arXiv subjects

Duncan J. Watts

Publications and source records attributed to Duncan J. Watts.

At least 19 recordsLinked to original sources

arXiv2026

All-sky modeling of Galactic emission at radio and microwave frequencies

We present a new all-sky model of low-frequency diffuse Galactic emission in the regime where synchrotron, free-free, and spinning dust dominate. The model extends the Planck 2015 diffuse component-separation analysis by incorporating recent radio and microwave surveys. We fit 35 full- and partial-sky maps at 1 degree resolution, including S-PASS at 2.30 GHz, C-BASS at 4.76 GHz, and QUIJOTE at 10-20 GHz, together with reprocessed WMAP and Planck LFI data from the Cosmoglobe collaboration and Planck HFI channels. Using a Bayesian parametric approach with Commander, we derive spatially varying amplitude and spectral parameter maps for the dominant low-frequency foreground components in total intensity. The main products are a full-sky synchrotron amplitude and spectral-index solution, an all-sky characterization of spinning dust emission with a single-component log-normal spectral model, and a reconstructed all-sky total-intensity map at 4.76 GHz tracing diffuse synchrotron emission with reduced systematics relative to Haslam 408 MHz. The revised low-frequency anchoring increases the recovered synchrotron amplitude: at 4.76 GHz, it is approximately a factor of two higher than the Planck 2015 prediction. The model achieves RMS temperature residuals below 10 $\mathrmμ$K over 95% of the sky up to 353 GHz, with fractional residuals below 1.5% in the Galactic plane and below 5% across QUIJOTE bands. Residual angular power spectra lie more than two orders of magnitude below the CMB spectrum. These products describe the transition between radio and microwave emission and provide a new reference for foreground modeling and sky-simulation applications.

astro-ph.GA↗

arXiv2026

LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems

Recommender systems have grown from content-organization tools into sophisticated systems that shape daily behavior. By controlling what we see, they shape what we perceive, raising concerns about filter bubbles, radicalization, polarization, and social inequality. Large language models (LLMs) enable more powerful personalization, intensifying these dynamics. Yet most recommenders are tuned for engagement or limited accuracy metrics, with little attention to broader social implications, e.g. how personalization reshapes exposure in socially consequential domains. We investigate whether LLM-assisted reranking, while improving personalization, inadvertently amplifies exposure to ideologically extreme or conspiratorial political content, a risk theorized but not empirically characterized in news recommendation. Using real news-consumption histories, we rerank YouTube's sidebar candidates through zero-shot, instruction-based prompting. We compare a baseline prompt with a constrained variant that preserves topical relevance and broadens ideological exposure while reducing conspiratorial or extreme content. Without constraints, reranking strengthened personalization but increased exposure to conspiratorial and extremist material for users whose histories contained such content. Lightweight prompt-level regularization reduced promotion of extreme content and increased ideological diversity, with modest relevance loss. Synthetic experiments suggest that LLMs rerank via statistical regularities in language rather than semantic understanding of ideology, clarifying why naive prompts amplify these patterns and why regularization can reshape them. Together, our results highlight the power of LLMs to operationalize contextual nuance in high-stakes recommendation, and the need to evaluate LLM-assisted personalization beyond accuracy and treat prompt design as a value-laden rather than neutral default.

