Decision engineering

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.The car is becoming an expression of identity, values, and personal control in ways that move far beyond traditional segmentation and branding. For example, fuel efficiency will be only one consideration for a socially responsible vehicle (SRV). What percent of the parts are recyclable? What is the vehicle's total carbon footprint? Are there child labor inputs? Toxic paints, glues, or plastics? How transparent is the supply chain? Is the seller accountable for recycling? What methods are used? Are fair labor practices employed?We live in a dynamic world in which the pace, scope, and complexity of change are increasing. The continued march of globalization, the growing number of independent actors, and advancing technology have increased global connectivity, interdependence and complexity, creating greater uncertainties, systemic risk and a less predictable future. These changes have led to reduced warning times and compressed decision cycles. 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. The use of a visual design language representing decisions (see § Visual decision design) is an important element of decision intelligence, since it provides an intuitive common language readily understood by all decision participants. A visual metaphor improves the ability to reason about complex systems as well as to enhance collaboration.