Abstract
An online seller or platform is technically able to offer every consumer a different price for the same product, based on information it has about the customers. Such online price discrimination exacerbates concerns regarding the fairness and morality of price discrimination, and the possible need for regulation. In this chapter, we discuss the underlying basis of price discrimination in economic theory, and its popular perception. Our surveys show that consumers are critical and suspicious of online price discrimination. A majority consider it unacceptable and unfair, and are in favour of a ban. When stores apply online price discrimination, most consumers think they should be informed about it. We argue that the General Data Protection Regulation (GDPR) applies to the most controversial forms of online price discrimination, and not only requires companies to disclose their use of price discrimination, but also requires companies to ask customers for their prior consent. Industry practice, however, does not show any adoption of these two principles.

Abstract
Reforming alcohol price regulations in wine-producing countries is challenging, as current price regulations reflect the alignment of cultural preferences with economic interests rather than public health concerns. We evaluate and compare the impact of counterfactual alcohol pricing policies on consumer behaviors, firms, and markets in France. We develop a micro-founded partial equilibrium model that accounts for consumer preferences over purchase volumes across alcohol categories and over product quality within categories, and for firms' strategic price-setting. After calibration on household scanner data, we compare the impacts of replacing current taxes by ethanol-based volumetric taxes with a minimum unit price (MUP) policy of 0.50 Euro per standard drink. The results show that the MUP in addition to the current tax outperforms a tax reform in reducing ethanol purchases (-15% vs. -10% for progressive taxation), especially among heavy drinking households (-17%). The MUP increases the profits of small and medium wine firms (+39%) while decreasing the profits of large manufacturers and retailers (-39%) and maintaining tax revenues stable. The results support the MUP as a targeted strategy to reduce harmful consumption while benefiting small and medium wine producers. This study provides ex-ante evidence that is crucial for alcohol pricing policies in wine-producing countries.

Abstract
This study investigates the impact of artificial intelligence (AI) adoption on job loss rates using the Global AI Content Impact Dataset (2020--2025). The panel comprises 200 industry-country-year observations across Australia, China, France, Japan, and the United Kingdom in ten industries. A three-stage ordinary least squares (OLS) framework is applied. First, a full-sample regression finds no significant linear association between AI adoption rate and job loss rate ($\beta \approx -0.0026$, $p = 0.949$). Second, industry-specific regressions identify the marketing and retail sectors as closest to significance. Third, interaction-term models quantify marginal effects in those two sectors, revealing a significant retail interaction effect ($-0.138$, $p < 0.05$), showing that higher AI adoption is linked to lower job loss in retail. These findings extend empirical evidence on AI's labor market impact, emphasize AI's productivity-enhancing role in retail, and support targeted policy measures such as intelligent replenishment systems and cashierless checkout implementations.

Abstract
The benefits of adopting artificial intelligence (AI) in manufacturing are undeniable. However, operationalizing AI beyond the prototype, especially when involved with cyber-physical production systems (CPPS), remains a significant challenge due to the technical system complexity, a lack of implementation standards and fragmented organizational processes. To this end, this paper proposes a new process model for the lifecycle management of AI assets designed to address challenges in manufacturing and facilitate effective operationalization throughout the entire AI lifecycle. The process model, as a theoretical contribution, builds on machine learning operations (MLOps) principles and refines three aspects to address the domain-specific requirements from the CPPS context. As a result, the proposed process model aims to support organizations in practice to systematically develop, deploy and manage AI assets across their full lifecycle while aligning with CPPS-specific constraints and regulatory demands.

AI Insights

• The AIM4M model is tool‑agnostic, enabling any ML framework to plug into CPPS workflows.
• It embeds audit‑ready traceability, so every model change is logged for regulatory compliance.
• Rollouts become predictable across heterogeneous factories thanks to a standardized deployment pipeline.
• SMEs can adopt bundled roles, while large plants can scale to fine‑grained governance without redesign.
• Future iterations will be driven by real‑world customer projects, iteratively tightening the process logic.
• The project is backed by EU “AI Matters” funding and Baden‑Württemberg state support, ensuring cross‑border collaboration.
• For deeper dives, consult “Machine Learning in Manufacturing” and the MLOps maturity model paper cited in the study.

