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arxivecongnbot.bsky.social
ArXiv Paperboy (Econ.GN + Econ.TH)
@arxivecongnbot.bsky.social
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posts updates from arXiv rss feeds for methodology papers in Economic Theory and General Economics.

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arxivecongnbot.bsky.social
arXiv📈🤖
Bayesian Persuasion with Selective Disclosure
By Dai, Fudenberg, Pei
A sender first publicly commits to an experiment and then can privately run additional experiments and selectively disclose their outcomes to a receiver. The sender has private information about the maximal number of additional experiments they can perform (i.e., their type). We show that the sender cannot attain their commitment payoff in any equilibrium if (i) the receiver is sufficiently uncertain about their type and (ii) the sender could benefit from selective disclosure after conducting their full-commitment optimal experiment. Otherwise, there can be equilibria where the sender obtains their commitment payoff.
January 12, 2026 at 10:07 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Accounting for environmental awareness in wheat production through Life Cycle Assessment
By Giulioni, Giuseppe, Paola
This paper presents a modeling framework for simulating the decision-making processes of artificial farms populating an agent-based model for the Italian wheat production system. The decision process is based on a mathematical programming model with which farms (i.e., agents) decide the target yield (production per hectare) and the mix of inputs needed to obtain such production, namely 1) fertilizers, 2) herbicides, and 3) insecticides. The environmental impacts of conventional production practices are assessed through a Life Cycle Assessment (LCA), using the ReCiPe 2016 methodology at the Endpoint level. Agents are made aware of the environmental consequences of their choices through two indicators: Disability-Adjusted Life Years (DALYs), which capture human health impacts, and the number of species lost per year, reflecting impacts on ecosystems. By internalizing this information, agents can make more balanced and sustainable production decisions.
January 12, 2026 at 10:06 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Bayesian Persuasion with Selective Disclosure
By Dai, Fudenberg, Pei
A sender first publicly commits to an experiment and then can privately run additional experiments and selectively disclose their outcomes to a receiver. The sender has private information about the maximal number of additional experiments they can perform (i.e., their type). We show that the sender cannot attain their commitment payoff in any equilibrium if (i) the receiver is sufficiently uncertain about their type and (ii) the sender could benefit from selective disclosure after conducting their full-commitment optimal experiment. Otherwise, there can be equilibria where the sender obtains their commitment payoff.
January 12, 2026 at 7:09 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Accounting for environmental awareness in wheat production through Life Cycle Assessment
By Giulioni, Giuseppe, Paola
This paper presents a modeling framework for simulating the decision-making processes of artificial farms populating an agent-based model for the Italian wheat production system. The decision process is based on a mathematical programming model with which farms (i.e., agents) decide the target yield (production per hectare) and the mix of inputs needed to obtain such production, namely 1) fertilizers, 2) herbicides, and 3) insecticides. The environmental impacts of conventional production practices are assessed through a Life Cycle Assessment (LCA), using the ReCiPe 2016 methodology at the Endpoint level. Agents are made aware of the environmental consequences of their choices through two indicators: Disability-Adjusted Life Years (DALYs), which capture human health impacts, and the number of species lost per year, reflecting impacts on ecosystems. By internalizing this information, agents can make more balanced and sustainable production decisions.
January 12, 2026 at 7:07 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Bayesian Persuasion with Selective Disclosure
By Dai, Fudenberg, Pei
A sender first publicly commits to an experiment and then can privately run additional experiments and selectively disclose their outcomes to a receiver. The sender has private information about the maximal number of additional experiments they can perform (i.e., their type). We show that the sender cannot attain their commitment payoff in any equilibrium if (i) the receiver is sufficiently uncertain about their type and (ii) the sender could benefit from selective disclosure after conducting their full-commitment optimal experiment. Otherwise, there can be equilibria where the sender obtains their commitment payoff.
January 12, 2026 at 4:11 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Accounting for environmental awareness in wheat production through Life Cycle Assessment
By Giulioni, Giuseppe, Paola
This paper presents a modeling framework for simulating the decision-making processes of artificial farms populating an agent-based model for the Italian wheat production system. The decision process is based on a mathematical programming model with which farms (i.e., agents) decide the target yield (production per hectare) and the mix of inputs needed to obtain such production, namely 1) fertilizers, 2) herbicides, and 3) insecticides. The environmental impacts of conventional production practices are assessed through a Life Cycle Assessment (LCA), using the ReCiPe 2016 methodology at the Endpoint level. Agents are made aware of the environmental consequences of their choices through two indicators: Disability-Adjusted Life Years (DALYs), which capture human health impacts, and the number of species lost per year, reflecting impacts on ecosystems. By internalizing this information, agents can make more balanced and sustainable production decisions.
