EconLit

Journal Crisis at Games and Economic Behavior

Joy Buchanan2 Comments

I used Grok for this query because economists were discussing the issue on X(Twitter) this week and Grok would have quick access to that discourse. Note that Grok provided links to legitimate web sources, not just Twitter speculation. I am providing those same links in the place where Grok put them. I do not know how much the chatter of the week affected the response below.

Prompt from J. B. on 8/22/26 to Grok: What happened to the economics journal Games and Economic Behavior?

The leading game theory journal Games and Economic Behavior (GEB), published by Elsevier since its founding in 1989, remains active and is still publishing issues (including volumes into 2026), but it is in a major editorial crisis due to a mass resignation of its leadership. (https://www.sciencedirect.com/journal/games-and-economic-behavior)

In late July 2026, Editor-in-Chief Hervé Moulin (who had held the role since January 2021) was informed by Elsevier that his 3-year contract would not be renewed, despite his willingness to continue. Elsevier cited a desire to move “towards a closer alignment with Elsevier strategic priorities and publishing policies.” Requests for further explanation (from Moulin and then collectively from the other six editors) were dismissed; Elsevier simply asserted its contractual right to make the decision and began searching for new editors without input from the existing board. (https://gametheorysociety.org/message-from-herve-moulin-geb-editor/)

In response, by August 14, 2026, all seven editors (Moulin plus Sushil Bikhchandani, Aislinn Bohren, Nicole Immorlica, Roberto Serrano, Emanuel Vespa, and Nicolas Vieille) resigned with the required three-month notice. Nearly all of the advisory editors (the equivalent of associate editors) also resigned in solidarity. The outgoing team is handling papers already under review and new submissions until November 14, 2026 (with some flexibility for papers already assigned by mid-August); after that, they will only finish evaluations already in progress. Elsevier has publicly thanked Moulin for his service. (https://gametheorysociety.org/message-from-herve-moulin-geb-editor/)

The journal continues under Elsevier ownership (it is one of the two official journals of the Game Theory Society, alongside the International Journal of Game Theory). Community discussion has focused on the implications for the field, the role of commercial publishers, and possibilities such as launching a successor journal. Similar recent mass resignations have occurred at other Elsevier titles over governance and editorial control issues. (https://casrai.org/news/2026-editorial-board-mass-resignations)

In short, GEB has not shut down, but its longstanding editorial team has largely departed over Elsevier’s non-renewal of the Editor-in-Chief and related decisions.

Prompt from J. B. on 8/22/26 to Grok: What will be the implications for the reputation of the journal?

The reputation of Games and Economic Behavior (GEB) is likely to face significant short- to medium-term damage, with longer-term outcomes depending heavily on how Elsevier handles the transition and whether a strong successor journal emerges.

GEB has long been viewed as the leading specialized journal in game theory (and one of the stronger outlets in theoretical economics more broadly). Its prestige rests on attracting high-quality, creative work; the expertise and independence of its editorial team; rigorous standards; and community trust. The mass resignation of all seven editors (including Editor-in-Chief Hervé Moulin) plus nearly the entire advisory board in August 2026, following Elsevier’s non-renewal of Moulin’s contract, directly undermines those foundations. (https://gametheorysociety.org/message-from-herve-moulin-geb-editor/)

Short-term effects

  • Loss of trust and submissions: Top researchers often choose journals partly based on the known editors and the expectation of fair, expert handling. With the board depleted (current editors only fully process papers until mid-November 2026), many authors are likely to pause or divert submissions. Community discussion already includes calls for boycotts of the Elsevier GEB and support for alternatives. @Afinetheorem
  • Perception of publisher interference: Elsevier’s stated rationale (“closer alignment with Elsevier strategic priorities and publishing policies”) and its decision to search for new editors without board input have fueled views that commercial priorities overrode academic ones. This echoes other recent mass resignations (e.g., Journal of Approximation Theory at Elsevier, where resigning editors declared the journal “as we have known it has effectively ceased to exist”). casrai.org
  • Operational disruption: Delays in reviews, potential quality variation under interim or new leadership, and uncertainty can further erode confidence among authors, referees, and readers.

Metrics such as impact factor or AJG ranking (currently 3) change slowly and will not immediately reflect the crisis, but subjective reputation in the game theory and economics communities can shift quickly.

