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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📈🤖
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 9, 2026 at 10:39 PM
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 9, 2026 at 10:37 PM
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 9, 2026 at 10:34 PM
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 9, 2026 at 10:30 PM
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 9, 2026 at 10:28 PM
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 9, 2026 at 10:24 PM
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 9, 2026 at 10:20 PM
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 9, 2026 at 10:16 PM
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 9, 2026 at 10:14 PM
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 9, 2026 at 10:11 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Bimodal Bias against Chinese Scientists in the American Academy: Penalties for Men, Bonuses for Women
By Cook
Given the recent targeting of Chinese scientists by the Department of Justice and sizable contributions of Chinese scientists to American science, it is urgent to investigate the presence and the particulars of anti-Chinese discrimination in the American academy. Across a sample of all faculty in the top 100 departments of sociology, economics, chemistry, and physics in the United States, we show that female Chinese scientists comprise a much higher percentage of the female professoriate than male Chinese scientists in the male professoriate. Using an exact matching approach, we then find that male Chinese scientists suffer from a dramatic citation penalty but that female Chinese scientists enjoy a persistent citation bonus. On average, female Chinese scientists require fewer citations on average than non-Chinese women where male Chinese scientists require more citations than their non-Chinese counterparts to attain a tenure-track professorial job of a given prestige rating.
January 9, 2026 at 10:09 PM
arxivecongnbot.bsky.social
arXiv📈🤖
The Endogenous Grid Method for Epstein-Zin Preferences
By Lujan
The endogenous grid method (EGM) accelerates dynamic programming by inverting the Euler equation, but it appears incompatible with Epstein-Zin preferences where the value function enters the Euler equation. This paper shows that a power transformation resolves the difficulty. The resulting algorithm requires no root-finding, achieves speed gains of one to two orders of magnitude over value function iteration, and improves accuracy by more than one order of magnitude. Holding accuracy constant, the speedup is two to three orders of magnitude. VFI and time iteration face a speed-accuracy tradeoff; EGM sidesteps it entirely.
January 9, 2026 at 10:07 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Constrained Assortment and Price Optimization under Generalized Nested Logit Models
By Pham, Mai
We study assortment and price optimization under the generalized nested logit (GNL) model, one of the most general and flexible modeling frameworks in discrete choice modeling. Despite its modeling advantages, optimization under GNL is highly challenging: even the pure assortment problem is NP-hard, and existing approaches rely on approximation schemes or are limited to simple cardinality constraints. In this paper, we develop the first exact and near-exact algorithms for constrained assortment and joint assortment--pricing optimization (JAP) under GNL. Our approach reformulates the problem into bilinear and exponential-cone convex programs and exploits convexity, concavity, and submodularity properties to generate strong cutting planes within a Branch-and-Cut framework (B\&C). We further extend this framework to the mixed GNL (MGNL) model, capturing heterogeneous customer segments, and to JAP with discrete prices. For the continuous pricing case, we propose a near-exact algorithm based on piecewise-linear approximation (PWLA) that achieves arbitrarily high precision under general linear constraints. Extensive computational experiments demonstrate that our methods substantially outperform state-of-the-art approximation approaches in both solution quality and scalability. In particular, we are able to solve large-scale instances with up to 1000 products and 20 nests, and to obtain near-optimal solutions for continuous pricing problems with negligible optimality gaps. To the best of our knowledge, this work resolves several open problems in assortment and price optimization under GNL.
January 9, 2026 at 10:06 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 9, 2026 at 7:47 PM
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 9, 2026 at 7:42 PM
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 9, 2026 at 7:37 PM
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 9, 2026 at 7:36 PM
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 9, 2026 at 7:34 PM
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 9, 2026 at 7:29 PM
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 9, 2026 at 7:25 PM
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 9, 2026 at 7:22 PM
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 9, 2026 at 7:18 PM
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 9, 2026 at 7:16 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Bimodal Bias against Chinese Scientists in the American Academy: Penalties for Men, Bonuses for Women
By Cook
Given the recent targeting of Chinese scientists by the Department of Justice and sizable contributions of Chinese scientists to American science, it is urgent to investigate the presence and the particulars of anti-Chinese discrimination in the American academy. Across a sample of all faculty in the top 100 departments of sociology, economics, chemistry, and physics in the United States, we show that female Chinese scientists comprise a much higher percentage of the female professoriate than male Chinese scientists in the male professoriate. Using an exact matching approach, we then find that male Chinese scientists suffer from a dramatic citation penalty but that female Chinese scientists enjoy a persistent citation bonus. On average, female Chinese scientists require fewer citations on average than non-Chinese women where male Chinese scientists require more citations than their non-Chinese counterparts to attain a tenure-track professorial job of a given prestige rating.
January 9, 2026 at 7:13 PM
arxivecongnbot.bsky.social
arXiv📈🤖
The Endogenous Grid Method for Epstein-Zin Preferences
By Lujan
The endogenous grid method (EGM) accelerates dynamic programming by inverting the Euler equation, but it appears incompatible with Epstein-Zin preferences where the value function enters the Euler equation. This paper shows that a power transformation resolves the difficulty. The resulting algorithm requires no root-finding, achieves speed gains of one to two orders of magnitude over value function iteration, and improves accuracy by more than one order of magnitude. Holding accuracy constant, the speedup is two to three orders of magnitude. VFI and time iteration face a speed-accuracy tradeoff; EGM sidesteps it entirely.
January 9, 2026 at 7:09 PM