Abstract
Online marketplaces will be transformed by autonomous AI agents acting on behalf of consumers. Rather than humans browsing and clicking, vision-language-model (VLM) agents can parse webpages, evaluate products, and transact. This raises a fundamental question: what do AI agents buy, and why? We develop ACES, a sandbox environment that pairs a platform-agnostic VLM agent with a fully programmable mock marketplace to study this question. We first conduct basic rationality checks in the context of simple tasks, and then, by randomizing product positions, prices, ratings, reviews, sponsored tags, and platform endorsements, we obtain causal estimates of how frontier VLMs actually shop. Models show strong but heterogeneous position effects: all favor the top row, yet different models prefer different columns, undermining the assumption of a universal "top" rank. They penalize sponsored tags and reward endorsements. Sensitivities to price, ratings, and reviews are directionally human-like but vary sharply in magnitude across models. Motivated by scenarios where sellers use AI agents to optimize product listings, we show that a seller-side agent that makes minor tweaks to product descriptions, targeting AI buyer preferences, can deliver substantial market-share gains if AI-mediated shopping dominates. We also find that modal product choices can differ across models and, in some cases, demand may concentrate on a few select products, raising competition questions. Together, our results illuminate how AI agents may behave in e-commerce settings and surface concrete seller strategy, platform design, and regulatory questions in an AI-mediated ecosystem.

Abstract
We investigate whether artificial intelligence can autonomously recover known structures of the Standard Model of particle physics using only experimental data and without theoretical inputs. By applying unsupervised machine learning techniques -- including data dimensionality reduction and clustering algorithms -- to intrinsic particle properties and decay modes, we uncover key organizational features of particle physics, such as the relative strength of different interactions and the difference between baryons and mesons. We also identify conserved quantities such as baryon number, strangeness and charm as well as the structure of isospin and the Eightfold Way multiplets. Our analysis then reveals that clustering can separate particles by interaction, flavor symmetries as well as quantum numbers. Additionally, we observe patterns consistent with Regge trajectories in baryon excitations. Our results demonstrate that machine learning can reproduce key aspects of the Standard Model directly from data, suggesting a promising path toward data-driven discovery in fundamental physics.

Abstract
US-China trade tensions, the COVID-19 pandemic, and the Russia-Ukraine conflict have disrupted and reshaped global supply chains. Existing studies caution that these tensions may not meaningfully reduce U.S. dependence on China-linked supply chains. This study examines the drivers of this unmet reallocation under overlapping geopolitical and public health disruptions. It investigates how these shocks jointly reconfigured bilateral trade and global value chain (GVC) participation and positioning among the U.S., China, and major trading partners during 2016-2023. Using monthly bilateral trade data across all sectors and multi-regional input-output tables for GVC decomposition, we combine a multi-period event-study with structural analysis to evaluate trade-flow disruptions and shifts in participation and functional positioning within GVCs. We find that China's exports remained robust, expanded across global markets, and sustained a rise in GVC participation, becoming more embedded in upstream segments through increased intermediate shipments to Asia and Europe. Meanwhile, U.S. imports increasingly shifted toward "China+1" partners, especially ASEAN, whose trade structures remain closely tied to Chinese upstream supply chains. These strengthening triangular relationships reveal how global reallocation and GVCs have evolved around the U.S. and China across successive shocks. Based on the evidence, we propose a supply chain resilience framework defined by three interacting dimensions: the level of GVC participation, the functional position within the value chain, and a country's capacity to re-couple in the post-shock landscape, conditioned by market diversification, economic complexity, and institutional capability. These findings carry significant implications for trade policy and industrial strategy in an era of geopolitical and geoeconomic fragmentation.

Abstract
Rising electricity demand underscores the need for secure and reliable generation expansion planning that accounts for upstream supply chain constraints. Traditional models often overlook limitations in materials, manufacturing capacity, lead times for deployment, and field availability, which can delay availability of planned resources and thus to threaten system reliability. This paper introduces a multi-stage supply chain-constrained generation expansion planning (SC-GEP) model that optimizes long-term investments while capturing material availability, production limits, spatial and temporal constraints, and material reuse from retired assets. A decomposition algorithm efficiently solves the resulting MILP. A Maryland case study shows that supply chain constraints shift technology choices, amplify deployment delays caused by lead times, and prompt earlier investment in shorter lead-time, low-material-intensity options. In the low-demand scenario, supply chain constraints raise investment costs by $1.2 billion. Under high demand, persistent generation and reserve shortfalls emerge, underscoring the need to integrate upstream constraints into long-term planning.

Abstract
Financial models do not merely analyse markets, but actively shape them. This effect, known as performativity, describes how financial theories and the subsequent actions based on them influence market processes, by creating self-fulfilling prophecies. Although discussed in the literature on economic sociology, this deeply rooted phenomenon lacks mathematical formulation in financial markets. Our paper closes this gap by breaking down the canonical separation of diffusion processes between the description of the market environment and the financial model. We do that by embedding the model in the process itself, creating a closed feedback loop, and demonstrate how prices change towards greater conformity to the prevailing financial model used in the market. We further show, with closed-form solutions and machine learning, how a performative market maker can reverse engineer the current dominant strategies in the market and effectively arbitrage them while maintaining competitive quotes and superior P&L.

