While by no means exhaustive, this page outlines some of the topics of the workshop.
History of pricing technologies
One of the earliest examples of pricing innovations can be found in Edo era Japan. In 1673 Mitsui Takatoshi founded a new branch of a family shop in Nihonbashi at the heart of Edo (now Tokyo), a large gofukuya (a kimono shop) called Echigo-ya (越後屋). Ten years later the shop offered an innovation - a fixed price trading directly at the shop at zero markups. Until then the kimono industry used a "markup" system to set prices - the price is set higher at first, the goods are taken to the customer and the final price is negotiated with payment done on credit every six months. Echigo-ya introduced the convenience of a fixed price tag provided that the payment is done in cash. The advertisement flyers mentioning the fixed priced cash system can be found in Waseda University Library.
Since then price displays went through a series of independent developments in many countries, moving from hand-written and encoded prices to typed and printed public price tags and e-ink displays.
Price differentiation and technology
Now algorithmic pricing has shifted from a remote area of stock-market trading and anecdotes on temperature-sensitive vending machines that raise the price during heat season into a daily reality.
Personalized and dynamic pricing begin to feature in the law and policy debates concerned with competition and with consumer and data protection. With 2.5 million daily price changes on Amazon, rapid adoption of electronic shelf labels by grocery stores, surge pricing in ride-sharing apps, and seasonal market tracking in the hospitality industry, there is a growing need for a new legislative vocabulary, economic models, ethical studies and regulatory approaches.
There is mounting evidence that even the simplest learning algorithms are capable of reaching collusive outcomes without input from (and possibly unbeknownst to) humans. Simulated oligopoly markets have been shown to converge to collusive outcomes as a direct result of reinforcement learning. Algorithmic collusion also poses a new challenge to regulators from a technical perspective of detecting and mitigating collusion.
However like many pricing innovations and similarly to price differentiation, it poses a trade-off between efficiency and fairness -- the overall impact is unclear and algorithmic collusion is a topic of active research.
An interplay of folk theorems and convergence properties of stochastic processes have been used to study the phenomena in addition to in silico experiments. More recently the regulators have also acquired an interest in the problem, including both the U.S. Federal Trade Commission and the European Commission.