As I step into the role of Director at SSRN, I am both honoured and excited to lead an organisation that has played such a transformative role in scholarly communication. 

For more than 30 years, SSRN has helped researchers share their work, discover new ideas, and accelerate the exchange of knowledge across disciplines. What began as a pioneering platform for the social sciences has grown into one of the world’s largest cross-disciplinary preprint server, supporting researchers and institutions across the globe. 

As I take on this role, one thing is clear: SSRN’s mission has never been more important. 

The way research is communicated continues to evolve. Researchers increasingly need trusted platforms that enable them to share findings quickly, establish priority, receive feedback, and reach the widest possible audience. At the same time, the scholarly community rightly expects high standards of quality, integrity, transparency, and service. 

At SSRN, we believe these goals go hand-in-hand. 

Our ambition is not simply to maintain what SSRN has already achieved, but to build the world’s most trusted and effective preprint platform. We want researchers to choose SSRN because it offers the right balance of speed, quality, integrity, and visibility. We want to make research available faster, while continuing to meet the expectations of researchers, institutions, funders, publishers, and the broader scholarly community. 

While recent changes across the research landscape have created new challenges and opportunities, our purpose remains constant. We are aware of the value we provide to researchers, and the important role we play within the broader ecosystem of scholarly communication. 

Looking ahead, I am optimistic about what we can achieve. SSRN already serves as a trusted home for millions of researchers and scholarly works. The opportunity now is to build on those strong foundations, to continue improving the researcher experience, strengthen trust in preprints, and ensure that important discoveries can be shared, discovered, and built upon more quickly than ever before. 

The future of research communication will be defined by collaboration, openness, innovation, and trust. SSRN is uniquely positioned to contribute to that future, and I am excited to work alongside our authors, partners, institutions, and research communities as we enter this next chapter. 

Together, we can continue advancing our mission of helping researchers share tomorrow’s research today. 

Clare Stone 
Director, SSRN 

By the end of July authors submitting to SSRN via our Web site Submission experience will be able to choose exactly how their work may be shared and reused. Instead of one implicit set of terms in which all rights are reserved, you will now get a new menu as part of Submission: this will offer the standard Creative Commons licences, a public domain option, or the choice to stay with the current default option and keep all rights reserved. 

Why we’re doing this 

This change gives authors much more fine-grain control over exactly how their work can be shared and re-used by others. It also means that for grant-funded research where the funder has stipulated preprinting under a CC BY, authors can now easily select a CC BY licence at the point of submission.  

We will again emphasize that this is an option, not a requirement. In the past, authors always retained their rights on SSRN, governed by the language in our terms and conditions. For those who do not wish to select a CC licence for any reason, selecting All Rights Reserved will provide the same protection as before but now made a little clearer and more explicit. The licence you choose will be displayed on the Abstract page, so all readers of your work understand any rights you have decided to share. 

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All Rights Reserved. You keep full control. Anyone wanting to share, remix, or reuse your article needs your permission first. If you’re not sure which licence you want, this is the best default as you can change your mind and choose a more permissive licence later. 

One thing to know before you choose 

You can make your licence more permissive after posting (say, moving from All Rights Reserved to CC BY), but you can’t make it more restrictive, so please choose with that in mind. As shown in the graphic below, you can move right to the less restrictive options but not left to the more restrictive options. 

We’re excited to be able to offer our users more explicit controls about the licensing terms for their work on SSRN, and this is something that authors and funders have been requesting for a while. We’d love to hear your thoughts on this new functionality so please let us know what you think at ideas@ssrn.com 

At SSRN, we have always believed that research should speak for itself. As we refocus our platform around the needs of individual researchers, faster posting, better discoverability, and stronger research integrity measures, we have made the difficult decision to retire our current Rankings features on the 15th of July 2026

Rankings of papers, authors, and institutions within some disciplines have been features of SSRN for many years, and we know they matter to those communities. However, maintaining them requires significant product and development resourcing, which as some have noticed we have struggled to allocate. Going forward, we want to put that investment into the parts of the platform that impact researchers across all the disciplines who use SSRN. 