cs.HC↗

arXiv2026

The Effect of Mobility Trajectory Sparsity on Epidemic Modeling Outcomes

GPS mobility data are increasingly used in epidemic modeling, allowing the construction of co-location networks or population flows. These trajectories typically exhibit high temporal sparsity because data collection is opportunistic and tied to phone use. Despite growing awareness of this limitation, the analysis and treatment of biases derived from it have been largely overlooked in existing epidemic modeling studies, raising concerns about the robustness of downstream inferences. We introduce a principled framework to quantify the impact of trajectory sparsity on key epidemic modeling outcomes across different levels of missingness. Our approach leverages a highly-complete dataset that exhibits both near-complete and sparse GPS trajectories. Near-complete trajectories provide baseline epidemic outcomes, while sparse trajectories provide realistic missingness patterns that we impose on the baseline to measure bias. In this way, we show how missing records can result in substantial underestimation of key measures of epidemic intensity, explained not only by the amount of missing data, but by more complex features of data missingness that should be taken into account when designing correction methods. Finally, we propose and evaluate a correction based on inverse probability weighting of network edges before epidemic model calibration, which is shown to reduce bias and parameter misspecification. We also demonstrate this correction on a separate anonymized sample from a commercial GPS mobility dataset and report on its effect. Together, our findings provide a first rigorous quantification of trajectory-sparsity bias in epidemic modeling, offering initial guidance on the treatment of this issue.

cs.SI↗

arXiv2026

A large-scale evaluation of commonsense knowledge in humans and large language models

Commonsense knowledge, a major constituent of artificial intelligence (AI), is primarily evaluated in practice by human-prescribed ground-truth labels. An important, albeit implicit, assumption of these labels is that they accurately capture what any human would think, effectively treating human common sense as homogeneous. However, recent empirical work has shown that humans vary enormously in what they consider commonsensical; thus what appears self-evident to one benchmark designer may not be so to another. Here, we propose a method for assessing commonsense knowledge in AI, specifically in large language models (LLMs), that incorporates empirically observed heterogeneity among humans by measuring the correspondence between a model's judgment and that of a human population. We first find that, when treated as independent survey respondents, most LLMs remain below the human median in their individual commonsense competence. Second, when used as simulators of a hypothetical population, LLMs correlate with real humans only modestly in the extent to which they agree on the same set of statements. In both cases, smaller, open-weight models are surprisingly more competitive than larger, proprietary frontier models. Our evaluation framework, which ties commonsense knowledge to its cultural basis, contributes to the growing call for adapting AI models to human collectivities that possess different, often incompatible, social stocks of knowledge.

cs.AI↗

arXiv2025

The Media Bias Detector: A Framework for Annotating and Analyzing the News at Scale

Mainstream news organizations shape public perception not only directly through the articles they publish but also through the choices they make about which topics to cover (or ignore) and how to frame the issues they do decide to cover. However, measuring these subtle forms of media bias at scale remains a challenge. Here, we introduce a large, ongoing (from January 1, 2024 to present), near real-time dataset and computational framework developed to enable systematic study of selection and framing bias in news coverage. Our pipeline integrates large language models (LLMs) with scalable, near-real-time news scraping to extract structured annotations -- including political lean, tone, topics, article type, and major events -- across hundreds of articles per day. We quantify these dimensions of coverage at multiple levels -- the sentence level, the article level, and the publisher level -- expanding the ways in which researchers can analyze media bias in the modern news landscape. In addition to a curated dataset, we also release an interactive web platform for convenient exploration of these data. Together, these contributions establish a reusable methodology for studying media bias at scale, providing empirical resources for future research. Leveraging the breadth of the corpus over time and across publishers, we also present some examples (focused on the 150,000+ articles examined in 2024) that illustrate how this novel data set can reveal insightful patterns in news coverage and bias, supporting academic research and real-world efforts to improve media accountability.

cs.CL↗

arXiv2025

Integrative Experiments Identify How Punishment Impacts Welfare in Public Goods Games

