Keywords and Phrases

A Collection of Keywords and Phrases
for Decision Making


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Action learning: Learning focused on outcomes and the future conditions that should have been foreseeable and that produced these outcomes.

Action: Committing resources, usually following a choice.

Active Listening: Active listening is a way of listening that focuses entirely on what the other person is saying and confirms understanding of both the content of the message and the emotions and feelings underlying the message to ensure that understanding is accurate.

Actor learning: Determining the accuracy of information offered by experts in a decision process, such as the likelihood of a future condition (interest rates), and forming by what has been obtained by the typical experts.

Actual judgment: Based on what the assessor really knows, it is the opposite of hypothetical judgment.

Adaptation of the Decision Process:

Additive Component: Elements of a quantity, e.g. utility partitioned into additive criteria.

Aiding method: Variant of a decision tool.

Alternatives: Optional courses of action from which a decision maker is expected to choose that are obtained from memory, vendor search, research and development, and the like and provide ways to achieve objectives.
One of the mutually exclusive courses of action attaining the objectives. Alternatives differ in their nature or character, not only in quantitative details. By mutually exclusive we mean that the alternatives are competitive in the sense that if A is selected, B cannot be chosen. A course of action that combines features selected from both A and B would be a new alternative.

Ambiguity: The inability to characterize or describe important aspects of a decision, such as core problems, future conditions, alternatives, or criteria.

Analysis: Separating or breaking up the component parts needed to value alternatives so that estimates for each component can be made and their effects combined.

Analytical decision process: The process by which a dominant stakeholder can act as the decision maker by applying steps to explore possibilities, access alternatives, ask "what-if" questions, and reflect in order to learn.

Analytical Components of Decision Making:

Analytical Problem Solving: This is an approach to deep-rooted or intractable conflicts that brings disputants together to analyze the underlying human needs that cause their conflict, and then helping them work together to develop ways to provide the necessary needs to resolve the problem.

Analyze: To break into component parts and study those parts to gain a better understanding of the whole.

Anchor effect: The failure to make adjustments after the initial estimate has been proved incorrect. This resistance is greater when the initial estimate was very high or very low.

Approximate equivalent: Substitute close enough to be useful.

Arbitrary Assumption: An assignment of value or probability, without reference to its realism, e.g., for analytic convenience.

Arena: The context in which a decision is made that captures background and motivating information that characterize a decision. Arenas identify contexts that are assumed by decision makers, which may or may not frame a decision in the most opportune manner.

Assessment Updating: use of Bayes theorem to revise a probability, based on new evidence, i.e., Bayesian updating of prior to obtain the posterior probabilities.

Assessor: Judger of a factual possibility based on empirical frequency function.

Assumptions: Suppositions about values for key factors in a decision process, such as future conditions, that are taken for granted unless subjected to sensitivity analysis.

Attribute: Any property, descriptive or prescriptive, of possibility distinction.

Availability heuristic: A rule of thumb that is used to acquire information in which easily accessible information is treated as diagnostic.

Average Personal Utility: Probability-weighted average personal utility, i.e., subjective expected utility.

Average value: Sum of possible values times probability, e.g., average personal utility.


Background knowledge: In addition to any specified recent knowledge.

Bad decisions: Failures to deal with foreseeable events and cope with behavioral factors that inhibit and mislead decision makers, whether the outcome is favorable or unfavorable.

Behavioral: A description of how decision makers act when making decisions without the aid of normative tactics (also called descriptive).

Benchmarking: Setting reference points or standards by which behaviors or developments can be measured at a point in time or over time; a practical tool for improving performance by learning from best practices and the processes by which they are achieved.

Bias: Partiality or prejudice in interpreting information and applying it to a decision situation.

Binary: Having two possibilities.


Casual attributes: The assignment of causes to outcomes or events that are observed.

Casual Dependence: Co-variation where direction matters to determine the influence.

Causality: What is the relationship between variables? Causes make their effects happen. That is more than, and different from, mere association. But it need not be one single different thing. One factor can contribute to the production or prevention if another in a great variety of ways. There are standing conditions, auxiliary conditions, precipitating conditions, agents, interventions, contraventions, modifications, contributory factors, enhancements, inhibitions, factors that raise the number of effects and factors that only raise the level, etc.

Certain Equivalent: Single quantity, judgmentally equated to a gamble, i.e., certainty equivalent in construction of a utility function.

Choice Fork: Branches are options, i.e., act fork.

Choice: Selecting among identified options, a phase of the decision process.

Civic decision: Decision-maker takes a private position on a public issue, e.g., government policy.

Clairvoyance: Having perfect information, e.g., the insider.

Coalition: A temporary union or alliance of stakeholders as a decision group to make a particular decision.

Coarse model: Structurally a simpler model.

Coherent: Obeys logical rules of consistency.

Common Errors in Understanding the Decision Problem:

Comparative statics: Comparative statics is the process of changing the value of a variable in a model, in order to see its individual impacts on a value function or outcome of the decision.

Complete model: Covers all relevant considerations comprehensively, implicitly or explicitly.

Complex model: That has elaborate structure.

Comprehensive coherence: Decision maker judgments are logically consistent; he/she is fully rational.

Concept: A word or group of words that summarizes or classifies certain facts, events or ideas into one category. Concepts are labels or categories, or selected properties of objects. They are the bricks from which theories are constructed. Theories are constructed by linking concepts representing different attributes or belonging to different classes and by developing sets of interrelated statements concerning the relationship(s) between such concepts.

Conceptualization: the process of clarifying and specifying the meaning(s) of variables in a problem statement or hypothesis in order to facilitate examination of relevant research by refining and developing a clear, precise, testable hypothesis

Conditional probability: Given some specified possibility based on knowledge of other events.

Conditioned assessment: That is, "it all depends"

Conditioning possibility: Affects uncertainty about another possibility, e.g. contributor.

Conflict: Disagreement among people with different interests, views, or agendas, causing emotional disturbance and stress.

Consequences: Prospects due to decision-maker action, they might be any defined outcomes or ill-defined.

Conspicuous alternatives: Traditional or habitual ways of responding to core problems that arise early in a decision process.

Constituent: Interested party on whose behalf decision-maker is expected to act, stakeholder.

Constraint: A constraint is a mathematical representation in a mathematical programming problem of some situational factors that must be taken into consideration when an attempt is made to optimize a value function with respect to its key variables. For example, when a manager formulates a business plan, she will not be able to spend more money on advertising than the overall budgeted dollar amount for the department allows. There are various kinds of constraints that occur, including policy constraints, financial constraints, physical constraints, ethical constraints and logical constraints, depending on the specifics of the setting that the analyst is modeling.

Consultant: Someone who assembles information, creates a knowledge base and provides professional advise relating to a decision problem of an individual, group or organization on a volunteer basis or for remuneration.