Abstract
This study examines the behavioral and environmental implications of shared autonomous micro-mobility systems, focusing on autonomous bicycles and their integration with transit in the U.S. While prior research has addressed operational and lifecycle aspects, a critical gap remains in understanding which modes these services are likely to substitute, who is most inclined to adopt them, and how service attributes influence user decisions. We design a context-aware stated preference survey grounded in real-world trips and estimate discrete choice models, including a hybrid model incorporating latent attitudes. Findings indicate that adoption, mode shift, and environmental impacts are highly sensitive to service design. Scenarios with minimal wait and cost yield high adoption but increase emissions, while moderate waits are more likely to reduce impacts. Adoption likelihood varies with demographic characteristics, and outcomes depend on city type, context, and infrastructure assumptions. These insights can inform the development of more sustainable and equitable mobility systems.

Abstract
Online sellers have been adopting AI learning algorithms to automatically make product pricing and advertising decisions on e-commerce platforms. When sellers compete using such algorithms, one concern is that of tacit collusion - the algorithms learn to coordinate on higher than competitive. We empirically investigate whether these concerns are valid when sellers make pricing and advertising decisions together, i.e., two-dimensional decisions. Our empirical strategy is to analyze competition with multi-agent reinforcement learning, which we calibrate to a large-scale dataset collected from Amazon.com products. Our first contribution is to find conditions under which learning algorithms can facilitate win-win-win outcomes that are beneficial for consumers, sellers, and even the platform, when consumers have high search costs. In these cases the algorithms learn to coordinate on prices that are lower than competitive prices. The intuition is that the algorithms learn to coordinate on lower advertising bids, which lower advertising costs, leading to lower prices. Our second contribution is an analysis of a large-scale, high-frequency keyword-product dataset for more than 2 million products on Amazon.com. Our estimates of consumer search costs show a wide range of costs for different product keywords. We generate an algorithm usage and find a negative interaction between the estimated consumer search costs and the algorithm usage index, providing empirical evidence of beneficial collusion. Finally, we analyze the platform's strategic response. We find that reserve price adjustments will not increase profits for the platform, but commission adjustments will. Our analyses help alleviate some worries about the potentially harmful effects of competing learning algorithms, and can help sellers, platforms and policymakers to decide on whether to adopt or regulate such algorithms.

Abstract
We consider decision-making problems under decision-dependent uncertainty (DDU), where the distribution of uncertain parameters depends on the decision variables and is only observable through a finite offline dataset. To address this challenge, we formulate a decision-dependent distributionally robust optimization (DD-DRO) problem, and leverage multivariate interpolation techniques along with the Wasserstein metric to construct decision-dependent nominal distributions (thereby decision-dependent ambiguity sets) based on the offline data. We show that the resulting ambiguity sets provide a finite-sample, high-probability guarantee that the true decision-dependent distribution is contained within them. Furthermore, we establish key properties of the DD-DRO framework, including a non-asymptotic out-of-sample performance guarantee, an optimality gap bound, and a tractable reformulation. The practical effectiveness of our approach is demonstrated through numerical experiments on a dynamic pricing problem with nonstationary demand, where the DD-DRO solution produces pricing strategies with guaranteed expected revenue.

Abstract
The increasing vulnerability of electrical distribution systems to extreme weather events and cyber threats necessitates the development of economically viable frameworks for resilience enhancement. While existing approaches focus primarily on technical resilience metrics and enhancement strategies, there remains a significant gap in establishing market-driven mechanisms that can effectively commercialize resilience features while optimizing their deployment through intelligent decision-making. Moreover, traditional optimization approaches for distribution network reconfiguration often fail to dynamically adapt to both normal and emergency conditions. This paper introduces a novel framework integrating dual-agent Proximal Policy Optimization (PPO) with market-based mechanisms, achieving an average resilience score of 0.85 0.08 over 10 test episodes. The proposed architecture leverages a dual-agent PPO scheme, where a strategic agent selects optimal DER-driven switching configurations, while a tactical agent fine-tunes individual switch states and grid preferences under budget and weather constraints. These agents interact within a custom-built dynamic simulation environment that models stochastic calamity events, budget limits, and resilience-cost trade-offs. A comprehensive reward function is designed that balances resilience enhancement objectives with market profitability (with up to 200x reward incentives, resulting in 85% of actions during calamity steps selecting configurations with 4 DERs), incorporating factors such as load recovery speed, system robustness, and customer satisfaction. Over 10 test episodes, the framework achieved a benefit-cost ratio of 0.12 0.01, demonstrating sustainable market incentives for resilience investment. This framework creates sustainable market incentives