Decision quality

Last updated

Decision quality (DQ) is the quality of a decision at the moment the decision is made, regardless of its outcome. Decision quality concepts permit the assurance of both effectiveness and efficiency in analyzing decision problems. [1] In that sense, decision quality can be seen as an extension to decision analysis. Decision quality also describes the process that leads to a high-quality decision. Properly implemented, the DQ process enables capturing maximum value in uncertain and complex scenarios. [2]

Contents

Decision and outcome

Fundamental to all decision quality concepts is the distinction between the decision and its outcome. [3] They are different because of the uncertainties when making a choice—a high-quality decision can still result in a poor outcome, and vice versa. In the face of uncertainty, the decision maker only has control over the decision, but no control over the outcome of external circumstances. Consequently, the outcome of a decision does not allow an assessment of its quality. A decision has quality at the time it is made, which is not changed by hindsight. Concepts of decision quality focus on measuring and improving the quality of the decision at the time it is being made.

Elements

The confidence a decision maker has in its choice, and related to it the commitment a decision maker has to act upon that choice, depends on the quality of the decision at the time of making the decision. A high-quality decision is characterized by the following elements:

Quality in decision making requires quality in each of the elements listed above, and the overall quality of the decision is limited by the weakest element. Decision quality is achieved when for each element the cost to obtain additional information or insight to improve its quality exceeds the added value.

A variety of specific tools and processes exist to improve the quality of each element.

Framing

The first element to achieve decision quality is framing. Having the appropriate frame ensures the right decision problem is addressed. Quality in framing is achieved when the decision makers have alignment on purpose, perspective, and scope of the decision problem to be solved. It means the right people will work the right problem the right way.

Options

A decision cannot be better than the best available alternative (or option if there is more than one). A wide variety of approaches, tools, and methods exist to generate high quality options, ranging from systematic search approaches to identify options to approaches that aim to creatively synthesize options.[ citation needed ] Quality in options is achieved by applying a suitable options generation process, where the process itself leads to a variety of feasible and diverse options, which are hybrid solutions of originally considered options that combine their best features, and where for each options an understanding of its implementation exists.

Information

The quality of a decision depends on the quality of the information to inform the decision. Quality in information is achieved when the information is meaningful and reliable, is based on appropriate data and judgment, reflects properly all uncertainties, biases, intangibles, and interdependencies, and the limits to the information are known. A wide variety of tools exist to improve the quality of the information used in the decision problem.

Values and trade-offs

Quality in this element requires the identification of the right decision criteria and the definition of trade-off rules among them. This necessitates at first the identification of all key stakeholders, and what each of them values. Quality in this element of decision quality is characterized by transparent value metrics, a clear line of sight of the primary metric, and explicit trade-off rules between key metrics.

Sound reasoning

This element is the domain of decision analysis, which aims to produce insight. Decision analysis provides the logic and analytic tools to find the best choice in a complex situation and should serve as a guide to facilitate the conversation about the decision. A wide variety of tools, ranging from decision trees, over hierarchies to complex network models is available to match the decision problem. Quality in this element is achieved when the value and uncertainty of each alternative are understood, and the best choice is clear.

Commitment to action

The quality of a decision also depends on the commitment to act upon the choice that is made. Quality in this element is achieved by involving all key decision-makers and stakeholders in an effective and efficient decision-making process. At the end of the process, quality is characterized by buy-in across all stakeholders and an organization that is ready to take action and commit resources.

History and industry implementation

Decision quality concepts were first developed in 1964, building on developments in statistical decision theory and game theory by Professor Howard Raiffa of Harvard University, and dynamic probabilistic systems by Professor Ronald A. Howard of Stanford University. The First implementation of DA/DQ concepts in a professional application is documented in Prof. Howard's paper "Decision Analysis: Applied Decision Theory." published in 1966. [4] Since then, decision analysis tools and decision quality concepts have been adopted by many corporations to guide and improve their decisions. [5] Starting in 2014, the Society of Decision Professionals is recognizing organizations that made DQ a core competency across the entire organization with the Raiffa-Howard Award, presented annually. [6] Beyond organization-wide implementation, decision quality concepts can also be applied on multi-company projects. [7]

See also

Related Research Articles

In common usage, evaluation is a systematic determination and assessment of a subject's merit, worth and significance, using criteria governed by a set of standards. It can assist an organization, program, design, project or any other intervention or initiative to assess any aim, realisable concept/proposal, or any alternative, to help in decision-making; or to ascertain the degree of achievement or value in regard to the aim and objectives and results of any such action that has been completed.

