'Reed's law' is the assertion of
David P. Reed that the
utility of large , particularly
social networks, can
scale exponentially with the size of the network.
The reason for this is that the number of possible sub-groups of network participants is
, where
is the number of participants. This grows much more rapidly than either
★ the number of participants,
, or
★ the number of possible pair connections,
(which follows
Metcalfe's law)
so that even if the utility of groups available to be joined is very small on a per-group basis, eventually the
network effect of potential group membership can dominate the overall economics of the system.
Derivation
Given a
set ''A'' of ''N'' people, it has
possible subsets. This is not difficult to see, since we can form each possible subset by simply choosing for each element of ''A'' one of two possibilities: whether to include that element, or not.
However, this includes the (one) empty set, and ''N''
singletons, which are not properly subgroups. So
subsets remain, which is exponential, like
.
Quote
From David P. Reed's, "The Law of the Pack" (Harvard Business Review, February 2001, pp 23-4):
:"[E]ven Metcalfe's Law understates the value created by a group-forming network as it grows. Let's say you have a GFN with n members. If you add up all the potential two-person groups, three-person groups, and so on that those members could form, the number of possible groups equals
. So the value of a GFN increases exponentially, in proportion to
. I call that Reed's Law. And its implications are profound."
See also
★ Coase's penguin
★
Social capital
★
Metcalfe's law
★
Andrew Odlyzko's "Content is Not King"
★
List of adages named after people
External links
★
That Sneaky Exponential—Beyond Metcalfe's Law to the Power of Community Building
★
Weapon of Math Destruction: A simple formula explains why the Internet is wreaking havoc on business models.
★
KK-law for Group Forming Services, XVth International Symposium on Services and Local Access, Edinburgh, March 2004, presents an alternative way to model the effect of social networks.