Punishment as a mechanism for promoting cooperation has been studied extensively for more than two decades, but its effectiveness remains a matter of dispute. Here, we examine how punishment's impact varies across cooperative settings through a large-scale integrative experiment. We vary 14 parameters that characterize public goods games, sampling 360 experimental conditions and collecting 147,618 decisions from 7,100 participants. Our results reveal striking heterogeneity in punishment effectiveness: while punishment consistently increases contributions, its impact on payoffs (i.e., efficiency) ranges from dramatically enhancing welfare (up to 43% improvement) to severely undermining it (up to 44% reduction) depending on the cooperative context. To characterize these patterns, we developed models that outperformed human forecasters (laypeople and domain experts) in predicting punishment outcomes in new experiments. Communication emerged as the most predictive feature, followed by contribution framing (opt-out vs. opt-in), contribution type (variable vs. all-or-nothing), game length (number of rounds), peer outcome visibility (whether participants can see others' earnings), and the availability of a reward mechanism. Interestingly, however, most of these features interact to influence punishment effectiveness rather than operating independently. For example, the extent to which longer games increase the effectiveness of punishment depends on whether groups can communicate. Together, our results refocus the debate over punishment from whether or not it "works" to the specific conditions under which it does and does not work. More broadly, our study demonstrates how integrative experiments can be combined with machine learning to uncover generalizable patterns, potentially involving interactions between multiple features, and help generate novel explanations in complex social phenomena.

econ.GN↗

arXiv2024

Sensitivity-Improved Polarization Maps at 40 GHz with CLASS and WMAP data

Improved polarization measurements at frequencies below 70 GHz with degree-level angular resolution are crucial for advancing our understanding of the Galactic synchrotron radiation and the potential polarized anomalous microwave emission and ultimately benefiting the detection of primordial $B$ modes. In this study, we present sensitivity-improved 40 GHz polarization maps obtained by combining the CLASS 40 GHz and WMAP $Q$-band data through a weighted average in the harmonic domain. The decision to include WMAP $Q$-band data stems from similarities in the bandpasses. Leveraging the accurate large-scale measurements from WMAP $Q$ band and the high-sensitivity information from CLASS 40 GHz band at intermediate scales, the noise level at $\ell\in[30, 100]$ is reduced by a factor of $2-3$ in the map space. A pixel domain analysis of the polarized synchrotron spectral index ($β_s$) using WMAP $K$ band and the combined maps (mean and 16/84th percentile across the $β_s$ map: $-3.08_{-0.20}^{+0.20}$) reveals a stronger preference for spatial variation (PTE for a uniform $β_s$ hypothesis smaller than 0.001) than the results obtained using WMAP $K$ and $Ka$ bands ($-3.08_{-0.14}^{+0.14}$). The cross-power spectra of the combined maps follow the same trend as other low-frequency data, and validation through simulations indicates negligible bias introduced by the combination method (sub-percent level in the power spectra). The products of this work are publicly available on $\mathtt{LAMBDA}$.

astro-ph.CO↗

arXiv2024

Cosmology Large Angular Scale Surveyor (CLASS): 90 GHz Telescope Pointing, Beam Profile, Window Function, and Polarization Performance

The Cosmology Large Angular Scale Surveyor (CLASS) is a telescope array that observes the cosmic microwave background (CMB) over ~75% of the sky from the Atacama Desert, Chile, at frequency bands centered near 40, 90, 150, and 220 GHz. CLASS measures the large angular scale CMB polarization to constrain the tensor-to-scalar ratio and the optical depth to last scattering. This paper presents the optical characterization of the 90GHz telescope, which has been observing since July 2018. Observations of the Moon establish the pointing while dedicated observations of Jupiter are used for beam calibration. The standard deviations of the pointing error in azimuth, elevation, and boresight angle are 1.3, 2.1, and 2.0 arcminutes, respectively, over the first 3 years of observations. This corresponds to a pointing uncertainty ~7% of the beam's full width at half maximum (FWHM). The effective azimuthally-symmetrized instrument 1D beam estimated at 90 GHz has an FWHM of 0.620+/-0.003 deg and a solid angle of 138.7+/-0.6(stats.)+/-1.1(sys.) usr integrated to a radius of 4 deg. The corresponding beam window function drops to b_ell^2 = 0.93, 0.71, 0.14 at ell = 30, 100, 300, respectively. Far-sidelobes are studied using detector-centered intensity maps of the Moon and measured to be at a level of 10^-3 or below relative to the peak. The polarization angle of Tau A estimated from preliminary survey maps is 149.6+/-0.2(stats.) deg in equatorial coordinates. The instrumental temperature-to-polarization (T-to-P) leakage fraction, inferred from per-detector demodulated Jupiter scan data, has a monopole component at the level of 1.7 x 10^-3, a dipole component with an amplitude of 4.3 x 10^-3, with no evidence of quadrupolar leakage.

