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1. Taxation and Innovation: What Do We Know?

   https://doi.org/10.3386/w27109  

   Akcigit, Ufuk; Stantcheva, Stefanie (November 2021, Innovation and Public Policy)

   Goolsbee, Austan; Jones, Benjamin (Ed.)

   Tax policies are a wide array of tools, commonly used by governments to influence the economy. In this paper, we review the many margins through which tax policies can affect innovation, the main driver of economic growth in the long-run. These margins include the impact of tax policy on i) the quantity and quality of innovation; ii) the geographic mobility of innovation and inventors across U.S. states and countries; iii) the declining business dynamism in the U.S., firm entry, and productivity; iv) the quality composition of firms, inventors, and teams; and v) the direction of research effort, e.g., toward applied versus basic research, or toward dirty versus clean technologies. We give ideas drawn from research on how the design of policy can allow policy makers to foster the most productive firms without wasting public funds on less productive ones. 

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2. Do Owners Know How Impulsive Their Dogs Are?

   https://doi.org/10.26451/abc.09.03.02.2022  

   Stevens, Jeffrey; Mathias, Madeline; Herridge, Megan; Hughes-Duvall, Kylie; Wolff, London; Yohe, McKenna (August 2022, Animal Behavior and Cognition)

   Impulsivity is an important behavioral trait in dogs that affects many aspects of their relationship with humans. But how well do owners know their dog’s levels of impulsivity? Two studies have investigated how owner perceptions of their dog’s impulsivity correlate with the distance traveled in a spatial impulsivity task requiring choices between smaller, closer vs. larger, more distant food treats (Brady et al., 2018; Mongillo et al., 2019). However, these studies have demonstrated mixed results. The current project aimed to replicate these studies by correlating owner responses to the Dog Impulsivity Assessment Survey (DIAS) and the dog’s maximum distance traveled in a spatial impulsivity task. We found that neither the DIAS overall score nor its three subcomponent scores correlated with dogs’ distance traveled. This result replicates Mongillo et al.’s lack of a relationship but does not replicate Brady et al.’s effect, questioning the generalizability of owner reports of dog impulsivity. The lack of replication could result from differences in methodology and sample populations, but it raises intriguing questions about possible differences in dog characteristics and owner knowledge of their dogs across cultures. 

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3. Craniofacial skeletal response to encephalization: How do we know what we think we know?

   https://doi.org/10.1002/ajpa.23766  

   Lesciotto, Kate M.; Richtsmeier, Joan T. (January 2019, American Journal of Physical Anthropology)

   Abstract Dramatic changes in cranial capacity have characterized human evolution. Important evolutionary hypotheses, such as the spatial packing hypothesis, assert that increases in relative brain size (encephalization) have caused alterations to the modern human skull, resulting in a suite of traits unique among extant primates, including a domed cranial vault, highly flexed cranial base, and retracted facial skeleton. Most prior studies have used fossil or comparative primate data to establish correlations between brain size and cranial form, but the mechanistic basis for how changes in brain size impact the overall shape of the skull resulting in these cranial traits remains obscure and has only rarely been investigated critically. We argue that understanding how changes in human skull morphology could have resulted from increased encephalization requires the direct testing of hypotheses relating to interaction of embryonic development of the bones of the skull and the brain. Fossil and comparative primate data have thoroughly described the patterns of association between brain size and skull morphology. Here we suggest complementing such existing datasets with experiments focused on mechanisms responsible for producing the observed patterns to more thoroughly understand the role of encephalization in shaping the modern human skull. 

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4. How do we know how smart AI systems are?

   https://doi.org/10.1126/science.adj5957  

   Mitchell, Melanie (July 2023, Science)

   In 1967, Marvin Minksy, a founder of the field of artificial intelligence (AI), made a bold prediction: “Within a generation…the problem of creating ‘artificial intelligence’ will be substantially solved.” Assuming that a generation is about 30 years, Minsky was clearly overoptimistic. But now, nearly two generations later, how close are we to the original goal of human-level (or greater) intelligence in machines? 

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5. What Design Methods do DataVis Practitioners Know and Use?

   https://doi.org/10.1145/3334480.3383048  

   Parsons, Paul; Baigelenov, Ali; Hung, Ya-Hsin; Schrank, Connor (April 2020, CHI '20 EA: Extended Abstracts of the ACM SIGCHI Conference on Human Factors in Computing Systems)

   Data visualization as a profession has been growing rapidly in recent years. Although some initiatives are in place to increase engagement between the academic and practitioner communities, we currently do not have a good understanding of how practitioners do their design work, including what methods, approaches, and principles they know and use in their everyday practice. We present a subset of results of a survey in which 87 DataVis practitioners identified their familiarity with popular design methods and the frequency with which they use them in their own work. We also discuss follow-up work to develop a deeper understanding of practitioners’ perspectives on design methods. 

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