What we are keeping is the data that we hope matters most to you as an author. Download counts and citation statistics will continue to be recorded and displayed on your individual papers and author profile, so you can always see how your work is travelling and who is engaging with it. 

We recognize this change will not be welcome to everyone, and we are genuinely interested in your feedback. If rankings are important to your work or institution, we want to hear about why you find them useful. We may revisit the idea of various rankings as the platform evolves. As always, you can share your thoughts with us at ideas@ssrn.com. 

SSRN rankings will no longer be updated or accessible from 15th July 2026.

Artificial intelligence has rapidly evolved from a productivity tool into something more profound: a constant cognitive companion. Millions of people now consult AI systems before writing emails, making decisions, solving problems, conducting research, and even forming opinions. Yet, while public discussion has focused heavily on whether AI is intelligent, accurate, or capable of replacing human labour, a more fundamental question remains largely unexplored: What happens to human thinking when AI becomes part of the reasoning process itself?

In their thought-provoking 2026 paper, Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender, Wharton researchers Steven D. Shaw and Gideon Nave propose a new framework for understanding cognition in the age of generative AI. At the heart of the paper is a striking concept the authors call cognitive surrender: the tendency to adopt AI-generated answers with minimal scrutiny, allowing artificial cognition to override both intuition and deliberate reasoning.

In this edition of A Closer Look, we explore the motivations behind the research, the development of Tri-System Theory, and the emergence of cognitive surrender as a defining feature of human-AI interaction.

Q: What inspired you to write this paper, and what question were you trying to answer?

Steven Shaw: The ubiquity of AI in life and work, and our own experience using it and teaching students who use it. A lot of research to date has focused on AI use-cases or how accurate AI models are, how they work, and where they fail. As cognitive scientists, we wanted to ask a different question: what happens to human thinking when people have access to a technology that can generate ready-made answers, explanations, and reasoning-like outputs across so many domains?

So, rather than studying AI itself, we wanted to understand the human–AI interface. How do people think with AI? When do they use it? When do they defer to it? And how does access to artificial cognition change confidence, judgment, and reasoning itself? That was the core motivation behind the paper.

Q: Your paper is titled Thinking—Fast, Slow, and Artificial. Why do you believe AI deserves to be thought of as a third system of thinking?

Gideon Nave: What makes AI fundamentally different from previous technologies, and positions it as a third system of thinking, is that it operates in the language of human thought. A calculator helps us do arithmetic, and a GPS helps us navigate, but they are specialized tools. AI can engage with virtually any question we can articulate in natural language: from solving problems and generating ideas to evaluating arguments and making decisions.

It’s also different from relying on another person. We have always turned to friends, colleagues, and experts for advice, but they are costly to access, not always available, and social interactions inevitably involve concerns about judgment or embarrassment. AI is available 24 hours a day, responds almost instantly, costs little or nothing, and allows people to ask almost anything without fear of being judged.

That’s why we think AI represents a fundamentally new element in human cognition. It is not just another tool or another source of information. It is becoming a constant cognitive partner that people increasingly consult whenever they think.

Q: One of the paper’s key ideas is “cognitive surrender.” What does that term mean, and where do we see it in everyday life?

Steven Shaw: We define cognitive surrender as adopting AI-generated outputs with minimal scrutiny or insufficient verification. Put more simply, it is the deferral of thought to AI: ceding the reasoning process to an artificial system and accepting its answer as your own.

I think most of us can recognize this in everyday life. AI is very good at many things, and in some domains, it can feel almost perfectly reliable. This property makes it easy, even tempting, to simply accept what AI gives us rather than think for ourselves. AI gives us a low-friction way to avoid cognitive effort. Cognitive surrender gives us a name for this uncritical adoption.

Q: Is relying on AI always a bad thing, or are there situations where “cognitive surrender” is actually the smartest choice?

Gideon Nave: No, relying on AI is not always a bad thing. We are careful in the paper to document cognitive surrender as a novel mode of human-AI interaction and discuss how this can be adaptive or risky. Many technologies change the cognitive skills people need to maintain. Calculators, for example, made it less necessary to do long division by hand, and that is not necessarily a problem. In some domains, automating cognition is efficient and reasonable.

