Monopolistically competitive markets are much more common in the real world than perfect markets, but perfect markets are the usual assumption in theoretical economics, mainly because they are more tractable from a mathematical perspective. However, there are many empirically observed distributions that lack a formal model that describes them, and thus tend to receive less attention from the economics community. One notable example was the observation that the distribution of intervals between random points corresponded to the market share distribution of the top 4 auto companies in 1967.

Other distributions obeyed the so-called broken stick rule: world religions, price patterns. However, there has been no theoretical model in which to fit the empirical result. More recently, stochastic proportional growth models were proposed for these, but not without problems: distributions are not perfectly log-log, but rather more like power-log on one end, and exponential on the other. One problem with the stochastic proportional growth model is that it relies on a so-called preferential attachment model. But this is not always the case in reality. This raises the question of what kind of distribution would arise in a market without preferential attachment.

Fiona offers one such model: assume uniform distribution of consumers over a given parameter, for instance, "sweetness" for a soft drink. Firms select their "sweetness" parameter at random. One can then (assuming a given distribution of tastes among consumers), assume that consumers will make their choices based on which products lie closest to their preference.

The distributions achieved through explicit simulation of the model that are more consistent with empirical observations than the Pareto distribution. The idea in Fiona's talk is similar to the discussion of Zipf's law (NKS 1014): a simple model using randomness could be the actual underlying generator of this and other phenomena that exhibit power law behavior.