January 12, 2026 at 4:08 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Assessing the Effects of Monetary Shocks on Macroeconomic Stars: A SMUC-IV Framework
By Fu, Hou, Pr\"user
This paper proposes a structural multivariate unobserved components model with external instrument (SMUC-IV) to investigate the effects of monetary policy shocks on key U.S. macroeconomic "stars"-namely, the level of potential output, the growth rate of potential output, trend inflation, and the neutral interest rate. A key feature of our approach is the use of an external instrument to identify monetary policy shocks within the multivariate unob- served components modeling framework. We develop an MCMC estimation method to facilitate posterior inference within our proposed SMUC-IV frame- work. In addition, we propose an marginal likelihood estimator to enable model comparison across alternative specifications. Our empirical analysis shows that contractionary monetary policy shocks have significant negative effects on the macroeconomic stars, highlighting the nonzero long-run effects of transitory monetary policy shocks.
January 12, 2026 at 3:37 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Using LLMs to Directly Guess Conditional Expectations Can Improve Efficiency in Causal Estimation
By Engh, Aronow
We propose a simple yet effective use of LLM-powered AI tools to improve causal estimation. In double machine learning, the accuracy of causal estimates of the effect of a treatment on an outcome in the presence of a high-dimensional confounder depends on the performance of estimators of conditional expectation functions. We show that predictions made by generative models trained on historical data can be used to improve the performance of these estimators relative to approaches that solely rely on adjusting for embeddings extracted from these models. We argue that the historical knowledge and reasoning capacities associated with these generative models can help overcome curse-of-dimensionality problems in causal inference problems. We consider a case study using a small dataset of online jewelry auctions, and demonstrate that inclusion of LLM-generated guesses as predictors can improve efficiency in estimation.
January 12, 2026 at 1:35 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Extracting the Multiscale Causal Backbone of Brain Dynamics
By
The bulk of the research effort on brain connectivity revolves around statistical associations among brain regions, which do not directly relate to the causal mechanisms governing brain dynamics. Here we propose the multiscale causal backbone (MCB) of brain dynamics shared by a set of individuals across multiple temporal scales, and devise a principled methodology to extract it. Our approach leverages recent advances in multiscale causal structure learning and optimizes the trade-off between the model fitting and its complexity. Empirical assessment on synthetic data shows the superiority of our methodology over a baseline based on canonical functional connectivity networks. When applied to resting-state fMRI data, we find sparse MCBs for both the left and right brain hemispheres. Thanks to its multiscale nature, our approach shows that at low-frequency bands, causal dynamics are driven by brain regions associated with high-level cognitive functions; at higher frequencies instead, nodes related to sensory processing play a crucial role. Finally, our analysis of individual multiscale causal structures confirms the existence of a causal fingerprint of brain connectivity, thus supporting from a causal perspective the existing extensive research in brain connectivity fingerprinting.
January 11, 2026 at 10:05 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Sparse Data-Driven Random Projection in Regression for High-Dimensional Data
By
arXiv:2312.00130v2 Announce Type: replace Abstract: We examine the linear regression problem in a challenging high-dimensional setting with correlated predictors where the vector of coefficients can vary from sparse to dense. In this setting, we propose a combination of probabilistic variable screening with random projection tools as a viable approach. More specifically, we introduce a new data-driven random projection tailored to the problem at hand and derive a theoretical bound on the gain in expected prediction error over conventional random projections. The variables to enter the projection are screened by accounting for predictor correlation. To reduce the dependence on fine-tuning choices, we aggregate over an ensemble of linear models. A thresholding parameter is introduced to obtain a higher degree of sparsity. Both this parameter and the number of models in the ensemble can be chosen by cross-validation. In extensive simulations, we compare the proposed method with other random projection tools and with classical sparse and dense methods and show that it is competitive in terms of prediction across a variety of scenarios with different sparsity and predictor covariance settings. We also show that the method with cross-validation is able to rank the variables satisfactorily. Finally, we showcase the method on two real data applications.