The key question is whether and when we are comfortable losing or reducing a human skill. There may be many tasks where AI can safely take over parts of the reasoning process. But there are also domains where we want people to retain judgment, responsibility, and expertise.

Experiment 2, with time pressure, illustrates the positive side well. When people were under time pressure, their unaided performance on our task declined. But when AI was accurate, and participants engaged in cognitive surrender, it helped participants avoid that decline. So the issue is not AI reliance itself. The issue is whether reliance is calibrated to the task, the system, and the stakes.

Q: Your experiments deliberately made AI right sometimes and wrong at other times. What did that reveal about how people interact with AI?

Steven Shaw: That manipulation was the clever experimental mechanism that allowed us to observe cognitive surrender. It lets us move beyond the question “Does AI improve accuracy?” and ask a deeper process question: when people use AI, are they checking it, strategically offloading to it, or surrendering to it?

If we had simply given participants access to AI, then AI would often be right on these structured problems, and we would only observe what appears to be performance improvement. This is what many of the deskilling studies show; unaided, optional AI access improves performance when System 3 is online, but there is an accumulation of cognitive debt due to repeated cognitive surrender. But if we only observed performance improvement, we can’t causally say why or how these processes are happening.

By deliberately making AI correct on some trials and incorrect on others, we could see how participants’ answers tracked AI’s recommendation. The pattern was clear: when AI was right, people benefited; when AI was wrong, many people followed it anyway, even though it was quite easy to verify the answer (elementary school arithmetic).

Q: One of your findings is that people often became more confident after using AI even when the AI was wrong. Why do you think that happens?

Gideon Nave: One reason is that people have been trained by experience to think AI is good, and in many cases, it really is. AI often provides useful, fluent, and confident answers. When people consult it, they may experience the output as a kind of high-quality second opinion.

That has metacognitive consequences. If I am unsure, and then a powerful system gives me an answer with a convincing explanation, I may feel that the uncertainty has been resolved. The problem is that this confidence can attach to the presence of an AI-generated answer rather than to the actual correctness of that answer.

That is what we saw in the studies. Confidence went up even when AI gave incorrect information, and confidence did not differ much depending on whether the AI was correct or incorrect. That suggests participants had difficulty distinguishing good AI outputs from bad ones. And our task was relatively easy to verify. In many real-world uses of AI, there may be no clear ground truth, which makes that problem much harder.

Q: Were there any results that genuinely surprised you or challenged your own assumptions?

Steven Shaw: We knew cognitive surrender existed because we’ve observed it in nature, in others, in ourselves. What we didn’t know is how well we would be able to observe it in the lab. The participants in our experiments engaged with AI often, and it was surprising how strongly they tended to follow AI once they opened the chat.

Even when AI was giving incorrect information, participants still followed it at high rates. In Study 1, for example, once participants engaged with AI, they followed faulty AI recommendations roughly four out of five times. That was striking. It suggests that once people turn to AI, they often shift from evaluating the answer to adopting the answer (at least on our task). Even in Study 3, for example, when we paid participants more for correct answers and immediately told them if they got each question correct or incorrect, we still saw many participants engage in cognitive surrender.

Q: What do your findings mean for how we should use AI at work, in education, or in our everyday decision-making?

Steven Shaw: I think the distinction we make between cognitive offloading and cognitive surrender is especially important. Cognitive offloading is strategic delegation. You remain in control of the decision, but you choose to offload a specific component of cognition to AI. That can be very beneficial. It can help people think faster, generate alternatives, summarize information, or handle parts of a task that would otherwise be time-consuming.

Cognitive surrender is different. It occurs when the locus of control shifts to artificial cognition. Instead of System 1 or System 2 guiding the reasoning process, System 3 becomes the main thinker. The person is no longer using AI to support their own reasoning; they are mostly following what AI tells them.

That distinction gives us a useful framework for work, education, and everyday decisions. The goal should not be to avoid AI. The goal should be to design tasks, habits, and institutions that encourage strategic offloading while guarding against passive surrender (unless we accept the consequences of that surrender).

Q: As AI becomes more integrated into our lives, how can people benefit from it without losing their own critical thinking skills?

