Abstract: Client decision-making behavioral preferences for evaluating or coping with unknown outcomes (risk temperament or “RT”) and absorbing information (preference typology or “PT”) during decision-making events are identified and classified by processing client responses to questions. Behavioral preferences are classified and stored in a behavior-influenced decision-making data set (BDDS). The simulator also receives an objective decision-making data set (ODDS) of factually objective inputs. The simulator combines and triangulates the PT and RT information contained in the BDSS with the ODDS and derives a new data set of resultant output goals (OGDS). The transformed OGDS output is displayed as a triangulation of the simulator's RT, PT, and ODDS, with a balanced OGDS displayed as a bubble or spirit level. Out of balance status identifies deviation from intended outcome. OGDS decision-making tools can be recursively combined as modular blocks to create decision-making tools for other desired outcomes.

Abstract: Client decision-making behavioral preferences for evaluating or coping with unknown outcomes (risk temperament or “RT”) and absorbing information (preference typology or “PT”) during decision-making events are identified and classified by processing client responses to questions. Behavioral preferences are classified and stored in a behavior-influenced decision-making data set (BDDS). The simulator also receives an objective decision-making data set (ODDS) of factually objective inputs. The simulator combines and triangulates the PT and RT information contained in the BDSS with the ODDS and derives a new data set of resultant output goals (OGDS). The transformed OGDS output is displayed as a triangulation of the simulator's RT, PT, and ODDS, with a balanced OGDS displayed as a bubble or spirit level. Out of balance status identifies deviation from intended outcome. OGDS decision-making tools can be recursively combined as modular blocks to create decision-making tools for other desired outcomes.