1. A method for modeling, solving and analyzing sequential, probabilistic and multi-objective decision problems, said method stored and executed as a stand-alone application on a computer having a processor, a color display screen, a pointing and selection device, a keyboard, and means for storing data and information, the method comprising the steps of: (a) receiving, as a first specification for a decision problem, one or more prioritized objectives for said decision problem, including a parent-child structure relating any and every parent objective to its offspring, (b) organizing said objectives and parent-child structure received in Step (a) as a multi-level objectives hierarchy in accordance with said first specification, (c) receiving, as a second specification, a collection of one or more performance measures for the decision problem together with a pairing of each performance measure with one and only one of said objectives, (d) for each performance measure in said collection of performance measures, receiving, as a third specification, a not necessarily monotonic, linear or exponential mathematical function, said function assigning a real number to each of a plurality of values of said performance measure, or receiving a collection of parameters and parameter values enabling the construction of said mathematical function, (e) receiving, as a fourth specification, a plurality of nodes, each node assigned a name and further specified as either a decision node, a chance node, a terminal node or a continuation node, together with a parent-child structure relating every parent node to its offspring, further receiving a selection of the principal node for the decision problem and, for every parent node, a specification of a contribution to each performance measure in said collection of performance measures, said contribution accrued during a transition from the parent node to each of its offspring and, for every chance node, a specification of a probability of transition from said chance node to each of its offspring and, for every continuation node, the name of the node to which it is continued, (f) creating a decision model for said decision problem by processing said second through fourth specifications and organizing said specifications into a sequential decision network, (g) solving the decision problem by processing said sequential decision network with the solver engine of the method, the resulting solution comprising an optimal decision policy for the problem and an optimal principal choice for a root node selected by a user, whereby an optimal solution is obtained for any sequential, probabilistic and multi-objective decision problem specified in accordance with Steps (a) through (d), said solution comprising an optimal decision policy and an optimal choice for the principal decision node selected by a user.