Context: The situation that captures background information and insights relevant to a decision and from which meaning and understanding are extracted (also called environment).

Contributor: Attribute of interest because it influences some criterion but is not itself a criterion.

Core model: A coarse model addressing the target evaluation directly.

Core problem: the most important and most central problem provoking action, which can be difficult to identify.

Cost-benefit Analysis: Assesses whether the cost of an intervention is worth the benefit by measuring both in the same units; monetary units are usually used.

Covaries: Probability of one possibility varies with the value of another possibility, depends on, relevant to.

Criteria weight: A quantitative value that specifies the relative important criteria.

Criteria: The means used to make a comparison among alternatives such as cost and quality.

Criterion domain: The proxy measure selected to capture a criterion, such as a morale survey that is used to measure satisfaction.

Criterion score: Criterion or attribute, (e.g., impact, value; to assign a score.

Criterion: A criterion is a rule or standard by which to rank the alternatives in order of desirability. The use of "criterion" to mean "objective" is incorrect.
An attribute (i.e., objective) decision-maker ultimately wants more or less of, e.g., wealth.

Culminating prospect: Possible places that an incremental commitment strategy may lead to.

Current decision: One to be made now, before learning anything new.


Danger of Over Simplification of Decision Models:

Decentralized Decision-Making: The locus of decision-making is decentralized to the extent to which there are multiple decision-makers involved with their own goals and objectives (utility function) and to the extent to which they follow their own expectations about the developments in the environment including the activities of other actors and competitors.

Decider: Person responsible for making choice, i.e., committing resources.

Decision aid: Help with decision-maker by providing some insight to the decision problem.

Decision aider: Provides decision analyst or other aid to decision-maker.

Decision analysis: A technique used to explore various facets of a decision, such as consistency or accuracy, the importance of criteria, the influence of extraneous factors, and norms within and among factions to promote insight that leads to learning.

Decision levels: A level corresponds to increases in the complexity of decisions, as assumptions about future conditions become more ambiguous.

Decision Maker: A decision maker is a person who makes the final choice among the alternatives.

Decision making process: The decision making process is the process that is used to make a decision. It can be an expert process, where the decision is made by one or more "experts" who look at the "facts" and make the decision based on those facts; it can be a political process through which a political representative or body makes the decision based on political considerations, or it might be a judicial process where a judge or a jury makes a decision based on an examination of legal evidence and the law.

Decision making Terms and Phrases: In a field such as decision analysis, where precision of communication is critical, it is important to use consistent terms for related but distinct concepts. Language in this field is still evolving and professional decision analyst practice varies.

Decision mode: Role of decider in relation to decision, e.g., professional.

Decision rule: Procedure that are set in advance, for making a prospective decision, in the event of a specified development.
Guides that specify how to use objective and subjective information to compare the merits of alternatives.

Decision Support Systems: A computer based system that helps the decision maker utilize data and models to solve unstructured problems.

Decision theory: Decision theory is a body of knowledge and related analytical techniques of different degrees of formality designed to help a decision maker choose among a set of alternatives in light of their possible consequences. Decision theory can apply to conditions of certainty, risk, or uncertainty. Decision under certainty] means that each alternative leads to one and only one consequence and a choice among alternatives is equivalent to a choice among consequences. In decision under risk each alternative will have one of several possible consequences, and the probability of occurrence for each consequence is known. Therefore, each alternative is associated with a probability distribution, and a choice among probability distributions.
When the probability distributions are unknown, one speaks about decision under uncertainty. Decision theory recognizes that the ranking produced by using a criterion has to be consistent with the decision maker's objectives and preferences. The theory offers a rich collection of techniques and procedures to reveal preferences and to introduce them into models of decision. It is not concerned with defining objectives, designing the alternatives or assessing the consequences; it usually considers them as given from outside, or previously determined. Given a set of alternatives, a set of consequences, and a correspondence between those sets, decision theory offers conceptually simple procedures for choice. Decision theory for risk conditions is based on the concept of utility. The decision maker's preferences for the mutually exclusive consequences of an alternative are described by a utility function that permits calculation of the expected utility for each alternative. The alternative with the highest expected utility is considered the most preferable. For the case of uncertainty, decision theory offers two main approaches.
The first exploits criteria of choice developed in a broader context by game theory, as for example the maxmin rule, where we choose the alternative such that the worst possible consequence of the chosen alternative is better than (or equal to) the best possible consequence of any other alternative. The second approach is to reduce the uncertainty case to the case of risk by using subjective probabilities, based on expert assessments or on analysis of previous decisions made in similar circumstances.

Decision Tree Analysis: It is a method for evaluating how user's make decisions in performing their tasks. Through interviews and observations, a decision tree is created to model options a user must choose among in performing a task. The decision tree may be developed with a domain expert as opposed to a typical user to help optimize decision-making performance. The decision tree is then used as a resource in designing user tasks.

Decision Tree and Sequential Decision Making: Decision tree refers to the use of the network-theoretical concept of tree to the uncertainty-related structuring of decision options. A tree is a fully connected network without circuits, i.e. every node is connected to every other node, but only once. A decision tree distinguishes between nodes from which decision options branch off (i.e., decision nodes) and nodes which have branches representing "environmental" options, i.e. environmental states which are associated with alternative decisions ( i.e., chance nodes or, environmental nodes) Every branch eventually leads to a payoff which, together with the environmental branch - specific probability permits the calculation of an expected payoff for each alternative decision and thereby to the selection of decision for action.

Decision tree:Graphic representation of potential developments following a choice.
It is a visual representation of a decision problem intended to help people make better decisions. Each node represents either a choice by the decision-maker or a probabilistic outcome. A decision tree can be used to determine the probability and net value of outcomes depending on which decisions are chosen. An analytical technique that merges chance occurrences about several future conditions with information that describes hybrid alternatives to value courses of action open to a decision maker.

Any software designed to facilitate decision-making, particularly group decision-making. Decision support systems may provide tools for such things as brainstorming, critiquing ideas, putting weights and probabilities on events and alternatives, and voting. Such systems enable presumably more rational and evenhanded decisions. Usually designed as part of tools to facilitate meetings in general, they encourage equal participation by, for instance, providing anonymity or enforcing turn-taking. They may also provide tools such as decision trees to help analyze the structure of the decision being made.

Decision:

Decision-aiding tool: Specific analytic procedure to aid decision, aide.

Decomposition: Expressing a quantity in terms of other quantities, e.g., net benefit = gross benefit Ė cost, whence decomposed (quantity) assessment.

Definitive Choice: Once-and-for-all irreversible action- not incremental.

Descriptive analysis: Answers to the questions similar to: What is; how the real world works?

