A system and method for projecting the likely future path of the subject of a sequential decision problem. The subject of the sequential decision problem takes an action beginning with the starting state of affairs and probabilistically transitions into other states according to the structure of the decision problem, the solution to the decision problem, possibly random events, and the decisions of the subject. The likely future path consists of a sequence of actions taken by the subject, the states the subject will likely be in after taking the projected actions, and the rewards the subject is likely to receive along the future path.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-aided decision making system, comprising: (a) a user input device; (b) a user output device; and (c) a processor programmed to evaluate decision problems available to a user; the programmed processor: (A) facilitating input of information from the user via the user input device, the information including (i) the decision problem to be solved (ii) an action set, the action set has elements representing actions available to the subject, each element in the action set having a corresponding action cost, the corresponding action costs forming an action cost set, (iii) at least one state dimension representing conditions relevant to the decision problem, (iv) each state dimension having a corresponding reward vector representing the reward associated with the elements of the state dimension, before consideration of the action cost set, (v) each state dimension having a corresponding transition matrix containing, for each element in the state dimension, a probability of moving from each state in the state dimension to each state in the state dimension for each action in the action set, (vi) a time index and a discount factor, the time index containing decision points available to the user, each decision point representing a point in time when the user selects from the action set, and the discount factor representing the subject's preference for rewards relative to time, (B) the programmed processor combining the reward vectors with the action cost set to form a reward matrix and the programmed processor combining the transition matrices with the action set to form a total transition matrix; (C) the programmed processor forming a functional equation from the state dimensions, the reward matrix, the total transition matrix, and all remaining inputs; (D) the programmed processor evaluating the functional equation, including error-checking and validating the inputs and performing a convergence check to ensure that the functional equation will be solvable, and the programmed processor solving the functional equation; (E) the programmed processor generating an optimal policy by using the solved functional equation to find, for every point in the decision index, the overall value-maximizing action, the set of decisions made at each point in the decision index forming the optimal policy; (F) the programmed processor generating a likely path for at least one starting state by (i) identifying an assumed action by selecting the value-maximizing action for each potential state at the initial point in the decision index, based upon the optimal policy (ii) evaluating, for the assumed action, a likely transition to occur by comparing the probabilities in the transition matrix for the combination of state dimensions; (iii) generating the likely path for each decision point in the decision index by selecting the likely transition from the possible transitions at each decision point based upon the current state, the reward in the current state given the assumed action and the most likely transition at the next decision point in the decision index (G) the programmed processor outputting the likely path to the user through the user output device. 2. A computer-aided decision making system according to claim 1, wherein the programmed processor evaluates, for the assumed actions, the most likely transition and generates the likely path for each decision point in the decision index using the most likely transition. 3. A computer-aided decision making system according to claim 1, wherein the programmed processor evaluates, for the assumed action, the most likely transition and generates the likely path for each decision point in the decision index using the most likely transition and the programmed processor generates a second likely path for a less likely transition based upon user-provided alternate path-selection criteria. 4. A computer-aided decision making system according to claim 3, wherein the programmed processor evaluates the most likely transition and at least one less likely transition and determines if the transitions are alternate paths where the likely transition probabilities, are close, or within a user-defined parameter, the programmed processor selects among alternate paths according to alternate path-selection criteria. 5. A computer-aided decision making system according to claim 1, wherein the programmed processor displays the optimal policies along the likely paths for different starting states at the initial point in the decision index. 6. A computer-aided decision making system according to claim 1, wherein the programmed processor displays the rewards along the likely paths for different starting states at the initial point in the decision index. 7. A computer-aided decision making system according to claim 1, wherein the programmed processor receives subject personality information and uses the subject personality information to determine the subject's path-selection criteria or otherwise define the assumed action. 8. A computer-aided decision making system according to claim 3, wherein the programmed processor, upon evaluating at least two alternate paths, selects the least likely transition. 