A geographically-resolved, multi-level Bayesian model is used to analyze the data presented in the U.S. Police-Shooting Database (USPSD) in order to investigate the extent of racial bias in the ...shooting of American civilians by police officers in recent years. In contrast to previous work that relied on the FBI's Supplemental Homicide Reports that were constructed from self-reported cases of police-involved homicide, this data set is less likely to be biased by police reporting practices. County-specific relative risk outcomes of being shot by police are estimated as a function of the interaction of: 1) whether suspects/civilians were armed or unarmed, and 2) the race/ethnicity of the suspects/civilians. The results provide evidence of a significant bias in the killing of unarmed black Americans relative to unarmed white Americans, in that the probability of being {black, unarmed, and shot by police} is about 3.49 times the probability of being {white, unarmed, and shot by police} on average. Furthermore, the results of multi-level modeling show that there exists significant heterogeneity across counties in the extent of racial bias in police shootings, with some counties showing relative risk ratios of 20 to 1 or more. Finally, analysis of police shooting data as a function of county-level predictors suggests that racial bias in police shootings is most likely to emerge in police departments in larger metropolitan counties with low median incomes and a sizable portion of black residents, especially when there is high financial inequality in that county. There is no relationship between county-level racial bias in police shootings and crime rates (even race-specific crime rates), meaning that the racial bias observed in police shootings in this data set is not explainable as a response to local-level crime rates.
Modelling animal network data in R using STRAND Ross, Cody T.; McElreath, Richard; Redhead, Daniel
Journal of animal ecology,
March 2024, 2024-03-00, 20240301, Letnik:
93, Številka:
3
Journal Article
Recenzirano
Odprti dostop
There have been recent calls for wider application of generative modelling approaches in applied social network analysis. At present, however, it remains difficult for typical end users—for example, ...field researchers—to implement generative network models, as there is a dearth of openly available software packages that make application of such models as simple as other, permutation‐based approaches.
Here, we outline the STRAND R package, which provides a suite of generative models for Bayesian analysis of animal social network data that can be implemented using simple, base R syntax.
To facilitate ease of use, we provide a tutorial demonstrating how STRAND can be used to model proportion, count or binary network data using stochastic block models, social relation models or a combination of the two modelling frameworks.
STRAND facilitates the application of generative network models to a broad range of data found in the animal social networks literature.
Using simple base‐R syntax, users can create complex Bayesian network analysis models that integrate actor, recipient and dyadic random effects, and accommodate individual‐level, dyad‐level and block‐level covariates. This R package thus allows biologists, ecologists and social scientists to study social relationships using scientifically plausible generative models of network ties.
Social network analysis provides an important framework for studying the causes, consequences, and structure of social ties. However, standard self-report measures-for example, as collected through ...the popular "name-generator" method-do not provide an impartial representation of such ties, be they transfers, interactions, or social relationships. At best, they represent perceptions filtered through the cognitive biases of respondents. Individuals may, for example, report transfers that did not really occur, or forget to mention transfers that really did. The propensity to make such reporting inaccuracies is both an individual-level and item-level characteristic-variable across members of any given group. Past research has highlighted that many network-level properties are highly sensitive to such reporting inaccuracies. However, there remains a dearth of easily deployed statistical tools that account for such biases. To address this issue, we provide a latent network model that allows researchers to jointly estimate parameters measuring both reporting biases and a latent, underlying social network. Building upon past research, we conduct several simulation experiments in which network data are subject to various reporting biases, and find that these reporting biases strongly impact fundamental network properties. These impacts are not adequately remedied using the most frequently deployed approaches for network reconstruction in the social sciences (i.e., treating either the union or the intersection of double-sampled data as the true network), but are appropriately resolved through the use of our latent network models. To make implementation of our models easier for end-users, we provide a fully documented R package, STRAND, and include a tutorial illustrating its functionality when applied to empirical food/money sharing data from a rural Colombian population. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Cesario et al. argue that benchmarking the relative counts of killings by police on relative crime rates, rather than relative population sizes, generates a measure of racial disparity in the use of ...lethal force that is unbiased by differential crime rates. Their publication, however, lacked any formal derivation showing that their benchmarking methodology has the statistical properties required to establish such a claim. We use the causal model of lethal force by police conditional on relative crime rates implicit in their analyses and prove that their benchmarking methodology does not, in general, remove the bias introduced by crime rate differences. Instead, it creates strong statistical biases that mask true racial disparities, especially in the killing of unarmed noncriminals by police. Reanalysis of their data using formally derived criminality-correcting benchmarks shows that there is strong and statistically reliable evidence of anti-Black racial disparities in the killing of unarmed Americans by police in 2015–2016.
