Legal decision-making and enforcement under uncertainty are always difficult and always potentially costly. The risk of error is always present given the limits of knowledge, but it is magnified by the precedential nature of judicial decisions: an erroneous outcome affects not only the parties to a particular case, but also all subsequent economic actors operating in “the shadow of the law.” The inherent uncertainty in judicial decision-making is further exacerbated in the antitrust context where liability turns on the difficult-to-discern economic effects of challenged conduct. And this difficulty is still further magnified when antitrust decisions are made in innovative, fast-moving, poorly-understood, or novel market settings—attributes that aptly describe today’s digital economy. Rational decision-makers will undertake enforcement and adjudication decisions with an eye toward maximizing social welfare (or, at the very least, ensuring that nominal benefits outweigh costs). But “[i]n many contexts, we simply do not know what the consequences of our choices will be. Smart people can make guesses based on the best science, data, and models, but they cannot eliminate the uncertainty.” Because uncertainty is pervasive, we have developed certain heuristics to help mitigate both the direct and indirect costs of decision-making under uncertainty, in order to increase the likelihood of reaching enforcement and judicial decisions that are on net beneficial for society. One of these is the error-cost framework. In simple terms, the objective of the error-cost framework is to ensure that regulatory rules, enforcement decisions, and judicial outcomes minimize the expected cost of (1) erroneous condemnation and deterrence of beneficial conduct (“false positives,” or “Type I errors”); (2) erroneous allowance and under-deterrence of harmful conduct (“false negatives,” or “Type II errors”); and (3) the costs of administering the system (including the cost of making and enforcing rules and judicial decisions, the costs of obtaining and evaluating information and evidence relevant to decision-making, and the costs of compliance). In the antitrust context, a further premise of the error-cost approach is commonly (although not uncontroversially) identified: the assumption that, all else equal, Type I errors are relatively more costly than Type II errors. “Mistaken inferences and the resulting false condemnations ‘are especially costly, because they chill the very conduct the antitrust laws are designed to protect.’” Thus the error-cost approach in antitrust typically takes on a more normative objective: a heightened concern with avoiding the over-deterrence of procompetitive activity through the erroneous condemnation of beneficial conduct in precedent-setting judicial decisions. Various aspects of antitrust doctrine—ranging from antitrust pleading standards to the market definition exercise to the assignment of evidentiary burdens—have evolved in significant part to constrain the discretion of judges (and thus to limit the incentives of antitrust enforcers) to condemn uncertain, unfamiliar, or nonstandard conduct, lest “uncertain” be erroneously identified as “anticompetitive.” The concern with avoiding Type I errors is even more significant in the enforcement of antitrust in the digital economy because the “twin problems of likelihood and costs of erroneous antitrust enforcement are magnified in the face of innovation.” Because erroneous interventions against innovation and the business models used to deploy it threaten to deter subsequent innovation and the deployment of innovation in novel settings, both the likelihood and social cost of false positives are increased in digital and other innovative markets. Thus the avoidance of error costs in these markets also raises the related question of the proper implementation of dynamic analysis in antitrust.
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Open Access
Sep 9, 2020
Andrew P Wheeler + 1