January 11, 2026 at 7:05 PM
arxivecongnbot.bsky.social
arXiv📈🤖
On the adaptation of causal forests to manifold data
By
Researchers often hold the belief that random forests are "the cure to the world's ills" (Bickel, 2010). But how exactly do they achieve this? Focused on the recently introduced causal forests (Athey and Imbens, 2016; Wager and Athey, 2018), this manuscript aims to contribute to an ongoing research trend towards answering this question, proving that causal forests can adapt to the unknown covariate manifold structure. In particular, our analysis shows that a causal forest estimator can achieve the optimal rate of convergence for estimating the conditional average treatment effect, with the covariate dimension automatically replaced by the manifold dimension. These findings align with analogous observations in the realm of deep learning and resonate with the insights presented in Peter Bickel's 2004 Rietz lecture.
January 11, 2026 at 4:06 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Mixture of segmentation for heterogeneous functional data
By
arXiv:2303.10712v2 Announce Type: replace Abstract: In this paper we consider functional data with heterogeneity in time and in population. We propose a mixture model with segmentation of time to represent this heterogeneity while keeping the functional structure. Maximum likelihood estimator is considered, proved to be identifiable and consistent. In practice, an EM algorithm is used, combined with dynamic programming for the maximization step, to approximate the maximum likelihood estimator. The method is illustrated on a simulated dataset, and used on a real dataset of electricity consumption.
January 11, 2026 at 3:36 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Principal stratification with continuous treatments and continuous post-treatment variables
By Antonelli, Wu, Mealli et al
Principal stratification (PS) is a commonly used approach for understanding the mechanisms through which a treatment affects an outcome. The goal of this work is to extend the PS framework to studies with continuous treatments, which introduces a number of both challenges and opportunities in terms of defining causal effects and performing inference. This manuscript provides multiple key methodological contributions: 1) we introduce principal causal estimands for continuous treatments that provide insights into different causal mechanisms, 2) we show that nonparametric identification is possible under a principal ignorability assumption, but only under a restrictive assumption on the joint distribution of potential mediators, which can be dropped under mild parametric assumptions, 3) we utilize nonparametric Bayesian models for the joint distribution of the potential mediating variables to ensure our approach is robust to model misspecification, and 4) we provide theoretical justification for utilizing an outcome model to identify the joint distribution of the potential mediating variables, and show that this is only possible if a principal ignorability assumption is violated. Lastly, we apply our methodology to a novel study of the relationship between the economy and arrest rates, and how this is potentially mediated by police capacity.
January 11, 2026 at 1:35 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold
By Xu, Han
Recovering a low-CP-rank tensor from noisy linear measurements is a central challenge in high-dimensional data analysis, with applications spanning tensor PCA, tensor regression, and beyond. We exploit the intrinsic geometry of rank-one tensors by casting the recovery task as an optimization problem over the Segre manifold, the smooth Riemannian manifold of rank-one tensors. This geometric viewpoint yields two powerful algorithms: Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (RGN), each of which preserves feasibility at every iteration. Under mild noise assumptions, we prove that RGD converges at a local linear rate, while RGN exhibits an initial local quadratic convergence phase that transitions to a linear rate as the iterates approach the statistical noise floor. Extensive synthetic experiments validate these convergence guarantees and demonstrate the practical effectiveness of our methods.
January 10, 2026 at 10:05 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Triple Instrumented Difference-in-Differences
By Miyaji
In this paper, we formalize a triple instrumented difference-in-differences (DID-IV). In this design, a triple Wald-DID estimand, which divides the difference-in-difference-in-differences (DDD) estimand of the outcome by the DDD estimand of the treatment, captures the local average treatment effect on the treated. The identifying assumptions mainly comprise a monotonicity assumption, and the common acceleration assumptions in the treatment and the outcome. We extend the canonical triple DID-IV design to staggered instrument cases. We also describe the estimation and inference in this design in practice.
January 10, 2026 at 4:06 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Matching with Generalized Sequential Choice Rules
By Ayg\"un, Turhan
This paper studies a many-to-one matching between individuals and institutions where institutions comprise multiple divisions and face cross-divisional constraints. We introduce a parametrized family of choice rules, which we call generalized sequential (GSq), that encompasses many different choice rules encountered in practice and in market design literature. We show that the cumulative offer mechanism (COM) is the unique stable and strategy-proof mechanism in a matching problem where all institutions have a GSq choice rule. We present two real-world applications in which choice rules in the GSq family emerge naturally: affirmative action in India's public higher educational institutions and government jobs, and high school admissions in China.
