 the probability that the combined transmission exceeds capacity K is thus Pr{Q1+Q>K=2-2K2 This is the probability of information loss when two consumers are served by a shared network with capacity K. The probability of information loss with shared capacity can be compared with that for two separate networks of capacity K/2. The probability that a consumer does not experience an information loss is then F(K/2) = K/2. So the probability that at least one consumer experiences an information loss is Pr{Q' > K/2 or Q2 > K/2} = 1 - K 24 It can easily be verified that the probability of information loss is greater when consumers have separate networks.18 By pooling information transmission, the probability of information loss is reduced. However, congestion remains a problem. If a consumer increases the amount of information transmitted, this increases the probability of information loss for all users of the network. For example, suppose that there are many consumers using the network and each has a range of possible transmissions [0, hi], i = 1,..., , If a consumer increases hi, expected information loss increases for all consumers. When retail access is priced based on a consumer's usage of the network, prices can be adjusted to reflect congestion effects. Sufficiently high prices for usage will fully eliminate congestion effects. If consumers choose the range of transmissions, then congestion effects are fully eliminated when [h's k. However, as we have seen, a consumer will tolerate some information loss in return for a higher range of transmissions. Thus, the efficient price will allow for some possibility of information loss; that is, hi  k. The price of retail access will reflect several considerations. The price will reflect the value that consumers place on transmission of information. Also on the demand side, the price of retail access will reflect the cost to consumers of information 17 The derivation involves application of the convolution formula for two (independent) distribu- tion functions. If Q = Q1 +