--- a/src/DataStruct.v	Mon Nov 16 13:27:34 2009 -0500
+++ b/src/DataStruct.v	Wed Nov 18 15:38:01 2009 -0500
@@ -830,11 +830,11 @@
 
    Inductive types are often the most pleasant to work with, after someone has spent the time implementing some basic library functions for them, using fancy [match] annotations.  Many aspects of Coq's logic and tactic support are specialized to deal with inductive types, and you may miss out if you use alternate encodings.
 
-   Recursive types usually involve much less initial effort, but they can be less convenient to use with proof automation.  For instance, the [simpl] tactic (which is among the ingredients in [crush]) will sometimes be overzealous in simplifying uses of functions over recursive types.  Consider a call [get l f], where variable [l] has type [filist A (S n)].  This expression would be simplified to an explicit pair, even though we know nothing about the structure of [l] beyond its type.  In a proof involving many recursive types, this kind of unhelpful "simplification" can lead to rapid bloat in the sizes of subgoals.
+   Recursive types usually involve much less initial effort, but they can be less convenient to use with proof automation.  For instance, the [simpl] tactic (which is among the ingredients in [crush]) will sometimes be overzealous in simplifying uses of functions over recursive types.  Consider a call [get l f], where variable [l] has type [filist A (S n)].  The type of [l] would be simplified to an explicit pair type.  In a proof involving many recursive types, this kind of unhelpful "simplification" can lead to rapid bloat in the sizes of subgoals.
 
    Another disadvantage of recursive types is that they only apply to type families whose indices determine their "skeletons."  This is not true for all data structures; a good counterexample comes from the richly-typed programming language syntax types we have used several times so far.  The fact that a piece of syntax has type [Nat] tells us nothing about the tree structure of that syntax.
 
-   Reflexive encodings of data types are seen relatively rarely.  As our examples demonstrated, manipulating index values manually can lead to hard-to-read code.  A normal inductive type is generally easier to work with, once someone has gone through the trouble of implementing an induction principle manually with the techniques we studied in Chapter 3.  For small developments, avoiding that kind of coding can justify the use of reflexive data structures. *)
+   Reflexive encodings of data types are seen relatively rarely.  As our examples demonstrated, manipulating index values manually can lead to hard-to-read code.  A normal inductive type is generally easier to work with, once someone has gone through the trouble of implementing an induction principle manually with the techniques we studied in Chapter 3.  For small developments, avoiding that kind of coding can justify the use of reflexive data structures.  There are also some useful instances of co-inductive definitions with nested data structures (e.g., lists of values in the co-inductive type) that can only be deconstructed effectively with reflexive encoding of the nested structures. *)
 
 
 (** * Exercises *)