Design Rationale: the reasoning that leads to design decisions. Documenting design rationale is important for validating that the correct design decision was made, to help those who are trying to interpret ambiguous design decisions or examples that don't fall clearly within a design principle, and to avoid going back and changing design decisions without knowing the original reasons in the first place. A design rationale can be an important tool in arriving at the initial design decision in the first place. Rationale should give advantages and disadvantages of a choice and include rejected alternatives, so that those alternatives don't keep popping up for reconsideration.

Deterministic Model: Mathematical models that are constructed for a condition of assumed certainty. The models assume there is only one possible result (which is known) for each alternative course or action.

Diagnosis: The action taken by decision makers to determine the underlying factors prompting a decision, using powers of observation and intuition.

Diagnostic judgment: How strongly a piece of evidence indicates that a possibility is true.

Diagnosticity of evidence of a possibility: Probability of evidence conditioned on that possibility, likelihood.

Disaggregated utility/uncertainty: Expressed as some function of multiple criteria scores/probabilities.

Discrete possibilities: Take on only a limited number of values

Dominance decision rule: Find an alternative that is better or worse than all other alternatives, no matter what future conditions arise.

Dummy option: unrealistic but analytically convenient option.


Elicitor: Determines someone elseís judgment, e.g., of uncertainty or preference.

Elimination by aspects: A technique used with multicriteria to speed decision making in short-fuse situations. Many alternatives are ruled out, one at a time, on the basis of criteria norms (for example, budget constraints or user views), thus eliminating all of the alternatives that fail to satisfy these norms.

Emotional inoculation: A technique used to reduce stakeholdersí fears about decision outcomes over which they have no control.

Emotions: Emotions are psychological feelings that people have that usually result from--and contribute to--a conflict. Examples are anger, shame, fear, distrust, and a sense of powerlessness. If emotions are effectively managed, they can become a resource for effective conflict resolution. If they are not effectively managed, however, they can intensify a conflict, heightening tensions and making the situation more difficult to resolve.

Empirical decisions: Information-poor decisions that have an accepted way to generate and analyze information for decision-making purposes.

Emulation: Aid that mimics some expertís judgment

Enhancement: Judgment made more rational.

Equivalent substitute: Assessor is judgmentally indifferent between them, to equate.

Ethical consideration: Identification of actions that seem acceptable according to organizational standards and modes of conduct but that would be unacceptable or questionable according to personal standards and personal modes of conduct.

Evaluation: putting a number to a judgment, not necessarily a preference judgment, e.g., assessing a probability.

Evaluatory: Refers to a judgment of preference.

Evidence: Information relevant to assessing a possibility.

Exogenous: External to the inner workings of a system or model; variables are exogenous to the extent that they are "given" and not the result of the operation of the system, or anything going on in the model itself. It is the opposite of Endogenous.

Expected-value decision rule: Value alternatives by weighting payoffs according to the relative importance of criteria and the likelihood of all future conditions. This is the only decision rule that incorporates what can be determined about uncertainty and applies compensatory logic.

External/Reality check: External to model, based on observed real world, external validation.


Fact-based disputes: Fact-based disputes are disputes about what has occurred or is occurring. Such disputes can be generated from misunderstandings or inaccurate rumors (when someone is accused of doing something they did not actually do). Facts-based disputes can also be generated by differing perceptions or judgments about what has occurred or is now occurring.

Factual judgment assessment: Evaluation, probabilistic or deterministic, of a possibility.

Factual possibility: Possible fact, event, quantity, property, proposition.

Feasibility Study: It is a study of the technical and economic prospects for developing a system prior to actually committing resources to actually developing it.

Feedback: Information gleaned about a decision after its outcome has been observed.

Feeder model: A model whose output corresponds to the input of a core model.

Fixed base: Unrelated to options.

Flipping probabilities: Deriving one set of discrete probabilities from another set.

Form: Shape or outline as a criterion in decisions, such as the style of a building that gives it a distinctive appearance.

Formal Models: Formal models are models said from the outset to be such, and built as such. That is they are built knowing that each component is labeled a component, or a process, or a phenomenon or a part, and that the model, when "finished", will perform in a certain way, that is, (a) specific outcome/s is/are anticipated.Such models can be loosely arranged packages of variables (forces, vectors, such as "population," or "agriculture" or "population growth" or "taxes"), with or without quantification. That is, the variable might have a numerical value--or it might not. The easiest to imagine models are those with variables that are both linked and quantified. Such as the following, where population and land each have values and so much labor can be applied to so much land, producing so many yields (food). Add space (more land), time (days, years, centuries), and population growth (as a rate), and you have a very dynamic situation, i.e., lots of different outcomes are possible. Why possible? Because they are dependent, dependent upon which values of which variables are set into the model. The society might grow, might expand beyond its "means" of subsistence, might adopt a new technology, might experience drought, or abundant agro-conditions. Who knows?

Formal simulation: A tactic used to promote learning by creating hypothetical decision situations. Decision makers consider hypothetical decisions and make choices that are correlated to the cues (criteria) buried in them to extract values and determine consistency in the use of information.

Frame: A window that focuses attention on what a decision is about, thereby providing direction to subsequent steps in a decision-making process.

Function: A relation between two or more variables so that the values of one are dependent on, determined by or correspond to values in the other variables, its arguments; a transformation whose range is uniquely specified by its domain. In algebra and set theory, functions are often called many-to-one mappings or images.
Use as a criterion in decisions, such as the use of a building that gives rise to space allocations and other user requirements that stipulates its design. A function is a relation in which each element in the domain is matched with only one element of the range. A function may be specified: numerically: by means of a table, algebraically: by means of a formula, and graphically: by means of a graph.

Future conditions: Factors beyond the decision makerís control that influence the payoffs associated with alternatives, such as the demand for a product (also called states of nature or states).


Gamble: Choice with uncertain outcomes.

Gamble-defined utility: Probability in an equivalent gamble between arbitrary good and bad.

Gamblerís fallacy: Treating random events such as a dice roll as if the outcome of one roll implies something about the outcome of the next roll.

Game theory: A reference to a set of optimal strategies for decisions which involve uncertainty resulting from conflicting objectives of players with interdependent decision situations. Distinction is made between zero-sum and non-zero-sum games. Best known among the strategies is the minimax/maximin solution strategy in a two-person game which, if a saddle point (i.e., or equilibrium) is present, results in each player achieving the best of all possible worst outcomes, or pay-off.

Goal: A state that decision-maker prefers to be in, e.g., an increase in a positive criterion.

Goal-seeking: The capability of asking what values certain variables must have in order to attain desired goals. It is a tool that uses iterative calculations to find the value required in one parameter in order to achieve a desired outcome.

Going through the GOO: Analyzing goals, options, and outcomes often in a tabular form.

Good/bad anchors: Hi/lo anchors for utility rating scale, e.g., 100, and zero, ends of rating scale.

