About

Cheri Ross is the Department Chair of Comparative Literature and a Professor of Teaching in the same department at the University of California, Davis. She holds a Ph.D. and M.A. from Stanford University and a B.A. from Wellesley College. Her teaching focuses on ancient myth and literature, classical literary genres such as epic and drama, as well as medieval and early modern European literary traditions. Ross has contributed to the development of programs at Stanford, including the Western Culture Program, its successor Cultures, Ideas, and Values, and Stanford's Introduction to the Humanities Program, where she was responsible for pedagogical training and supervision of post-doctoral assistants. Throughout her career, she has received numerous awards for teaching and program administration, including the University Honors Program Outstanding Faculty Award at UC Davis.

Research topics

• Political Science
• Machine Learning
• Social Science
• Computer Science
• Psychology
• Sociology
• Biology
• Evolutionary biology
• Mathematics
• Developmental psychology
• Microeconomics
• Ecology
• Environmental ethics
• Epistemology
• Econometrics
• Economics
• Engineering ethics
• Geography
• Communication
• Mathematical economics

Selected publications

• Robust Bayesian analysis of animal networks subject to biases in sampling intensity and censoring

  Methods in Ecology and Evolution · 2025-04-15 · 2 citations

  articleOpen accessSenior author

  Abstract Data collection biases are a persistent issue for studies of social networks. This issue has been particularly important in animal social network analysis (ASNA), where data are often unevenly sampled and such biases may potentially lead to incorrect inferences about animal social behaviour. Here, we address the issue by developing a Bayesian model, which not only estimates network structure but also explicitly accounts for sampling and censoring biases. Using a set of simulation experiments designed to reflect various sampling and observational biases encountered in real‐world scenarios, we systematically validate our model and evaluate its performance relative to other common ASNA methodologies. By accounting for differences in node‐level censoring (i.e. individual variation in undetected ties), our model permits the recovery of true latent social connections, even under a wide range of conditions where some key individuals are intermittently unobserved. Our model outperformed all other existing approaches and accurately captured network structure, as well as individual‐level and dyad‐level effects. Antithetically, permutation‐based and simple linear regression approaches performed the worst across many conditions. These results highlight the advantages of generative network models for ASNA, as they offer greater flexibility, robustness and adaptability to real‐world data complexities. Our findings underscore the importance of generative models that jointly estimate network structure and measurement biases typical in empirical studies of animal social behaviour.

  PublisherDOI

• Social Networks, cooperation and social status in rural Colombia

  Bulletins et Mémoires de la Société d anthropologie de Paris · 2025-01-01

  articleOpen accessSenior author

  Within any given hierarchy, individuals differ in their social influence and decision-making authority (i.e., in social status) and these differences govern their access to the resources available to their group. Existing work has demonstrated two broad routes to achieving social status: through prestige, the perceived ability and willingness to confer benefits to others, and dominance, the perceived ability and willingness to inflict harm. I will present a study that examines whether distin...

  PublisherDOI

• Bayesian multiplex network models in R using STRAND: methods for biologists and social scientists

  Royal Society Open Science · 2025-10-01 · 1 citations

  articleOpen access1st authorCorresponding

  The social networks of interest to biologists, ecologists and social scientists are often multi-layered, with the same set of individuals interacting with one another in complex, multifaceted ways. Each type of interaction can be represented as one layer in a larger multiplex network. Important research questions often hinge on how ties or flows in one network layer impact ties or flows in another layer. Similar questions focus on the relationship between nodal characteristics across layers: for example, is an individual with a high out-degree in one layer more likely to have a high in-degree in another layer? These questions are effectively addressed by multiplex extensions of generative social network models, like the social relations model (SRM). Here, we present a multiplex implementation of the SRM in the STRAND R package and provide tutorials teaching end-users how to run the model on their own data. We provide worked examples of data analysis, parameter visualization and results interpretation, using datasets from experimental economics and animal behaviour. Our software package allows end-users to deploy powerful multiplex models, using only simple base-R model syntax, permitting wider use of generative network modelling approaches across disciplines.

  PublisherDOI

• Generating positive-definite correlation matrices with additional structure

  2025-07-05 · 2 citations

  preprintOpen accessSenior author

  Many applied problems in the biological and social sciences involve estimating correlation matrices with special constraints. A consequence of these constraints is that the correlations in the random effects exhibit special symmetries, like block structure and/or Toeplitz-like diagonal-constant banding. Generating valid, positive-definite prior correlation matrices with such structure is typically non-trivial in the context of Bayesian model fitting. Here, we provide two effective solutions to the problem, the first based on an l2-norm penalty, and the second based on a flexible Cholesky factor parameterization that permits a priori constraints on the elements of the correlation matrix. We compare and contrast the methods using empirical and simulated network data, test for differences in efficiency, and highlight the partial strengths of each method, before discussing opportunities for further improvements. We also provide generalizable implementations of both methods in Stan code.

