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.
A related field, decision engineering, also investigates the improvement of decision-making processes but is not always as closely tied to data science.
Decision intelligence is based on the recognition that, in many organizations, decision-making could be improved if a more structured approach were used. Decision intelligence seeks to overcome a decision-making "complexity ceiling", which is characterized by a mismatch between the sophistication of organizational decision-making practices and the complexity of situations in which those decisions must be made. As such, it seeks to solve some of the issues identified around complexity theory and organizations.
In this sense, decision intelligence represents a practical application of the field of complex systems, which helps organizations to navigate the complex systems in which they find themselves. Decision intelligence can also be thought of as a framework that brings advanced analytics and machine learning techniques to the desktop of the non-expert decision maker, as well as incorporating, and then extending, data science to overcome the problems articulated in black swan theory.
Decision intelligence proponents believe that many organizations continue to make poor decisions. In response, decision intelligence seeks to unify a number of decision-making best practices, described in more detail below.
Decision intelligence builds on the insight that it is possible to design the decision itself, using principles previously used for designing more tangible objects like bridges and buildings.
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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.
A decision model in decision theory is the starting point for a decision method within a formal (axiomatic) system. Decision models contain at least one action axiom. An action is in the form "IF is true, THEN do ". An action axiom tests a condition (antecedent) and, if the condition has been met, then (consequent) it suggests (mandates) an action: from knowledge to action. A decision model may also be a network of connected decisions, information and knowledge that represents a decision-making approach that can be used repeatedly (such as one developed using the Decision Model and Notation standard).
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. 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.
This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons.
This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons.
Learn how to apply the Market Opportunity Navigator - a three-step tool for identifying, evaluating and strategizing market opportunities - to get the most value for your innovation.
This course is devoted to the psychology of risk (How do people make decisions in real-life situations characterized by risk and/or uncertainty?) and to risk competencies (How to make better decisions
L'étudiant.e applique les compétences acquises au cours de ses études dans une recherche effectuée dans l'un des laboratoires de la section de physique sous l'encadrement d'un.e enseignant.e de la sec
L'étudiant·e applique les compétences acquises au cours de ses études dans une recherche effectuée dans l'un des laboratoires de la section de physique sous l'encadrement d'un·e enseignant·e de la sec
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