Group Decision Making: Several tactics can improve the performance of groups. In particular, organizations that stress participation emphasize the formulation and cohesion stages and tend to ignore the process and control stages. This practice reduces the effectiveness of groups. Carefully considering each phase is the key to successful group performance.
Several guides that can improve the performance of a group are summarized below:

Group decision process: The process used when there are many known stakeholders. In this process, a decision group made up of these stakeholders takes steps to explore possibilities, assess options, ask "what-if" questions, and reflect in order to learn.

Group process: A set of procedures used to manage the activities of a group engaged in decision-making activities, such as identifying problems and uncovering alternatives.


Heuristic: A trial-and-error tactic used by a decision maker to speed the process of learning or finding out.

Hindsight bias: The tendency to treat observed outcomes as if they were more likely than facts would warrant or even as if they were preordained.

Holistic characterization: Single description of total possibilities.

Human agency: Reference to the independent decision-making intentions, opportunities, capabilities and activities of human beings.

Hypothetical judgment: Based on a possibility that has not, or not yet, occurred, e.g., likelihood in statistical theory. It is the opposite of actual judgment.

Ideal rationality: Perfect analysis of all available knowledge.


Illusion of control: An act of prediction that makes the predicted outcome seem more certain- for example, attributing good outcomes to skill and bad ones to chance, which leads to treating out-of-control situations as if they were under control and to minimal learning about missed opportunities.

Illusory associations: Misleading connections between signs and conditions they are thought to predict.

Impact: Difference in value, criterion score or contributor value, due to exercising an option, i.e., compared with null option.

Implementing choice: Narrow variant of a broader choice.

Importance weights: Relative importance of criteria, trading off units, i.e., coefficient.

Importance-weighted criteria evaluation: Utility of options = (approximately) sum of criteria scores times importance weights, linear additive multi-attribute utility.

Incremental commitment: At least partially reversible option.

Indicative (of possibility): Evidence supports truth of a possibility, i.e., a diagnostic.

Individual learning: Decision makersí gaining of insight as they reflect on decision outcomes as missed opportunities.

Inference: A conclusion that is drawn by applying reasoning to available information to arrive at a decision.

Influence sketch: Qualitative influence diagram shows casual linkages between choice and utility.

Informal simulation: A technique applied in important and recurring decisions to promote learning by recreating actual decision situations. The parties involved are disguised, and the decision is described with the facts available when the actual decision was rendered. A comparison of the simulated decisions and the original choices in terms of outcomes indicates how well one can do.

Informant: Source of judgment to be assessed.

Informational decisions: Unique or problematic interpretations drawn from information-rich situations in which the means used to assess information are controversial.

Information-processing capability: The ability of decision makers to observe, catalogue, and make judgments on the basis of information they observe.

Innovate: 1. To use a new, not necessarily better, way of responding to aims or objectives.
2. Knowing without the conscious use of reasoning or logic.

Input judgment: Term to be supplied in a model or inference procedure, from which output is calculated.

Inside organizational act: Action within organization, e.g., designing a purchasing procedure, i.e., internal org act. It is the opposite of outside act.

Interdependent Decisions: A series of decisions that are interrelated. A sequential set of decisions are usually interdependent.

Interest-Based Problem Solving: Interest-based problem solving defines problems in terms of interests and works to reconcile the interests to obtain a mutually-satisfactory solution.

Interested party: Their interests are affected by choice.

Internal check: Validation internal to model, e.g., by test of technical soundness, science, or logical coherence.

Intractable Conflicts: It is used to refer to conflicts that go on for a long time, resisting most (if not all) attempts to resolve them. Typically they involve fundamental value disagreements, high stakes distributional questions, domination issues, and/or denied human needs--all of which are non-negotiable problems. They often involve unavoidable win-lose situations as well.


Janusian thinking: The ability to hold multiple views while making a decision. The term comes from the Roman god Janus, who was the patron of beginnings and endings and is usually shown with two faces symbolize the need for multiple views as a decision is made.

Joint probability: Probability that two or more events occur together.

Judger: Makes a judgment.

Judgment: Personal assessment or other evaluation.
The act of deciding by an individual or group vested with authority.

Judgment-intensive model: Main analytic effort is on assessing model inputs, not structure.


Knowledge enriched assessment: Improved by new knowledge.

Knowledge structure: A set of beliefs formed by experience and assembled as theories or scripts that suggest relationships among people, events, and objects.

Knowledge: Knowledge refers to what one knows and understands. Knowledge is sometimes categorized as unstructured, structured, explicit or tacit. What we know we know is explicit knowledge. Knowledge that is unstructured and understood, but not clearly expressed is implicit knowledge. If the knowledge is organized and easy to share then it is called structured knowledge. To convert implicit knowledge into explicit knowledge, it must be extracted and formatted.


LaPlace decision rule: Assume nothing about the likelihood of future conditions, making the valuation of alternatives an average of the payoffs under each future condition (also called the uncertainty rule).

Law of Diminishing Returns: A reference to the law which states that additional inputs of a variable factor of production combined with fixed factors of production will eventually lead to a decreasing marginal output.

Learning to learn: A form of learning in which reflection on both process and outcome occurs, leading to a reappraisal of norms and values applied in making decisions and the steps to follow (also called double-loop learning and learning two).


Learning: Discovering pitfalls to avoid in future decisions and ways to avoid these pitfalls.

Limited coherence: Some of assessorís judgments are shown logically to be consistant.

Linear function: A function whose graph is a straight line

Linear programming: Linear programming is a mathematical procedure (it has nothing to do with computer programming languages) that is implemented with computers so that systems of linear equations can be solved to determine the optimal values of variables that affect a value function. This technique is often used in business for solving problems as varied as workforce scheduling, production planning and input selection, loan portfolio funding, gasoline blend mixing, advertising targeting, and many other problems where the allocation of scarce resources is an important consideration.


Main Components of Decision Making:

Making Tough Decisions:

Marginal analysis: The analytical approach which stresses the importance of the margins of an activity: what happens to the costs, benefits (e.g., utility, profits) or combination of substitutable facets or activities as incremental changes are made to an independent variable e.g. in search of an equilibrium, a maximum, minimum or optimum.

Marginal Cost: The addition to total cost resulting from the addition of the last unit of output, to the total quantity of output.

Marginal Principle: To maximize net benefits, the strategic or action variable should be increased until MB = MC, i.e. marginal benefits equal marginal costs.

Middleman: Person or group dealing with aider on behalf of decision-maker.

Missed opportunities: Ways of responding to core problems that were overlooked or rejected during a decision process.

Mixed-mode decision process: The process recommended when unknown or competing factions can be identified as stakeholders. A coalition of stakeholders with aims that correspond to those of the organization is formed to explore possibilities, assess options, ask "what-if" questions, and reflect in order to learn.

Model Base: A collection of preprogrammed quantitative models (e.g., statistical, financial, optimization) organized as a single unit.

