In multiplayer games, self-interested behavior among the players can harm the social welfare. Tax mechanisms are a common method to alleviate this issue and induce socially optimal behavior. In this work, we take the initial step of learning the optimal tax that can maximize social welfare with limited feedback in congestion games. We propose a new type of feedback named equilibrium feedback, where the tax designer can only observe the Nash equilibrium after deploying a tax plan. Existing algorithms are not applicable due to the exponentially large tax function space, nonexistence of the gradient, and nonconvexity of the objective. To tackle these challenges, we design a computationally efficient algorithm that leverages several novel components: (1) a piece-wise linear tax to approximate the optimal tax; (2) extra linear terms to guarantee a strongly convex potential function; (3) an efficient subroutine to find the exploratory tax that can provide critical information about the game.

In using "LLMs", we are referring to language models which evince in-context learning capabilities (also referred to as "foundation models" [13]). This behavior has traditionally been observed in models with at least a billion parameters.

The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform?

With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering - the movement of illicit funds to conceal their origins - can cross bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5% of global GDP or $0.8 - $2.0 trillion dollars are laundered globally each year. Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-available benchmark is needed for comparing models and for the advancement of the area. To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets. We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different machine learning models in terms of their AML abilities. In a key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected.

The Internal Revenue Service (IRS) has plans to take advantage of the "AI boom" to fill glaring workforce gaps, following the layoff of thousands of tax agents. In a May 6 oversight hearing of the House Appropriations Committee, U.S. Treasury Secretary Scott Bessent explained that the agency would be leaning into AI solutions in order to accommodate further reductions in the IRS' budget and staff and not fall behind on tax collection. The Treasury's budget proposal includes the removal of another 40,000 jobs. According to Bessent, proposed cuts to the agency's IT budget could be an opportunity to modernize and restructure the agency's existing IT infrastructure, as the current administration hones in on "wasteful" spending. "I believe, through smarter IT, through this AI boom, that we can use that to enhance collections. And I would expect that collections would continue to be very robust, as they were this year," he said.