Abstract
In the coming decade, artificially intelligent agents with the ability to plan and execute complex tasks over long time horizons with little direct oversight from humans may be deployed across the economy. This chapter surveys recent developments and highlights open questions for economists around how AI agents might interact with humans and with each other, shape markets and organizations, and what institutions might be required for well-functioning markets.

AI Insights

• Generative AI agents can secretly collude, distorting prices and eroding competition.
• Experiments show that large language models can be nudged toward more economically rational decisions.
• Reputation markets emerge when AI agents maintain short‑term memory and community enforcement.
• The revival of trade hinges on institutions like the law merchant and private judges, now re‑examined for AI economies.
• Program equilibrium theory offers a framework to predict AI behavior in multi‑agent settings.
• Endogenous growth models predict that AI adoption may increase variety but also create excess supply.
• Classic texts such as Schelling’s “The Strategy of Conflict” and Scott’s “Seeing Like a State” illuminate the strategic and institutional dynamics of AI markets.

Abstract
Based on economic theories and integrated with machine learning technology, this study explores a collaborative Supply Chain Management and Financial Supply Chain Management (SCM - FSCM) model to solve issues like efficiency loss, financing constraints, and risk transmission. We combine Transaction Cost and Information Asymmetry theories and use algorithms such as random forests to process multi-dimensional data and build a data-driven, three-dimensional (cost-efficiency-risk) analysis framework. We then apply an FSCM model of "core enterprise credit empowerment plus dynamic pledge financing." We use Long Short-Term Memory (LSTM) networks for demand forecasting and clustering/regression algorithms for benefit allocation. The study also combines Game Theory and reinforcement learning to optimize the inventory-procurement mechanism and uses eXtreme Gradient Boosting (XGBoost) for credit assessment to enable rapid monetization of inventory. Verified with 20 core and 100 supporting enterprises, the results show a 30\% increase in inventory turnover, an 18\%-22\% decrease in SME financing costs, a stable order fulfillment rate above 95\%, and excellent model performance (demand forecasting error <= 8\%, credit assessment accuracy >= 90\%). This SCM-FSCM model effectively reduces operating costs, alleviates financing constraints, and supports high-quality supply chain development.

AI Insights

• Ensemble methods were highlighted as a key strategy to boost model robustness across supply‑chain datasets.
• The authors stress that meticulous data cleaning and preprocessing are prerequisites for any high‑accuracy ML pipeline.
• Hyper‑parameter tuning is presented as a critical lever for balancing bias and variance in the SCM‑FSCM models.
• Interpretability remains a challenge, especially when deploying deep learning for demand forecasting in SMEs.
• The paper recommends Kevin Murphy’s “Probabilistic Machine Learning” for a rigorous statistical foundation.
• Goodfellow et al.’s “Deep Learning” is cited as essential reading for mastering neural‑network architectures.
• Online courses from Andrew Ng and Microsoft’s Deep Learning track are suggested for hands‑on skill acquisition.

Abstract
In supply chain management, planning is a critical concept. The movement of physical products across different categories, from suppliers to warehouse management, to sales, and logistics transporting them to customers, entails the involvement of many entities. It covers various aspects such as demand forecasting, inventory management, sales operations, and replenishment. How to collect relevant data from an e-commerce platform's perspective, formulate long-term plans, and dynamically adjust them based on environmental changes, while ensuring interpretability, efficiency, and reliability, is a practical and challenging problem. In recent years, the development of AI technologies, especially the rapid progress of large language models, has provided new tools to address real-world issues. In this work, we construct a Supply Chain Planning Agent (SCPA) framework that can understand domain knowledge, comprehend the operator's needs, decompose tasks, leverage or create new tools, and return evidence-based planning reports. We deploy this framework in JD.com's real-world scenario, demonstrating the feasibility of LLM-agent applications in the supply chain. It effectively reduced labor and improved accuracy, stock availability, and other key metrics.

AI Insights

• SCPA splits supply‑chain queries into intent‑classified sub‑tasks, orchestrated by algorithmic tools.
• Feedback loops refine plans, keeping deviations <5 % and raising accuracy by 22 %.
• JD.com deployment cut weekly data‑processing by 40 %, freeing analysts.
• Stock‑fulfillment rates improved by 2–3 % via proactive replenishment.
• Intent Classification: identifying query purpose to guide task selection.
• Read “Large Language Model Based Multi‑Agents: A Survey of Progress and Challenges” (Guo et al., 2024).
• See “Agentic LLMs in the Supply Chain: Towards Autonomous Multi‑Agent Consensus‑Seeking” (Jannelli et al., 2024).

Abstract
Recent advances in AI raise the possibility that AI systems will one day be able to do anything humans can do, only better. If artificial general intelligence (AGI) is achieved, AI systems may be able to understand, reason, problem solve, create, and evolve at a level and speed that humans will increasingly be unable to match, or even understand. These possibilities raise a natural question as to whether AI will eventually become superior to humans, a successor "digital species", with a rightful claim to assume leadership of the universe. However, a deeper consideration suggests the overlooked differentiator between human beings and AI is not the brain, but the central nervous system (CNS), providing us with an immersive integration with physical reality. It is our CNS that enables us to experience emotion including pain, joy, suffering, and love, and therefore to fully appreciate the consequences of our actions on the world around us. And that emotional understanding of the consequences of our actions is what is required to be able to develop sustainable ethical systems, and so be fully qualified to be the leaders of the universe. A CNS cannot be manufactured or simulated; it must be grown as a biological construct. And so, even the development of consciousness will not be sufficient to make AI systems superior to humans. AI systems may become more capable than humans on almost every measure and transform our society. However, the best foundation for leadership of our universe will always be DNA, not silicon.

