Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …

We consider a ubiquitous scenario in the internet economy when individual decision makers
(henceforth, agents) both produce and consume information as they make strategic choices …

Individual decision-makers consume information revealed by the previous decision makers,
and produce information that may help in future decision makers. This phenomenon is …

In this article we analyze the ethical aspects of multistakeholder recommendation systems
(RSs). Following the most common approach in the literature, we assume a consequentialist …

As self-interested individuals (“agents”) make decisions over time, they utilize information
revealed by other agents in the past and produce information that may help agents in the …

We consider a ubiquitous scenario in the Internet economy when individual decision-makers
(henceforth, agents) both produce and consume information as they make strategic choices …

We propose and design recommendation systems that incentivize efficient exploration.
Agents arrive sequentially, choose actions and receive rewards, drawn from fixed but …

Algorithmic decision-making in high-stakes domains often involves assigning decisions to
agents with incentives to strategically modify their input to the algorithm. In addition to …

Many online platforms offer time-locked sales campaigns, whereby products are sold at fixed
prices for prespecified lengths of time. Platforms often display some information about …