Allen Lee, Ph.D. - US grants

With National Science Foundation Support, Dr. Katherine Spielmann will lead an interdisciplinary team of archaeologists and software developers in the analysis of the factors that led to the over-hunting of large game (principally antelope and deer) in the American Southwest between A.D. 1200 and 1500. This period of time is characterized by large-scale migrations, and aggregation of people into towns of 500-1000 residents. During this time some archaeological animal bone assemblages indicate a decline in the availability of large game. There is currently no regional-scale understanding of the factors that promoted over-hunting in some areas but not in others. The two primary factors to be investigated are 1) population size, concentration, and persistence on the landscape, and 2) environmental variation. The team will then investigate the contexts in which people chose to intensify turkey husbandry in response to declining access to meat from wild game. Although domesticated turkeys are known from the Southwest by A.D. 500, until the 1100s they were largely raised for feathers. After that time some populations intensified turkey production for consumption, while others did not. Why this variation existed is a target of the project. From this perspective the project is important because it will provide insight into long term interactions between a society at a relatively "simple" level of social and subsistence organization and the environment within which it existed. The results will be generalizable to many other cultures and regions of the world.

The project is innovative in the scale at which animal bone data from a diversity of prehistoric village sites across the Southwestern US will be integrated to address the issue of human impacts on the environment. Although in the past researchers have undertaken synthetic analyses at smaller scales, these have generally used animal bone data presented in summary form in the published literature. Rather than depend on published data, these syntheses will rely on analyses of an integrated composite of original faunal datasets. The PI's approach to synthesis using the original datasets is made possible through use of tDAR (the Digital Archaeological Record; http://tdar.org), a repository for digital archaeological data. Members of the project's Southwestern Faunal Working Group are uploading their datasets into tDAR. The software development team will then work with the Faunal Working Group to streamline tDAR's groundbreaking analytical tool that allows the integration of datasets (or spreadsheets) that were recorded by different investigators using inconsistent analytical protocols. This integration brings the animal bone data into a single classificatory scheme, thus making it possible to analyze faunal datasets in a manner that allows the research team to address anthropological questions in ways that have not heretofore been possible.

The intellectual merits of the project lie in demonstrating the scientific value of large-scale dataset sharing and integration for addressing a diversity of anthropological questions. The project will significantly enhance the anthropological understanding of long-term interaction between people and their environment, and will improve research using archaeological animal bone data. The broader impacts of the project include training of undergraduate and graduate students through internships and research assistantships, and workshops on tDAR for cultural resource management communities. In addition, scientists in other fields will be able to access these archaeological data and use this project's tools and protocols to address broader issues of biodiversity and environmental change.

Norms of transparency and knowledge sharing in science encourage new research to build on prior discoveries, leading to rapid innovation and significant societal returns. This project establishes a new activity to help make scientific computation and data science more transparent and accessible. Computation has evolved from tools for assisting scientific research to digital laboratories where fundamental scientific discoveries take place. This is increasingly so for social and ecological sciences, which are combining big data with computational models to better understand the social and earth systems whose complex dynamics underlie many of the grand challenges faced by humanity today. Organized as a Spoke in the National Science Foundation's Big Data Innovation Hub and Spoke network, this project establishes a next generation, online Computational Model Library (CMLX).

In the CMLX, software code for computational models used in social and ecological sciences is published and freely accessible to the scientific community and general public, and linked with an online database of scientific papers reporting on associated model-based science. In addition to the code library, the CMLX is developing an online repository to archive end-to-end workflows of model-based science, where the entire research process can be followed and reproduced, from data synthesis, to modeling, analysis, and visualization. A regional Working Group - including software developers, data providers, and user communities - will provide advice and expertise, as well as help establish community-wide standards for transparency and accessibility in scientific computation. The CMLX is enabling transparent access to scientific procedures, models, and data that can be used to assess the consequences of alternative scenarios, policies, and assumptions for more sustainable management of complexly coupled human and earth systems.

Cooperation is vital to a well-functioning society. Cooperation is needed to solve difficult societal problems, sustain shared social systems and resources (common-pool resources), and provide essential goods and services (public goods). Society's governance systems promote cooperation by protecting shared interests, encouraging people to follow rules, and reducing conflicts. Widespread and sustained cooperation, however, is not guaranteed. Recent world events show many examples of cooperative failures (e.g., global conflict, political upheaval, environmental degradation, and resource scarcity). The psychological mechanisms involved in cooperative decision making must be better understood to develop more effective solutions to these problems. The current research project examines the psychological processes associated with three important aspects of governance: communication, democratic decision making (e.g., voting), and enforcement (e.g., monitoring and financial punishments). The goal is to understand (a) when and why these governance mechanisms improve cooperation, or hinder it, and (b) what motivates groups of people to self-govern, creating their own effective solutions for shared problems. The research project looks at five core aspects of social cognition that influence people's group commitment and policy acceptance: perceptions of governmental legitimacy, satisfaction of fundamental needs (e.g., fair decision making, security), perceived reasons others act the way they do (causal attributions), group identity (e.g., liking, trust), and intrinsic and extrinsic motivations. By studying these fundamental aspects of social cognition, this research seeks to identify core principles of cooperation (e.g., enforcement must be legitimized) that can better explain cooperative failures in society (e.g., why regulatory systems sometimes backfire), reconcile competing theories in social science, and improve public policy. The project is mentoring many graduate and undergraduate students, with diverse backgrounds, in research design, data collection, analyses, and communication. Educational resources based on this research are incorporated into a new decision-making course, existing courses in sustainability and experimental economics, and developed for elementary schools. These materials are shared for public use. A free podcast series is created to share important concepts, research designs, and results with the public and scientific community; these podcasts also discuss societal implications, in light of major world events.