Model: Mathematical function equated to, or approximating, some entity, e.g., option utility as a function of probabilities and component utilities.
A model is a set of propositions or equations describing in simplified form some aspects of our experience. Every model is based upon a theory, but the theory may not be stated in concise form. An object or process which shares crucial properties of an original, modeled object or process, but is easier to manipulate or understand. A scale model (i.e., Iconic model) has the same appearance as the original save for size and detail. However, increasing use is made of computer simulation: the model is a program that enables a computer to determine how key properties of the original will change over time. It is easier to change a program than to rebuild a scale model if we want to explore the effect of changes in policy or design.
A model is a device, scheme, or procedure typically used in systems analysis to predict the consequences of a course of action; a model usually aspires to represent the real world (to the degree needed in analysis)--for example, a relation between some observed phenomena. A model can be formal, e.g., a mathematical expression, a diagram, a table.
A deterministic model generates the response to a given input by one fixed law; a stochastic model picks up the response from a set of possible responses according to a fixed probability distribution, stochastic models are used to simulate the behavior of real systems under random conditions. A dynamic model can describe the time-spread phenomena, dynamic processes, in a system. A static model describes the system at a given instant of time and in an assumed state of equilibrium. Among the formal, mathematical models an analytical model is formed by explicit equations. It may permit an analytic or numerical solution. An analytic model is linear if all equations in the model are linear. We speak of a simulation model if the solution, i.e., the answer to the question which the analyst has posed, is obtained by experiments on the model rather than by an explicit solution algorithm. A typical example is stochastic simulation, where one wants to obtain probabilistic properties of a system's response by evaluating the results of a large number of simulation runs on the model. In some analyses the model by which one predicts the outcome of a course of action must take into account that this outcome depends also on actions taken by other decision makers. If the assumption can be made that those decision makers optimize some defined objective functions, and all the other aspects of the system can also be formalized, an optimization model (e.g., a linear programming model) can be used to determine the system's response to a course of action. A formal model has a structure (the form of an equation, for example) and parameters (the value of coefficients in an equation for example). Determination of both the structure and parameters is model identification; determination of the parameters on the basis of experimental data is model estimation. The check of a proposed model against experimental data other than those used for parameter estimation is model validation.
A model is a system that stands for or represents another typically more comprehensive system. A model consists of a set of objects, described in terms of variables and relations, and interactions defined on these and either (a) embodies a theory of that portion of reality which it claims to represent.

Modeling: Modeling is an attempt to represent the bare bones of reality, so that aspects of it can be described, explained, optimized or predicted what the reality is; so that we can predict it. We can use a model to understand how changes in the environment impact the decision problem that a manager faces. The outputs or findings from the modeling process enable an analyst to determine the logical results of decision, and choose an optimal course of action. Similarly, changes in the environment and variables surrounding the decision problem can be studied to determine the effects that they have on the decision problem.

Monetary conversion: Partitioned evaluation, summing money-equivalent components of criteria scores.

Monte-Carlo simulation: A simulation technique that allows combining various factors with probabilistic outcomes to characterize the distribution of an end result. This is especially useful when the number of factors affecting the outcome is large. It allows modeling of complex problems with relative ease. We use Monte Carlo simulation for solving problems such as demand and capacity forecasting, process optimization and real options analysis

Multicriteria decisions: Decisions in which several outcomes can occur and in which each outcome has a value that must be considered in order to compare the merits of alternatives.

Multicriteria model: Only addresses conflicting criteria considerations.

Multiple perspectives: Technical, personal, and organization views of a decision that are used to incorporate a balanced view of factors that merit consideration.


Natural Metric: Measure of a real quantity, e.g., money.

Neural Nets: This technique is useful when a large number of independent factors affect an outcome in complex and non-linear fashion. Multivariate and non-linear regressions can be used as substitutes. Regressions have the advantage of being able to demonstrate the individual factorial relationships. However, neural nets are useful when relationships are non linear and complex.

Norm: A standard identifying a level of performance that is expected or required.

Normative: A type of statement that suggests how a decision should be carried out (also called prescriptive).

Null option: do nothing, may be a dummy option.


Objective estimates: Estimates of quantitative information obtained by traditional means, such as accounting systems for costs.

Objective: An objective is something that a decision maker seeks to accomplish or to obtain by means of his decision. A decision maker should have one objective, while formulating the other objectives as constraints.
An objective must be quantified. The term goal (or target) is sometimes used to achieve specific value for the objective. For example the goal of a company is to reduce the cost by 10%.
Something to which an effort is directed, the goal, purpose or criterion a decision maker uses to evaluate alternative courses of actions. The choice of objective constrains possible behaviors.

Objective: The intentions of the decision process that set out what is to be strived for or sought (also called aims).

Objectivist position: Relies on data-based analysis rather than on human judgment.

Opportunism: Refers to the suggestion (widely associated with transaction cost analysis) that a decision-maker may unconditionally seek his/her self-interests, and that such behavior cannot necessarily be predicted. This proposition extends the simple self-interest seeking assumption to include "self-interest seeking with guile" thereby making allowance for strategic behavior. For example, strategic manipulation of information or misrepresentation of intentions; false or empty, i.e. self-disbelieved, threats or promises, has profound implications for choosing between alternative contractual relationships. Opportunistic behavior contrasts with stewardship behavior which involves a trust relation in which the word of a party can be taken as his bond.

Opportunity Cost: The cost of an activity in terms of foregone or sacrificed next best alternative uses of the assets involved. Can also be formulated as "amount of product B we must give up to produce a unit of product A.

Optimal option: Best, highest utility.

Optimal Production Function: A technical relationship identifying the maximum output or combination of outputs (products) capable of being produced by a specified input or combinations of inputs (factors of production).

Optimism Decision Rule: Using a maximum rule to find the best payoff for the most likely future conditions or the best payoff regardless of future conditions.

Optimization: Optimization is an activity that aims at finding the best (i.e., optimal) solution to a problem. For optimization to be meaningful there must be an objective function to be optimized and there must a set of constraints.
The optimal solution (or "solution to the optimization problem") is values of decision variables that satisfy the constraints and for which the objective function attains a maximum (or a minimum, in a minimization problem). Very few optimization problems can be solved analytically, that is, by means of explicit formulae. In most practical cases appropriate computational techniques of optimization (numerical procedures of optimization) must be used. Among those techniques linear programming permits the solution of problems in which the objective function and all constraint relations are linear.
Optimization problems are made up of three basic ingredients. An objective function which we want to minimize or maximize, for instance, in a manufacturing process, we might want to maximize the profit or minimize the cost. In fitting experimental data to a user-defined model, we might minimize the total deviation of observed data from predictions based on the model. In designing an automobile panel, we might want to maximize the strength. It contains a set of unknowns or decision variables which affect the value of the objective function. In the manufacturing problem, the variables might include the amounts of different resources used or the time spent on each activity. In fitting-the-data problem, the unknowns are the parameters that define the model. In the panel design problem, the variables used define the shape and dimensions of the panel. Finally, it has a set of constraints that allow the unknowns to take on certain values but exclude others. For the manufacturing problem, it does not make sense to spend a negative amount of time on any activity, so we constrain all the "time" variables to be non-negative. In the panel design problem, we would probably want to limit the weight of the product and to constrain its shape. The optimization problem is then to find values of the variables that minimize or maximize the objective function while satisfying the constraints.

