Introduction to the Milestones series

This new series will publish expository commentaries celebrating key contributions (Milestones) to our scholarly heritage

Feature-weighted categorized play across symmetric games

Experimental game theory studies the behavior of agents who face a stream of one-shot games as a form of learning. Most literature focuses on a single recurring identical game. This paper embeds single-game learning in a broader perspective, where learning can take place across similar games. We posit that agents categorize games into a few classes and tend to play the same action within a class. The agent’s categories are generated by combining game features (payoffs) and individual motives. An individual categorization is experience-based, and may change over time. We demonstrate our approach by testing a robust (parameter-free) model over a large body of independent experimental evidence over 2 × 2 symmetric games. The model provides a very good fit across games, performing remarkably better than standard learning models

On the decomposition of stochastic discounted cash flows

This paper extends previous results by Peccati [7] and Beccacece [1] on the decomposition of the discounted cash flow for deterministic financial operations to the stochastic case. Modelling financial operations as processes whose cumulant is a semimartingale, we obtain a very general decomposition formula which allows one to consider even random discount factors. © 1991 Springer-Verlag

Market equilibrium via classifications of commodities

When a rich variety of available goods is classified into a smaller number of tradable commodities, we have a market economy that relies on a simplified representation. We study how the classification of goods into commodities affects the allocation of scarce resources among agents. We also consider a notion of decentralized equilibrium that is achieved merely by an appropriate classification of the goods, regardless of the presence of a price-based mechanism