We conduct a detailed investigation of the relationship among the obesity rate of urban areas and expressions of happiness, diet and physical activity on social media. We do so by analyzing a massive, geo-tagged data set comprising over 200 million words generated over the course of 2012 and 2013 on the social network service Twitter. Among many results, we show that areas with lower obesity rates: (1) have happier tweets and frequently discuss (2) food, particularly fruits and vegetables, and (3) physical activities of any intensity. Additionally, we provide evidence that each of these results offer different and unique insight into the variation of the obesity rate in urban areas within the United States. Our work shows how the contents of social media may potentially be used to estimate real-time, population-scale measures of factors related to obesity.

We propose a generative agent-based model of the emergence and escalation of xenophobic anxiety in which individuals from two di erent religious groups encounter various hazards within an artificial society. The architecture of the model is informed by several empirically validated theories about the role of religion in intergroup conflict. Our results identify some of the conditions and mechanisms that engender the intensification of anxiety within and between religious groups. We define mutually escalating xenophobic anxiety as the increase of the average level of anxiety of the agents in both groups over time. Trace validation techniques show that the most common conditions under which longer periods of mutually escalating xenophobic anxiety occur are those in which the di erence in the size of the groups is not too large and the agents experience social and contagion hazards at a level of intensity that meets or exceeds their thresholds for those hazards. Under these conditions agents will encounter out-group members more regularly, and perceive them as threats, generating mutually escalating xenophobic anxiety. The model's capacity to grow the macro-level emergence of this phenomenon from micro-level agent behaviors and interactions provides the foundation for future work in this domain.

We employ existing data sets and agent-based modeling to forecast changes in religiosity and existential security among a collective of individuals over time. Existential security reflects the extent of economic, socioeconomic and human development provided by society. Our model includes agents in social networks interacting with one another based on the education level of the agents, the religious practices of the agents, and each agent's existential security within their natural and social environments. The data used to inform the values and relationships among these variables is based on rigorous statistical analysis of the International Social Survey Programme Religion Module (ISSP) and the Human Development Report (HDR). We conduct an evaluation that demonstrates, for the countries and time periods studied, that our model provides a more accurate forecast of changes in existential security and religiosity than two alternative approaches. The improved accuracy is largely due to the inclusion of social networks with educational homophily which alters the way in which religiosity and existential security change in the model. These dynamics grow societies where two individuals with the same initial religious practices (or belief In God, or supernatural beliefs) evolve differently based on the educational backgrounds of the individuals with which they surround themselves. Finally, we discuss the limitations of our model and provide direction for future work.

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent-based models and simulations consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Simulated agents generate large volumes of data and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents’ perspectives on key life events by providing natural language narratives. However, these narratives should be factual, transparent, and reproducible. Therefore, we present a structured narrative prompt for sending queries to LLMs, we experiment with the narrative generation process using OpenAI’s ChatGPT, and we assess statistically significant differences across 11 Positive and Negative Affect Schedule (PANAS) sentiment levels between the generated narratives and real tweets using chi-squared tests and Fisher’s exact tests. The narrative prompt structure effectively yields narratives with the desired components from ChatGPT. In four out of forty-four categories, ChatGPT generated narratives which have sentiment scores that were not discernibly different, in terms of statistical significance (alpha level $\alpha =0.05$), from the sentiment expressed in real tweets. Three outcomes are provided: (1) a list of benefits and challenges for LLMs in narrative generation; (2) a structured prompt for requesting narratives of an LLM chatbot based on simulated agents’ information; (3) an assessment of statistical significance in the sentiment prevalence of the generated narratives compared to real tweets. This indicates significant promise in the utilization of LLMs for helping to connect a simulated agent’s experiences with real people.

Background: Forecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. Objective: COVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes and outcomes is needed to advance knowledge of health statistics for policy makers and the public. Using publicly available real-time data provided online, we aimed to evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts were evaluated based on how well they forecast 1, 3, and 7 days forward when utilizing 1-, 3-, 7-, or all prior–day cumulative case counts during early virus onset. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels. Methods: We created 1-, 3-, and 7-day forecasts at the county, health district, and state levels using (1) a naïve approach, (2) Holt-Winters (HW) exponential smoothing, (3) a growth rate approach, (4) a moving average (MA) approach, (5) an autoregressive (AR) approach, (6) an autoregressive moving average (ARMA) approach, and (7) an autoregressive integrated moving average (ARIMA) approach. Forecasts relied on Virginia’s 3464 historical county-level cumulative case counts from March 7 to April 22, 2020, as reported by The New York Times. Statistically significant results were identified using 95% CIs of median absolute error (MdAE) and median absolute percentage error (MdAPE) metrics of the resulting 216,698 forecasts. Results: The next-day MA forecast with 3-day look-back length obtained the lowest MdAE (median 0.67, 95% CI 0.49-0.84, P<.001) and statistically significantly differed from 39 out of 59 alternatives (66%) to 53 out of 59 alternatives (90%) at each geographic level at a significance level of .01. For short-range forecasting, methods assuming stationary means of prior days’ counts outperformed methods with assumptions of weak stationarity or nonstationarity means. MdAPE results revealed statistically significant differences across geographic levels. Conclusions: For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset, the following were found: (1) the MA method was effective for forecasting 1-, 3-, and 7-day cumulative case counts; (2) exponential growth was not the best representation of case growth during early virus onset when the public was aware of the virus; and (3) geographic resolution was a factor in the selection of forecasting methods.

Agentic AI marks a major shift in how autonomous systems reason, plan, and execute multi-step tasks. Unlike traditional single model prompting, agentic workflows integrate multiple specialized agents with different Large Language Models(LLMs), tool-augmented capabilities, orchestration logic, and external system interactions to form dynamic pipelines capable of autonomous decision-making and action. As adoption accelerates across industry and research, organizations face a central challenge: how to design, engineer, and operate production-grade agentic AI workflows that are reliable, observable, maintainable, and aligned with safety and governance requirements. This paper provides a practical, end-to-end guide for designing, developing, and deploying production-quality agentic AI systems. We introduce a structured engineering lifecycle encompassing workflow decomposition, multi-agent design patterns, Model Context Protocol(MCP), and tool integration, deterministic orchestration, Responsible-AI considerations, and environment-aware deployment strategies. We then present nine core best practices for engineering production-grade agentic AI workflows, including tool-first design over MCP, pure-function invocation, single-tool and single-responsibility agents, externalized prompt management, Responsible-AI-aligned model-consortium design, clean separation between workflow logic and MCP servers, containerized deployment for scalable operations, and adherence to the Keep it Simple, Stupid (KISS) principle to maintain simplicity and robustness. To demonstrate these principles in practice, we present a comprehensive case study: a multimodal news-analysis and media-generation workflow. By combining architectural guidance, operational patterns, and practical implementation insights, this paper offers a foundational reference to build robust, extensible, and production-ready agentic AI workflows.