astro-ph.IM↗

arXiv2024

CLASS Angular Power Spectra and Map-Component Analysis for 40 GHz Observations through 2022

Measurement of the largest angular scale ($\ell < 30$) features of the cosmic microwave background (CMB) polarization is a powerful way to constrain the optical depth to reionization and search for the signature of inflation through the detection of primordial $B$-modes. We present an analysis of maps covering 73.6\% of the sky made from the $40\,\mathrm{GHz}$ channel of the Cosmology Large Angular Scale Surveyor (CLASS) from 2016 August to 2022 May. Taking advantage of the measurement stability enabled by front-end polarization modulation and excellent conditions from the Atacama Desert, we show this channel achieves higher sensitivity than the analogous frequencies from satellite measurements in the range $10 < \ell < 100$. Simulations show the CLASS linear (circular) polarization maps have a white noise level of $125 \,(130)\,\mathrm{μK\, arcmin}$. We measure the Galaxy-masked $EE$ and $BB$ spectra of diffuse synchrotron radiation and compare to space-based measurements at similar frequencies. In combination with external data, we expand measurements of the spatial variations of the synchrotron spectral energy density (SED) to include new sky regions and measure the diffuse SED in the harmonic domain. We place a new upper limit on a background of circular polarization in the range $5 < \ell < 125$ with the first bin showing $D_\ell < 0.023$ $\mathrm{μK^2_{CMB}}$ at 95\% confidence. These results establish a new standard for recovery of the largest-scale CMB polarization from the ground and signal exciting possibilities when the higher sensitivity and higher-frequency CLASS channels are included in the analysis.

astro-ph.CO↗

arXiv2023

Causally estimating the effect of YouTube's recommender system using counterfactual bots

In recent years, critics of online platforms have raised concerns about the ability of recommendation algorithms to amplify problematic content, with potentially radicalizing consequences. However, attempts to evaluate the effect of recommenders have suffered from a lack of appropriate counterfactuals -- what a user would have viewed in the absence of algorithmic recommendations -- and hence cannot disentangle the effects of the algorithm from a user's intentions. Here we propose a method that we call ``counterfactual bots'' to causally estimate the role of algorithmic recommendations on the consumption of highly partisan content. By comparing bots that replicate real users' consumption patterns with ``counterfactual'' bots that follow rule-based trajectories, we show that, on average, relying exclusively on the recommender results in less partisan consumption, where the effect is most pronounced for heavy partisan consumers. Following a similar method, we also show that if partisan consumers switch to moderate content, YouTube's sidebar recommender ``forgets'' their partisan preference within roughly 30 videos regardless of their prior history, while homepage recommendations shift more gradually towards moderate content. Overall, our findings indicate that, at least since the algorithm changes that YouTube implemented in 2019, individual consumption patterns mostly reflect individual preferences, where algorithmic recommendations play, if anything, a moderating role.

cs.SI↗

arXiv2023

Pre-registration for Predictive Modeling

Amid rising concerns of reproducibility and generalizability in predictive modeling, we explore the possibility and potential benefits of introducing pre-registration to the field. Despite notable advancements in predictive modeling, spanning core machine learning tasks to various scientific applications, challenges such as overlooked contextual factors, data-dependent decision-making, and unintentional re-use of test data have raised questions about the integrity of results. To address these issues, we propose adapting pre-registration practices from explanatory modeling to predictive modeling. We discuss current best practices in predictive modeling and their limitations, introduce a lightweight pre-registration template, and present a qualitative study with machine learning researchers to gain insight into the effectiveness of pre-registration in preventing biased estimates and promoting more reliable research outcomes. We conclude by exploring the scope of problems that pre-registration can address in predictive modeling and acknowledging its limitations within this context.

