Blog: Prediction Markets for Science: Beyond the Quick Bucks

Throughout the 2024 US election season, as opinion polls predicted roughly even chances of either party winning, news media paid special attention to the odds on prediction markets such as Polymarket.

More recently, prediction markets made headlines for a different reason. When Venezuelan President Maduro was captured in January 2026, the prediction market reacted quicker than the actual news, helping a lucky insider make $436,000 in a few hours.

But what are some uses for scientists, besides making some (or half a million) quick bucks?

Determining the Reproducibility of Scientific Studies

Several studies have explored the use of market-based prediction of reproducibility to guide scientific funding and replication efforts.

Dreber et al. (2015) created their own prediction markets with recruited participants during psychology's Reproducibility Project, effectively leveraging the wisdom of the crowd with real financial stakes. The predictions coming from the prediction market were more accurate and more confident than those from a survey—even with the same participants. Market prices correctly predicted replication outcomes 71% of the time, compared to just 58% for surveys.

A Scientific Funding Market That Rewards Discovery and Disclosure

Almenberg et al. (2009) conducted an experiment with three information settings:

  1. Everyone had the same information
  2. Some people had privileged information
  3. Some people had privileged information that would later be made public

They found that Setting 3 combined the best of the two other settings: the prediction probabilities were as accurate as in Setting 1, and the privileged people (scientists who gained information asymmetry through their research) were able to reap a profit.

The researchers entertained the idea that this could become a new funding structure for science: a market-based approach that allows funding agencies to identify the most viable projects and scientists to be financially incentivized to produce and make public their quality research.

The Manipulation Problem

Of course, prediction markets have their limitations, especially given that financial incentives can encourage participants to manipulate the outcome or its metrics. For example, an unauthorized edit on the interactive war map of Institute for the Study of War showed an incredibly swift Russian advance into Myrnohrad. Since the map was used by Polymarket to determine the outcome of a bet, a few betters bagged a 33,000% return.

Despite the imperfections, prediction markets are poised to become integrated with the mainstream financial system and evolve into a powerful tool for opinion polling and forecasting—one that works best when incentives align with societal goals and outcomes are protected from manipulation.

This blog post is a recap of Jianlong Zhu's presentation in the I2SC group meeting on 4 Feb 2026.

References:

  • Dreber, A., Pfeiffer, T., Almenberg, J., et al. (2015). "Using prediction markets to estimate the reproducibility of scientific research." PNAS, 112(50), 15343-15347.
  • Almenberg, J., Kittlitz, K., & Pfeiffer, T. (2009). "An Experiment on Prediction Markets in Science." PLOS ONE, 4(12), e8500.

Popular posts from this blog

AI Girlfriend or AI Boyfriend? Social Determinants of Human-AI Relationships

In the West and across the English-language internet, the idea of using AI for intimate companionship often conjures images of female personas. From Sam Altman's cryptic one-word X post—"her," a clear nod to the 2013 film—to Grok's launch of its AI companion Ani, the dominant narrative seems tailored to a heterosexual male audience. The archetype of the AI girlfriend, designed to be emotionally available, submissive, and aesthetically pleasing, has become a cultural fixture. In China, however, the discourse around human-AI intimacy takes a different shape. Media coverage and user engagement tend to center on women's experiences. The most talked-about AI bachelor? Dan—a character created using the "Do Anything Now" jailbreak prompt—who has captured the hearts of many Chinese women. Online discussions about Dan and similar AI companions flourish on Xiaohongshu, a lifestyle platform with a predominantly female user base, often dubbed "China's Inst...
Read more

Blog: The Importance of Good Data in Satellite Imagery Analysis

Introduction The phrase "Garbage-In-Garbage-Out" is well-known in data science, but it takes on new meaning in real-world projects, especially those involving satellite imagery. In these contexts where ground truth labels are often sparse, preprocessing becomes not just a step, but a cornerstone of success. Reflecting on my project, I have realized that understanding and preparing data account for about 70% (if not more) of the work and determines the quality of the results. Data preprocessing ensures that the inputs to your model are clean, structured, and tailored to the problem at hand. This is true for all kinds of data, whether tabular data, text, or images. However, the proper preprocessing steps come from initially "looking" at the data. That means preprocessing is dependent on the data and the task at hand.   Understanding Satellite Images Satellite images are far more than just pictures; they encode a wealth of information about the spectral signature of a ...
Read more

Job: Student Research Assistant (m/w/d) for AI & Misinformation detection

Image
Join Us in Fighting Misinformation! We are seeking a passionate and skilled Research Assistant to contribute to cutting-edge projects at the intersection of AI, digital literacy, and societal impact. Be part of a team dedicated to developing AI-powered tools to help young people combat misinformation. If you are interested, submit your CV and a brief cover letter detailing your relevant experience and interest in combating misinformation through AI to Katja Raak - kraak@cs.uni-saarland.de .
Read more