Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or
LMSR) for prediction markets, have become important building blocks, called'primitives,'for …

We present a study on the evolution of publications about applications of proper scoring
rules. Specifically, we consider articles reporting the use of proper scoring rules when either …

Automated market makers are algorithmic agents that enable participation and information
elicitation in electronic markets. They have been widely and successfully applied in artificial …

We propose a general framework for the design of securities markets over combinatorial or
infinite state or outcome spaces. The framework enables the design of computationally …

We study axiomatic foundations for different classes of constant function automated market
makers (AMMs). We focus particularly on independence, homogeneity and convexity …

Automated market makers (AMMs) are automata that trade electronic assets at rates set by
mathematical formulas. AMMs are usually implemented by smart contracts on blockchains …

We investigate the effects of market making on market performance, focusing on allocative
efficiency as well as gains from trade accrued by background traders. We employ empirical …

The flourishing of online labor markets such as Amazon Mechanical Turk (MTurk) makes it
easy to recruit many workers for solving small tasks. We study whether information elicitation …

Abstract Machine Learning competitions such as the Netflix Prize have proven reasonably
successful as a method of “crowdsourcing” prediction tasks. But these compe-titions have a …

We consider the design of proper scoring rules, equivalently proper losses, when the goal is
to elicit some function, known as a property, of the underlying distribution. We provide a full …