Forecasting Effects of Influence Operations: A Generative Social Science Methodology

Simulation enables analysis of social systems that would be difficult or unethical to experiment upon directly. Agent-based models have been used successfully in the field of generative social science to discover parsimonious sets of factors that generate social behavior. This methodology provides an avenue to explore the spread of anti-government sentiment in populations and to compare the effects of potential Military Information Support Operations (MISO) actions. This research develops an agent-based model to investigate factors that affect the growth of rebel uprisings in a notional population. It adds to the civil violence model developed by Epstein (2006) by enabling communication between agents in the manner of a genetic algorithm and friendships based on shared beliefs. A designed experiment is performed. Additionally, two counter-propaganda strategies are compared and explored. Analysis identifies factors that have effects that can explain some real-world observations, and provides a methodology for MISO operators to compare the effectiveness of potential actions

Generating Strong Diversity of Opinions: Agent Models of Continuous Opinion Dynamics

Opinion dynamics is the study of how opinions in a group of individuals change over time. A goal of opinion dynamics modelers has long been to find a social science-based model that generates strong diversity -- smooth, stable, possibly multi-modal distributions of opinions. This research lays the foundations for and develops such a model. First, a taxonomy is developed to precisely describe agent schedules in an opinion dynamics model. The importance of scheduling is shown with applications to generalized forms of two models. Next, the meta-contrast influence field (MIF) model is defined. It is rooted in self-categorization theory and improves on the existing meta-contrast model by providing a properly scaled, continuous influence basis. Finally, the MIF-Local Repulsion (MIF-LR) model is developed and presented. This augments the MIF model with a formulation of uniqueness theory. The MIF-LR model generates strong diversity. An application of the model shows that partisan polarization can be explained by increased non-local social ties enabled by communications technology

Re-visiting Meltsner: Policy Advice Systems and the Multi-Dimensional Nature of Professional Policy Analysis

10.2139/ssrn.15462511-2

Agent Scheduling in Opinion Dynamics: A Taxonomy and Comparison Using Generalized Models

Opinion dynamics models are an important field of study within the agent-based modeling community. Agent scheduling elements within existing opinion dynamics models vary but are largely unjustified and only minimally explained. Furthermore, previous research on the impact of scheduling is scarce, partially due to a lack of a common taxonomy with which to discuss and compare schedules. The Synchrony, Actor type, Scale (SAS) taxonomy is presented, which aims to provide a common lexicon for agent scheduling in opinion dynamics models. This is demonstrated using a generalized repeated averaging model (GRAM) and a generalized bounded confidence model (GBCM). Significant differences in model outcomes with varied schedules are given, along with the results of intentional model biasing using only schedule variation. We call on opinion dynamics modelers to make explicit their choice of schedule and to justify that choice based on realistic social phenomena.Abstract © JASSS

Aluminum Nitride Hydrolysis Enabled by Hydroxyl-Mediated Surface Proton Hopping

Aluminum nitride (AlN) is used extensively in the semiconductor industry as a high-thermal-conductivity insulator, but its manufacture is encumbered by a tendency to degrade in the presence of water. The propensity for AlN to hydrolyze has led to its consideration as a redox material for solar thermochemical ammonia (NH₃) synthesis applications where AlN would be intentionally hydrolyzed to produce NH₃ and aluminum oxide (Al₂O₃), which could be subsequently reduced in nitrogen (N₂) to reform AlN and reinitiate the NH₃ synthesis cycle. No quantitative, atomistic mechanism by which AlN, and more generally, metal nitrides react with water to become oxidized and generate NH₃ yet exists. In this work, we used density-functional theory (DFT) to examine the reaction mechanisms of the initial stages of AlN hydrolysis, which include: water adsorption, hydroxyl-mediated proton diffusion to form NH₃, and NH₃ desorption. We found activation barriers (<i>E</i>_a) for hydrolysis of 330 and 359 kJ/mol for the cases of minimal adsorbed water and additional adsorbed water, respectively, corroborating the high observed temperatures for the onset of steam AlN hydrolysis. We predict AlN hydrolysis to be kinetically limited by the dissociation of strong Al–N bonds required to accumulate protons on surface N atoms to form NH₃. The hydrolysis mechanism we elucidate is enabled by the diffusion of protons across the AlN surface by a hydroxyl-mediated Grotthuss mechanism. A comparison between intrinsic (<i>E</i>_a = 331 kJ/mol) and mediated proton diffusion (<i>E</i>_a = 89 kJ/mol) shows that hydroxyl-mediated proton diffusion is the predominant mechanism in AlN hydrolysis. The large activation barrier for NH₃ generation from AlN (<i>E</i>_a = 330 or 359 kJ/mol, depending on water coverage) suggests that in the design of materials for solar thermochemical ammonia synthesis, emphasis should be placed on metal nitrides with less covalent metal–nitrogen bonds and, thus, more-facile NH₃ liberation

Initial Clinical Needs Among Transgender and Non-binary Individuals in a Large, Urban Gender Health Program

BackgroundTransgender and gender-diverse individuals are particularly vulnerable to healthcare discrimination and related health sequelae.ObjectiveTo demonstrate diversity in demographics and explore variance in needs at the time of intake among patients seeking care at a large, urban gender health program.DesignWe present summary statistics of patient demographics, medical histories, and gender-affirming care needs stratified by gender identity and sexual orientation.ParticipantsWe reviewed all intake interviews with individuals seeking care in our gender health program from 2017 to 2020.Main measuresClients reported all the types of care in which they were interested at the time of intake as their "reason for call" (i.e., establish primary care, hormone management, surgical services, fertility services, behavioral health, or other health concerns).Key resultsOf 836 patients analyzed, 350 identified as trans women, 263 as trans men, and 223 as non-binary. The most prevalent sexual identity was straight among trans women (34%) and trans men (38%), whereas most (69%) non-binary individuals identified as pansexual or queer; only 3% of non-binary individuals identified as straight. Over half of patients reported primary care, hormone management, or surgical services as the primary reason for contacting our program. Straight, transgender women were more likely to report surgical services as their primary reason for contacting our program, whereas gay transgender men were more likely to report primary care as their reason.ConclusionsIndividuals contacting our gender health program to establish care were diverse in sexual orientation and gender-affirming care needs. Care needs varied with both gender identity and sexual orientation, but primary care, hormone management, and surgical services were high priorities across groups. Providers of gender-affirming care should inquire about sexual orientation and detailed treatment priorities, as trans and gender-diverse populations are not uniform in their treatment needs or goals