Gideon Nave: People can benefit from AI without losing critical thinking, but only if they are intentional about how they use it. AI should not simply be treated as an answer machine. It can also be used as a tool for comparison, reflection, and improvement.

One useful approach is to use AI to augment the reasoning process rather than replace it. For example, a person might ask AI to generate alternatives, identify weaknesses in an argument, summarize competing views, or explain why a proposed answer might be wrong. In those cases, AI can actually increase critical engagement because it gives the user more material to evaluate.

The broader point is that AI can either weaken or strengthen critical thinking depending on how it is used. Passive acceptance encourages surrender. Intentional use can support better reasoning.

Q: What responsibilities do AI developers and technology companies have in designing systems that encourage thoughtful rather than passive use?

Steven Shaw: That is a really interesting question because it depends on our goals when integrating AI into society. Is AI primarily a technology for economic growth? Is it a technology for training machines to replace human labour? Or is it a technology that should benefit people in humane and broadly shared ways?

An individual AI developer has responsibilities within their job and employer, but the larger responsibility sits with companies, institutions, and governments that shape how these systems are built and deployed. If AI is understood as a pervasive technology with public-good implications, then there is a strong responsibility to design it in ways that enhance human thinking rather than encourage passive use, de-skilling, or loss of critical judgment. At the moment, sycophantic and highly-engaging elements of AI systems seem to err towards more capitalistic motivations.

That means building systems that help people use AI ethically and effectively. The goal should be augmentation, not replacement of human agency. AI should make people more capable without making them less reflective.

Q: Looking ahead, what do you think is the biggest unanswered question about AI and human reasoning?

Gideon Nave: I think the biggest unanswered questions are concerning what I call the soft singularity. Most discussions focus on AI becoming smarter than humans. I’m interested in a different possibility: what if humans gradually stop thinking for themselves?

As AI becomes easier, cheaper, and available for virtually every cognitive task, people may increasingly outsource reasoning, memory, writing, and decision-making. In education, this could create “never-skilling”—a generation that never develops core cognitive abilities because AI performs those tasks from the outset.

Will AI augment human intelligence, or will it slowly erode the very capacities that make us effective thinkers? I believe that is one of the defining scientific questions of the AI era, though it’s difficult to study. It might just take a long time.

Q: Finally, if readers take away just one message from this paper, what would you like it to be?

Steven Shaw: We need to think carefully and intentionally about the psychology of AI. This is not just another tool. It’s a powerful system that is already being integrated into everyday life and everyday thought.

The most dangerous response may be to do nothing: to fail to recognize that cognitive surrender is likely to become a common feature of human–AI interaction. If we care about preserving creativity, autonomy, and human judgment, then we need to understand the conditions under which people surrender their thinking to AI.

AI changes the architecture of thinking. We need user habits, institutional norms, and policy protections that allow people to benefit from AI without surrendering the parts of cognition that make us human.

Gideon Nave: The main message is that cognitive surrender is not a distant possibility. It’s already here. Humans naturally conserve cognitive effort, and AI makes it easier than ever to outsource thinking. That’s not a flaw in human nature; it’s how our minds are built.

The real question isn’t whether we’ll rely on AI—we will. The question is how much of our thinking we’re willing to give away in exchange for convenience and short-term productivity.

ABOUT THE AUTHORS

Gideon Nave

Gideon Nave is the Carlos and Rosa de la Cruz Associate Professor of Marketing at The Wharton School, University of Pennsylvania, where his research sits at the intersection of neuroscience, technology, and consumer behaviour.

With a PhD from the California Institute of Technology (Caltech) in decision neuroscience and an earlier background in electrical engineering, Nave combines methods from psychology, neuroscience, economics, and data science to investigate how people think, decide, and behave. His work spans topics including artificial intelligence, decision-making, genetics, consumer psychology, and scientific reproducibility.

Nave has received numerous recognitions, including the APS Rising Star Award and the ACR Early Career Award, and was named one of Poets & Quants’ Best 40-Under-40 Professors. He is affiliated with the Wharton Neuroscience Initiative, Wharton Human-AI Research (WHOAR), and Wharton Behavioral Change for Good (BCFG).