Optimize: Decision strategy to seek optimal option. The decision strategy of choosing the alternative that gives the best or optimal overall value.

Option base: One option, e.g., null option is base.

Option: A possible action to be chosen by decision-maker, alternative, deal.

Order effect: Information received early in a decision process is given more weight than information received later in the process.

Organizational decision analysis: Where the organization is treated as the decider.

Organizational learning: Improving the capacity of the organization to make good decisions, neutralizing coverups by removing the incentives for decision makers in the organization to offer only good news.

Outcomes: Distinct events due to action; a special case of consequences.
The results of a decision process that value the alternative that was selected by measuring the results in terms of the decision criteria (for example, cost, satisfaction, quality, and use).

Output judgment: Calculated from a model or inference procedure, including their inputs.

Outside organizational act: Transaction between organization (as decision-maker) and outside world, e.g., to purchase equipment.


Pallid: A characteristic of information that makes it hard to recall because of a lack of personal identity, distance, or abstractness.

Partitioned utility: Disaggregated into additive components.

Path dependency: Reference to effects of past commitments or acquired knowledge on subsequent actions and decisions. Recognizing that "history matters" for a future course of action or development, such past commitments or learning activities could entail previous investments, e.g. in transaction-specific assets, contracts, research & development, or the pool of usually locally, learned behaviors and organizational routines which constrain, including spatially future activities.

Personal decision analysis: Evaluating options by quantifying decision-maker judgments, based on statistical decision theory, i.e., Bayesian statistics.

Personal decision: A private decision involving decision-makerís own action, e.g., whom to marry.

Personal Probability: Judgerís uncertainty, expressed as a number, 0 to 1, i.e., the frequency function.

Personal Utility: any quantified measure of welfare, satisfaction, happiness, etc, for decision-maker, reflecting decision-makerís preferences

Personalist position: Models human judgment, e.g., to maximize personal average utility.

Pessimism decision rule: When high stakes call for conservative choices, a minimax rule is used to identify future conditions that lead to the worst outcomes and to make choices that avoid these future conditions by selection of the alternative with the best payoff that is left.

Planning: A managerial function concerned with making forecasts, formulating outlines of things to do, and identifying methods to accomplish them.

Plus-minus tally: Deriving utility of an option as the sum of pluses/minuses for each criterion, without explicitly considering the relative importance of the criteria.

Possibility fork: Branches of outcomes, i.e., event fork.

Prediction: The act of estimating something before it occurs.

Preference covariation: Related to value judgments.

Preferences: decision-makerís underlying or reported value judgments, tastes.

Prescriptive: Prescribes action that should be taken.

Present equivalent: An amount received now as a judgmental equivalent to future amount, e.g., stream of money, present value.

Preventive Strategies for Bad Decisions: Tough decisions can become bad decisions when ambiguity, uncertainty, and conflict are ignored, treated superficially, or assumed away during decision making.

Prior judgment: Before some specified evidence is learned.

Prisoners' Dilemma: A decision situation which illustrates the benefits of cooperation or collective action but also the difficulty of arriving at such an outcome. The decision payoffs are structured so that it is individually beneficial not to collaborate (with the fellow prisoner) even though collaboration by both would yield acceptable outcomes (and clearly better than if both defect). Each prisoner feels that she has to defect due to the uncertainty about the "partner's" action.

Private mode: Decision in private life, personal or civic, i.e., nonprofessional.

Probability distribution: probability assigned to all values of a possibility, mass/density function.

Probability tree: Displays unconditional and conditional probabilities of covarying possibilities.

Probability: Metric 0-1 obeying certain formal rules (e.g., sum to 1), frequency, chance.
The likelihood or chance that a certain event will occur. Probabilities may be based on "objective" statistical (frequency-of- occurrence procedures) or on subjective procedures and personal beliefs. In Bayesian analysis, a distinction is made between "prior" (before... ) and "posterior" probabilities (after specific additional information had a chance to change the prior beliefs or estimations).

Probability-weighted average: Sum of all possible outcomes of a gamble times the probability of each, expected value, e-value, and expectation, mean.

Problem situations: Concerns that specify what a decision is about, including background information that depicts its origins and the motivations of stakeholders.

Problem Solving: This term is sometimes used to refer to analytical problem solving workshops that seek to analyze and resolve conflicts based on identifying and providing the underlying human needs. In other situations, it refers to an approach to mediation that focuses primarily on resolving the conflict

Procedural problems: Procedural problems are problems with decision making procedures. Examples are decisions that are made without considering relevant and important facts, decisions that are made arbitrarily without considering the interests or needs of the affected people, or decisions that are made without following the established and accepted process. Often, procedural problems can intensify and complicate disputes which could be resolved relatively easily if proper procedures were followed.

Professional decision: Made in a professional capacity (e.g., as manager).

Prospect: Future possibility.

Prospective decision: One not to be made now, but possibly later. It is the opposite of current.

Prototyping: A strategy in system development in which a scaled down system or portion of a system is constructed in a short time, tested, and improved in several iterations. A prototype is an initial version of a system that is quickly developed to test the effectiveness of the overall design being used to solve a particular problem.

Psychological field: All that is in a personís mind.

Pure Uncertainty model: Elaborates only uncertainty considerations.

Puzzlement: A condition that arises when cues that describe what a decision is about are obscure or vague.

Qualitative information: Descriptions of the basic nature of a decision according to features that characterize sentiments of key stakeholders, winner and losers for particular options, problem definitions, and so on, expressed as criteria weights and likelihood of future conditions.


Quantitative information: Measurements of factors pertinent to decisions, such as costs or questionnaire results that capture sentiments.


Rating scale: Artificially constructed, e.g., 0 to 100, for an attribute that has no natural metric

Rational Decision Behavior: Behavior that is goal-oriented in reaching a decision. Behavior is guided by the consequences likely to result from the selection of a given alternative. A decision maker believes based upon analysis that a chosen alternative will result in achieving one or more desired objectives.

Rational: Advances decision-makerís welfare effectively, based on all decision-makerís knowledge and judgments, constrained to be globally coherent.

Rationale: Explanation of reasons for an input.

Rationality: A way of thinking about a decision that stresses political, logical, and ethical means of drawing an inference to make a judgment.

