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1. Population structure drives cultural diversity in finite populations: A hypothesis for localized community patterns on Rapa Nui (Easter Island, Chile)

   https://doi.org/10.1371/journal.pone.0250690  

   Lipo, Carl P.; DiNapoli, Robert J.; Madsen, Mark E.; Hunt, Terry L. (May 2021, PLOS ONE)

   Esteve-Altava, Borja (Ed.)

   Understanding how and why cultural diversity changes in human populations remains a central topic of debate in cultural evolutionary studies. Due to the effects of drift, small and isolated populations face evolutionary challenges in the retention of richness and diversity of cultural information. Such variation, however, can have significant fitness consequences, particularly when environmental conditions change unpredictably, such that knowledge about past environments may be key to long-term persistence. Factors that can shape the outcomes of drift within a population include the semantics of the traits as well as spatially structured social networks. Here, we use cultural transmission simulations to explore how social network structure and interaction affect the rate of trait retention and extinction. Using Rapa Nui (Easter Island, Chile) as an example, we develop a model-based hypothesis for how the structural constraints of communities living in small, isolated populations had dramatic effects and likely led to preventing the loss of cultural information in both community patterning and technology. 

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2. Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections

   https://doi.org/10.1155/2018/8760371  

   Gevertz, Jana L.; Wares, Joanna R. (October 2018, Computational and Mathematical Methods in Medicine)

   Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty. 

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3. An enactivist account of the dynamics of lying

   https://doi.org/10.1177/10597123231166421  

   Kronsted, Christian; Gallagher, Shaun; Tollefsen, Deborah; Windsor, Leah (April 2023, Adaptive Behavior)

   Enactivist accounts of communication have focused almost exclusively on honest, cooperative communication. However, much of human life involves deception and lies. Using the generally agreed upon definition of lying, we here develop an enactive account of the dynamics of lying. At face, lying poses a problem for enactive theories of cognition since lying seemingly requires the ability to represent counterfactual states of affairs and implant those representations in other agents' belief systems. On our account, lying involves the active manipulation of the short- and long-term dynamics of social cognitive systems so that agents have access to different sets of affordances from the one’s they counterfactually would have had access to without the lie. Representing truths and falsehoods are replaced with competency within social-cultural and material practices. 

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4. Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions

   Xie, Tian; Zhang, Xueru (November 2024, Advances in Neural Information Processing Systems)

   As machine learning (ML) models are increasingly used in social domains to make consequential decisions about humans, they often have the power to reshape data distributions. Humans, as strategic agents, continuously adapt their behaviors in response to the learning system. As populations change dynamically, ML systems may need frequent updates to ensure high performance. However, acquiring high-quality human-annotated samples can be highly challenging and even infeasible in social domains. A common practice to address this issue is using the model itself to annotate unlabeled data samples. This paper investigates the long-term impacts when ML models are retrained with model-annotated samples when they incorporate human strategic responses. We first formalize the interactions between strategic agents and the model and then analyze how they evolve under such dynamic interactions. We find that agents are increasingly likely to receive positive decisions as the model gets retrained, whereas the proportion of agents with positive labels may decrease over time. We thus propose a refined retraining process to stabilize the dynamics. Last, we examine how algorithmic fairness can be affected by these retraining processes and find that enforcing common fairness constraints at every round may not benefit the disadvantaged group in the long run. Experiments on (semi-)synthetic and real data validate the theoretical findings. 

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5. Archaeological networks, community detection, and critical scales of interaction in the U.S. Southwest/Mexican Northwest

   https://doi.org/10.1016/j.jaa.2023.101511  

   Peeples, Matthew A.; J. Bischoff, Robert (June 2023, Journal of Anthropological Archaeology)

   Archaeologists have long recognized that spatial relationships are an important influence on and driver of all manner of social processes at scales from the local to the continental. Recent research in the realm of complex networks focused on community detection in human and animal networks suggests that there may be certain critical scales at which spatial interactions can be partitioned, allowing researchers to draw potential boundaries for interaction that provide insights into a variety of social phenomena. Thus far, this research has been focused on short time scales and has not explored the legacies of historic relationships on the evolution of network communities and boundaries over the long-term. In this study, we examine networks based on material cultural similarity drawing on a large settlement and material culture database from the U.S. Southwest/Mexican Northwest (ca. 1000–1450 CE) divided into a series of short temporal intervals. With these temporally sequenced networks we: 1) demonstrate the utility of network community detection for partitioning interactions in geographic space, 2) identify key transitions in the geographic scales of network communities, and 3) illustrate the role of previous network configurations in the evolution of network communities and their spatial boundaries through time. 

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