general equilibrium model construction

This study addresses the uncertainty surrounding the real-world impacts of carbon offset policies on aggregate emissions and welfare, which stems in part from conventional carbon accounting metrics’ inability to capture general equilibrium spillovers. The authors develop an analytical general equilibrium model incorporating carbon offsets to systematically evaluate the effects of changes in offset prices and identify four marginal mechanisms through which offsets influence outcomes—one of which is a novel channel uncovered in this work. By integrating two dominant carbon accounting approaches into both parameterization and theoretical analysis, the study demonstrates that raising offset prices yields ambiguous effects on total emissions and welfare, suggesting that offset efficacy may be systematically over- or underestimated. These findings underscore the critical importance of incorporating general equilibrium considerations into offset policy design.

This paper addresses the identification of marginal policy effects (MPE) in centralized markets—specifically, how to nonparametrically assess the impact of marginal reforms on equilibrium outcomes without exogenous variation in policy rules. Method: We propose constructing “equilibrium-adjusted outcome variables” by modeling and estimating market-level equilibrium externalities, rendering these variables invariant to policy perturbations. This enables decomposition of the MPE into a covariance structure of observable variables, embedding equilibrium externalities directly into the structural outcome design and bridging naturally with the marginal treatment effect (MTE) framework. Contribution/Results: We establish theoretical identifiability of the MPE and validate the method via simulations and empirical applications. Our approach breaks from conventional policy evaluation’s reliance on exogenous policy shifts, offering the first nonparametric, intervention-free local policy evaluation paradigm for centralized markets—such as matching markets and platform mechanisms—where equilibrium externalities are inherent and policy interventions are often infeasible or unethical.

This study addresses the ambiguity in policy analysis arising from the lack of a unified equilibrium selection mechanism in dynamic stochastic general equilibrium (DSGE) models under multiple equilibria. Viewing DSGE models as fixed-point selection devices within self-referential economies, the paper proposes a unified framework comprising model specification, a self-reference operator, and a programmable equilibrium selector. It formalizes equilibrium selection as a computable operation for the first time, demonstrating that the Blanchard–Kahn conditions correspond to a specific selector and introducing alternative rules—such as minimum variance and fiscal anchoring—to better reflect policy intent. Leveraging linear rational expectations systems and standard solution techniques like QZ decomposition and OccBin, the framework enables efficient computation and validation of selectors. This approach reinterprets mainstream DSGE solution methods, facilitates systematic comparison of selection rules, and significantly enhances model transparency and policy relevance.

This paper addresses the computational challenge of general equilibrium in exchange economies featuring real financial markets, household production, and asset retention. Method: It formulates equilibrium computation as a max-inf optimization problem subject to no-arbitrage constraints, introduces the Walrasian dual function—novelly capturing market disequilibrium—and establishes its rigorous equivalence to equilibrium; further develops a lopsided-convergence theory for approximating max-inf points, overcoming existence and computability barriers in incomplete markets. Contribution/Results: The authors design an augmented Walrasian algorithm enabling efficient numerical solution of diverse complex exchange economies. Numerical experiments validate its accuracy and robustness, and the framework is successfully extended to applications including financial stability analysis and macroeconomic policy simulation.

This paper addresses the challenge of welfare analysis for dynamic models in high-dimensional state spaces. Methodologically, it proposes an estimable and inferential welfare metric framework grounded in doubly robust estimation and dynamic dual representation, enabling unbiased inference on average welfare and its marginal effects without explicit value function estimation. The approach accommodates arbitrary value function estimators—including Lasso and deep neural networks—and automatically corrects their estimation bias without imposing restrictive assumptions on bias structure. Theoretically, it establishes consistent estimation and asymptotically valid inference procedures for average welfare, average marginal welfare effects, and decomposition into direct and indirect effects under high-dimensional dynamic environments. Empirically, the method is applied to a dynamic model of teacher absenteeism, successfully estimating average teacher welfare and demonstrating strong performance, validity, and robustness in a real-world high-dimensional dynamic setting.

This work establishes the first infinite-dimensional general equilibrium theory for large language model (LLM) agent systems under centralized coordination, grounded in the Arrow–Debreu framework. Each LLM agent is modeled as a firm with fixed weights determining its production set, while the coordinator acts as a consumer who optimizes routing under a budget constraint to maximize system welfare. Performance trajectories are represented in the Hilbert space \(L^2([0,T],\mathbb{R}^r)\). By combining Brouwer’s fixed-point theorem with Banach’s contraction mapping principle, the authors prove the existence of equilibrium, its Pareto optimality, and decentralizability; under contraction conditions, uniqueness and global convergence are also guaranteed. This framework extends general equilibrium theory to dynamic LLM systems for the first time, overcoming the non-convergence issues exemplified by Scarf’s counterexample and satisfying a functional Walras’ law.

This study addresses the absence of a systematic framework in empirical economics for translating analytical findings into normative policy recommendations. Integrating statistical decision theory with the literature on policy choice, the authors develop a unified analytical framework and introduce two types of navigational maps to guide research design. They also implement an R package that automatically generates standardized, publication-ready visualizations of policy impacts. Demonstrated through applications in development economics, this approach substantially enhances the transparency, cross-study comparability, and empirical grounding of policy advice, thereby offering the first end-to-end methodological pipeline that bridges theoretical analysis and practical policy evaluation.

This study investigates the impact of machine learning algorithms on information aggregation in asset markets with dispersed information, focusing on whether price mechanisms can fully reflect the information extracted by such algorithms. The authors introduce the Chow-Liu tree into a general equilibrium framework à la Hellwig (1980), constructing an equilibrium model in which agents employ this algorithm for Bayesian inference. The analysis reveals that even when agents are initially homogeneous, they endogenously develop heterogeneous beliefs, demand functions, and utilities. More importantly, while machine learning enhances information processing in partial equilibrium, it leads to less informative prices in general equilibrium compared to the rational expectations benchmark, indicating that market prices fail to efficiently aggregate the information uncovered by machine learning.

This study addresses the lack of a theoretical framework for analyzing strategic interactions among platforms, model providers, and users in generative model markets. It proposes a three-layer game-theoretic model that formally characterizes this multi-sided market, incorporating user heterogeneity in preferences and model performance evaluation to examine the conditions for pure-strategy Nash equilibria. The analysis reveals that localized model appeal drives platform differentiation and introduces a strategic entry mechanism for model providers, alongside an optimal-response-based training strategy. Theoretical results demonstrate that expanding the model pool does not necessarily enhance user welfare or diversity; in contrast, the proposed strategic deployment approach effectively guides model provision, leading to improved market equilibrium and social welfare.

This study addresses equilibrium in a two-sided labor market where users perceive tasks as goods while workers view them as burdens. To this end, it introduces a unified Fisher market model that simultaneously treats tasks as goods for users—maximizing utility under budget constraints—and as burdens for workers—minimizing disutility under income constraints. By leveraging KKT conditions and a variable substitution technique, the inherently non-convex problem is transformed into a linear program, enabling a rigorous proof of equilibrium existence and the validity of welfare theorems. For the case of linear preferences, the work further proposes a strongly polynomial-time combinatorial algorithm that efficiently solves linear programs with irrational coefficients, thereby overcoming longstanding challenges in constructing such LPs within traditional goods-market frameworks.