Medium- to long-term scenarios

Historical patterns from similar mass resignations offer useful analogies:

  • “Zombie journal” risk: The title, ISSN, indexing, and historical impact factor stay with Elsevier. If the new editorial team is perceived as less distinguished or more aligned with volume/publisher goals, submissions from leading researchers can decline, citation patterns can shift (sometimes toward more geographically concentrated or lower-prestige work), and the journal’s standing within the field can permanently weaken. Cases like Lingua (Elsevier, 2015) and others show that the brand can persist while the scholarly niche contracts. sciencedirect.com
  • Recovery path: If Elsevier rapidly appoints highly respected new editors who maintain rigorous standards and rebuild community confidence, the damage can be limited. Some journals have stabilized after board changes, though full restoration of prior prestige is uncommon when the exit was acrimonious.
  • Successor journal effect: Strong community interest already exists in launching a new outlet (possible names floated include variations on “Theory of Games and Economic Behavior” or society-backed alternatives; comparisons are drawn to how JEEA or other breakaways have functioned). If a credible successor is established—especially one endorsed by the departing editors and the Game Theory Society—and is treated as the “true” continuation by tenure/promotion committees, the original GEB’s relative prestige would likely decline further. Successful breakaways (e.g., Glossa after Lingua, or Imaging Neuroscience after NeuroImage) have often captured much of the field’s attention and high-quality papers. journalmetrics.org

**end of Grok responses **
If you want some chatter, you can put “GEB” into the search bar at x.com, LinkedIn, or BlueSky this week and see some more speculation.

There is a lot happening with publishing, including a huge shock of strangeness from the ability to both write and review papers with LLMs. Tumult will be normal. Read Mike on Will AI kill the research paper? or Mike on The actual AI problem in academic economics or Mike on Academic economists are overcommitted . The real perennial problem is that nothing is free but no one wants to pay.

Lastly, dear reader, you might wonder why so much of my post was AI-generated this week. I see this as burning my tokens so you don’t have to. You can find this with a free web search. Some people predict that “cheap AI” is coming to an end, so we might find it worthwhile to publish responses as well as prompts for each other. Regards, fellow humans and AI readers.

AI Innate Preferences Paper on Arxiv

Joy BuchananLeave a comment

Please check out my new paper, with Joshua Foster

The Innate Economic Preferences of Language Models (arXiv link)

Abstract: Language models increasingly settle real resource tradeoffs on behalf of principals yet their economic preferences remain unobserved. We demonstrate their generation rule is isomorphic to the random utility model of discrete choice. This allows internal logit scores to structurally identify preferences. Estimating risk attitudes across twelve models in a portfolio task reveals universal but heterogeneous risk aversion. Although models reject strictly dominated options, their elicited preferences fail invariance tests and violate the independence of irrelevant alternatives across varying experimental prompts. Finally, fine tuning establishes that a principal can explicitly engineer a target risk attitude.

I hope you will refer to the manuscript for details, but I will share one picture here. This is panel (a) of Figure 3: Empirical indifference curves for open-weight models mapped over the portfolio space.

In simple language, what the red/blue picture shows is that the Qwen language model is picking the portfolios that offer more money (in expectation, with a distaste for excessive risk). That’s basically what a rational actor should do. We find that the language models make fairly consistent choices and rarely violate the monotonicity requirement for a well-behaved utility function.

How we describe this figure in the paper: “Starting from a base bundle with expected return µ = 10 and risk σ = 30, we sweep over the dense grid of alternative portfolios from our experimental protocol and record the position-corrected logit gap between each grid portfolio and the base. The yellow dashed line overlays the indifference curve implied by the mean-variance structural estimates, and the heatmap colors encode the sign and magnitude of the logit difference, with blue regions preferred to the base and red regions dispreferred. Several patterns emerge from these plots. All six models produce upward-sloping indifference curves, confirming that higher risk must be compensated by higher expected return.”

We think this basic research on behavior is important, for alignment research and for business applications with delegating work to AI agents. The first question to ask, before testing whether we can impose our preferences on AI agents, is whether those agents have preferences at all in a consistent sense.

Suggested citation: Buchanan, J., & Foster, J. (2026). The innate economic preferences of language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.26288

Uncertainty Increases Profits?

Zachary BartschLeave a comment

Most people have an intuition that uncertainty can harm economic outcomes. Baker, Bloom, & Davis (2016) and Bloom (2009) demonstrated that industrial production and manufacturing decline in the face of policy uncertainty. The typical mechanism that people suggest is that uncertainty about the future causes people to engage in precautionary saving, resulting in fewer sales.

