I introduce a reduced form two-sided market model to study prediction and identification in two-sided markets. The model generates the hallmark features of two-sided markets: potentially below cost or even negative prices to one side of the market, and the "see-saw" or "waterbed" effect of a...

I introduce a reduced form two-sided market model to study prediction and identification in two-sided markets. The model generates the hallmark features of two-sided markets: potentially below cost or even negative prices to one side of the market, and the “see-saw” or “waterbed” effect...

I introduce a reduced form two-sided market model to study prediction and identification in two-sided markets. The model generates the hallmark features of two-sided markets: potentially below cost or even negative prices to one side of the market, and the “see-saw” or “waterbed” effect...

We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question....

We build a game-theoretic model to examine how better demand forecasting due to algorithms, machine learning and artificial intelligence affects the sustainability of collusion in an industry. We find that while better forecasting allows colluding firms to better tailor prices to demand...