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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Dewhurst, David Rushing Gray, Tyler J. Frank, Morgan R. Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States MassMutual Data Science AmherstMA01002 United States Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States

complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry. Copyright © 2020, The Authors. All rights reserved.

关键词： Turbulence

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The Resume Paradox: Greater Language Differences, Smaller Pay Gaps

arXiv

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arXiv 2023年

作者： Minot, Joshua R. Maier, Marc Demarest, Bradford Cheney, Nicholas Danforth, Christopher M. Dodds, Peter Sheridan Frank, Morgan R. Computational Story Lab The University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center The University of Vermont BurlingtonVT05401 United States Department of Computer Science The University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States Neurobotics Lab The University of Vermont BurlingtonVT05401 United States Department of Mathematics and Statistics The University of Vermont BurlingtonVT05401 United States Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15260 United States Digital Economy Lab Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Over the past decade, the gender pay gap has remained steady with women earning 84 cents for every dollar earned by men on average. Many studies explain this gap through demand-side bias in the labor market represented through employers' job postings. However, few studies analyze potential bias from the worker supply-side. Here, we analyze the language in millions of US workers' resumes to investigate how differences in workers' self-representation by gender compare to differences in earnings. Across US occupations, language differences between male and female resumes correspond to 11% of the variation in gender pay gap. This suggests that females' resumes that are semantically similar to males' resumes may have greater wage parity. However, surprisingly, occupations with greater language differences between male and female resumes have lower gender pay gaps. A doubling of the language difference between female and male resumes results in an annual wage increase of $2,797 for the average female worker. This result holds with controls for gender-biases of resume text and we find that per-word bias poorly describes the variance in wage gap. The results demonstrate that textual data and self-representation are valuable factors for improving worker representations and understanding employment inequities. Copyright © 2023, The Authors. All rights reserved.

关键词： Employment

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Learning deterministic hydrodynamic equations from stochastic active particle dynamics

arXiv

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arXiv 2022年

作者： Maddu, Suryanarayana Vagne, Quentin Sbalzarini, Ivo F. Technische Universität Dresden Faculty of Computer Science Dresden01069 Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden01307 Germany Center for Systems Biology Dresden Dresden01307 Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Dresden Leipzig Germany Cluster of Excellence Physics of Life TU Dresden Germany Max Planck Institute for the Physics of Complex Systems Dresden01187 Germany Department of Genetics and Evolution University of Geneva Switzerland

We present a principled data-driven strategy for learning deterministic hydrodynamic models directly from stochastic non-equilibrium active particle trajectories. We apply our method to learning a hydrodynamic model for the propagating density lanes observed in self-propelled particle systems and to learning a continuum description of cell dynamics in epithelial tissues. We also infer from stochastic particle trajectories the latent phoretic fields driving chemotaxis. This demonstrates that statistical learning theory combined with physical priors can enable discovery of multi-scale models of non-equilibrium stochastic processes characteristic of collective movement in living systems. © 2022, CC BY.

关键词： Stochastic systems

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Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance

arXiv

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arXiv 2020年

作者： Dewhurst, David Rushing Alshaabi, Thayer Arnold, Michael V. Minot, Joshua R. Danforth, Christopher M. Dodds, Peter Sheridan Vermont Complex Systems Center Computational Story Lab University of Vermont BurlingtonVT05405 MassMutual Data Science BostonMA02110 Department of Mathematics and Statistics University of Vermont BurlingtonVT05405

Using a random 10% sample of tweets authored from 2019-09-01 through 2020-04-30, we analyze the dynamic behavior of words (1-grams) used on Twitter to describe the ongoing COVID-19 pandemic. Across 24 languages, we find two distinct dynamic regimes: One characterizing the rise and subsequent collapse in collective attention to the initial Coronavirus outbreak in late January, and a second that represents March COVID-19-related discourse. Aggregating countries by dominant language use, we find that volatility in the first dynamic regime is associated with future volatility in new cases of COVID-19 roughly three weeks (average 22.49 ± 3.26 days) later. Our results suggest that surveillance of change in usage of epidemiology-related words on social media may be useful in forecasting later change in disease case numbers, but we emphasize that our current findings are not causal or necessarily predictive. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Say their names: Resurgence in the collective attention toward black victims of fatal police violence following the death of george floyd

arXiv

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arXiv 2021年

作者： Wu, Henry H. Gallagher, Ryan J. Alshaabi, Thayer Adams, Jane L. Minot, Joshua R. Arnold, Michael V. Welles, Brooke Foucault Harp, Randall Dodds, Peter Sheridan Danforth, Christopher M. Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05401 United States Communication Media and Marginalization Lab Network Science Institute Northeastern University BostonMA02115 United States Communication Studies Department Northeastern University BostonMA02115 United States Department of Philosophy University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

