Can Social Norms Explain Long-Term Trends in Alcohol Use? Insights from Inverse Generative Social Science.

Social psychological theory posits entities and mechanisms that attempt to explain observable differences in behavior. For example, dual process theory suggests that an agent's behavior is influenced by intentional (arising from reasoning involving attitudes and perceived norms) and unintentional (i.e.

Inverse Generative Social Science: Backward to the Future.

The agent-based model is the principal scientific instrument of generative social science. Typically, we design completed agents-fully endowed with rules and parameters-to grow macroscopic target patterns from the bottom up. Inverse generative science (iGSS) stands this approach on its head: Rather than handcrafting completed agents to grow a target-the problem-we start with the macro-target and evolve micro-agents that generate it, stipulating only primitive agent-rule constituents and permissible combinators.

Privacy and contact tracing efficacy.

As the COVID-19 pandemic emerged, public health authorities and software designers considered the possibility that smartphones could be used for contact tracing to control disease spread. Smartphone-based contact tracing was attractive in part because it promised to allow the tracing of contacts that might not be reported using traditional contact tracing methods. Comprehensive contact tracing raises distinctive privacy concerns, however, that have not been previously explored.

Triple contagion: a two-fears epidemic model.

We present a differential equations model in which contagious disease transmission is affected by contagious fear of the disease and contagious fear of the control, in this case vaccine. The three contagions are coupled. The two fears evolve and interact in ways that shape distancing behaviour, vaccine uptake, and their relaxation.

A review and agenda for integrated disease models including social and behavioural factors.

Social and behavioural factors are critical to the emergence, spread and containment of human disease, and are key determinants of the course, duration and outcomes of disease outbreaks. Recent epidemics of Ebola in West Africa and coronavirus disease 2019 (COVID-19) globally have reinforced the importance of developing infectious disease models that better integrate social and behavioural dynamics and theories. Meanwhile, the growth in capacity, coordination and prioritization of social science research and of risk communication and community engagement (RCCE) practice within the current pandemic response provides an opportunity for collaboration among epidemiological modellers, social scientists and RCCE practitioners towards a mutually beneficial research and practice agenda.

Data analysis and modeling pipelines for controlled networked social science experiments.

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors.

The Normative Underpinnings of Population-Level Alcohol Use: An Individual-Level Simulation Model.

. By defining what is "normal," appropriate, expected, and unacceptable, social norms shape human behavior. However, the individual-level mechanisms through which social norms impact population-level trends in health-relevant behaviors are not well understood.

Toward inverse generative social science using multi-objective genetic programming.

Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intra-agent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening.

Social conformity despite individual preferences for distinctiveness.

We demonstrate that individual behaviours directed at the attainment of distinctiveness can in fact produce complete social conformity. We thus offer an unexpected generative mechanism for this central social phenomenon. Specifically, we establish that agents who have fixed needs to be distinct and adapt their positions to achieve distinctiveness goals, can nevertheless self-organize to a limiting state of absolute conformity.

Modeling the regional spread and control of vancomycin-resistant enterococci.

Background: Because patients can remain colonized with vancomycin-resistant enterococci (VRE) for long periods of time, VRE may spread from one health care facility to another.

Methods: Using the Regional Healthcare Ecosystem Analyst, an agent-based model of patient flow among all Orange County, California, hospitals and communities, we quantified the degree and speed at which changes in VRE colonization prevalence in a hospital may affect prevalence in other Orange County hospitals.

Results: A sustained 10% increase in VRE colonization prevalence in any 1 hospital caused a 2.

A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission.

The Global-Scale Agent Model (GSAM) is presented. The GSAM is a high-performance distributed platform for agent-based epidemic modeling capable of simulating a disease outbreak in a population of several billion agents. It is unprecedented in its scale, its speed, and its use of Java.

Combining Computational Fluid Dynamics and Agent-Based Modeling: a new approach to evacuation planning.

We introduce a novel hybrid of two fields-Computational Fluid Dynamics (CFD) and Agent-Based Modeling (ABM)-as a powerful new technique for urban evacuation planning. CFD is a predominant technique for modeling airborne transport of contaminants, while ABM is a powerful approach for modeling social dynamics in populations of adaptive individuals. The hybrid CFD-ABM method is capable of simulating how large, spatially-distributed populations might respond to a physically realistic contaminant plume.

Modeling the spread of methicillin-resistant Staphylococcus aureus (MRSA) outbreaks throughout the hospitals in Orange County, California.

Background: Since hospitals in a region often share patients, an outbreak of methicillin-resistant Staphylococcus aureus (MRSA) infection in one hospital could affect other hospitals.

Methods: Using extensive data collected from Orange County (OC), California, we developed a detailed agent-based model to represent patient movement among all OC hospitals. Experiments simulated MRSA outbreaks in various wards, institutions, and regions.

Economic cost and health care workforce effects of school closures in the U.S.

School closure is an important component of U.S. pandemic flu mitigation strategy, but has important costs.

Modelling to contain pandemics.

Agent-based computational models can capture irrational behaviour, complex social networks and global scale--all essential in confronting H1N1, says Joshua M. Epstein.

Coupled contagion dynamics of fear and disease: mathematical and computational explorations.

Background: In classical mathematical epidemiology, individuals do not adapt their contact behavior during epidemics. They do not endogenously engage, for example, in social distancing based on fear. Yet, adaptive behavior is well-documented in true epidemics.

Controlling pandemic flu: the value of international air travel restrictions.

Background: Planning for a possible influenza pandemic is an extremely high priority, as social and economic effects of an unmitigated pandemic would be devastating. Mathematical models can be used to explore different scenarios and provide insight into potential costs, benefits, and effectiveness of prevention and control strategies under consideration.

Methods And Findings: A stochastic, equation-based epidemic model is used to study global transmission of pandemic flu, including the effects of travel restrictions and vaccination.

Individual-based computational modeling of smallpox epidemic control strategies.

In response to concerns about possible bioterrorism, the authors developed an individual-based (or "agent-based") computational model of smallpox epidemic transmission and control. The model explicitly represents an "artificial society" of individual human beings, each implemented as a distinct object, or data structure in a computer program. These agents interact locally with one another in code-represented social units such as homes, workplaces, schools, and hospitals.

Containing a large bioterrorist smallpox attack: a computer simulation approach.

Background: A bioterrorist release of smallpox is a constant threat to the population of the USA and other countries.

Design: A stochastic simulation model of the spread of smallpox due to a large bioterrorist attack in a structured population was constructed. Disease natural history parameter estimates, time lines of behavioral activities, and control scenarios were based on the literature and on the consensus opinion of a panel of smallpox experts.

The evolution of social behavior in the prehistoric American southwest.

Long House Valley, located in the Black Mesa area of northeastern Arizona (USA), was inhabited by the Kayenta Anasazi from circa 1800 B.C. to circa A.

Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley.

Long House Valley in the Black Mesa area of northeastern Arizona (U.S.) was inhabited by the Kayenta Anasazi from about 1800 before Christ to about anno Domini 1300.

Modeling civil violence: an agent-based computational approach.

This article presents an agent-based computational model of civil violence. Two variants of the civil violence model are presented. In the first a central authority seeks to suppress decentralized rebellion.