  PublisherOA PDFDOI

• Author response for "Bayesian multiplex network models in R using STRAND: methods for biologists and social scientists"

  2025-08-16

  peer-review1st authorCorresponding
  PublisherDOI

• Five misunderstandings in animal social network analysis

  2025-08-04 · 1 citations

  preprintOpen access

  Animal social network analysis has become central to behavioural ecology, offering powerful tools to explore the links between social behaviour and ecological or evolutionary processes. While rooted in the broader field of social network analysis, the methods used in animal studies have diverged from contemporary practices in the broader field. This divergence has led to conflicting guidance on best practices and in confusion among behavioural ecologists on how to analyse animal network data. Here, we identify and resolve five key misunderstandings in animal social network analysis. We start by tracing a brief history of the field. We then define each misunderstanding, discuss the flaws in the methodology that they are premised on, and outline their consequences for scientific inference. Finally, we examine how these issues might be overcome by using models that reflect the generative mechanisms that underlay the structural features of social network data---building upon tools and ideas from the wider social networks literature. Our goal is to help bridge the gap between behavioural ecologists and the broader social network analysis community, encouraging methodological realignment and facilitating fundamental advances in how we understand the ecological and evolutionary foundations of animal social behaviour.

  PublisherOA PDFDOI

• Social and economic consequences of prestige and dominance in rural Colombian social networks

  2025-08-22

  preprintOpen accessSenior author

  Social status regulates influence and well-being in most social-animals. In humans, social status can be attained via two distinct routes: prestige (freely-conferred deference, typically tracking the ability of individuals to confer benefits) and dominance (fear-based deference, typically tracking the ability of individuals to inflict costs). While prestige and dominance are well-studied from a psychological perspective, their influence on dyadic behavior, including social leveling, remains under-explored---especially in small-scale communities. Here, we present data from four Colombian communities (N_ind=496), where we collected peer nominations of prestige, dominance, trust, affinity, fear, and friendship, and ran network-structured economic games measuring altruistic giving, exploitation, and costly punishment. Applying a multiplex network model to these data (N_obs=865,944; N_ties=76,427), we analyze how perceptions of status relate to dyadic game behavior. More-prestigious individuals were more trusted, had more friends, received more cooperative transfers, and were less frequently punished or exploited. More-dominant individuals experienced discrepant outcomes: they too had more friends and received more cooperative transfers, but they were also more feared and distrusted, and were preferential targets of exploitation and costly punishment. In short, prestige conferred clear social and economic advantages, while dominance carried net costs. Our work provides the first large-scale test of dyad-level dominance leveling in real-world networks, and yields support for the idea that dominance in human communities is a precarious strategy. Although dominant individuals may be targets of friendship and cooperation, perhaps due to a linkage between dominance and local authority, they are more heavily leveled and face difficulty in obtaining positive, community-wide standing.

  PublisherOA PDFDOI

• Perceived inequality and variability in the expression of parochial altruism

  Evolutionary Human Sciences · 2025-01-01 · 3 citations

  articleOpen access1st authorCorresponding

  Abstract It is commonly argued that humans have generalised predispositions for within-group favouritism and between-group animus (i.e. that humans are parochially altruistic ), leading to higher levels of internal conflict in societies with greater diversity. Other research, however, has questioned both the ubiquity of parochial altruism and the role of diversity per se in causing social discord. Here, we use ethnographic, social network and experimental economic game data to explore this topic in two multi-ethnic Colombian communities. We examine the extent to which Afrocolombian and Emberá residents express parochial altruism, finding appreciable variability between communities, and across individuals within communities. When present, parochial altruism appears to be driven by divergent perceptions of group-based economic need, not group identity per se . Our results suggest that diversity may be less likely to cause social discord than past work has suggested, as long as group-based inequalities in wealth, well-being and representation – that can destabilise positive inter-group relationships – are minimised.

  PublisherOA PDFDOI

• Author response for "Robust Bayesian analysis of animal networks subject to biases in sampling intensity and censoring"

  2025-02-03

  peer-reviewSenior author
  PublisherDOI

• Reproductive inequality in humans and other mammals

  London School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2025-03-19

  articleOpen access

  To address claims of human exceptionalism, we determine where humans fit within the greater mammalian distribution of reproductive inequality. We show that humans exhibit lower reproductive skew (i.e., inequality in the number of surviving offspring) among males and smaller sex differences in reproductive skew than most other mammals, while nevertheless falling within the mammalian range. Additionally, female reproductive skew is higher in polygynous human populations than in polygynous nonhumans mammals on average. This patterning of skew can be attributed in part to the prevalence of monogamy in humans compared to the predominance of polygyny in nonhuman mammals, to the limited degree of polygyny in the human societies that practice it, and to the importance of unequally held rival resources to women's fitness. The muted reproductive inequality observed in humans appears to be linked to several unusual characteristics of our species-including high levels of cooperation among males, high dependence on unequally held rival resources, complementarities between maternal and paternal investment, as well as social and legal institutions that enforce monogamous norms.

  PublisherOA PDFDOI

Frequent coauthors

• Monique Borgerhoff Mulder

  University of California, Davis

  27 shared

• Daniel Redhead

  23 shared

• Richard McElreath

  Max Planck Institute for Evolutionary Anthropology

  23 shared

• Bruce Winterhalder

  University of California, Davis

  21 shared

• Anne C. Pisor

  21 shared

• Jeremy Koster

  Max Planck Institute for Evolutionary Anthropology

  20 shared

• Benjamin Grant Purzycki

  Aarhus University

  16 shared

• Russell D. Greaves

  University of New Mexico

  12 shared

Education

• PhD, Anthropology

  University of California Davis

  2014

Awards & honors

• University Honors Program Outstanding Faculty Award