Abstract
There are inefficiencies in financial markets, with unexploited patterns in price, volume, and cross-sectional relationships. While many approaches use large-scale transformers, we take a domain-focused path: feed-forward and recurrent networks with curated features to capture subtle regularities in noisy financial data. This smaller-footprint design is computationally lean and reliable under low signal-to-noise, crucial for daily production at scale. At Increase Alpha, we built a deep-learning framework that maps over 800 U.S. equities into daily directional signals with minimal computational overhead. The purpose of this paper is twofold. First, we outline the general overview of the predictive model without disclosing its core underlying concepts. Second, we evaluate its real-time performance through transparent, industry standard metrics. Forecast accuracy is benchmarked against both naive baselines and macro indicators. The performance outcomes are summarized via cumulative returns, annualized Sharpe ratio, and maximum drawdown. The best portfolio combination using our signals provides a low-risk, continuous stream of returns with a Sharpe ratio of more than 2.5, maximum drawdown of around 3\%, and a near-zero correlation with the S\&P 500 market benchmark. We also compare the model's performance through different market regimes, such as the recent volatile movements of the US equity market in the beginning of 2025. Our analysis showcases the robustness of the model and significantly stable performance during these volatile periods. Collectively, these findings show that market inefficiencies can be systematically harvested with modest computational overhead if the right variables are considered. This report will emphasize the potential of traditional deep learning frameworks for generating an AI-driven edge in the financial market.

Abstract
In volatile financial markets, balancing risk and return remains a significant challenge. Traditional approaches often focus solely on equity allocation, overlooking the strategic advantages of options trading for dynamic risk hedging. This work presents DeltaHedge, a multi-agent framework that integrates options trading with AI-driven portfolio management. By combining advanced reinforcement learning techniques with an ensembled options-based hedging strategy, DeltaHedge enhances risk-adjusted returns and stabilizes portfolio performance across varying market conditions. Experimental results demonstrate that DeltaHedge outperforms traditional strategies and standalone models, underscoring its potential to transform practical portfolio management in complex financial environments. Building on these findings, this paper contributes to the fields of quantitative finance and AI-driven portfolio optimization by introducing a novel multi-agent system for integrating options trading strategies, addressing a gap in the existing literature.

Abstract
The web continues to grow, but dependency-monitoring tools and standards for resource integrity lag behind. Currently, there exists no robust method to verify the integrity of web resources, much less in a generalizable yet performant manner, and supply chains remain one of the most targeted parts of the attack surface of web applications. In this paper, we present the design of LiMS, a transparent system to bootstrap link integrity guarantees in web browsing sessions with minimal overhead. At its core, LiMS uses a set of customizable integrity policies to declare the (un)expected properties of resources, verifies these policies, and enforces them for website visitors. We discuss how basic integrity policies can serve as building blocks for a comprehensive set of integrity policies, while providing guarantees that would be sufficient to defend against recent supply chain attacks detailed by security industry reports. Finally, we evaluate our open-sourced prototype by simulating deployments on a representative sample of 450 domains that are diverse in ranking and category. We find that our proposal offers the ability to bootstrap marked security improvements with an overall overhead of hundreds of milliseconds on initial page loads, and negligible overhead on reloads, regardless of network speeds. In addition, from examining archived data for the sample sites, we find that several of the proposed policy building blocks suit their dependency usage patterns, and would incur minimal administrative overhead.

