Neural networks are able to emulate economic subjects' behavior, without the utilization of a priori economic laws. This work states the possibility of explaining economic regularities about demand curve following connectionist models. Targets for training are generated by the model itself while it learns and acts with a cross-target method, i.e., the artificial neural network produces guesses both of the actions of the economic subject and of their effects. Actual effects are estimated by economic environment rules and results are used to train the effect guess mechanism; the evaluation of actions necessary to match guessed effects are, on the contrary, employed to train the decision mechanism (which guesses actions). Two kinds of learning -- short term and long term learning -- take place. The former only with local capability to react to the changes of the environment and the latter one with full capability to do it. For an observer the behavior produced by this kind of artificial agent (AA) matches the actions of a real agent as stated in economic handbooks, with goals and plans. Obviously, AA has no such symbolic entities, which are inventions of the observer. The assumption is that the observations of economists about real world agents' behavior suffer from the same bias. Economic regularities arising from present versions of the model encourage further developments, in particular toward the interaction of micro-models to build macroeconomic ones.