cs.LG↗

arXiv2023

The diminishing state of shared reality on US television news

The potential for a large, diverse population to coexist peacefully is thought to depend on the existence of a ``shared reality:'' a public sphere in which participants are exposed to similar facts about similar topics. A generation ago, broadcast television news was widely considered to serve this function; however, since the rise of cable news in the 1990s, critics and scholars have worried that the corresponding fragmentation and segregation of audiences along partisan lines has caused this shared reality to be lost. Here we examine this concern using a unique combination of data sets tracking the production (since 2012) and consumption (since 2016) of television news content on the three largest cable and broadcast networks respectively. With regard to production, we find strong evidence for the ``loss of shared reality hypothesis:'' while broadcast continues to cover similar topics with similar language, cable news networks have become increasingly distinct, both from broadcast news and each other, diverging both in terms of content and language. With regard to consumption, we find more mixed evidence: while broadcast news has indeed declined in popularity, it remains the dominant source of news for roughly 50\% more Americans than does cable; moreover, its decline, while somewhat attributable to cable, appears driven more by a shift away from news consumption altogether than a growth in cable consumption. We conclude that shared reality on US television news is indeed diminishing, but is more robust than previously thought and is declining for somewhat different reasons.

cs.IR↗

arXiv2023

CLASS Observations of Atmospheric Cloud Polarization at Millimeter Wavelengths

The dynamic atmosphere imposes challenges to ground-based cosmic microwave background observation, especially for measurements on large angular scales. The hydrometeors in the atmosphere, mostly in the form of clouds, scatter the ambient thermal radiation and are known to be the main linearly polarized source in the atmosphere. This scattering-induced polarization is significantly enhanced for ice clouds due to the alignment of ice crystals under gravity, which are also the most common clouds seen at the millimeter-astronomy sites at high altitudes. This work presents a multifrequency study of cloud polarization observed by the Cosmology Large Angular Scale Surveyor (CLASS) experiment on Cerro Toco in the Atacama Desert of northern Chile, from 2016 to 2022, at the frequency bands centered around 40, 90, 150, and 220 GHz. Using a machine-learning-assisted cloud classifier, we made connections between the transient polarized emission found in all four frequencies with the clouds imaged by monitoring cameras at the observing site. The polarization angles of the cloud events are found to be mostly $90^\circ$ from the local meridian, which is consistent with the presence of horizontally aligned ice crystals. The 90 and 150 GHz polarization data are consistent with a power law with a spectral index of $3.90\pm0.06$, while an excess/deficit of polarization amplitude is found at 40/220 GHz compared with a Rayleigh scattering spectrum. These results are consistent with Rayleigh-scattering-dominated cloud polarization, with possible effects from supercooled water absorption and/or Mie scattering from a population of large cloud particles that contribute to the 220 GHz polarization.

astro-ph.IM↗

arXiv2023

Microwave Observations of Venus with CLASS

We report on the disk-averaged absolute brightness temperatures of Venus measured at four microwave frequency bands with the Cosmology Large Angular Scale Surveyor (CLASS). We measure temperatures of 432.3 $\pm$ 2.8 K, 355.6 $\pm$ 1.3 K, 317.9 $\pm$ 1.7 K, and 294.7 $\pm$ 1.9 K for frequency bands centered at 38.8, 93.7, 147.9, and 217.5 GHz, respectively. We do not observe any dependence of the measured brightness temperatures on solar illumination for all four frequency bands. A joint analysis of our measurements with lower frequency Very Large Array (VLA) observations suggests relatively warmer ($\sim$ 7 K higher) mean atmospheric temperatures and lower abundances of microwave continuum absorbers than those inferred from prior radio occultation measurements.