The murder of George Floyd by police in May 2020 sparked international protests and renewed attention in the Black Lives Matter movement. Here, we characterize ways in which the online activity following George Floyd's death was unparalleled in its volume and intensity, including setting records for activity on Twitter, prompting the saddest day in the platform's history, and causing George Floyd's name to appear among the ten most frequently used phrases in a day, where he is the only individual to have ever received that level of attention who was not known to the public earlier that same week. Further, we find this attention extended beyond George Floyd and that more Black victims of fatal police violence received attention following his death than during other past moments in Black Lives Matter's history. We place that attention within the context of prior online racial justice activism by showing how the names of Black victims of police violence have been lifted and memorialized over the last 12 years on Twitter. Our results suggest that the 2020 wave of attention to the Black Lives Matter movement centered past instances of police violence in an unprecedented way, demonstrating the impact of the movement's rhetorical strategy to "say their names. © 2021, CC BY-SA.

关键词： Law enforcement

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Evolving ab initio trading strategies in heterogeneous environments

arXiv

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arXiv 2019年

作者： Dewhurst, David Rushing Li, Yi Bogdan, Alexander Geng, Jasmine MassMutual Center of Excellence in Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center Computational Story Lab Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States MassMutual Data Science BostonMA02210 United States

Securities markets are quintessential complex adaptive systems in which heterogeneous agents compete in an attempt to maximize returns. Species of trading agents are also subject to evolutionary pressure as entire classes of strategies become obsolete and new classes emerge. Using an agent-based model of interacting heterogeneous agents as a flexible environment that can endogenously model many diverse market conditions, we subject deep neural networks to evolutionary pressure to create dominant trading agents. After analyzing the performance of these agents and noting the emergence of anomalous superdiffusion through the evolutionary process, we construct a method to turn high-fitness agents into trading algorithms. We backtest these trading algorithms on real high-frequency foreign exchange data, demonstrating that elite trading algorithms are consistently profitable in a variety of market conditions-even though these algorithms had never before been exposed to real financial data. These results provide evidence to suggest that developing ab initio trading strategies by repeated simulation and evolution in a mechanistic market model may be a practical alternative to explicitly training models with past observed market data. Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous agents

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Fame and Ultrafame: Measuring and comparing daily levels of 'being talked about' for United States' presidents, their rivals, God, countries, and K-pop

arXiv

引用

arXiv 2019年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Dewhurst, David Rushing Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States

When building a global brand of any kind-a political actor, clothing style, or belief system-developing widespread awareness is a primary goal. Short of knowing any of the stories or products of a brand, being talked about in whatever fashion-raw fame-is, as Oscar Wilde would have it, better than not being talked about at all. Here, we measure, examine, and contrast the day-to-day raw fame dynamics on Twitter for US Presidents and major US Presidential candidates from 2008 to 2020: Barack Obama, John McCain, Mitt Romney, Hillary Clinton, Donald Trump, and Joe Biden. We assign "lexical fame" to be the number and (Zipfian) rank of the (lowercased) mentions made for each individual across all languages. We show that all five political figures have at some point reached extraordinary volume levels of what we define to be "lexical ultrafame": An overall rank of approximately 300 or less which is largely the realm of function words and demarcated by the highly stable rank of 'god'. By this measure, 'trump' has become enduringly ultrafamous, from the 2016 election on. We use typical ranks for country names and function words as standards to improve perception of scale. We quantify relative fame rates and find that in the eight weeks leading up the 2008 and 2012 elections, 'obama' held a 1000:757 volume ratio over 'mccain' and 1000:892 over 'romney', well short of the 1000:544 and 1000:504 volumes favoring 'trump' over 'hillary' and 'biden' in the 8 weeks leading up to the 2016 and 2020 elections. Finally, we track how only one other entity has more sustained ultrafame than 'trump' on Twitter: The K-pop (Korean pop) band BTS. We chart the dramatic rise of BTS, finding their Twitter handle '@bts twt' has been able to compete with 'a' and 'the', reaching a rank of three at the day scale and a rank of one at the quarter-hour scale. Our findings for BTS more generally point to K-pop's growing economic, social, and political power. Copyright © 2019, The Authors. All rights reser