The research team conducts five interdisciplinary lab experiments in 5 years. Groups manage a shared resource simulated on the computer in VCWeb (Virtual Commons software), earning money based on their harvests. Surveys measure motivations and perceptions corresponding to each of the five fundamental social cognitions. Experiment 1 examines (a) how communication improves cooperation and (b) the motivations involved in voluntary cooperation and self-organization. Participants will complete the resource governance task in different phases, with and without communication. Psychological effects of communication are correlated with observed levels of cooperation, and used to develop principles for institutional design and public policy. Experiment 2 assesses both communication and enforcement (economic sanctions). This study also examines how non-punitive aspects of enforcement (e.g., justification of sanctions, rehabilitative techniques) improve enforcement and counteract potentially harmful side-effects. Experiment 3 investigates how different voting processes potentially justify, or legitimize enforcement. This experiment clarifies when and how democratic processes and enforcement enhance one another. Experiment 4 examines how preferences for different governance systems (e.g., voting mechanisms) change over time, identifying core motivations that drive institutional evolution and dynamic fluctuations in cooperation. Experiment 5 tests boundary conditions and integrates key findings across each experiment. All results are related to a core theory of how fundamental needs contribute to group commitment, policy acceptance, and voluntary cooperation.

PROJECT SUMMARY/ABSTRACT Candidate: Dr. Allen Lee, MD is a gastroenterologist with advanced training in gastrointestinal motility whose research focuses on identifying abnormalities in host-microbial interactions to improve care in irritable bowel syndrome (IBS). Dr. Lee?s long-term career goals are to identify novel subgroups of IBS patients which inform biological responses to therapies and guide management. The proposed K23 mentored career development award includes a 3-year plan for training, didactics, and research activities that will provide Dr. Lee with the necessary skills and experience to become a successful independent investigator. Career Development: Dr. Lee will develop skills in the following four areas: 1) culture-independent approaches to study host-microbial interactions in IBS; 2) advanced biostatistical methods to study longitudinal datasets, including mixed models or generalized additive models; 3) laboratory-based translational techniques; 4) predictive analytics, such as machine learning algorithms. These training goals will directly contribute to Dr. Lee?s long-term career goals and prepare him to submit a successful R01 application. Research Context: Common treatments in diarrhea-predominant IBS (IBS-D) are effective in ?50% patients. Additionally, there are currently no methods to identify patients more likely to respond to different treatments. The current paradigm for managing IBS patients revolves around identifying and treating the predominant symptom complexes. However, this is a hugely inefficient model and leads to frustration for both patients and health care providers. This proposal seeks to determine whether a systems biology approach, which incorporates microbiome and metabolomics data along with detailed clinical phenotype information, may identify novel subgroups that inform treatment decisions in IBS-D. Research Plan: This career development award leverages an on-going clinical trial comparing the effects of a nonabsorbable antibiotic rifaximin with a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) in IBS-D patients to identify differences in microbial community structure and function in responders vs. non-responders to therapy. Specific Aim 1 will identify fecal microbial features by 16S rRNA sequence analysis characteristic of treatment response to rifaximin or a low FODMAP diet. Specific Aim 2 will determine how treatments with rifaximin or low FODMAP diet affect the fecal metabolome, including short chain fatty acids and bile acids, in IBS-D patients. Specific Aim 3 will develop predictive models to identify subsets of IBS-D patients responsive to treatment. We will also identify risk factors for non-response to either rifaximin or low FODMAP diet. Results from this proposal will inform two future R01 proposals to validate these findings as well as to understand the mechanisms by which host-microbial interactions may mediate response to different therapies in IBS.

