For use cases where there is no explicit "negative" it is a recommended pattern to do it like you described, posting a negative for any impression then posting a "positive" when things get clicked. You are technically correct that this does, theoretically, skew the propensities a bit. 

However, first of all this effect is almost always very small. Typical response rates are very low, so when propensity gets calculated as pos/(2*pos+neg) this is almost always very close to the actual number pos/(pos+neg). Secondly, we don't use the propensities as such. We (almost always) only use them in a relative way, and the ranking of the propositions is hardly affected by this "double counting". If you really, really want, there is a closed for formula to recover the unbiased propensity (divide by (1-p)) but we don't recommend doing that because it adds unnecessary complexity.

Lastly, in recent Pega versions we can deal with implicit negatives differently: you can define a "time out" on a response and only define the "positive" behavior. After the time out we set a negative if there is no positive. Since 7.4 this is a feature in CDH ("Outcome optimization") and recently, in Pega 8, this is a feature of Predictions.