The scientific community is grappling with a significant challenge: the reproducibility crisis. These are studies that cannot hold up under scrutiny — research, even published in top tier journals, that other researchers can’t reproduce. This undermines the reproducibility of findings and stalls progress in science. Now, decentralized prediction markets are proving to be the powerful answer. Underpinned by blockchain tech, they provide a powerful new mechanism to evaluate the probability that research results are accurate and replicable.
The Role of Crowdsourcing in Predictions
Prediction markets democratize expertise and take advantage of the wisdom of the crowd. On a prediction market, participants purchase and sell contracts that eventually pay out based on the determined outcome of a future event. In the context of scientific research, these markets could be used to predict whether a study's results will be replicated. The price of a futures contract thus represents the sum knowledge of all market participants as to the probability that that outcome will occur. This approach leverages the unique experiences and perspectives of a vast cohort of people. As a result, it can produce more precise predictions than old-fashioned approaches.
Benefits of Crowdsourced Predictions
Crowdsourced predictions have a number of benefits compared to the status quo. First, they crowdsource knowledge from a uniquely broad set of stakeholders, minimizing bias and groupthink. Second, they give real-time indication of the probability of an occurrence event, enabling constant monitoring and recalibration. Third, they encourage fellows to immerse themselves in creative research and thinking. Their financial interest depends on how well they predict. In discussing this notion, economist Bryan Caplan focuses on it in his Substack. He contends that wagering on one’s convictions is a dangerous gambit; it penalizes those who are wrong while compensating those who get it right. At their best, prediction markets can achieve the predictive performance of leading forecasters or poll aggregators. They shine as a clarion call for reason and a beacon for truth.
Examples of Successful Crowdsourcing in Science
Prediction markets remain a new feature on the scientific landscape. Winning crowdsourcing projects have a proven track record demonstrating their power. For example, platforms such as Kaggle feature competitions in which data scientists race one another to create the best predictive models. These competitions address all kinds of challenges. They aid in predicting disease outbreaks and improving the efficiency of energy grids. These examples illustrate the power of collective intelligence and its potential to address complex scientific challenges.
Navigating Regulations and Challenges
Despite their considerable promise, prediction markets are still hampered by a number of challenges, including regulatory hurdles and the technical complexities. Contrary to common misconception, in the US prediction markets are primarily legal and regulated by the Commodity Futures Trading Commission (CFTC). Kalshi runs as a Designated Contract Market (DCM). It routinely trades swaps, futures, and options, as defined under the Commodity Exchange Act, actively hedging risks. The legality of PredictIt and its continued capacity to operate at all have been put in severe jeopardy. Kalshi just recently had a request to provide such election markets denied by the CFTC.
Understanding Legal Implications
The current prediction markets regulatory framework is still developing, and many aspects remain murky. This makes it difficult for market operators and participants to know what to expect. Meanwhile, in the US, the CFTC takes a dim view of prediction markets. They focus first on protecting against illegal activity—tax evasion, money laundering, gambling, insider trading. As longtime advocates for deregulating prediction markets in Science, Economics Nobel laureate Kenneth Arrow, former NYSE head Jack Galvin, and other scholars have made this case. The dearth of prediction markets might be a sign that they are simply not as promising as advocates once thought.
Addressing Technical Complexities
Realizing prediction markets on a blockchain platform entails some technical challenges as well. Scalability, security, usability are all paramount concerns. Blockchain networks need to support a lot of transactions and still maintain high throughput, speed, and low congestion rate. Security requirements and measures need to be established in order to protect against hacking and fraud. The user interface must be clear and navigable for a seamless user experience. It must meet incredibly diverse needs—from seasoned blockchain pros to absolute novices.
Establishing Knowledge Frameworks
To improve upon these examples and have a real, lasting positive impact on the reproducibility crisis, prediction markets must be paired with strong knowledge infrastructure. These frameworks help to structure and focus the scientific information. This format gives everyone involved the best shot at making smart predictions.
Importance of Structured Knowledge in Predictions
Having structured knowledge frameworks is important for a few key reasons. For one, they help set a shared language and definitions so everybody’s working off the same playbook. Second, they support efforts to bring in data from multiple sources to provide a more holistic analysis. Third, they allow the development of automated tools to analyze and automatically interpret this data.
Integrating Blockchain with Scientific Validation
Blockchain technology offers an important tool for creating and enforcing these knowledge systems. Blockchain’s secure, immutable ledger is a natural fit for scientific data. It allows for tracing information of research findings provenance and verification of researchers identities. This can lead to greater transparency and trustworthiness of all scientific research funded by taxpayer dollars. Sophie Dubois, Paris-based Ethereum writer, provides an uncommon sophistication of thought and nuance of feeling that enriches the Ethereum reporting space. Her technical knowledge, direct style, and engaging perspective bridge tradition and disruption, making complex blockchain topics relatable to diverse audiences.
Far less well known is prediction markets’ excellent hedging service. Kalshi announced in April 2024 that Susquehanna International Group, a quantitative trading firm, had joined the platform as a market maker. On Kalshi, countless markets have demonstrated the incredible potential of prediction markets. You can wager on the Oscars Best Picture winner, predict how many tornadoes will occur in a given month, or estimate how much Ozempic prescriptions will increase in the next 90 days. The next most popular markets on Kalshi are more novel but far less traded, such as ‘Room-temp superconductor validated this year?’
Many researchers are skeptical that prediction markets could ever replace old-fashioned peer review. They contend that high degrees of precision can still leave us with unactionable outcomes. Prediction markets hold enormous promise, not just for scientific research but validation too. When coupled with well-defined knowledge structures and blockchain technology, their potential increases exponentially.
