Can we shift belief in the ‘Law of Small Numbers’?

‘Sample size neglect’ is a tendency to underestimate how the variability of mean estimates changes with sample size. We studied 100 participants, from science or social science backgrounds, to test whether a training task showing different-sized samples of data points (the ‘beeswarm’ task) can help overcome this bias. Ability to judge if two samples came from the same population improved with training, and 38% of participants reported that they had learned to wait for larger samples before making a response. Before and after training, participants completed a 12-item estimation quiz, including items testing sample size neglect (S-items). Bonus payments were given for correct responses. The quiz confirmed sample size neglect: 20% of participants scored zero on S-items, and only two participants achieved more than 4/6 items correct. Performance on the quiz did not improve after training, regardless of how much learning had occurred on the beeswarm task. Error patterns on the quiz were generally consistent with expectation, though there were some intriguing exceptions that could not readily be explained by sample size neglect. We suggest that training with simulated data might need to be accompanied by explicit instruction to be effective in counteracting sample size neglect more generally

Thermal Regulation and the Star-forming Main Sequence

We argue that the interplay between cosmic rays, the initial mass function (IMF), and star formation plays a crucial role in regulating the star-forming "main sequence." To explore these phenomena we develop a toy model for galaxy evolution in which star formation is regulated by a combination of a temperature-dependent IMF and heating due to starlight, cosmic rays, and (at very high redshift) the cosmic microwave background. This produces an attractor, near-equilibrium solution which is consistent with observations of the star-forming main sequence over a broad redshift range. Additional solutions to the same equations may correspond to other observed phases of galaxy evolution, including quiescent galaxies. This model makes several falsifiable predictions, including higher metallicities and dust masses than anticipated at high redshift and isotopic abundances in the Milky Way. It also predicts that stellar mass-to-light ratios are lower than produced using a Milky Way–derived IMF, such that inferences of stellar masses and star formation rates for high redshift galaxies are overestimated. In some cases, this may also transform inferred dark matter profiles from core-like to cusp-like

Responsible AI Considerations in Text Summarization Research: A Review of Current Practices

AI and NLP publication venues have increasingly encouraged researchers to reflect on possible ethical considerations, adverse impacts, and other responsible AI issues their work might engender. However, for specific NLP tasks our understanding of how prevalent such issues are, or when and why these issues are likely to arise, remains limited. Focusing on text summarization -- a common NLP task largely overlooked by the responsible AI community -- we examine research and reporting practices in the current literature. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals. We also discuss current evaluation practices and consider how authors discuss the limitations of both prior work and their own work. Overall, we find that relatively few papers engage with possible stakeholders or contexts of use, which limits their consideration of potential downstream adverse impacts or other responsible AI issues. Based on our findings, we make recommendations on concrete practices and research directions