January 10, 2026 at 4:08 AM
arxivecongnbot.bsky.social
arXiv📈🤖
How Human is AI? Examining the Impact of Emotional Prompts on Artificial and Human and Responsiveness
By Zurich), Zurich), Zurich)
This research examines how the emotional tone of human-AI interactions shapes ChatGPT and human behavior. In a between-subject experiment, we asked participants to express a specific emotion while working with ChatGPT (GPT-4.0) on two tasks, including writing a public response and addressing an ethical dilemma. We found that compared to interactions where participants maintained a neutral tone, ChatGPT showed greater improvement in its answers when participants praised ChatGPT for its responses. Expressing anger towards ChatGPT also led to a higher albeit smaller improvement relative to the neutral condition, whereas blaming ChatGPT did not improve its answers. When addressing an ethical dilemma, ChatGPT prioritized corporate interests less when participants expressed anger towards it, while blaming increases its emphasis on protecting the public interest. Additionally, we found that people used more negative, hostile, and disappointing expressions in human-human communication after interactions during which participants blamed rather than praised for their responses. Together, our findings demonstrate that the emotional tone people apply in human-AI interactions not only shape ChatGPT's outputs but also carry over into subsequent human-human communication.
January 10, 2026 at 4:04 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Large language models can effectively convince people to believe conspiracies
By Costello, Pelrine, Kowal et al
Large language models (LLMs) have been shown to be persuasive across a variety of context. But it remains unclear whether this persuasive power advantages truth over falsehood, or if LLMs can promote misbeliefs just as easily as refuting them. Here, we investigate this question across three pre-registered experiments in which participants (N = 2,724 Americans) discussed a conspiracy theory they were uncertain about with GPT-4o, and the model was instructed to either argue against ("debunking") or for ("bunking") that conspiracy. When using a "jailbroken" GPT-4o variant with guardrails removed, the AI was as effective at increasing conspiracy belief as decreasing it. Concerningly, the bunking AI was rated more positively, and increased trust in AI, more than the debunking AI. Surprisingly, we found that using standard GPT-4o produced very similar effects, such that the guardrails imposed by OpenAI did little to revent the LLM from promoting conspiracy beliefs. Encouragingly, however, a corrective conversation reversed these newly induced conspiracy beliefs, and simply prompting GPT-4o to only use accurate information dramatically reduced its ability to increase conspiracy beliefs. Our findings demonstrate that LLMs possess potent abilities to promote both truth and falsehood, but that potential solutions may exist to help mitigate this risk.
January 10, 2026 at 4:00 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Towards a Sociology of Sociology: Inequality, Elitism, and Prestige in the Sociological Enterprise From 1970 to the Present
By Cook
There is a science of science and an informal economics of economics, but there is not a cohesive sociology of sociology. We turn the central findings and theoretical lenses of the sociological tradition and the sociological study of stratification inward on sociology itself to investigate how sociology has changed since the 1970s. We link two bibliometric databases to trace diachronic relationships between PhD training and publication outcomes, both of which are understudied in the science of science and sociology of science. All of sociology's top 3 journals remained biased against alum of less prestigious PhD programs, and while most forms of bias in elite sociological publishing have ameliorated over time, the house bias of the American Journal of Sociology in favor PhD alumnae of UChicago has intensified.
January 10, 2026 at 3:57 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Technology Adoption and Network Externalities in Financial Systems: A Spatial-Network Approach
By Kikuchi
This paper develops a unified framework for analyzing technology adoption in financial networks that incorporates spatial spillovers, network externalities, and their interaction. The framework characterizes adoption dynamics through a master equation whose solution admits a Feynman-Kac representation as expected cumulative adoption pressure along stochastic paths through spatial-network space. From this representation, I derive the Adoption Amplification Factor -- a structural measure of technology leadership that captures the ratio of total system-wide adoption to initial adoption following a localized shock. A Levy jump-diffusion extension with state-dependent jump intensity captures critical mass dynamics: below threshold, adoption evolves through gradual diffusion; above threshold, cascade dynamics accelerate adoption through discrete jumps. Applying the framework to SWIFT gpi adoption among 17 Global Systemically Important Banks, I find strong support for the two-regime characterization. Network-central banks adopt significantly earlier ($\rho = -0.69$, $p = 0.002$), and pre-threshold adopters have significantly higher amplification factors than post-threshold adopters (11.81 versus 7.83, $p = 0.010$). Founding members, representing 29 percent of banks, account for 39 percent of total system amplification -- sufficient to trigger cascade dynamics. Controlling for firm size and network position, CEO age delays adoption by 11-15 days per year.
