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Overruling Hanover Shoe? AI Puts Its Best Foot Forward in Antitrust Law
Overruling Hanover Shoe? AI Puts Its Best Foot Forward in Antitrust Law - Algorithmic Econometrics: Teaching Computers to Think Like Economists
Economists have long relied on econometric models to analyze complex economic behaviors and make predictions. These models are built on assumptions about human rationality and require significant data inputs. Now, advances in artificial intelligence are enabling a new field of algorithmic econometrics. This involves training machine learning algorithms on economic data to detect patterns and make forecasts without relying on pre-set theoretical models.
One example is using neural networks to predict financial market movements. By analyzing large datasets of historical prices, news events, and other factors, algorithms can identify correlations and make statistically-valid predictions on future price changes. This removes human biases and limitations in processing complex information. Algorithms have proven remarkably effective at trading strategies, beating most human investors.
Researchers are also applying AI to build agent-based computational models. These simulate the interactions of multiple economic actors to understand emergent macro-level behaviors. Each agent follows simple behavioral rules, but the collective interactions can replicate real-world phenomena like market crashes. This provides insights on market instability absent in equation-based models.
Overruling Hanover Shoe? AI Puts Its Best Foot Forward in Antitrust Law - Sizing Up the Competition: How AI Aids in Market Definition
Defining the relevant market is a crucial first step in many antitrust analyses. This establishes the competitive landscape and identifies who the major players are. For deals like mergers and acquisitions, getting the market definition right is critical. Traditionally, economists have had to rely on surveys, focus groups, and other manual methods to collect and analyze data on substitutable products, geographic scope, and more. This was often subjective and imprecise.
Now, algorithmic approaches are revolutionizing market definition. By scraping data on prices, product features, consumer search queries, and more, AI can empirically determine what products compete with one another from a consumer perspective. This data-driven method avoids reliance on anecdotal evidence or untested theories. Algorithms can analyze billions of data points to identify cross-elasticities of demand and recommender system correlations. This enhances accuracy.
Overruling Hanover Shoe? AI Puts Its Best Foot Forward in Antitrust Law - Who Needs Brandeis? Letting Data Determine "The Curse of Bigness"
The traditional approach to defining antitrust markets has long relied on the subjective judgments of economists and legal scholars. Concepts like the "relevant market" and "reasonable interchangeability" have been shaped by seminal cases like Brown Shoe and Supreme Court decisions like Cellophane. While these precedents have provided a framework, their application has often been criticized as inconsistent and prone to biases.
Now, AI-powered analysis of large datasets is offering a more objective and rigorous approach to market definition. By leveraging algorithms to sift through voluminous data on consumer behavior, product features, and market dynamics, antitrust authorities can identify competitive constraints with unprecedented precision. This data-driven approach can overcome the limitations of traditional "hypothetical monopolist" tests that often fail to capture real-world substitution patterns.
For example, in a merger between two major tech platforms, an AI-powered analysis could scour user search histories, app downloads, and online traffic data to map out the true competitive landscape. This might reveal that users view certain niche social media apps as closer substitutes than the merging parties' core products. Such insights would be difficult to uncover through manual market surveys or anecdotal evidence.
Similarly, in assessing dominance in a particular industry, algorithmic techniques can rigorously analyze factors like cross-price elasticities, network effects, and switching costs. This allows for more nuanced and granular analyses than the binary "monopoly/not monopoly" determinations of the past. Authorities can better understand the degree of market power and the specific sources of that power.
Importantly, this data-driven approach sidesteps the ideological baggage that has long plagued antitrust enforcement. Concepts like "bigness" and "concentration" can now be defined empirically rather than through the lens of Brandeisian populism or Chicago-school laissez-faire. Algorithms don't bring preconceptions about the role of government or the virtues of large-scale enterprise. They simply let the facts speak for themselves.
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