AI Insights

• LiMS uses service workers to intercept network requests, enabling real‑time integrity checks without site code changes.
• The backend API performs lightweight database lookups to decide policy compliance, keeping first‑load latency under 200 ms.
• Default policy sets include hash verification, MIME type matching, and origin whitelisting, composable into richer rulesets.
• Evaluation on 450 domains shows only a few hundred milliseconds overhead on first loads, dropping to near‑zero on reloads.
• Policy building blocks align with real‑world dependency patterns, implying low admin effort for operators.
• Literature shows service workers can degrade performance, but LiMS mitigates this by caching policy results and batching API queries.

Abstract
This paper presents data-driven approaches for integrated assortment planning and inventory allocation that significantly improve fulfillment efficiency at JD.com, a leading E-commerce company. JD.com uses a two-level distribution network that includes regional distribution centers (RDCs) and front distribution centers (FDCs). Selecting products to stock at FDCs and then optimizing daily inventory allocation from RDCs to FDCs is critical to improving fulfillment efficiency, which is crucial for enhancing customer experiences. For assortment planning, we propose efficient algorithms to maximize the number of orders that can be fulfilled by FDCs (local fulfillment). For inventory allocation, we develop a novel end-to-end algorithm that integrates forecasting, optimization, and simulation to minimize lost sales and inventory transfer costs. Numerical experiments demonstrate that our methods outperform existing approaches, increasing local order fulfillment rates by 0.54% and our inventory allocation algorithm increases FDC demand satisfaction rates by 1.05%. Considering the high-volume operations of JD.com, with millions of weekly orders per region, these improvements yield substantial benefits beyond the company's established supply chain system. Implementation across JD.com's network has reduced costs, improved stock availability, and increased local order fulfillment rates for millions of orders annually.

Abstract
Artificial Intelligence (AI) has transitioned from a futuristic concept reserved for large corporations to a present-day, accessible, and essential growth lever for Small and Medium-sized Enterprises (SMEs). For entrepreneurs and business leaders, strategic AI adoption is no longer an option but an imperative for competitiveness, operational efficiency, and long-term survival. This report provides a comprehensive framework for SME leaders to navigate this technological shift, offering the foundational knowledge, business case, practical applications, and strategic guidance necessary to harness the power of AI. The quantitative evidence supporting AI adoption is compelling; 91% of SMEs using AI report that it directly boosts their revenue. Beyond top-line growth, AI drives profound operational efficiencies, with studies showing it can reduce operational costs by up to 30% and save businesses more than 20 hours of valuable time each month. This transformation is occurring within the context of a seismic economic shift; the global AI market is projected to surge from $233.46 Billion in 2024 to an astonishing $1.77 Trillion by 2032. This paper demystifies the core concepts of AI, presents a business case based on market data, details practical applications, and lays out a phased, actionable adoption strategy.

AI Insights

• A phased, pilot‑driven rollout lets SMEs de‑risk AI investment while building internal momentum.
• AI Agents, Hyperautomation, and Democratization of AI are the next frontiers that will empower agile businesses.
• Building a unified data infrastructure and a data‑driven culture is essential for scaling AI across the organization.
• Responsible, ethical AI implementation can become a competitive advantage for SMEs in the coming decade.
• Key literature: “Assessing AI Adoption and Digitalization in SMEs” (arXiv) offers a practical framework for implementation.
• Definition: Hyperautomation – the strategic integration of AI, machine learning, and robotic process automation to maximize process automation.
• Definition: Machine Learning – training algorithms on data to enable predictive or decision‑making capabilities.

Abstract
This study examines how interest rate caps affect the demand for payday loans, using aggregate data from British Columbia (2012--2019) during which the province's maximum fee was reduced from \$23 to \$17 and then to \$15 per \$100 borrowed. Estimating a linear demand function via OLS, we find that lowering interest rate caps significantly increases loan demand. We estimate that the \$8 decrease, from \$23 to \$15 per \$100, raised annual consumer surplus by roughly \$28.6 million (2012 CAD). A further reduction to \$14, starting in January 2025, would add another \$3.9 million per year. These results suggest that stricter interest rate caps can yield substantial consumer welfare gains.