Abstract: In one embodiment, the present disclosure is directed to a method for providing a recommended wellness behavior. User data is received for different wellness components, the user data including data related to physical health, finances, and psychology. A user's wellness is assessed by, for each wellness component, determining a deviation between a comparison score and a relevant standard. A recommended behavior is determined based on the determined deviation. An indicator of the recommended behavior is then output to the user for the user to perform.

Abstract: In one embodiment, the present disclosure is directed to a method for providing a recommended wellness behavior. User data is received for different wellness components, the user data including data related to physical health, finances, and psychology. A user's wellness is assessed by, for each wellness component, determining a difference between the current user score and a relevant demographic standard, and determining a predicted future score based thereon. A recommended behavior is determined based on an identification of the wellness components having greatest differences between the predicted future score and the demographic standard. An indicator of the recommended behavior is then output to the user.

Abstract: In one embodiment, the present disclosure is directed to a method for providing a recommended wellness behavior. User data is received for different wellness components, the user data including data related to physical health, finances, and psychology. A user's wellness is assessed by, for each wellness component, determining a difference between the current user score and a relevant demographic standard, and determining a predicted future score based thereon. A recommended behavior is determined based on an identification of the wellness components having greatest differences between the predicted future score and the demographic standard. An indicator of the recommended behavior is then output to the user.

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.