Pay What You Want (PWYW) can be an attractive marketing strategy to price discriminate between fair-minded and selfish customers, to fully penetrate a market without giving away the product for free, and to undercut competitors that use posted prices. We report on laboratory experiments that...

We provide a new framework to identify demand elasticities in markets where managers rely on algorithmic recommendations for price setting, and apply it to a dataset containing bookings for a sample of mid-sized hotels in Europe. Using non-binding algorithmic price recommendations and observed...

In many markets, sellers advertise their good with an asking price. This is a price at which the seller will take his good off the market and trade immediately, though it is understood that a buyer can submit an offer below the asking price and that this offer may be accepted if the seller...

This paper discusses the role of secret versus public reserve prices when bidders’ valuations depend positively on the seller’s private signal. A public reserve price is announced before the auction starts, and a secret reserve price is disclosed after the highest bid has been reached. The...

We study experimentally the effect of bargaining power in two sequential mechanisms that offer the possibility to trade at a fixed price before an auction. In the "Buy-It-Now" format, the seller has the bargaining power and offers a price prior to the auction; whereas in the "Sell-It-Now"...

The price-setting behaviour of manufacturing plants is examined using a large panel of monthly surveyed plant- and product-specific prices. The sample shows a high frequency of zero changes, relatively small price changes, and a strong seasonal price-change pattern. The intermittent feature of...

This paper develops a dynamic programming method when the one-stage deviation principle in the sense of mechanism design literature doesn’t hold. The commonly used dynamic programming method is valid only if the one-stage deviation principle in the sense of mechanism design literature is...

We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The "curse of dimensionality" is avoided due to four complementary techniques: (1) exploiting...

Anthropogenic greenhouse gas emissions are changing the energy balance of our planet. Various climatic feedbacks make the resulting warming over the next decades and centuries highly uncertain. We quantify how this uncertainty changes the optimal carbon tax in a stochastic dynamic programming...