The theory continues that firms consequently decrease production as demand for their output declines. Firms aren’t interested in causing the quantities supplied and demanded to be equal. Rather, they don’t want to produce too many goods that don’t get sold or don’t get sold at an adequate markup. Production is costly.  A related theory is that more persistent or longer-run uncertainty can also depress investment, since the riskier future increases the tail risk of losses.

Rather than make a risky investment, one could instead just hold off and wait for some of that uncertainty to get resolved. There’s tradeoffs to this, of course. As future costs and benefits become clearer, they also get priced-in to asset values. So, there is an optimization problem. The possible downside outcome is big and uncertain. If the risk of the investment gets resolved and the downside outcome is still too likely or harmful, then a project manager did the ex-post ‘right thing’ by waiting.

But, if the downside risk disappears or is found to be very small, then waiting to invest in the project incurs an economic cost. Either 1) the profitable project and its associated profits will occur later and less valuably, or 2) other firms also resolve their uncertainty and bid up the price of the project’s inputs. Invest too early, and the downside is large and uncertain. Invest too late, and you may lose the potential upside partially or entirely.

But can uncertainty systematically increase profits?

Walter Oi said yes.

Continue reading →

The Journal of Healthcare Finance Is Back

James Bailey1 Comment

Most academic journals are run by big for-profit publishing companies, and most of the rest are run by universities or big academic societies. The Journal of Healthcare Finance was an extreme outlier from this norm, run single-handedly by Editor-In-Chief James Unland since 1994. It was the rare journal that was free both for readers and authors.

I loved the idea of having a single person truly in charge and accountable without being slowed by a complex bureaucracy. But eventually a single person will want to, or have to, move on. Having an institution run a journal can ease this process, though an individual can still try to find their own successor.

In this case, The Journal of Healthcare Finance had been on hiatus since its Editor-In-Chief stepped back, with its last issue published in 2023. Their old website domain had expired- not a great look for anyone who published there and was going up for a job or tenure.

But now it is officially back at a new domain, with the single Editor-In-Chief replaced by a full editorial team, and accepting submissions again with the hope of releasing a new issue this year.

Selfishly, I’m happy to see this both because it means they will continue hosting my past publication, and to have a potential outlet for my future work. I recommend that other health economists and health services researchers give it a try, though as of now I have no personal experience with the new editorial team.

Do NBA Teams Play Worse In Back-To-Back Games?

James BaileyLeave a comment

The conventional wisdom is that the NBA regular season has too many games. Teams play worse because they are tired, or injured, or resting their stars so they can be ready to actually play hard in the playoffs.

New research shows that the conventional wisdom is…. probably right. In particular, teams play worse by many measures when they have to play two days in a row. That’s what Max Aicardi and I found in a paper published today, “Running on Empty: How Back-to-Backs Impact Pace and the Four Factors of Basketball Success“:

Teams on the second night of a back-to-back shoot less efficiently (lower eFG%), grab fewer offensive rebounds, and play at a slower pace. On defense, they allow opponents to shoot more efficiently, force fewer turnovers, and give up more free throw attempts and second-chance opportunities. Turnover percentage and offensive free throw rate did not change significantly, consistent with our conceptual framework’s distinction between effort-dependent and execution-dependent metrics. While not every metric changed significantly, the overall pattern is clear: second-night back-to-back scheduling is associated with a measurable decline in team performance

The effect sizes here tend to be small, around 0.5-2%, but they are statistically significant given that we studied over 20,000 games, and practically significant given how close NBA games are.

Max had the idea for this paper and wrote the first draft as a student in my Economics Senior Capstone class in 2025. After he graduated, I joined the paper as a coauthor to get it ready for journals. We share the data and code for the paper here.

Video on You Wouldn’t Steal a Car

Joy Buchanan2 Comments

The brilliant content creator economist Matt Hill has posted a video “How Piracy Accidentally Created AI” to the @EconNerds channel on YouTube.

The video is so funny (and smart!) that I encourage you to sit back and watch it all the way through. Around minute 2, he gets to the topic of online piracy.

The 4 minute mark is where I am featured to explain my paper with Bart Wilson: You Wouldn’t Steal a Car: Moral Intuition for Intellectual Property (SSRN link)

The @EconNerds channel on YouTube has over 100 engaging videos like this to help you learn economics. You can also find Econ Nerds updates on X/Twitter

I summarized our findings in a previous blog “Summary of You Wouldn’t Steal a Car,” but Matt’s video is more fun and quite technically accurate, so now everyone can just watch it.