Active search for prey is energetically costly, so understanding how foragers optimize search has been central to foraging theory. Some theoretical work has suggested that foragers of randomly ...distributed prey should search using Lévy flights, while work on area-restricted and intermittent search strategies has demonstrated that foragers can use the information provided by prey encounters to more effectively adapt search direction and velocity. Previous empirical comparisons of these search modes have tended to rely on distribution-level analyses, due to the difficulty of collecting event-level data on encounters linked to the GPS tracks of foragers. Here we use a preliminary event-level data-set (18.7 hours of encounter-annotated focal follows over 6 trips) to show that two Colombian blowgun hunters use adaptive encounter-conditional heuristics, not non-conditional Lévy flights, when searching for prey. Using a theoretically derived Bayesian model, we estimate changes in turning-angle and search velocity as a function of encounters with prey at lagged time-steps, and find that: 1) hunters increase average turning-angle in response to encounters, producing a more tortuous search of patches of higher prey density, but adopt more efficient uni-directional, inter-patch movement after failing to encounter prey over a sufficient period of time; and, 2) hunters reduce search velocity in response to encounters, causing them to spend more of their search time in patches with demonstrably higher prey density. These results illustrate the importance of using event-level data to contrast encounter-conditional, area-restricted search and Lévy flights in explaining the search behavior of humans and other organisms.
Researchers studying social networks and inter-personal sentiments in bounded or small-scale communities face a trade-off between the use of roster-based and free-recall/name-generator-based survey ...tools. Roster-based methods scale poorly with sample size, and can more easily lead to respondent fatigue; however, they generally yield higher quality data that are less susceptible to recall bias and that require less post-processing. Name-generator-based methods, in contrast, scale well with sample size and are less likely to lead to respondent fatigue. However, they may be more sensitive to recall bias, and they entail a large amount of highly error-prone post-processing after data collection in order to link elicited names to unique identifiers. Here, we introduce an R package, DieTryin, that allows for roster-based dyadic data to be collected and entered as rapidly as name-generator-based data; DieTryin can be used to run network-structured economic games, as well as collect and process standard social network data and round-robin Likert-scale peer ratings. DieTryin automates photograph standardization, survey tool compilation, and data entry. We present a complete methodological workflow using DieTryin to teach end-users its full functionality.
Drawing on Skellam׳s (1958) work on sampling animal populations using transects, we derive a behavioral ecological model of the choice between sit-and-wait and active-search hunting. Using simple, ...biologically based assumptions about the characteristics of predator and prey, we show how an empirically definable parameter space favoring active-search hunting expands as: (1) the average rate of movement of prey decreases, or (2) the energetic costs of hunter locomotion decline. The same parameter space narrows as: (3) prey skittishness increases as a function of a hunter׳s velocity, or (4) prey become less detectable as a function of a hunter׳s velocity. Under either search tactic, encounter rate increases as a function of increasing prey velocity and increasing detection zone radius. Additionally, we investigate the roles of habitat heterogeneity and spatial auto-correlation or grouping of prey on the optimal search mode of a hunter, finding that habitat heterogeneity has the potential to complicate application of the model to some empirical examples, while the effects of prey grouping lead to relatively similar model outcomes. As predicted by the model, the introduction of the horse to the Great Plains and the introduction of the snowmobile to Arctic foraging communities decreased the metabolic costs of active-search and led to a change in normative hunting strategies that favored active-search in place of sit-and-wait hunting.
•We model the behavioral ecology of search mode for randomly moving predator and prey.•Active-search is favored at low prey movement velocity, ambush at high prey velocity.•Sit-and-wait mode is favored if predator movement is energetically costly.•Ambush is favored if faster predator velocity alerts prey or impedes their detection.•Optimal predator velocity in active-search mode balances costs against prey movement.
Stem cell scientists have developed methods for the self-formation of artificial organs, often referred to as organoids. Organoids can be used as model systems for research in multiple biological ...disciplines. Yoshiki Sasai's innovation for deriving mammalian retinal tissue from
stem cells has had a large impact on the study of the biology of vision. New developments in retinal organoid technology provide avenues for
models of human retinal diseases, studies of pathological mechanisms, and development of therapies for retinal degeneration, including electronic retinal implants and gene therapy. Moreover, these innovations have played key roles in establishing models for large-scale drug screening, studying the stages of retinal development, and providing a human model for personalized therapeutic approaches, like cell transplants to replace degenerated retinal cells. Here, we first discuss the importance of human retinal organoids to the biomedical sciences. Then, we review various functional features of retinal organoids that have been developed. Finally, we highlight the current limitations of retinal organoid technologies.
Human marriage systems, characterized by long-term partnerships and extended windows of parental care, differ from the mating systems of pulsed or seasonally breeding non-human animals in which ...Bateman's principles were originally tested. These features, paradigmatic of but not unique to humans, complicate the accurate measurement of mating success in evaluating Bateman's three principles. Here, we unpack the concept of mating success into distinct components: number of partners, number of years partnered, the timing of partnerships, and the quality of partners. Drawing on longitudinal records of marriage and reproduction collected in a natural-fertility East African population over a 20-year period, we test and compare various models of the relationship between mating success and reproductive success (RS), and show that an accurate assessment of male and female reproductive behaviour requires consideration of all major components of mating success. Furthermore, we demonstrate that while Bateman's third principle holds when mating success is defined in terms of years married, women's fitness increases whereas men's fitness decreases from an increase in the number of marriage partners, holding constant the total effective duration of marriages. We discuss these findings in terms of the distinct, sex-specific pathways through which RS can be optimized, and comment on the contribution of this approach to the broader study of sexual selection.
PDF
