Bad Science: Abuse And Effects In Online Markets

By Alexander M. Waksman[1]

Imagine a scientist who has total conviction that a particular hypothesis is correct.  The scientist’s belief in their hypothesis is so strong that they dispense with the need to test it in experiments or compare it against empirical data.  Even when third parties carry out such tests, the scientist considers it unnecessary to take account of the results.  We would condemn that approach as “bad science”.

Antitrust may not be a science in the strict sense, but the same logic applies.  When a competition authority (or claimant) identifies a theory of harm, that theory ought to be tested against actual market developments to determine whether its predictions are correct.  As Commission guidance states, “economic theory is used to develop a testable hypothesis that is later checked against the data. In that case, the economic analysis makes predictions about reality that can be tested by observations and potentially rejected or verified.”[2]

Thus, a theory of harm is useful as a starting point, but it cannot be the whole story.  In particular, when the challenged conduct has been ongoing for a significant period of time and sufficient data are available, it makes good sense to ask whether the hypothesised anticompetitive outcomes have actually transpired.

The need to “test the hypothesis” is a controversial topic in abuse of dominance cases, where competition authorities, courts, and commentators offer differing formulati…


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