You can see more work by Gideon Nave on his SSRN Author page here.

Steven D. Shaw

Steven Shaw is a Postdoctoral Researcher in the Marketing Department at The Wharton School of the University of Pennsylvania and incoming Assistant Professor at King’s Business School, King’s College London.

His research explores consumer behavior through an interdisciplinary lens, drawing on behavioural science, evolutionary psychology, genoeconomics, and neuropsychology to better understand how people make decisions and interact with emerging technologies. Shaw received his PhD from the Ross School of Business at the University of Michigan.

You can see more work by Steven Shaw his SSRN Author page here.

Prediction markets have earned a reputation for striking accuracy, yet the engine behind that accuracy has remained surprisingly opaque. In their 2026 paper, Prediction Market Accuracy: Crowd Wisdom or Informed Minority? Roberto Gómez‑Cram, Yunhan Guo, Theis Ingerslev Jensen, and Howard Kung take the first comprehensive look inside Polymarket’s transaction universe and uncover a result that overturns the dominant narratives. Accuracy, they show, comes neither from the broad crowd nor from insiders, but from a tiny, persistently skilled minority roughly 3% of traders who react instantly to public news, correct pricing violations, and trade against the crowd’s behavioural mistakes. 

This Closer Look explores the motivations behind the research, the methods the authors used to separate skill from luck, and what their findings mean for the future of prediction markets as forecasting tools. Through our conversation with the authors, we dig into how the project began, why the “wisdom of crowds” story falls short, and how a small, informed minority quietly shapes the probabilities that policymakers 

Q: What motivated you to investigate who makes prediction markets accurate? Prediction markets are often praised for their forecasting ability, but the source of that accuracy is less understood. What sparked this research question?

Theis Jensen: Roberto and I were working on another project with the same co‑authors, studying analyst expectations and whether surprises around earnings announcements explained returns. Most of what we tested didn’t explain much. Then one of our co‑authors , came across these new earnings prediction markets. We added them to the regression almost casually, and suddenly they explained a lot. That caught our attention. Analysts are smart, so seeing prediction markets add information beyond their forecasts made them worth studying. Since then, we haven’t really looked back: we’ve gone fully into prediction‑market research. 
 

Q: Before you started the project, did you expect the results to support the “wisdom of crowds” view? How did your expectations compare with what you ultimately found? 

Roberto Gomez Cram: When we saw how accurate prediction‑market prices were and confirmed that in papers, we wanted to understand why. So we looked at what people were saying. The first explanation was the classic “wisdom of crowds”: many traders each contribute a bit of information, and it all gets incorporated into prices. That’s also the narrative pushed by the companies running these markets. So yes, we initially expected to find something like that, that information was spread across many traders. 

The other view, especially in the media, was that accuracy comes from insider trading. There are several high‑profile cases, like the Maduro case or the recent one involving a Google employee betting on Google markets. We had these two competing explanations: broad crowd wisdom, versus insider trading – and the goal of the paper was to figure out which one actually drives the accuracy. 

Q: For readers unfamiliar with prediction markets, what is the single most important takeaway from the paper? 

Theis Jensen: I think one key takeaway is that if you don’t have any particular informational advantage or you’re not especially good at things like programming ,then you probably shouldn’t bet in prediction markets with the goal of making money. What we find is that only about 3% of all accounts show significant skill. Most retail investors are not part of that 3%. So one takeaway is that prediction markets are not something you should rely on for long‑term investment success. That said, prediction markets can still be really interesting. I use them myself for things like the World Cup. I don’t expect to win money, but I get something else out of it the excitement of having a bit of money on the line. They can be fun, but I wouldn’t treat them as a long‑term investment strategy. 

Roberto Gomez Cram: And even if you win in one period, if you keep betting, it’s very likely you’ll lose over time. That goes back to what Theis said: they’re not designed for long‑term investment returns. 

Q: The paper argues that accuracy comes neither from the crowd nor from insiders. Why is that distinction important? What does it tell us about how information gets incorporated into markets?   

