2. A method as in claim 1 , wherein the steps of presenting collectively include operating a simulation engine having a set of state variables, and having a set of rules describing permitted changes in value of those state variables, wherein substantially each collection of possible values for the set of state variables defines a context; and the set of decision models collectively include a set of behavior models, each capable of responding to a query from the simulation engine and capable of generating a response to that query.
2. A method as in claim 1 , wherein the steps of presenting collectively include operating a simulation engine having a set of state variables, and having a set of rules describing permitted changes in value of those state variables, wherein substantially each collection of possible values for the set of state variables defines a context; and the set of decision models collectively include a set of behavior models, each capable of responding to a query from the simulation engine and capable of generating a response to that query. 5. A method as in claim 2 , wherein the simulation engine includes at least one of: a self-learning mode including at least some of those behavior models and software agents having initial values, and reinforcing those behavior models and software agents which are successful according to an evaluator for the simulation engine; a demonstration mode including at least some of those behavior models and software agents demonstrating their learning to an observer; a real-time mode including substantially all of those behavior models and software agents being updated with each change in context; a stochastic mode including introduction of random or pseudorandom values for at least some of those state variables.