Information has long been a cornerstone of business strategy. Firms need to know where they fit into the broader market, what the competitive openings are, what their customers' tastes and needs are, and how to manipulate all this information to their advantage. In the Information Age, particularly with the proliferation of the Internet, data moves at hyperspeed and is ever more critical as businesses try to seek out exclusive information before their competitors. As information gathering, processing, and application accelerates, it steadily bears out an interesting theory of market efficiency known as Grossman's paradox.
Under traditional market theory, it is assumed that markets function best when market players have full and complete knowledge of all information relevant to the market. In this way, actors can predict the consequences of their behavior—and that of their competitors—and adjust their strategies accordingly, leading to the greatest overall efficiency for the market. Sanford Grossman, an economist at the University of Pennsylvania's Wharton School, pointed out an inherent flaw in such market assumptions. Perfectly efficient markets would provide no incentive to seek out new information. In other words, no profit could be gained by such activity. But if there's no profit, no business would gather information, and no one could possibly be perfectly informed, thus undoing the market equilibrium. This is to say, firms that have ceased gathering new information about the market couldn't possibly make informed decisions about prices or allocation of resources, and the market's efficiency would be destroyed. Grossman suggests that perfect information, or anything close to it, is purely fiction and is a contradiction in the efficient market theory. As a result, according to Grossman, no player in a market could ever be perfectly informed, and thus the drive to obtain more information than the next competitor will always be a component of market economics.
"The Devil's Derivatives Dictionary." The Derivatives Zine, July 2000. Available from www.derivativeszine.com.
SEE ALSO: Data Mining