Joy Explains how to integrate AI into a statistics class

Joy BuchananLeave a comment

I have put a working paper on SSRN describing three ways I incorporated AI into my Business Statistics 200-level class for undergraduates at Samford University in Spring 2026. Read the paper at the link.

Connecting Classroom Econometrics and Excel Training with Large Language Models (SSRN)

Abstract
Economics education almost always includes a component of statistics. Most undergraduate economics curricula require an upper-division econometrics course, and many economists teach data analysis. These courses help students become conversant with major contributions in economics research, but they can be challenging to teach. On its own, the mathematics of regression, error minimization, and statistical software may not feel exciting to students, especially compared with the intuitive economic reasoning that often draws them into the discipline. At the same time, students are increasingly interested in artificial intelligence and large language models. This note describes three practical teaching exercises designed to connect ordinary least squares, Excel training, and LLMs. The goal is to use students’ curiosity about AI to motivate classic statistical reasoning and practical spreadsheet skills.

The image is from ChatGPT 5.5 Thinking mode and it took over a minute to generate. The fact that their laptop screen is pointing away from them is funny (unrealistic). The portrayal of Tom Holland and Zendaya is good, which is what the audience cares about. So, this seems like a case of AI hallucinating up the thing that people want.

I posted back in February about the LLM Telephone game: Telephone Classroom Game for Teaching Large Language Models

Citation:
Buchanan, Joy, “Connecting Classroom Econometrics and Excel Training with Large Language Models” (May 27, 2026). Available at SSRN: https://ssrn.com/abstract=6839039

Note: I have been posting my papers to SSRN for a long time as a way to distribute them faster and more widely. I have heard rumors that SSRN might stop working, for my purposes. If anyone has suggestions for what I should do about the papers I have up there, please let me know! Or, if you are readying this post-summer-2026 and want a copy, send me a message at my Samford email so I can send you a copy.

The Welfare-Productivity Tradeoff in US-China Trade

James BaileyLeave a comment

Who benefits from trade between the US and China? If China subsidizes their exporting industries, should the US see this as a threat that undermines our industries, or thank China for lowering prices for US consumers? Does it matter that China runs a persistent trade surplus (exporting more than they import), while the US runs a persistent trade deficit?

Everyone has a take on these questions, but the answers I hear even among economists rarely draw from the leading modern models in the international trade literature. Krugman (1980) (10k citations) shows how large home markets matter for industries with increasing returns to scale. In a simple increasing returns model, unlike with Econ 101 comparative advantage, temporary subsidies can permanently flip which country an industry efficiently operates in.

Melitz (2003) (20k citations) extends the Krugman model to include firm-level productivity differences. Rubini (2014) extends the Melitz model to include innovation. Now Xiao (2025) has extended the Rubini model to include unbalanced trade, then calibrated the model with data from the US and China. Now that the mathematical models are able to incorporate more and more features of the real world, what do they show?

China’s trade surplus and the US trade deficit have tradeoffs. Specifically, China’s trade surplus leads them to be more productive than they otherwise would be, but have lower welfare, because so much of the fruit of their production is enjoyed by other countries. Conversely the US trade deficit leads us to produce less than we otherwise would, but to have higher welfare thanks to consumers enjoying the cheaper foreign goods.

In one sense this recapitulates some of the same debates people had without the math. Some people like trade because it benefits US consumers and overall present-day US wellbeing. Some don’t like it because it harms US manufacturing and our resiliency in any potential future conflict.

One advantage of the models is that it puts numbers on the tradeoffs. In this case, the welfare benefit to the US may be small relative to China’s welfare loss and relative to both countries’ productivity changes:

the average productivity increase caused by trade surplus ranges from 1.2 percentage points to 5.46 percentage points when the innovation cost changes. These results explain China’s long-term export promotion policies and align with its new policy goal of developing “new productivity forces”. I also identify a negative effect on China’s trade partners’ productivity (namely, the US), of between -2.74 percentage points and -5.89 percentage points. This comes at a welfare cost, equivalent to between 3 percentage points and 5.7 percentage points of consumption units. Correspondingly, China’s cheaper goods increase welfare in the US by between 0.26 percentage points and 1.22 percentage points

In addition to the big complex model, Xiao’s paper shares nice background on the sheer size of Chinese export subsidies, noting that they account for 2/3 of all manufacturing subsidies in G20 countries, and that export tax rebates are almost 2/3 as large as Chinese net exports. In short, China’s trade surplus is not simply driven by differing preferences and production capabilities across countries, but is largely driven by deliberate policy choices.

