Advances in Mechanism Design and Marketing Analytics

The field of mechanism design and marketing analytics is witnessing significant innovations, driven by the increasing use of artificial intelligence, machine learning, and deep learning techniques. Researchers are exploring new approaches to optimize auction mechanisms, model complex market interactions, and improve sales attribution accuracy. A key direction is the development of more sophisticated and generalizable models that can capture nuanced market dynamics and adapt to changing conditions. Noteworthy papers in this area include:

  • MMCE: A Framework for Deep Monotonic Modeling of Multiple Causal Effects, which proposes a novel framework for modeling multiple causal effects from observational data.
  • Deep Learning for Double Auction, which develops innovative deep learning methods for solving the complex problem of double auctions with imperfect information on both demand and supply sides.
  • NNN: Next-Generation Neural Networks for Marketing Mix Modeling, which presents a Transformer-based neural network approach to marketing mix modeling that captures complex interactions and improves sales attribution accuracy.

Sources

Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design

MMCE: A Framework for Deep Monotonic Modeling of Multiple Causal Effects

Tight Regret Bounds for Fixed-Price Bilateral Trade

Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents

Deep Learning for Double Auction

NNN: Next-Generation Neural Networks for Marketing Mix Modeling

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