Satisficing is a decision-making strategy or cognitive heuristic that entails searching through the available alternatives until an acceptability threshold is met. The term satisficing, a portmanteau of satisfy and suffice, was introduced by Herbert A. Simon in 1956, although the concept was first posited in his 1947 book Administrative Behavior. Simon used satisficing to explain the behavior of decision makers under circumstances in which an optimal solution cannot be determined. He maintained that many natural problems are characterized by computational intractability or a lack of information, both of which preclude the use of mathematical optimization procedures. He observed in his Nobel Prize in Economics speech that "decision makers can satisfice either by finding optimum solutions for a simplified world, or by finding satisfactory solutions for a more realistic world. Neither approach, in general, dominates the other, and both have continued to co-exist in the world of management science".

<span class="mw-page-title-main">Decision-making</span> Cognitive process to choose a course of action or belief

In psychology, decision-making is regarded as the cognitive process resulting in the selection of a belief or a course of action among several possible alternative options. It could be either rational or irrational. The decision-making process is a reasoning process based on assumptions of values, preferences and beliefs of the decision-maker. Every decision-making process produces a final choice, which may or may not prompt action.

<span class="mw-page-title-main">Decision theory</span> Branch of applied probability theory

Decision theory is a branch of applied probability theory and analytic philosophy concerned with the theory of making decisions based on assigning probabilities to various factors and assigning numerical consequences to the outcome.

Managerial economics is a branch of economics involving the application of economic methods in the organizational decision-making process. Economics is the study of the production, distribution, and consumption of goods and services. Managerial economics involves the use of economic theories and principles to make decisions regarding the allocation of scarce resources. It guides managers in making decisions relating to the company's customers, competitors, suppliers, and internal operations.

Development communication refers to the use of communication to facilitate social development. Development communication engages stakeholders and policy makers, establishes conducive environments, assesses risks and opportunities and promotes information exchange to create positive social change via sustainable development. Development communication techniques include information dissemination and education, behavior change, social marketing, social mobilization, media advocacy, communication for social change, and community participation.

Program evaluation is a systematic method for collecting, analyzing, and using information to answer questions about projects, policies and programs, particularly about their effectiveness and efficiency.

Policy analysis or public policy analysis is a technique used in the public administration sub-field of political science to enable civil servants, nonprofit organizations, and others to examine and evaluate the available options to implement the goals of laws and elected officials. People who regularly use policy analysis skills and techniques on the job, particularly those who use it as a major part of their job duties are generally known by the title Policy Analyst. The process is also used in the administration of large organizations with complex policies. It has been defined as the process of "determining which of various policies will achieve a given set of goals in light of the relations between the policies and the goals."

Decision analysis (DA) is the discipline comprising the philosophy, methodology, and professional practice necessary to address important decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing important aspects of a decision; for prescribing a recommended course of action by applying the maximum expected-utility axiom to a well-formed representation of the decision; and for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker, and other corporate and non-corporate stakeholders.

Adaptive management, also known as adaptive resource management or adaptive environmental assessment and management, is a structured, iterative process of robust decision making in the face of uncertainty, with an aim to reducing uncertainty over time via system monitoring. In this way, decision making simultaneously meets one or more resource management objectives and, either passively or actively, accrues information needed to improve future management. Adaptive management is a tool which should be used not only to change a system, but also to learn about the system. Because adaptive management is based on a learning process, it improves long-run management outcomes. The challenge in using the adaptive management approach lies in finding the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. This approach has more recently been employed in implementing international development programs.

In economics, rationalization is an attempt to change a pre-existing ad hoc workflow into one that is based on a set of published rules. There is a tendency in modern times to quantify experience, knowledge, and work. Means–end (goal-oriented) rationality is used to precisely calculate that which is necessary to attain a goal. Its effectiveness varies with the enthusiasm of the workers for the changes being made, the skill with which management applies the rules, and the degree to which the rules fit the job.