Realistic assumption: Actual (not arbitrary) position.

Redundancy: Double-counting.

Regret decision rule: Using a minmax rule to find alternatives with the greatest "lost/gain" if not adopted and select the alternative that best manages the postdecisional regret of missed opportunity.

Replacement: Decision tool that bypasses and substitutes for unaided judgment.

Representation heuristic: Use of a past decision as an example from which premises about information relationships are drawn and used to make future decisions.

Representation: The formulation or view of a problem. It is developed so the problem will be easier to solve.

Representational decisions: Choices for which there is a rich informational base and an accepted way to manipulate the data.

Representative value: Single value substituted for segment of a distribution.

Risk averse: decision-maker dislikes possibility of bad gamble outcome.

Risk neutral: decision-maker is prepared to play the averages.

Risk penalty: Amount by which average utility is to be reduced to account for risk, to produce a certain equivalent.

Risk: The chance that a bad outcome will occur, no matter what precautions are taken. Generally defined as the cost/expected value of an unfavorable outcome in decision situation where the probabilities are known. Thus, risk is used to describe the costs associated with the inability to predict exactly, even if you have (what you think are) "precise" probabilities, since any probability that you can expect a favorable outcome up to 99.9..% leaves the (possibly very small) chance that it does not come about.
In decision theory and in statistics, risk means uncertainty for which the probability distribution is known. Accordingly, risk analysis] it means a study to determine the outcomes of decisions along with their probabilities. In systems analysis, a decision maker is often concerned with the probability that a project (the chosen alternative) cannot be carried out with the time and money available. This risk of failure may differ from alternative to alternative and should be estimated as part of the analysis. In another usage, risk means an uncertain and strongly adverse impact, as in "the risks of nuclear power plants to the population are..." In that case, risk analysis or risk assessment is a study composed of two parts, the first dealing with the identification of the strongly adverse impacts, and the second with determination of their respective probabilities.

Roll back: to analyze a decision tree by progressively replacing judgments by logically equivalent substitutes, fold back.

Root cause: The source or origin of a decision that indicates the necessity to act.

Rule of thumb: An approach that is based on experience, not scientific knowledge, and is thought to be useful in carrying out some aspects of decision making.


Satisfice: settle on the first option satisfactory to decision-maker.

Satisficing: A decision rule calling for the first alternative that meets preset norms to be adopted.

Scenario: A prospect comprising a sequence of events.

Schema: A systematic program of action to attain an objective that is often unspoken and applied intuitively.

Search decisions: Decisions made in situations that are information-poor and in which there is no obvious or agreed-upon way to formulate the decision to collect and analyze information.

Selective perspective: Recognition of information only when it is consistent with the decision makerís inclinations or prejudices, a tendency that often leads the decision maker to ignore all other information.

Sensitivity analysis:A technique used to examine the risk in assumptions about key factors, such as interest rates and the relative weight of cost and quality criteria, in the choice among alternatives. Sensitivity analysis is the most general term used to describe through which one gauges the relative sensitivity of the value of the objective function to a one unit change in any of its solution variables. It is often done by running a decision model several times with different inputs so a modeler can analyze the alternative results.
Testing the impact of alternative input judgments on analysis findings.

Sequential decisions: Decisions in which opportunities to purchase clarifying information arise throughout the decision process.

Set of possibilities vs. a possibility: E.g., white color is a se of possibilities; red is a possibility within that set.

Setting: A possibility that affects an option consequence but is not itself a consequence.

Simulation Models A simulation is the execution of a model, represented by a computer program that gives information about the system being investigated. The simulation approach of analyzing a model is opposed to the analytical approach, where the method of analyzing the system is purely theoretical. As this approach is more reliable, the simulation approach gives more flexibility and convenience. The activities of the model consist of events, which are activated at certain points in time and in this way affect the overall state of the system. The points in time that an event is activated are randomized, so no input from outside the system is required. Events exist autonomously and they are discrete so between the executions of two events nothing happens.

Why are we running these simulations? Simulation is an appropriate methodology whenever a social phenomenon is not directly accessible, either because it no longer exits (as in archaeological studies) or because its structure or the effects of its structure , i.e., its behavior, are so complex that the observer cannot directly attain a clear picture of what is going on (as in some studies of world politics). The simulation is based upon a model constructed by the researcher that is more observable than the target phenomenon itself.

Single-pass evaluation: Produces only one evaluation of a given judgment.

Sole practitioner: Professional decision-maker in "private practice"

Stakeholders: Groups that have a share or interest in a decision and its outcome, or that are impacted by a decision, even if they are not part of the decision making process. That is, people who have legitimate interests or stakes in the decision.

Standard Base: Fixed base corresponding to a normal or average situation.

Strategic Decision-making: As different from programmed, routine decisions, strategic decisions tend to be relatively infrequent, not repetitive, involve the commitment of considerable resources (capital), and have long-time horizons with significant levels of uncertainty.

Structured Decision: It is any standard or repetitive decision situation for which solution techniques are already available. It is also sometimes called routine or programmed decisions. The structural elements in the situation, e.g. alternatives, criteria, environmental conditions, are known, defined and understood.

Structured opportunities: Opportunities that are clear because the set of favorable circumstances that can be exploited is known.

Structured problems: Problems that are understood because most of their component parts can be identified.

Structure-intensive model: Main analytic effort is on structure, not input.

Styles of Decision Making:

Subjective estimates: Best guesses about factors influenced by chance events, such as the prospects of high demand for a product or high usage for a service, and the relative importance of criteria used to compare alternatives.

Substitute: A hypothetical entity, model, judgment, treated as if it were another entity

Sufficient model: Minimum of analysis complexity that permit options to be evaluated.

Surrogate metric: Numerical natural substitute for a nonnatural measure.

Symptomatic problems: Signs and signals that suggest superficial concerns and not the underlying issues that are prompting the need to act.

Synergy: More of one criterion increases utility of other, e.g., advertising and sales.

