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NERA Consultants Emilie Feyler and Dr. Veronica Postal recently published an article in Competition Policy International (CPI) titled “Can Self-Preferencing Algorithms Be Pro-Competitive?”

Self-preferencing algorithms are allegedly biased by their creators to privilege the creator’s content and products. In their article, Ms. Feyler and Dr. Postal describe an ongoing debate about the competitive implications of algorithmic self-preferencing practices in digital markets, where large platforms often have a dual role: They operate the digital marketplace as information intermediaries and also offer their own competing products as players in the marketplace.  

The article concludes there is no consensus from economic literature on whether pro-competitive benefits or possible anticompetitive considerations prevail. Nor is there consensus on the welfare effects of policy intervention aimed at correcting biases in algorithmic recommendations. Determining the net impact of self-preferencing algorithms requires individualized analysis that accounts for the workings of specific algorithms, the competitive context, and the market environment.