Theis Jensen: I think it’s important because it speaks directly to the public narrative. People who like prediction markets say the accuracy comes from the “wisdom of the crowd,” and people who dislike them say it’s all insiders. There’s value in academics looking at this in a rigorous way. What we find is that neither extreme is right, the truth sits somewhere in between, and that helps inform the broader debate. 

Prediction markets are controversial, and to make progress on questions like whether they should be allowed, we need a clear understanding of what actually drives them. Our job is to cut through the hype and the incentives people may have to push one story or the other, and simply say: here’s what the data shows. We don’t see insider trading as the dominant force, and we don’t see accuracy coming from all users. Instead, it comes from a very small group of highly skilled traders. And then people can decide what they make of that. 

Q:  How do you distinguish genuine skill from simple good luck? Why was that challenge central to the study? 

Theis Jensen: It’s actually very difficult to separate luck from skill. Our procedure is a bit technical, but the intuition is simple. We look at how well a trader has done historically, and then we simulate how they would have done if they were essentially just flipping a coin. 

From those simulations, we get a distribution of random outcomes: sometimes you’d win by luck, sometimes you’d lose, but on average you shouldn’t make or lose much. Then we compare that random distribution to the trader’s actual profit and loss. If their real performance is far above what you’d expect from the coin‑flip world, we say there’s statistically significant evidence that this trader is doing better than luck. That’s the basic idea behind how we identify genuine skill. 

Q: What characteristics define the traders you classify as “skilled”? Are they experts, fast information processors, disciplined traders, or something else entirely? 

Theis Jensen: We don’t actually know who they are, all accounts are anonymous ,but we can see how they trade. We focus on three behaviours that only the skilled group consistently shows, all related to trading efficiently on public news, exploiting psychological biases, and acting on arbitrage opportunities in prediction markets. 

One pattern is how quickly they react to news. For example, Polymarket might have a contract on whether Tesla will beat earnings. As soon as Tesla announces results, the skilled traders are the ones who immediately trade on that information before others react. They’re extremely fast at processing news. 

Another pattern is that they trade against well‑known psychological biases. A classic one is the favourite–long‑shot bias: people like betting on long shots for the big payout and avoid favourites because the return is small. That means favourites tend to be slightly underpriced and long shots slightly overpriced. Skilled traders do the opposite; they buy the underpriced favourites and sell the overpriced long shots. In that sense, they behave like cold, unemotional machines, while the rest of us are more hot‑headed humans. 

Q: What are the findings that generally surprised you during your research? Like any result that made you rethink your assumption? 

Roberto Gomez Cram: For me, the most striking result was how few accounts actually make money in prediction markets. Only about 3% consistently earn profits, while the vast majority don’t trade any better than flipping a coin. And those skilled traders essentially rely on the other 97% they’re trading against them. One way to interpret it is that the majority is effectively funding the profits of the minority, which is the opposite of what Polymarket’s CEO often claims about “everyone doing great.” The tiny number of skilled accounts kept surprising me. 

Theis Jensen: I agree. Another surprising result was how persistent skill is. In most financial markets, it’s extremely hard to beat the market year after year. Mutual fund managers are the classic example. They might outperform in one year, but very few do it repeatedly. Skill just isn’t persistent. But in prediction markets, the small group of skilled traders shows very persistent performance. So while only a tiny subset is skilled, those traders consistently beat the market. I hadn’t expected that at all. 

Q: Your study suggests that many participants contribute volume but relatively little information. What broader lessons does that offer about collective decision-making and expertise? 

Theis Jensen: I think one broad takeaway is that markets do a very good job of aggregating information, but that’s usually driven by a small minority of people. When you have retail traders like people from Reddit trading stocks, they’re not contributing much information. Their trades don’t really push prices toward the correct value. But what they do provide, and this goes back to what Roberto said, is an incentive for the really smart traders to trade. If you were only trading against other smart people, you wouldn’t make much money and might not trade at all. But if most people are trading for fun or acting behaviourally, then skilled traders have a strong incentive because there’s a big pot of money to be won. 

Roberto Gomez Cram: And this is especially true in prediction markets because they’re a zero‑sum game: my losses are your gains. That’s very different from financial markets, which aren’t zero‑sum. If you invest in an ETF and the economy grows, everyone can do well. There the benchmark is beating a passive strategy. Here, the benchmark is simply having a positive profit, which makes the dynamic Theis described even more important. 

