RAND researchers have pioneered several different methodologies, such as the Delphi method and robust decisionmaking, and continue to apply their methodological expertise in multidisciplinary projects that may require a range of capabilities, including modeling and simulation, survey research, economic or statistical analysis, or planning and forecasting.
An overview of the 'mental models' methodology of communicating focuses on each step in the initial approach and offers a discussion of four specific challenges that a practitioner may face when implementing the approach and strategies for addressing them.
Large coverage expansions under the ACA have reignited concerns about physician shortages. These estimates result from models that forecast future supply and demand for physicians based on past trends and current practice. While useful exercises, they do not necessarily imply that intervening to boost physician supply would be worth the investment.
Large efficiency gains and substantial reduction in omitted variable bias are demonstrated in an application to sociodemographic differences in the risk of child obesity estimated from two nationally representative cohort surveys.
What will transportation look like in the United States in the year 2030? Multiple mobility scenarios are possible. Come hear how policymakers and planners can shape the future of mobility in the United States and what factors will influence the creation of the future transportation system.
To assess congestion charging policies where the charge varies according to the time of day, the Ørestad Transport Model (OTM) for the Greater Copenhagen area has been extended to predict the choice of time of travel for car drivers.
Predictive policing is not an end-all solution, but rather a tool that must be used in concert with other policing resources as part of a broader anti-crime effort. Used properly, predictive policing can predict the risk of future events, but not the events themselves.
The RAND Distinguished Speaker Series presents Nobel-prize winning economist Edmund Phelps, who discusses the effect of corporatist values on innovation and creativity.
A diagnostic tool maximizes the utility of security cooperation analyses and can help defense planners identify potential mismatches between security cooperation funding, priorities, and propensity for success with a given country.
Robust Decision Making showed El Dorado Irrigation District managers the results of key trade-offs among future strategies and how expectations for future vulnerable conditions can guide decisions to augment their long-term plan.
A collaboration among RAND, the Pardee RAND Graduate School, Evolving Logic, and network partners, RDMlab promotes the development and use of Robust Decision Making (RDM) methods for policy and decisionmaking.
RAND worked with the U.S. Bureau of Reclamation to explore the use of Robust Decision Making in the Bureau's long-term planning for the Colorado River.
The Colorado River Basin Study evaluated the river system's resiliency and compared resource management options using the Robust Decision Making methodology.
The Colorado River Basin Study evaluated the river system's resiliency and compared resource management options. The Robust Decision Making methodology helped to identify vulnerabilities and compare portfolios of options.
This report describes a proof-of-concept analysis using Robust Decision Making to evaluate water resource management response packages for California's Central Valley under future uncertainty for the California Water Plan Update 2013.
Upper-extremity and mobility subdomains shared about 35% of the variance in common, and produced comparable scores whether calibrated separately or together.
Although study characteristics, such as trial quality, may explain some proportion of heterogeneity across study results in meta-analyses, residual heterogeneity is a crucial factor in determining when associations between moderator variables and effect sizes can be statistically detected.
Scenario discovery offers a new means to characterize and communicate the information in computer simulation models under conditions of deep uncertainty.
A new set of scenarios, referred to as Shared Socio-economic Pathways (SSPs), examines challenges to mitigation and challenges to adaptation. Developing SSPs with a "backwards" approach could help inform the development of SSPs to ensure the storylines focus on the driving forces most relevant to distinguishing between the SSPs.
Mobility — the ability to travel from one location to another — may look very different in the United States in the year 2030. Three key drivers differentiate possible scenarios: the price of oil, the development of environmental regulations, and the amount of highway revenues and expenditures.
To make an impact on patient care within a 20-year timeframe, biomedical research funders and policymakers should focus resources on clinical rather than basic research, and support individuals who work across disciplinary boundaries and are motivated by patient need.