Platforms are everywhere. The rise of Uber, Netflix, and Facebook has attracted a lot of attention to this business model. However, despite its relevance and presence in the digital economy, the definition of platforms, their main characteristics, the intuitions about how they set prices, solve coordination issues, or choose their ownership structure seem to be scattered in many papers. This review attempts to organize the last two decades of research on multisided platforms around three essential elements of platforms: price structure, network effects, and control rights. We highlight which definitions are used in the literature, how they are related to the defining characteristics of platforms, and what research has been made on those characteristics. We pay special attention to the research done on pricing, coordination problems, and ownership structure. We conclude by reviewing the theoretical evidence around monopolization and competition policy in multisided markets.

The use of artificial intelligence (AI) in the form of pricing algorithms to increase profits is becoming ubiquitous. However, the literature has focused on specific markets and algorithms so far, but it is unclear what happens across algorithms and markets. To analyze the business and economic impact of pricing algorithms, we build a computational model that considers two sophisticated AI algorithms (Q-learning and Particle Swarm Optimization) competing in prices in three different market structures (Logit, Hotelling, and linear demand models). From a social perspective, we find that PSO outperforms Q-learning, which tends to set supracompetitive prices. However, small changes in the algorithm designs may drive them to set more competitive prices, implying that a proper analysis of algorithmic competition requires considering the details of the algorithms and the market structure. When firms compete on algorithms, algorithms may generate price dispersion. Additionally, when facing a traditional competitor that uses a best-response function, algorithms tend to set supracompetitive prices, and both firms earn extra profits, but the traditional competitor benefits the most. Overall, the article contributes to understanding algorithmic competition, discusses implications for managers and policymakers, and identifies opportunities for future research.

Purpose: Simulating markets using agent-based models must consider pricing. However, the strategic nature of prices limits the development of agent-based models with endogenous price competition.Methods: I propose an agent-based algorithm based on Game Theory that allows us to simulate the pricing in different markets. I test the algorithm in five theoretical economic models from the industrial organization literature. Results:In all cases, the algorithm is capable of simulating the optimal pricing of those markets. It is also tested in two more cases: one in which the original work fails to predict the optimal outcome, and another one that is quite complex to solve analytically. Lastly, I present two potential extensions of this algorithm: one dynamic, and another one based on quantity competition. Conclusions:This algorithm opens the door to the extensive inclusion of pricing in agent-based models, but also, it helps to establish a link between the industrial organization literature and the agent-based modeling.

Algorithmic pricing may lead to more efficient and contestable markets, but high-impact, low-probability events such as terror attacks or heavy storms may lead to price gouging, which may trigger injunctions or get sellers banned from platforms such as Amazon or eBay. This work addresses how such events may impact prices when set by an algorithm and how different markets may be affected. We analyze how to mitigate these high-impact events by paying attention to external (market conditions) and internal (algorithm design) features surrounding the algorithms. We find that both forces may help in partially mitigating price gouging, but it remains unknown which forces or features may lead to complete mitigation.

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