In a setting with multiple buyers, a buyer's expected value for an experiment may

We build a mechanism design framework where a platform designs GenAI models to screen users who obtain instrumental value from the generated conversation and privately differ in their preference for latency. We show that the revenue-optimal mechanism is simple: deploy a single aligned (user-optimal) model and use token cap as the only instrument to screen the user. The design decouples model training from pricing, is readily implemented with token metering, and mitigates misalignment pressures.

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A tech industry insider's guide to where to stay, eat, work, and play while visiting the tech scene's mothership, San Francisco. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. You've probably read plenty of recent news stories about how San Francisco is a failed city. Our infrastructure is crumbling, our streets are scary, our social fabric is torn and frayed. Most of that stuff is false. Yes, San Francisco has issues, but they're the same problems nearly all US cities are facing as they struggle to reorient themselves to our new, post-pandemic economic reality. The "doom loop" narrative that's often repeated in the national press is a gross exaggeration. The truth is that San Francisco is thriving.

We study hypothesis testing over a heterogeneous population of strategic agents with private information. Any single test applied uniformly across the population yields statistical error that is sub-optimal relative to the performance of an oracle given access to the private information. We show how it is possible to design menus of statistical contracts that pair type-optimal tests with payoff structures, inducing agents to self-select according to their private information. This separating menu elicits agent types and enables the principal to match the oracle performance even without a priori knowledge of the agent type. Our main result fully characterizes the collection of all separating menus that are instance-adaptive, matching oracle performance for an arbitrary population of heterogeneous agents. We identify designs where information elicitation is essentially costless, requiring negligible additional expense relative to a single-test benchmark, while improving statistical performance. Our work establishes a connection between proper scoring rules and menu design, showing how the structure of the hypothesis test constrains the elicitable information. Numerical examples illustrate the geometry of separating menus and the improvements they deliver in error trade-offs. Overall, our results connect statistical decision theory with mechanism design, demonstrating how heterogeneity and strategic participation can be harnessed to improve efficiency in hypothesis testing.

Motivated by the problem of selling large, proprietary data, we consider an information pricing problem proposed by Bergemann et al. that involves a decision-making buyer and a monopolistic seller. The seller has access to the underlying state of the world that determines the utility of the various actions the buyer may take. Since the buyer gains greater utility through better decisions resulting from more accurate assessments of the state, the seller can therefore promise the buyer supplemental information at a price. To contend with the fact that the seller may not be perfectly informed about the buyer's private preferences (or utility), we frame the problem of designing a data product as one where the seller designs a revenue-maximizing menu of statistical experiments. Prior work by Cai et al. showed that an optimal menu can be found in time polynomial in the state space, whereas we observe that the state space is naturally exponential in the dimension of the data. We propose an algorithm which, given only sampling access to the state space, provably generates a near-optimal menu with a number of samples independent of the state space. We then analyze a special case of high-dimensional Gaussian data, showing that (a) it suffices to consider scalar Gaussian experiments, (b) the optimal menu of such experiments can be found efficiently via a semidefinite program, and (c) full surplus extraction occurs if and only if a natural separation condition holds on the set of potential preferences of the buyer.

When you purchase through links in our articles, we may earn a small commission. Windows 11 25H2 is here! With Windows 11 25H2, Microsoft is providing numerous new features for Windows 11. We show you everything you need to know now. Windows 11 version 25H2, the next major feature update for Microsoft's operating system, started rolling out in waves on September 30.

These vegan meal kits and delivery services bring preprepared meals and meal kits to your door. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Vegan-specific meal kit services are a modern miracle for vegans, who usually aren't afforded the same conveniences as meat eaters or those without dietary restrictions. We at WIRED love meal kits, because they're all about modern convenience--you can eat what you want, even if you're on a specialty diet or have strong food preferences, without ever leaving your house. Gone are the days of grocery shopping and scouring online for recipes; these contemporary vegan meal kit services do the heavy lifting for you using curated menus and algorithms, with choices for both premade microwavable meals and kits where you do the cooking yourself. Some vegan meal kit services, like Hungryroot, use AI customization to curate menus based on your specific tastes. Others, like Daily Harvest, have a set selection of choices so you can always keep your freezer stocked with vegan, gluten-free meals to have on hand.