System Analysis: This term has many different meanings. The systems analysis is an explicit formal inquiry carried out to help someone (referred to as the decision maker) identify a better course of action and make a better decision than he might otherwise have made. The characteristic attributes of a problem situation where systems analysis is called upon are complexity of the issue and uncertainty of the outcome of any course of action that might reasonably be taken. Systems analysis usually has some combination of the following: identification and re-identification) of objectives, constraintS, and alternative courses of action; examination of the probable consequences of the alternatives in terms of costs, benefits, and risks; presentation of the results in a comparative framework so that the decision maker can make an informed choice from among the alternatives. A systems analysis that concentrates on comparison and ranking of alternatives on basis of their known characteristics is referred to as decision analysis.
The part or aspect of systems analysis that concentrates on finding out whether an intended course of action violates any constraints is referred to as feasibility analysis. A systems analysis in which the alternatives are ranked in terms of effectiveness for fixed cost or in terms of cost for equal effectiveness is referred to as cost-effectiveness analysis. Cost-benefit analysis is a study where for each alternative the time stream of costs and the time stream of benefits (both in monetary units) are discounted to yield their present values. The comparison and ranking are made in terms of net benefits (benefits minus cost) or the ratio of benefits to costs. In risk-benefit analysis, cost (in monetary units) is assigned to each risk so as to make possible a comparison of the discounted sum of these costs (and of other costs as well) with the discounted sum of benefits that are predicted to result from the decision. The risks considered are usually events whose probability of occurrence is low, but whose adverse consequences would be important, e.g., events such as an earthquake or explosion of a plant.
The diagnosis formulation, and solution of problems that arise out of the complex forms of interaction in systems, from hardware to corporations, that exist or are conceived to accomplish one or more specific objectives. Systems analysis provides a variety of analytical tools, design methods and evaluative techniques to aid in decision making regarding such systems.

Systems Thinking: A way of thinking about, and a language for describing and understanding, the forces and interrelationships that shape the behavior of systems... helps us to see how to change systems more effectively, and to act more in tune with the larger processes of the natural and economic world.


Tacit Knowledge: A reference to types of knowledge which cannot be stated explicitly and therefore cannot be easily communicated and transferred. It therefore contrasts with "codified" or "explicit" knowledge. Personal skills are frequently cited as an important example of tacit knowledge.

Tactics: Procedures that specify one or more steps to be taken to deal with a particular stage of the decision process.

Target judgment: Objective of the enquiry.

Theories: A Theory is a systematic explanatory statement comprising a deductively connected set of (inductively/empirically derived) laws or propositions which relate (dependent and independent) variables to each other. Theories are the propositions that describe what one believes to be true about people, object, and event relationships.

Tier: A level in hierarchy.

Tough decisions: A class of decisions plagued by ambiguity, conflict, and uncertainty.

Tree Diagram: It is a visual way of representing hierarchies, commonly used, for instance, for representing organization charts. A tree diagram represents the hierarchy by displaying each object in the hierarchy as a box with lines connecting to its parent and to each of its children.

Tree sequence: Single succession of possibilities on a decision or probability tree.


Uncertainties: In decision theory and statistics, a precise distinction is made between a situation of risk and one of certainty. There is an uncontrollable random event inherent in both of these situations. The distinction is that in a risky situation the uncontrollable random event comes from a known probability distribution, whereas in an uncertain situation the probability distribution is unknown.

Uncertainty: Doubt about the magnitude of key future conditions, such as the level of demand, interest rates, or inflation.


Uncertainty is a ubiquitous phenomenon in everyday life, but it is also a topic of fundamental significance to many scientific disciplines. Uncertainty taken here in a broad sense, has many facets - among them probability and vagueness, including possibility, confidence, fuzziness etc. These are captured by different theories which often seem to be conceptually and technically incompatible. Therefore there is no universally accepted theory covering all this area and there are many reasons why we shall neither expect nor want to have one. On the other hand there have been attempts to cross the borders - there are theories trying to bridge gaps between rival approaches and looking for their common background.

Unconscious Preference:

Uncovering the Core Decision:

Unstructured Decisions: This type of decision situation is complex and no standard solutions exist for resolving the situation. Some or all of the structural elements of the decision situation are undefined, ill-defined or unknown. For example, goals may be poorly defined, alternatives may be incomplete or non-comparable, choice criteria may be hard to measure or difficult to link to goals.

Unstructured opportunities: Opportunities that are vague because key events needed to produce favorable outcomes are uncertain.

Unstructured problems: Problems that are poorly understood because most of their component parts are unknown or contentious.

Utility: In economics, utility means the real or fancied ability of a good or service to satisfy a human want. An associated term is welfare function (synonym: utility function--not to be confused with utility function in decision theory; see below), which relates the utility derived by an individual or group to the goods and services that it consumes. Marginal utility is the change in utility due to a one unit change in the quantity of a good or service consumed.
In decision theory, utility is a measure of the desirability of consequences of courses of action that applies to decision making under risk--that is, under uncertainty with known probabilities.
Utility theory provides a basis for the assignment of utilities to consequences. There exist various methods for constructing utility functions. The best-known method is based on indifference judgments of the decision maker about specially constructed alternatives (lotteries).
It is the usefulness or the value, that is an attribute denoting the capacity to satisfy human desires, usually measured by the price someone is willing to pay. Marginal utility is the change in utility due to a one-unit change in the quantity consumed.
The relationship between value and changes in the level of a criterion, such as increases in satisfaction.


Valuation information: Criteria, such as cost or profit, used to determine the merit of alternatives so that those with the greatest merit can be selected.

Value: The determination of how much something is worth. If the item being valued is publicly traded, value can be observed in the market. For most real assets, it is not the case and we need to apply valuation techniques to impute a value.
Value can be either objective or subjective. In the latter case, it means subjective worth or importance. For example, "the value of future benefits to the decision maker," "the value of clean air to the society." For the purposes of analysis, the subjective values must be measured on some scale. These measures of value should be based on preferences expressed by the person or group of interest.
In value analysis, one considers that the value v is related to the physical or other objective measure y of a consequence by a subjectively defined value function so that v = f(y).
Values are the ideas we have about what is good and what is bad, and how things should be. Besides business objective, we have values about family relationships, about work relationships and about other personal and relationships issues. In decision making, values are expressed in terms of weights given to the criteria, either explicitly or implicitly. Implicit values are derived by examining several similar decisions and noting which criteria must have been emphasized for a series of choices to have been made.

Variable: A variable or variable number is a unspecified quantity that may assume any one of a set of values ( i.e., the "range" of a variable). Variables can be continuous or discontinuous and dependent and independent.

Vividness: A characteristic of information that makes it memorable because of its emotional appeal, proximity, or seeming reality.


Warrant: A basis for drawing a conclusion, such as statistical significance, mathematical proof, or agreement among experts.

Welfare: Satisfaction, happiness, good.

What are models?

"What-if" questioning: The logical inquiry applied to examine assumptions about key factors, such as product demand or level of risk inherent in seemingly viable alternatives.


X-Efficiency / X-Inefficiency: Managers are merely x-efficient due, to (1) selective rationality; (2) individual inadequacies; (3) discretionary effort; (4) pervasive inertia; (5) organizational entropy



Zero sum game: A game in which the payoffs to the players add up to zero, so that a gain for one is necessarily equaled by loss to others. It contrasts with positive sum game. Zero-sum games or situations are situations in which the only way one side can get ahead (or get more of something) is if the other side gets less. This occurs when there is a finite amount of a resource to be distributed, and the together the parties want more than is available. In this situation, no side can get what they want unless the other side gets less than they want. This is also referred to as win-lose situations.


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