astro-ph.EP↗

arXiv2023

The science of fake news

Fake news emerged as an apparent global problem during the 2016 U.S. Presidential election. Addressing it requires a multidisciplinary effort to define the nature and extent of the problem, detect fake news in real time, and mitigate its potentially harmful effects. This will require a better understanding of how the Internet spreads content, how people process news, and how the two interact. We review the state of knowledge in these areas and discuss two broad potential mitigation strategies: better enabling individuals to identify fake news, and intervention within the platforms to reduce the attention given to fake news. The cooperation of Internet platforms (especially Facebook, Google, and Twitter) with researchers will be critical to understanding the scale of the issue and the effectiveness of possible interventions.

cs.CY↗

arXiv2023

On-sky performance of new 90 GHz detectors for the Cosmology Large Angular Scale Surveyor (CLASS)

The Cosmology Large Angular Scale Surveyor (CLASS) is a polarization-sensitive telescope array located at an altitude of 5,200 m in the Chilean Atacama Desert and designed to measure the polarized Cosmic Microwave Background (CMB) over large angular scales. The CLASS array is currently observing with three telescopes covering four frequency bands: one at 40 GHz (Q); one at 90 GHz (W1); and one dichroic system at 150/220 GHz (HF). During the austral winter of 2022, we upgraded the first 90 GHz telescope (W1) by replacing four of the seven focal plane modules. These new modules contain detector wafers with an updated design, aimed at improving the optical efficiency and detector stability. We present a description of the design changes and measurements of on-sky optical efficiencies derived from observations of Jupiter.

astro-ph.IM↗

arXiv2022

Calibration of Transition-edge Sensor (TES) Bolometer Arrays with Application to CLASS

The current and future cosmic microwave background (CMB) experiments fielding kilo-pixel arrays of transition-edge sensor (TES) bolometers require accurate and robust gain calibration methods. We simplify and refactor the standard TES model to directly relate the detector responsivity calibration and optical time constant to the measured TES current $I$ and the applied bias current $I_{\mathrm{b}}$. The calibration method developed for the Cosmology Large Angular Scale Surveyor (CLASS) TES bolometer arrays relies on current versus voltage ($I$-$V$) measurements acquired daily prior to CMB observations. By binning Q-band (40GHz) $I$-$V$ measurements by optical loading, we find that the gain calibration median standard error within a bin is 0.3%. We test the accuracy of this "$I$-$V$ bin" detector calibration method by using the Moon as a photometric standard. The ratio of measured Moon amplitudes between detector pairs sharing the same feedhorn indicates a TES calibration error of 0.5%. We also find that for the CLASS Q-band TES array, calibrating the response of individual detectors based solely on the applied TES bias current accurately corrects TES gain variations across time but introduces a bias in the TES calibration from data counts to power units. Since the TES current bias value is set and recorded before every observation, this calibration method can always be applied to raw TES data and is not subject to $I$-$V$ data quality or processing errors.

astro-ph.IM↗

arXiv2022

Construction of a Large Diameter Reflective Half-Wave Plate Modulator for Millimeter Wave Applications

Polarization modulation is a powerful technique to increase the stability of measurements by enabling the distinction of a polarized signal from dominant slow system drifts and unpolarized foregrounds. Furthermore, when placed as close to the sky as possible, modulation can reduce systematic errors from instrument polarization. In this work, we introduce the design and preliminary drive system laboratory performance of a new 60 cm diameter reflective half-wave plate (RHWP) polarization modulator. The wave plate consists of a wire array situated in front of a flat mirror. Using \mbox{50 $μ$m} diameter wires with \mbox{175 $μ$m} spacing, the wave plate will be suitable for operation in the millimeter wavelength range with flatness of the wires and parallelism to the mirror held to a small fraction of a wavelength. The presented design targets the 77--108 GHz range. Modulation is performed by a rotation of the wave plate with a custom rotary drive utilizing an actively controlled servo motor.

astro-ph.IM↗