P.S. The paper’s author, Aochen Xiao, is on the econ job market.

arXiv will ban authors who submit papers with LLM mistakes

Joy Buchanan1 Comment

In the world of academic preprints, arXiv has long been the go-to platform for researchers to share work quickly. But with the explosion of generative AI tools, the repository is drawing a line in the sand.

On May 14, 2026, arXiv moderator Thomas Dietterich announced a clarified enforcement policy. If a submission contains incontrovertible evidence that authors didn’t properly check LLM-generated content, all listed authors face serious consequences.

What counts as “Incontrovertible Evidence”? The policy targets clear signs of unchecked AI output, including:

  • Hallucinated or fake references
  • Meta-comments left by the model (e.g., “Here is a 200-word summary; would you like me to make any changes?” or placeholder instructions like “fill in the real numbers from your experiments”)
  • Other obvious errors, plagiarized text, biased content, or misleading claims generated by AI

arXiv’s Code of Conduct already holds every author fully responsible for the entire paper’s contents.

The Penalty

  • One-year ban from submitting new papers to arXiv.
  • After the ban, future submissions must first be accepted at a reputable peer-reviewed venue before arXiv will host them.

At first researchers discussing the policy online seemed happy about the one-year ban, but when I pointed out that it is essentially a ban for life to use it at a pre-print venue, some people became nervous.

Why now? arXiv has been overwhelmed by low-effort “AI slop.” These papers are marked by fabricated citations and shallow summaries. This erodes trust in the entire preprint ecosystem.

In response to the complaints (someone like me would be worried that I’ll somehow let an error slip through and then be banned for life from posting working papers), Scientific Director Steinn Sigurðsson shared:

on the whole @arxiv flap about hallucinated references etc

you don’t see the stuff we reject… some of it is really really egregious

the decision to impose additional consequences is largely to throttle that stuff so n00bs and bad actors don’t trash us trying repeatedly

This is the problem that we face with every internet forum. A few bad actors ruin it for good people.

In 2022 I wrote Content moderation strategy

Elon Musk buying Twitter is the big news this week. He wants to enhance free speech on the site and, according to him, make it more open and fun. Some fans are hoping that he will make the content moderation and ban policy more transparent. Maybe that’s possible. 

If no one can be banned, then bad actors will bring the whole platform down. Inevitably, good people get caught in the net, and it’s devastating to be locked out of a platform where your peers are sharing.

However, if you want to be taken seriously by tech folk then ask for a system that is possible. A substantially better experience might be incompatible with the site being free to users.

Part of the problem that I don’t hear people talking about is that a free platform is not easily compatible with good customer service.

For some not-fake work and citations: Buchanan et al. (2024) provided early clear evidence that a mark of LLM-written work is fake citations. And, Buchanan and Hickman (2024) show that certain framings can prompt people to be more suspicious of AI-generated writing, such that they are pushed toward doing a fact-check before believing all claims.

Buchanan, Joy, and William Hickman. “Do people trust humans more than ChatGPT?.” Journal of Behavioral and Experimental Economics 112 (2024): 102239.

Buchanan, Joy, Stephen Hill, and Olga Shapoval. “ChatGPT hallucinates non-existent citations: Evidence from economics.” The American Economist 69.1 (2024): 80-87.

Most Published Research Findings Are Directionally Correct

James BaileyLeave a comment

As a new quick rule of thumb inspired by the Nature papers, you could do worse than “cut estimated effect sizes in half”. If a published paper says that a college degree raises wages 100%, then chances are the degree really does raise wages, but more like 40–50%. In 2005, John Ioannidis said that “most published research findings are false”. By 2026, we seem to have improved to “most published research findings are exaggerated.”

That’s the conclusion of my piece out today at Econlog: “Is Economics Finally Becoming Trustworthy?“

There’s plenty of both good and bad news for economics and the social sciences in both my piece and the Nature special issue it describes. It’s kind of like the Our World in Data motto:

In short, our attempt to replicate hundreds of papers showed that published social science results shouldn’t be trusted precisely today, but they seem to be getting more reliable over time, and they are much more reliable than chance. Economics and political science look the best, though we are still very far from perfect:

You can read the full piece here.