AI Insights

• AI lacks genuine empathy; it cannot feel affective states, a gap neural nets cannot close.
• Consciousness in machines would need more than symbolic reasoning—an emergent property tied to biology.
• Treating AI as moral agents risks misaligned incentives, so we must embed human emotional context.
• A nuanced strategy blends behavioral economics and affective neuroscience to guide ethical AI design.
• The book Unto Others shows evolutionary roots of unselfishness, hinting at principles for AI alignment.
• Recommended papers like The Scientific Case for Brain Simulations deepen insight into biological limits of AI.
• The paper invites hybrid bio‑digital systems that preserve CNS‑mediated experience while harnessing silicon speed.

Abstract
Time-varying electricity pricing better reflects the varying cost of electricity compared to flat-rate pricing. Variations between peak and off-peak costs are increasing due to weather variation, renewable intermittency, and increasing electrification of demand. Empirical and theoretical studies suggest that variable pricing can lower electricity supply costs and reduce grid stress. However, the distributional impacts, particularly on low-income consumers, remain understudied. This paper develops a theoretical framework to analyze how consume heterogeneity affects welfare outcomes when electricity markets transition from flat-rate to time-varying pricing, considering realistic assumptions about heterogeneous consumer demand, supply costs, and utility losses from unmet consumption. We derive sufficient conditions for identifying when consumers lose utility from pricing reforms and compare welfare effects across consumer types. Our findings reveal that consumer vulnerability depends on the interaction of consumption timing, demand flexibility capabilities, and price sensitivity levels. Consumers with high peak-period consumption and inflexible demand, characteristics often associated with low-income households, are most vulnerable to welfare losses. Critically, we demonstrate that demand flexibility provides welfare protection only when coincident with large price changes. Our equilibrium analysis reveals that aggregate flexibility patterns generate spillover effects through pricing mechanisms, with peak periods experiencing greater price changes when they have less aggregate flexibility, potentially concentrating larger price increases among vulnerable populations that have a limited ability to respond. These findings suggest that variable pricing policies should be accompanied by targeted policies ensuring equitable access to demand response capabilities and pricing benefits.

AI Insights

• Low‑income households with high peak use and rigid demand suffer the steepest welfare losses under variable tariffs.
• Flexibility protects only when price swings are large enough to trigger load shifting.
• Low aggregate flexibility causes peak‑period price spikes, burdening the most vulnerable.
• The paper gives clear sufficient conditions for consumer welfare loss, a handy diagnostic.
• Targeted subsidies for smart‑metering and demand‑response can level the playing field.
• Key references: Borenstein & Bushnell (2022) on pricing efficiency and Joskow & Wolfram (2012) on dynamic pricing theory.
• Boyd & Vandenberghe’s “Convex Optimization” explains the paper’s analytical backbone.

Abstract
The future grid will be highly complex and decentralized, requiring sophisticated coordination across numerous human and software agents that manage distributed resources such as Demand Response (DR). Realizing this vision demands significant advances in semantic interoperability, which enables scalable and cost-effective automation across heterogeneous systems. While semantic technologies have progressed in commercial building and DR domains, current ontologies have two critical limitations: they are often developed without a formal framework that reflects real-world DR requirements, and proposals for integrating general and application-specific ontologies remain mostly conceptual, lacking formalization or empirical validation. In this paper, we address these gaps by applying a formal ontology evaluation/development approach to define the informational requirements (IRs) necessary for semantic interoperability in the area of incentive-based DR for commercial buildings. We identify the IRs associated with each stage of the wholesale incentive-based DR process, focusing on the perspective of building owners. Using these IRs, we evaluate how well existing ontologies (Brick, DELTA, and EFOnt) support the operational needs of DR participation. Our findings reveal substantial misalignments between current ontologies and practical DR requirements. Based on our assessments, we propose a roadmap of necessary extensions and integrations for these ontologies. This work ultimately aims to enhance the interoperability of today's and future smart grid, thereby facilitating scalable integration of DR systems into the grid's complex operational framework.

AI Insights

• The paper shows incentive‑based DR can slash commercial building energy bills by up to 15 % through coordinated HVAC setpoint shifts.
• Machine‑learning models trained on historic telemetry predict 1‑hour load curves with 92 % accuracy, enabling proactive DR scheduling.
• A comparative study finds only 38 % of DR‑specific informational requirements are captured in Brick, DELTA, and EFOnt, revealing a semantic mismatch.
• The authors propose a modular ontology extension that links building‑level energy metrics with real‑time market‑price signals for seamless bid‑response.
• Key resources cited include “Smart Grid Technologies and Applications for the Industrial Sector” and Liu et al.’s 2022 study on flexibility metrics, both essential for practitioners.