9. A computer-aided decision making system according to claim 1, wherein the programmed processor prompts the user to select a subset of the state dimensions and projects a likely path for the selected state dimensions 10. A computer-aided decision making system according to claim 1, wherein the programmed processor prompts the user to select an analysis of the state dimensions in the decision problem, and the computer system conducts the analysis of dimensions to determine one or more significant state dimensions, and projects the likely path for each significant state dimension(s). 11. A computer implemented method for assisting a user in making a decision comprising: (a) providing a computer system having a user input device, a user output device, and a processor programmed with instructions to evaluate a decision problem available to the user, the instructions programming the processor: (b) using the computer system to provide the user with an option for selecting the decision problem to be solved, the user inputs information via the user input device to define the decision problem, the information including (i) the decision problem to be solved (ii) an action set, the action set has elements representing actions available to the subject, each element in the action set having a corresponding action cost, the corresponding action costs forming an action cost set, (iii) at least one state dimension representing conditions relevant to the decision problem, (iv) each state dimension having a corresponding reward vector representing the reward associated with the elements of the state dimension, before consideration of the action cost set, (v) each state dimension having a corresponding transition matrix containing, for each element in the state dimension, a probability of moving from each state in the state dimension to each state in the state dimension for each action in the action set, (vi) a time index and a discount factor, the time index containing decision points available to the user, each decision point representing a point in time when the user selects from the action set, and the discount factor representing the subject's preference for rewards relative to time, (c) forming, by the computer system manipulating the reward vectors with the action cost set, a reward matrix, and by the computer system manipulating the transition matrices with the set of actions, a total transition matrix, (d) forming, by the computer system manipulating the state dimensions, the reward matrix, the total transition matrix and all remaining inputs, a functional equation. (e) evaluating, by the computer system, the functional equation including error-checking and validating the inputs and performing a convergence check to ensure that the functional equation will be solvable, and solving the functional equation; (f) generating, by the computer system, an optimal policy by using the solved functional equation to find for every point in the decision index, the overall value-maximizing action, the set of decisions made at each point in the decision index forming the optimal policy; (g) generating, by the computer system, a likely path for at least one starting state by (i) identifying an assumed action by selecting the value-maximizing action for each potential state at the initial point in the decision index, based upon the optimal policy (ii) evaluating, for the assumed action, the most likely transition to occur by comparing the probabilities in the transition matrix for the combination of state dimensions; (iii) generating the likely path for each decision point in the decision index by selecting the most likely transition from the possible transitions at each decision point based upon the current state, the reward in the current state given the assumed action and the most likely transition at the next decision point in the decision index (E) outputting, by the computer system, the likely path to the user through the user output device. 12. A method as set forth in claim 11, wherein the computer system evaluates, for the assumed action, the most likely transition and generates the likely path for each decision point in the decision index using the most likely transition. 13. A method as set forth in claim 11, wherein the computer system evaluates, for the assumed action, the most likely transition and generates the likely path for each decision point in the decision index using the most likely transition and generates a second likely path for a less likely transition based upon user-provided alternate path-selection criteria 14. A method as set forth in claim 13, wherein the computer system evaluates the most likely transition and at least one less likely transition and determines if the transitions are alternate paths where the likely transition probabilities, are close, or within a user-defined parameter, the programmed processor selects among alternate paths according to alternate path-selection criteria. 15. A method as set forth in claim 11, wherein the computer system displays the optimal policies along the likely paths for different starting states at the initial point in the decision index. 16. A method as set forth in claim 11, wherein the computer system displays the rewards along the likely paths for different starting states at the initial point in the decision index. 17. A method as set forth in claim 11, wherein the computer system receives subject personality information and uses the subject personality information to determine the subject's path-selection criteria or otherwise define the assumed action. 18. A method as set forth in claim 13, wherein the computer system, upon evaluating at least two alternate paths, selects the least likely transition. 19. A method as set forth in claim 11 wherein the computer system prompts the user to select a subset of the state dimensions and projects a likely path for the selected state dimensions. 20. A method as set forth in claim 11 wherein the computer system prompts the user to select an analysis of the state dimensions in the decision problem, and the computer system conducts the analysis of dimensions to determine one or more significant state dimensions, and projects the likely path for each significant state dimension(s).