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When engaging in behaviors that may entail risks or outcomes that are unknown or uncertain, individuals often look beyond their own experiences (including past behaviors and subsequent outcomes) to consider the experiences of others in their immediate social networks. This social influence at the micro-scale (i.e., the way in which individuals are influenced by their immediate social networks) can affect change in the greater social web in such a way that social networks may have profound effects on decisionmaking at the population level. Such micro-level social influence is central to many theories of individual decisionmaking and behavior. Observations of population-level dynamics at the macro-level demonstrate the end result of these processes-for example, over time, people's behavior tends to look more like that of their peers. This article describes a general agent-based model (ABM) for studying social influence, and uses that general ABM to explore the relationship between micro-influence and macro-dynamics for broad classes of problems. We also describe an approach to tailor the general ABM to model a specific behavior influenced by social learning, which we illustrate using surveys designed to inform the ABM. The framework we developed could be useful for studying any system in which social learning may occur. But while our general ABM can produce dynamics reminiscent of those that might result from many different types of behaviors, it will typically need to be tailored when used to model any particular behavior.

The Veterans Access, Choice, and Accountability Act of 2014 addressed the need for access to timely, high-quality health care for veterans. Section 201 of the legislation called for an independent assessment of various aspects of veterans' health care. The RAND Corporation was tasked with an assessment of the Department of Veterans Affairs (VA) current and projected health care capabilities and resources. An examination of data from a variety of sources, along with a survey of VA medical facility leaders, revealed the breadth and depth of VA resources and capabilities: fiscal resources, workforce and human resources, physical infrastructure, interorganizational relationships, and information resources. The assessment identified barriers to the effective use of these resources and capabilities. Analysis of data on access to VA care and the quality of that care showed that almost all veterans live within 40 miles of a VA health facility, but fewer have access to VA specialty care. Veterans usually receive care within 14 days of their desired appointment date, but wait times vary considerably across VA facilities. VA has long played a national leadership role in measuring the quality of health care. The assessment showed that VA health care quality was as good or better on most measures compared with other health systems, but quality performance lagged at some VA facilities. VA will require more resources and capabilities to meet a projected increase in veterans' demand for VA care over the next five years. Options for increasing capacity include accelerated hiring, full nurse practice authority, and expanded use of telehealth.