Q: What does this research tell us about the future role of prediction markets in forecasting elections, economic events, sports, or technological developments? 

Roberto Gomez Cram: I think prediction markets are amazing, and they really do provide a new financial instrument, one where you can put money on very specific events. That specificity, with narrow questions and clear rules and timelines, is what makes prediction markets so interesting. Right now, most activity is speculation, like betting on whether GDP will be above a certain level. But there’s a lot of potential for hedging, because these markets let you hedge risks you can’t hedge with standard financial instruments. If volume and liquidity increase, more people could use them for hedging, and that would attract institutional investors, not just retail. At that point, prediction markets could become part of mainstream finance because they offer an instrument you simply can’t trade elsewhere. 

Theis Jensen: On future accuracy, our earnings paper shows prediction markets are extremely accurate, but our other paper shows that most of that accuracy comes from a small group of people. So going forward, our evidence suggests three things: prediction markets today are quite accurate; staying accurate requires that these skilled traders keep participating; and they also need people to trade against. For these markets to work well, they can’t be dominated only by not‑so‑smart money or only by very smart money they need a mix. That’s how I see their future. 

Q: If policymakers, businesses, or journalists rely on prediction markets, how should they interpret your findings? Should they have greater confidence in these markets or be more cautious? 

Theis Jensen: I would have confidence in these markets for accuracy. If you’re interested in some event and it’s traded on a prediction market, then more often than not, that’s probably the most accurate estimate you have. If I want to know the probability that Norway beats France in the World Cup, I check Polymarket. If I want to see where people think GDP is going, I still look at forecasts, but I’m increasingly drawn to prediction markets too. Most of our research suggests these markets are doing their job. They’re extremely accurate. 

Roberto Gomez Cram: And as liquidity improves and trading volume is increasing massively, these prices should become even more accurate. At least until the point where people start using them for hedging. Once they’re used for hedging, you’ll see a wedge between accuracy and the “true” price because risk premia start to matter. So they’ll stay very accurate up to that point, and then accuracy may decline a bit once hedging motives enter. 

Theis Jensen: I agree. Once they’re liquid enough to be used for hedging, they must already be extremely liquid. So there are two forces going in opposite directions, and it’s hard to know which one will dominate. 

Roberto Gomez Cram: But in the medium term, I think accuracy will keep improving. 

Q: What is the most important unanswered question that remains after this paper? If you were writing the sequel, what would you study next? 

Roberto Gomez Cram: We conclude the paper by saying that the next big question and what we’re currently working on is the hedging motives in prediction markets. 

Theis Jensen: Yes, essentially the hedging capacity. What can you actually hedge with prediction markets right now? How much can you hedge, and how valuable is that compared with standard financial markets? 

Roberto Gomez Cram: And we’ll have a working paper on this very soon , journalists, and investors increasingly rely on. 

ABOUT THE AUTHORS

Roberto Gomez Cram

Roberto Gómez‑Cram is an Assistant Professor of Finance at London Business School. His research spans asset pricing applied time‑series econometrics, and investments, with a focus on how information is incorporated into markets and how financial assets are priced. His work has been recognised with multiple awards and published in leading journals, including the Journal of Financial Economics. 
Roberto joined the LBS faculty in 2019, spent a year at NYU Stern, and returned to LBS in 2024. Before academia, he worked as an Assistant Economist at the Central Bank of Mexico. He holds a PhD in Finance from the Wharton School of the University of Pennsylvania. 

You can see more work by Roberto Gómez‑Cram on his SSRN Author page here.

Theis Ingerslev Jensen 

Theis Ingerslev Jensen is an Assistant Professor of Finance at Yale SOM. His research focuses on empirical asset pricing and the use of novel data especially measures of subjective expectations to understand how beliefs shape market outcomes. His projects are highly data‑intensive and often employ machine learning and other advanced statistical methods.
Originally from Denmark, Theis earned his PhD in Financial Economics from Copenhagen Business School in 2023. His expertise spans behavioural finance, big data, financial markets, and valuation.

