Since the provider's goal of maximizing revenue can only be achieved

Algorithmic pricing on online e-commerce platforms raises the concern of tacit collusion, where reinforcement learning algorithms learn to set collusive prices in a decentralized manner and through nothing more than profit feedback. This raises the question as to whether collusive pricing can be prevented through the design of suitable buy boxes, i.e., through the design of the rules that govern the elements of e-commerce sites that promote particular products and prices to consumers. In this paper, we demonstrate that reinforcement learning (RL) can also be used by platforms to learn buy box rules that are effective in preventing collusion by RL sellers. For this, we adopt the methodology of Stackelberg POMDPs, and demonstrate success in learning robust rules that continue to provide high consumer welfare together with sellers employing different behavior models or having out-of-distribution costs for goods.

We consider the Continuum Bandit problem where the goal is to find the optimal action under an unknown reward function, with an additional monotonicity constraint (or, markdown constraint) that requires that the action sequence be non-increasing. This problem faithfully models a natural single-product dynamic pricing problem, called markdown pricing, where the objective is to adaptively reduce the price over a finite sales horizon to maximize expected revenues. Jia et al '21 and Chen '21 independently showed a tight $T^{3/4}$ regret bound over $T$ rounds under *minimal* assumptions of unimodality and Lipschitzness in the reward (or, revenue) function. This bound shows that the demand learning in markdown pricing is harder than unconstrained (i.e., without the monotonicity constraint) pricing under unknown demand which suffers regret only of the order of $T^{2/3}$ under the same assumptions (Kleinberg '04). However, in practice the demand functions are usually assumed to have certain functional forms (e.g.

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.

We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly posts prices (over $T$ rounds) that depend on the observable $d$-dimensional context and receives binary purchase feedback. Unlike prior work assuming homogeneous buyer types, in our setting the buyer's valuation type is drawn from an unknown distribution with finite support size $K_{\star}$. We develop a contextual pricing algorithm based on optimistic posterior sampling with regret $\widetilde{O}(K_{\star}\sqrt{dT})$, which we prove to be tight in $d$ and $T$ up to logarithmic terms. Finally, we refine our analysis for the non-contextual pricing case, proposing a variance-aware zooming algorithm that achieves the optimal dependence on $K_{\star}$.

This paper proposes DeepRule, an integrated framework for automated business rule generation in retail assortment and pricing optimization. Addressing the systematic misalignment between existing theoretical models and real-world economic complexities, we identify three critical gaps: (1) data modality mismatch where unstructured textual sources (e.g. negotiation records, approval documents) impede accurate customer profiling; (2) dynamic feature entanglement challenges in modeling nonlinear price elasticity and time-varying attributes; (3) operational infeasibility caused by multi-tier business constraints. Our framework introduces a tri-level architecture for above challenges. We design a hybrid knowledge fusion engine employing large language models (LLMs) for deep semantic parsing of unstructured text, transforming distributor agreements and sales assessments into structured features while integrating managerial expertise. Then a game-theoretic constrained optimization mechanism is employed to dynamically reconcile supply chain interests through bilateral utility functions, encoding manufacturer-distributor profit redistribution as endogenous objectives under hierarchical constraints. Finally an interpretable decision distillation interface leveraging LLM-guided symbolic regression to find and optimize pricing strategies and auditable business rules embeds economic priors (e.g. non-negative elasticity) as hard constraints during mathematical expression search. We validate the framework in real retail environments achieving higher profits versus systematic B2C baselines while ensuring operational feasibility. This establishes a close-loop pipeline unifying unstructured knowledge injection, multi-agent optimization, and interpretable strategy synthesis for real economic intelligence.

The rapid diffusion of autonomous pricing algorithms has reshaped competitive dynamics in digital marketplaces, raising important economic and policy questions about their potential for collusive behavior. A substantial body of research demonstrates that reinforcement-learning (RL) agents can autonomously coordinate on supracompetitive outcomes even in the absence of explicit communication. Foundational contributions--including the work in [1]--show that algorithmic agents may systematically learn tacitly collusive strategies across multiple market structures, with Q-learning in particular generating prices above competitive levels in Logit, Hotelling, and linear demand environments. These concerns are reinforced by seminal work such as [2], which demonstrates that simple Q-learning agents reliably sustain collusion through structured punishment and reward cycles in repeated pricing games, as well as by [3], who document how algorithmic systems may generate sudden price spikes in response to high-impact, low-probability events (HILP), unintentionally coordinating on elevated prices. The study of [4] establishes a robust empirical and computational foundation demonstrating that pricing algorithms may autonomously learn to collude. A complementary line of research focuses specifically on Q-learning's capacity to learn collusive equilibria, as documented in papers [2], [5], and [6]. These findings are consistent with the theoretical properties of Q-learning established by [7], who show that the algorithm incrementally learns long-run discounted value-maximizing strategies in sequential decision problems. More recent studies further reveal that deep reinforcement-learning (deep RL) algorithms--including DDQN and SAC--may also display collusive tendencies. For instance, [8] documents that modern RL systems can coordinate on higher-than-competitive prices under a variety of market configurations.

A newly enacted New York law requires retailers to say whether your data influences the price of basic goods like a dozen eggs or toilet paper, but not how. If you're near Rochester, New York, the price for a carton of Target's Good & Gather eggs is listed as $1.99 on its website. It's unclear why the prices differ, but a new notice on Target's website offers a potential hint: "This price was set by an algorithm using your personal data." A recently enacted New York State law requires businesses that algorithmically set prices using customers' personal data to disclose that. According to the law, personal data includes any data that can be "linked or reasonably linked, directly or indirectly, with a specific consumer or device." The law doesn't require businesses to explicitly state what information about a person or device is being used or how each piece of information affects the final price a customer sees.

The Download: spotting crimes in prisoners' phone calls, and nominate an Innovator Under 35 A US telecom company trained an AI model on years of inmates' phone and video calls and is now piloting that model to scan their calls, texts, and emails in the hope of predicting and preventing crimes. Securus Technologies president Kevin Elder told that the company began building its AI tools in 2023, using its massive database of recorded calls to train AI models to detect criminal activity. It created one model, for example, using seven years of calls made by inmates in the Texas prison system, but it has been working on models for other states and counties. However, prisoner rights advocates say that the new AI system enables a system of invasive surveillance, and courts have specified few limits to this power. We have some exciting news: Nominations are now open for MIT Technology Review's 2026 Innovators Under 35 competition. This annual list recognizes 35 of the world's best young scientists and inventors, and our newsroom has produced it for more than two decades.