关键词： Social networking (online)

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Probability-turbulence divergence: A tunable allotaxonometric instrument for comparing heavy-tailed categorical distributions

arXiv

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05401 United States Vermont Advanced Computing Center University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States MassMutual Data Science AmherstMA01002 United States Howard Hughes Medical Institute University of California Berkeley BerkeleyCA94720 United States Data Visualization Lab Khoury College of Computer Sciences Northeastern University BostonMA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

Real-world complex systems often comprise many distinct types of elements as well as many more types of networked interactions between elements. When the relative abundances of types can be measured well, we further observe heavy-tailed categorical distributions for type frequencies. For the comparison of type frequency distributions of two systems or a system with itself at different time points in time—a facet of allotaxonometry—a great range of probability divergences are available. Here, we introduce and explore ‘probability-turbulence divergence’, a tunable, straightforward, and interpretable instrument for comparing normalizable categorical frequency distributions. We model probability-turbulence divergence (PTD) after rank-turbulence divergence (RTD). While probability-turbulence divergence is more limited in application than rank-turbulence divergence, it is more sensitive to changes in type frequency. We build allotaxonographs to display probability turbulence, incorporating a way to visually accommodate zero probabilities for ‘exclusive types’ which are types that appear in only one system. We explore comparisons of example distributions taken from literature, social media, and ecology. We show how probability-turbulence divergence either explicitly or functionally generalizes many existing kinds of distances and measures, including, as special cases, L(p) norms, the Sørensen-Dice coefficient (the F1 statistic), and the Hellinger distance. We discuss similarities with the generalized entropies of Rényi and Tsallis, and the diversity indices (or Hill numbers) from ecology. We close with thoughts on open problems concerning the optimization of the tuning of rank- and probability-turbulence divergence. Copyright © 2020, The Authors. All rights reserved.

关键词： Normal distribution

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Gauging the happiness benefit of US urban parks through Twitter

arXiv

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arXiv 2020年

作者： Schwartz, Aaron J. Dodds, Peter Sheridan O’Neil-Dunne, Jarlath P.M. Ricketts, Taylor H. Danforth, Christopher M. Gund Institute for Environment University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05401 United States Rubenstein School of Environment and Natural Resources University of Vermont BurlingtonVT05401 United States Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

The relationship between nature contact and mental well-being has received increasing attention in recent years. While a body of evidence has accumulated demonstrating a positive relationship between time in nature and mental well-being, there have been few studies comparing this relationship in different locations over long periods of time. In this study, we estimate a happiness benefit, the difference in expressed happiness between in- and out-of-park tweets, for the 25 largest cities in the US by population. People write happier words during park visits when compared with non-park user tweets collected around the same time. While the words people write are happier in parks on average and in most cities, we find considerable variation across cities. Tweets are happier in parks at all times of the day, week, and year, not just during the weekend or summer vacation. Across all cities, we find that the happiness benefit is highest in parks larger than 100 acres. Overall, our study suggests the happiness benefit associated with park visitation is on par with US holidays such as Thanksgiving and New Year’s Day. Copyright © 2020, The Authors. All rights reserved.

关键词： Parks

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Interpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance

arXiv

引用

arXiv 2021年

作者： Minot, Joshua R. Cheney, Nicholas Maier, Marc Elbers, Danne C. Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Neurobotics Lab University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States VA Cooperative Studies Program VA Boston Healthcare System BostonMA02130 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05401 United States

Medical systems in general, and patient treatment decisions and outcomes in particular, are affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how word choices made by healthcare practitioners and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce the potential for bias in natural language processing pipelines. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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