RDM Glossary

This document provides a glossary of terms commonly used in RDM analyses. Providing such a glossary is challenging for two reasons. First, many of these terms (e.g. uncertainty, scenario, policy) have different meanings across the different scholarly and practice communities with which RDM analysts interact. Second, the terms in this glossary describe both aspects of the analysis and corresponding attributes of the real world.

Since “the map is not the territory” it is important to situate the following terminology within RDM’s approach to mapping the world facing decision makers.



This is a run of the simulation model for one future and one strategy. RDM analyses typically generate databases of many simulation model runs. Each entry in such a database is a case. Each database entry typically includes numbers describing the future, the strategy, and the metrics that result from pursuing the strategy in that future.


A condition refers to a set of futures that are similar along one or more dimensions of uncertainty. For instance, a climate condition might be specified by two input parameters, one specifying the choice of a global greenhouse gas emissions trajectory and the other the choice of a climate model (GCM).

Some communities use the word “scenario” for what is defined here as a “condition.” For example, water agencies often have planning processes that use what they call demographic scenarios and climate scenarios as inputs into their water management planning simulations.

Future (or plausible future, future state of the world)

These synonymous terms describe a specific set of assumptions about the future. RDM represents uncertainty with sets of multiple futures. An RDM analysis typically represents each future with a vector of specific values for each of the uncertain input parameters to a simulation model.

Lever (or decision lever)

This is an individual component of a strategy and could include: specific infrastructure investments, development of programs, or changes to policies or laws. These are the “Ls” in XLRM. These are also called “measures,” “options,” and “policies” in some contexts.

Metric (or performance metric)

This is some criteria of interest to decision makers that they can use to judge the relative desirability of various cases. These are the “Ms” in XLRM. RDM analyses typically relate metrics to specific outputs of simulation models.

Model (or simulation model)

The model — also called scenario generator in some RDM literature — is a set of relationships that project how a strategy will perform in a future evaluated according to the metrics. The model comprises the relationships, or “Rs,” in XLRM.

The model is typically embodied in computer code. Bankes (1993) distinguishes between “consolidative” and “exploratory” models. The former are validated and predictive. The latter provide a mapping of assumptions to consequences without any judgment regarding the validity of alternative assumptions. RDM analyses typically regard models as exploratory.

Scenario (or decision-relevant scenario)

This is a set of cases that share some decision-relevant attribute. For instance, a region in a Tableau plot where a strategy performs poorly might be such a scenario. The clusters of cases generated by a scenario discovery analysis also represent a scenario.

The word scenario is used in many different ways in many different literatures (see Parson et al., 2007). For instance as noted above, some communities use the word “scenario” for what we call a “condition.” However, the scenario planning literature is often dismissive of such usage (see for instance Wack, 1985). Given these conflicting terminologies, RDM analysts may sometimes find it helpful to use the more focused term “decision-relevant scenario” rather than scenario.

The term “decision-relevant scenario” is a modification of the term “policy-relevant scenario,” first defined by Groves and Lempert (2007). It is also important to note that in one of the original pieces of RDM literature, Popper, Bankes, and Lempert referred to a “scenario ensemble” (Lempert et al., 2003). Under these proposed definitions the phrase would be an “ensemble of cases.”


A strategy (often used synonymously with policy) represents a distinct choice facing a planner or decision maker, and is often defined by the amount, location, and timing of different investments or programs, or “levers.” In the NEPA regulatory environment, different strategies represent different alternatives.


An uncertainty is a single exogenous factor affecting the future (the “Xs” in XLRM), typically represented in an RDM analysis by specific input parameters to the simulation model. The word uncertainty also clearly has a much broader span of meanings in the RDM and other literatures.


RDM exercises often employ an "XLRM" framework (Lempert et al. 2003) to help guide stakeholder elicitation, data gathering, and model development. The letters X, L, R, and M refer to four categories of factors important to an RDM analysis:

  • Exogenous uncertainties (X) are factors outside the decision makers' control that may affect the ability of near-term actions to achieve decision makers' goals;
  • Policy levers (L) are near-term actions that decision makers may want to consider;
  • Relationships (R), generally represented by simulation models, describe how the policy levers perform, as measured by the metrics, under the various uncertainties; and
  • Metrics (M) are the performance standards used to evaluate whether or not a choice of policy levers achieves decision makers' goals.


Bankes, S. C. (1993). "Exploratory Modeling for Policy Analysis." Operations Research 41(3): 435-449.

Groves, D. G. and R. J. Lempert (2007). "A New Analytic Method for Finding Policy-Relevant Scenarios." Global Environmental Change 17: 73-85.

Lempert, R. J., S. W. Popper and S. C. Bankes (2003). Shaping the Next One Hundred Years : New Methods for Quantitative, Long-term Policy Analysis. Santa Monica, CA, RAND Corporation.

Parson, E. A., V. Burkett, K. Fischer-Vanden, D. Keith, L. O. Mearns, H. Pitcher, C. Rosenweig and M. Webster (2007). Global-Change Scenarios: Their Development and Use, Synthesis and Assessment Product 2.1b, US Climate Change Science Program.

Wack, P. (1985). "The Gentle Art of Reperceiving - Scenarios: Uncharted Waters Ahead (part 1 of a two-part article)." Harvard Business Review (September-October): 73-89.