This project is designed to support and advance next generation, interdisciplinary science of the complexly interacting societal and natural processes that are critical to human life and well-being. Computational models are powerful scientific tools for understanding these coupled social-natural systems and forecasting their future conditions for evidenced-based planning and policy-making. This project is led by the Network for Computational Modeling in Social and Ecological Sciences (CoMSES.Net). CoMSES.Net's science gateway promotes knowledge sharing among scientists and with the general public, and enables open, online access to sophisticated computational models of social and ecological systems. CoMSES.Net's partners in this project (the Community Surface Dynamics Modeling System and Consortium of Universities for the Advancement of Hydrologic Science) also enable knowledge sharing and provide open, online repositories of models in the earth sciences. This project will enhance these science gateways and create online educational materials to make these critical technologies easier to find, understand, and use for scientists and non-scientists alike. By integrating innovative technology with training and incentives to engage in best practice standards, this project will stimulate innovation and diversity in modeling science. It will enable researchers to build on each other's work and combine it in new ways to address societal and environmental challenges. The cybertools and educational programs developed in the project will be openly accessible not just to research institutions but also to smaller colleges, state and local governments, and a broader audience beyond the science community. The project will give decision-makers and the data scientists who support them access to a larger and more varied toolkit with which to explore potential solutions to societal and environmental policy issues. A long-term aim of the project is to support an evolving ecosystem of diverse, reusable, and combinable models that are transparently accessible to anyone in the world. Sustainable planetary care and management is a challenge that confronts all of humanity, and requires knowledge, histories, methods, perspectives, and engagement of researchers, decision-makers, and private citizens across the country and throughout the world.

The project will develop an Integrative Cyberinfrastructure Framework (ICF) to enable innovative next-generation modeling of human and natural systems, and build capacity in modeling science. It will support a set of activities that integrate the human and technological components of cyberinfrastructure. 1) Software tools will be developed that augment model codebases with modern software development scaffolding to facilitate reuse, integration, and validation of model code. 2) The project will provide high-throughput computing (HTC) resources for simultaneously running numerous iterations of models needed to capture stochastic variability, explore a parameter space, and generate alternative scenarios; 3) Online training activities will build expertise and capacity to make effective use of the cybertools and the HTC resources; 4) The ICF will engage a global modeling science community to provide professional incentives that encourage researchers to adopt best practices and catalyze innovative science. Leveraging existing NSF investments, the ICF will be developed and deployed by the Network for Computational Modeling in Social and Ecological Sciences (CoMSES.Net), in partnership with the Community Surface Dynamics Modeling System (CSDMS), Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI), Open Science Grid, Big Data Hub/Spoke network, and Science Gateways Community Institute. Computational models have emerged as powerful scientific tools for understanding coupled social-biogeophysical systems and generating forecasts about future conditions under a range of climate, biogeophysical, and socioeconomic conditions. CoMSES.Net, CSDMS, and CUASI are scientific networks, with online science gateways and code archives that enable open access to computational models for an international community of social, ecological, environmental, and geophysical scientists. However, the full value of accessible, well-documented models only can be realized if their code is also widely reproducible and reusable, with a potential for integration with other models. In order to confront critical challenges for understanding the coupled human and natural systems of today's world, modeling scientists also need HTC environments for upscaling models and exploring high-dimensional parameter spaces inherent in representing these systems. The ICF is designed to meet these challenges. By integrating technology with intellectual capacity-building, the ICF will stimulate innovation and diversity in modeling science by letting creative researchers build on each other's work more readily and combine it in new ways to address societal-environmental challenges we have not yet perceived. The tools and training resources will be openly accessible not just to leading research institutions but also to the many smaller colleges, state and local governments, and a broader audience beyond science. They will provide decision-makers and the data scientists who support them access to a much larger and more varied toolkit with which to explore potential solution spaces to social and environmental policy issues. The proposed ICF is also designed to help transform scientific modeling practice, including incentives that can help early career researchers shift from creating models to solve problems specific to a particular project to models that are also useful for others. The project will help support a future evolving ecosystem of diverse, reusable, and integrable models that are transparently accessible to the broader community.

This project is funded by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering, with the Division of Social and Economic Sciences in the Directorate for Social, Behavioral & Economic Sciences also contributing funds.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This project investigates how patterns of connection between people affect their quality of life over the long term. The goal is to map out patterns of interaction between people in different locations and assess how these patterns impacted the development of their communities in a changing environment. Learning about these patterns can be accomplished with archaeological data, which show how ancient people worked together to meet basic human needs. Understanding how social networks grew and changed in the past can lead to a better understanding of how people today can work together for increased prosperity, inclusiveness, environmental sustainability, and peace. <br/><br/>This project brings together a team of researchers from multiple universities, not-for-profit organizations, and tribal communities. These scientists use archaeological evidence to investigate relationships between spatial patterns of social interaction and the quality of life over 800 years in the southwestern United States. They combine demographic, socioeconomic, health, and environmental reconstructions of the past built using data from previous NSF-funded projects into a single research platform. They also use this platform, together with ideas from complex systems and network analysis, to examine how spatial properties of human networks influenced other aspects of human development, using archaeological indicators of the UN Sustainable Development Goals as the basis for assessment. The results will advance understandings of how socio-spatial networks influence sustainability and the quality of life.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.