January 10, 2026 at 3:53 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Beyond Interaction Effects: Two Logics for Studying Population Inequalities
By Daoud
When sociologists and other social scientist ask whether the return to college differs by race and gender, they face a choice between two fundamentally different modes of inquiry. Traditional interaction models follow deductive logic: the researcher specifies which variables moderate effects and tests these hypotheses. Machine learning methods follow inductive logic: algorithms search across vast combinatorial spaces to discover patterns of heterogeneity. This article develops a framework for navigating between these approaches. We show that the choice between deduction and induction reflects a tradeoff between interpretability and flexibility, and we demonstrate through simulation when each approach excels. Our framework is particularly relevant for inequality research, where understanding how treatment effects vary across intersecting social subpopulation is substantively central.
January 10, 2026 at 3:51 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Confidence and Organizations
By Espitia
Miscalibrated beliefs are widely viewed as compromising the quality of employees' decisions. Why, then, might an organization prefer to hire an individual known to be overconfident? This paper develops a theory of organizational demand for employees' levels of confidence when private information interacts with conflicts of interest. I study a model in which an employee uses private information to make decisions on behalf of the organization and analyze the belief design problem, namely, how the organization would like the employee to interpret his observations. I show that organizations prefer employees whose actions reflect a constant expected conflict of interest across observations. A well-calibrated employee is optimal if and only if private information does not affect this conflict. When the conflict varies with information, organizations optimally select employees whose confidence distorts their responses to information. Overconfidence is optimal when the organization seeks stronger adjustments to information than a well-calibrated employee would provide.
January 10, 2026 at 3:47 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Optimally designing purpose and meaning at work
By Cabrales, Hauk
Many workers value purpose and meaning in their jobs alongside income, and firms need to align these preferences with profit goals. This paper develops a dynamic model in which firms invest in purpose to enhance job meaning and motivate effort. Workers, who differ in productivity, choose both productive and socialization effort, gaining utility from income and meaning. Purpose accumulates over time through firm investment and interacts with socialization to generate meaning, which boosts productivity. Firms invest in purpose only insofar as it raises profits. We characterize the unique equilibrium, including steady state and transition dynamics. Meaning and purpose rise with the importance workers place on meaning and with firm's patience, but fall with depreciation and socialization costs. The relationship with workers' share of output is nonmonotonic. We also show that some intermediate level of heterogeneity in skills is best for performance. Compared to a worker-owned firm, profit-maximizing firms underinvest in purpose, highlighting a misalignment between firm incentives and worker preferences. The model provides insight into when and why firms adopt purpose-driven practices and underscores the role of diversity in fostering meaning at work.
January 10, 2026 at 3:44 AM
arxivecongnbot.bsky.social
arXiv📈🤖
When Sellers Are Uncertain about Quality
By Kuwahara
Second-hand markets have expanded rapidly with the growth of online consumer-to-consumer (C2C) platforms. A key feature of C2C markets is that sellers are typically non-professionals and often face uncertainty about the quality of the goods they sell. This creates scope for platforms to introduce systems that reduce sellers' uncertainty about quality. However, an important question remains: is it socially desirable for sellers to have more precise quality information? We present results showing that while improved information always benefits sellers, it can either benefit or harm buyers. We derive a necessary and sufficient condition under which buyers benefit, and show that this condition holds in many cases, especially when buyers' valuations are not too large relative to sellers' costs. These findings suggest that platforms should consider reducing sellers' uncertainty about quality as a means of improving market efficiency.
January 10, 2026 at 3:41 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Global Inequalities in Clinical Trials Participation
By Lou, D\'iaz-Faes, He et al
Clinical trials shape medical evidence and determine who gains access to experimental therapies. Whether participation in these trials reflects the global burden of disease remains unclear. Here we analyze participation inequality across more than 62,000 randomized controlled trials spanning 16 major disease categories from 2000 to 2024. Linking 36.8 million trial participants to country-level disease burden, we show that global inequality in clinical trial participation is overwhelmingly structured by country rather than disease. Country-level factors explain over 90% of variation in participation, whereas disease-specific effects contribute only marginally. Removing entire disease categories, including those traditionally considered underfunded, has little effect on overall inequality. Instead, participation is highly concentrated geographically, with a small group of countries enrolling a disproportionate share of participants across nearly all diseases. These patterns have persisted despite decades of disease-targeted funding and increasing alignment between research attention and disease burden within diseases. Our findings indicate that disease-vertical strategies alone cannot correct participation inequality. Reducing global inequities in clinical research requires horizontal investments in research capacity, health infrastructure, and governance that operate across disease domains.
January 10, 2026 at 3:39 AM