How can lazy learning systems simultaneously solve multiple problems?
Let's see:


  Because the target function is approximated locally for each query to
  the system, 


If i am going to predict the class (for example) of a brand new instance i compare it with the most similar in the train dataset. Imagine a 2D graphic with lots of points and i put the new point, so i compare with the other points locally, and the class of the nearest to it will help me say what is its class.


  lazy learning systems can simultaneously solve multiple problems 
  and deal successfully with changes in the problem domain.


Maybe, these problems are the ones created by the eager methods that try to simulate the hyperplanes (boudaries) in a function, in logic predicates, etc between the classes and these hyperplanes make lots of mistakes. And simple local comparisons with the similarity with the train dataset could do a better work.