We introduce the theoretical study of a Platform Equilibrium. There are
...
Large language models have astounded the world with fascinating new
capa...
Traditionally, social choice theory has only been applicable to choices ...
In this paper, we study belief elicitation about an uncertain future eve...
We introduce the use of generative adversarial learning to compute equil...
AI methods are used in societally important settings, ranging from credi...
Stackelberg equilibria arise naturally in a range of popular learning
pr...
We study the use of reinforcement learning to learn the optimal leader's...
Trading on decentralized exchanges has been one of the primary use cases...
Understanding emerging behaviors of reinforcement learning (RL) agents m...
Doctoral programs often have high rates of depression, anxiety, isolatio...
For a prediction task, there may exist multiple models that perform almo...
Decentralized exchanges (DEXs) provide a means for users to trade pairs ...
Driven by rapid digitization and expansive internet access, market-drive...
Algorithmic pricing on online e-commerce platforms raises the concern of...
AI and reinforcement learning (RL) have improved many areas, but are not...
We initiate the use of a multi-layer neural network to model two-sided
m...
Uniswap is the largest decentralized exchange for digital currencies. Th...
In recent years, prominent blockchain systems such as Bitcoin and Ethere...
We outline two dishonest strategies that can be cheaply executed on the
...
We introduce the use of reinforcement learning for indirect mechanisms,
...
We introduce the decision-aware time-series conditional generative
adver...
Blockchains are intended to be immutable, so an attacker who is able to
...
In this paper, we investigate the problem of transforming an
ε-BIC mecha...
Machine learning is a powerful tool for predicting human-related outcome...
Tackling real-world socio-economic challenges requires designing and tes...
Collaboration requires agents to coordinate their behavior on the fly,
s...
Collaboration requires agents to coordinate their behavior on the fly,
s...
Proof-of-Work mining is intended to provide blockchains with robustness
...
Recent work in the domain of misinformation detection has leveraged rich...
Proof-of-Stake consensus protocols give rise to complex modeling challen...
Algorithmic systems have been used to inform consequential decisions for...
From skipped exercise classes to last-minute cancellation of dentist
app...
Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dot...
We study the behavior of stochastic bandits algorithms under strategic
b...
We study revenue-optimal pricing and driver compensation in ridesharing
...
We propose a new, complementary approach to interpretability, in which
m...
The Computing Community Consortium (CCC), along with the White House Off...
We initiate the study of mechanism design without money for common goods...
Without monetary payments, the Gibbard-Satterthwaite theorem proves that...
Cluster-based randomized experiments are popular designs for mitigating ...
Ridesharing platforms match drivers and riders to trips, using dynamic p...
Designing an auction that maximizes expected revenue is an intricate tas...
In this paper, we take a statistical decision-theoretic viewpoint on soc...
Random utility theory models an agent's preferences on alternatives by
d...
In mechanism design it is typical to impose incentive compatibility and ...
In addressing the challenge of exponential scaling with the number of ag...