relatively small number of individuals have made a profound and lasting impact on science and society. Despite long-standing, multi-disciplinary interests in understanding careers of elite scientists, there have been limited attempts for a quantitative, career-level analysis. Here, we leverage a comprehensive dataset we assembled, allowing us to trace the entire career histories of nearly all Nobel laureates in physics, chemistry, and physiology or medicine over the past century. We find that, although Nobel laureates were energetic producers from the outset, producing works that garner unusually high impact, their careers before winning the prize follow relatively similar patterns as ordinary scientists, being characterized by hot streaks and increasing reliance on collaborations. We also uncovered notable variations along their careers, often associated with the Nobel prize, including shifting coauthorship structure in the prize-winning work, and a significant but temporary dip in the impact of work they produce after winning the Nobel. Together, these results document quantitative patterns governing the careers of scientific elites, offering an empirical basis for a deeper understanding of the hallmarks of exceptional careers in science.
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