Howard Raiffa was an American academic who was the Frank P. Ramsey Professor (Emeritus) of Managerial Economics, a joint chair held by the Business School and Harvard Kennedy School at Harvard University. He was an influential Bayesian decision theorist and pioneer in the field of decision analysis, with works in statistical decision theory, game theory, behavioral decision theory, risk analysis, and negotiation analysis. He helped found and was the first director of the International Institute for Applied Systems Analysis.

The rational planning model is a model of the planning process involving a number of rational actions or steps. Taylor (1998) outlines five steps, as follows:

In decision theory, the evidential reasoning approach (ER) is a generic evidence-based multi-criteria decision analysis (MCDA) approach for dealing with problems having both quantitative and qualitative criteria under various uncertainties including ignorance and randomness. It has been used to support various decision analysis, assessment and evaluation activities such as environmental impact assessment and organizational self-assessment based on a range of quality models.

<span class="mw-page-title-main">Decision intelligence</span> Subfield of machine learning

Decision intelligence is an engineering discipline that augments data science with theory from social science, decision theory, and managerial science. Its application provides a framework for best practices in organizational decision-making and processes for applying machine learning at scale. The basic idea is that decisions are based on our understanding of how actions lead to outcomes. Decision intelligence is a discipline for analyzing this chain of cause and effect, and decision modeling is a visual language for representing these chains.

Value-driven design (VDD) is a systems engineering strategy based on microeconomics which enables multidisciplinary design optimization. Value-driven design is being developed by the American Institute of Aeronautics and Astronautics, through a program committee of government, industry and academic representatives. In parallel, the U.S. Defense Advanced Research Projects Agency has promulgated an identical strategy, calling it value-centric design, on the F6 Program. At this point, the terms value-driven design and value-centric design are interchangeable. The essence of these strategies is that design choices are made to maximize system value rather than to meet performance requirements.

Decision-making software is software for computer applications that help individuals and organisations make choices and take decisions, typically by ranking, prioritizing or choosing from a number of options.

Robust decision-making (RDM) is an iterative decision analytics framework that aims to help identify potential robust strategies, characterize the vulnerabilities of such strategies, and evaluate the tradeoffs among them. RDM focuses on informing decisions under conditions of what is called "deep uncertainty", that is, conditions where the parties to a decision do not know or do not agree on the system models relating actions to consequences or the prior probability distributions for the key input parameters to those models.

<span class="mw-page-title-main">Bayesian inference in marketing</span>

In marketing, Bayesian inference allows for decision making and market research evaluation under uncertainty and with limited data.

<span class="mw-page-title-main">Value tree analysis</span>

Value tree analysis is a multi-criteria decision-making (MCDM) implement by which the decision-making attributes for each choice to come out with a preference for the decision makes are weighted. Usually, choices' attribute-specific values are aggregated into a complete method. Decision analysts (DAs) distinguished two types of utility. The preferences of value are made among alternatives when there is no uncertainty. Risk preferences solves the attitude of DM to risk taking under uncertainty. This learning package focuses on deterministic choices, namely value theory, and in particular a decision analysis tool called a value tree.

References

  1. ↑ Ronald A. Howard, "Decision Analysis: Practice and Promise", Management Science, Vol. 34, No.6, June 1988
  2. ↑ Larry Neal and Carl Spetzler, "An Organization-Wide Approach to Good Decision Making", Harvard Business Review, May 27, 2015
  3. ↑ Spetzler, Carl; Winter, Hannah; Meyer, Jennifer (2016). Decision Quality: Value Creation from Better Business Decisions. Wiley.
  4. ↑ Decision Analysis: Applied Decision Theory, Ronald A. Howard., The Proceedings of the Fourth International Conference on Operational Research, ed. D. B. Hertz and J. Melese. New York: John Wiley & Sons, Inc., 1966.
  5. ↑ Strategic Decision Group, "Embedding Decision Quality in Chevron’s Culture Helps Them Outperform the Competition", date missing
  6. ↑ Society of Decision Professionals, "The Raiffa-Howard Award for Organizational Decision Quality", 2014
  7. ↑ SPE Technical Report, "Guidance for Decision Quality for Multi-Company Upstream Projects", SPE-181246-TR, March 2016
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.