You can see more work by Theis Ingerslev Jensen his SSRN Author page here.

At SSRN, we have always believed that research should speak for itself. As we refocus our platform around the needs of individual researchers, faster posting, better discoverability, and stronger research integrity measures, we have made the difficult decision to retire our current Rankings features. 

Rankings of papers, authors, and institutions within some disciplines have been features of SSRN for many years, and we know they matter to those communities. However, maintaining them requires significant product and development resourcing, which as some have noticed we have struggled to allocate. Going forward, we want to put that investment into the parts of the platform that impact researchers across all the disciplines who use SSRN. 

What we are keeping is the data that we hope matters most to you as an author. Download counts and citation statistics will continue to be recorded and displayed on your individual papers and author profile, so you can always see how your work is travelling and who is engaging with it. 

We recognize this change will not be welcome to everyone, and we are genuinely interested in your feedback. If rankings are important to your work or institution, we want to hear about why you find them useful. We may revisit the idea of various rankings as the platform evolves. As always, you can share your thoughts with us at ideas@ssrn.com. 

Rankings will be live on SSRN until the end of June and will then be removed, which means that SSRN rankings will no longer be updated or accessible after July 1st.

We are aware that Professor Bainbridge raises understandable concerns about changes at SSRN, particularly given how central the platform is to legal scholarship. For many of us, SSRN is not just a repository; it is the backbone of early dissemination, discovery, and intellectual exchange in the legal academy. 

That said, it is important to emphasize that SSRN’s core mission remains unchanged: enabling the rapid sharing and discovery of scholarly work. SSRN continues to operate as an open-access platform designed specifically to help scholars disseminate early-stage research widely and efficiently. 

Recent and planned updates should be understood in that context. SSRN has explicitly reaffirmed a “renewed focus on our core research sharing mission,” emphasizing preprints, working papers, and global scholarly connection as its central priorities. Far from retreating from academic support, we are re-centering on what legal scholars value most: visibility, speed, and reach.  

For legal academics in particular, SSRN’s Legal Scholarship Network remains a robust and highly active ecosystem. It continues to: 

• Provide a venue for posting draft articles, accepted papers, and teaching materials 

• Cooperate with Google Scholar so that posted papers are rapidly indexed and widely discoverable via popular search paths 

• Enable rapid dissemination to a global audience without paywalls 

• Power discovery through the Legal Scholarship Network and email alerts 

• Facilitate interdisciplinary visibility and citation-building 

• Permit early drafts to meaningfully influence ongoing policy debates and judicial discourse 

We would like to clarify a few specific points raised by Professor Bainbridge:  

• Institutional research paper series will be closed on a rolling basis at the end of each institution’s existing term, or at the end of December 2026, whichever comes first. 

• Published work in legal scholarship will continue to be posted in most cases, except where prohibited by copyright, as we have done in the past. 

• While SSRN will introduce the ability to select a CC-BY license at submission later this year, options will include CC-BY, CC-BY-NC and CC-BY-NC-ND, providing additional flexibility and choice for authors. Additionally, authors may opt out of applying a CC license.

• SSRN will continue to highlight specific papers on its social media channels and engage with authors via our blog interview series. 

• Metrics such as downloads and views will continue to be aggregated at the paper and author level, although institutional level aggregation will end this year. We will consider improved institutional discovery and aggregation features in the future product roadmap. 

• With the exception of some sponsored niche areas, existing areas of legal scholarship on the site and their associated email alerts will continue unchanged. 

• We are aware that many legal scholars rely on the help of their law librarians and/or department administrative support to submit research to SSRN. We are evaluating ways to balance this need in the future with the need to ring-fence the platform against bad actors and spam submissions. This will likely involve additional identity verification measures. We expect to be able to share additional details on this point in Q4 of 2026. 

Change in platform strategy understandably creates uncertainty; we at SSRN are grappling with change and uncertainty along with our disciplinary communities. But SSRN is not moving away from legal scholarship; rather, we hope to continue serving as the primary hub for sharing and discovering legal research by focusing on our strengths rather than peripheral offerings. Please continue to share your thoughts and concerns with us and thank you for your patience as we grow into the future.