Professor Sakai of Keio University Discusses "The Century of Prediction Markets: Social Implementation of Collective Intelligence" at WebX 2026

On July 14, 2026, Professor Toyoki Sakai of Keio University took the stage at the Limitless stage of WebX 2026 for a session titled "The Century of Prediction Markets: Social Implementation of Collective Intelligence." He positioned prediction markets as "an invention that shines in human history," explaining the structural flaws of opinion polls, the two implementation forms of board trading and market maker types, and their application to corporate governance through internal utilization, all from an economist's perspective over approximately 30 minutes.

Professor Sakai began his lecture by listing the social scientific inventions humanity has achieved so far: representative democracy, the concept of human rights, equality under the law, corporations, markets, fiat currency, and credit creation. He asserted that a new invention has recently been added to this list.
"I am convinced that a new item has been added to this list in recent years: prediction markets."
In prediction markets, a single question is posed in one market. Professor Sakai cited the example of Polymarket, explaining the mechanism using the question, "Will the traffic in the Strait of Hormuz normalize within this year?" For this question, tickets are bought and sold where a yes answer is worth one dollar and a no answer is worth zero dollars. Since the ticket price is determined by supply and demand, it will always take a value between zero and one. Professor Sakai emphasized that the core point of prediction markets is that this price P can be interpreted directly as "the probability that the event will occur." It is known in economics that prices aggregate people's expectations very effectively.
Supplement: The pioneer of prediction markets was the "Iowa Electronic Markets (IEM)" started by the University of Iowa in 1988. Polymarket gained attention for indicating Trump's advantage earlier than opinion polls in the 2024 U.S. presidential election.

Professor Sakai posed a paradoxical question: "It is strange when opinion polls are wrong." Major newspapers randomly sample voters, and if they collect a sufficient number, they should almost certainly obtain correct results according to the mathematical statistics' "law of large numbers." Yet, why do opinion polls go wrong? Professor Sakai organized the reasons into three points.
The first point is the possibility that respondents "answer without much thought." When someone who does not usually think about politics is suddenly asked, serious answers are hard to expect. The second point is the issue of "lying." Residents of California may respond that they support Trump due to social pressure, giving answers that differ from their true feelings (the so-called social desirability bias). The third point, which Professor Sakai emphasized as the most important, is:
"People who are knowledgeable about the question and those who are not are counted as the same individual. The answers of knowledgeable individuals should be weighted more heavily than those of less knowledgeable individuals, yet opinion polls are extremely unreasonable in this regard."
Prediction markets overcome all three points. If one trades without thinking, they will incur losses, thus they must engage seriously. They do not lie as they act faithfully to their own interests. Knowledgeable individuals trade more, naturally increasing their influence (weight) on the price.

Professor Sakai classified prediction markets into two main forms. The first is the "board trading type," with Polymarket as a representative example. Participants trade with each other through board trading, and prices are determined by supply and demand. The basic format involves gathering participants from around the world and betting money.
The second is the "market maker type," with Japan's IGS (listed on the Tokyo Stock Exchange Growth Market) operating "Signals" as a representative example. Participants buy and sell tickets with the market designer (market maker) and do not trade with each other. It is often based on limited internal operations, using low liquidity points to avoid financial and gambling regulations.
"In the market maker type, prices are determined not by supply and demand but by a price determination function. Log scoring rules and quadratic scoring rules are used, and the operator can control the maximum amount they pay, which is a characteristic feature."
Supplement: Research on the price determination function of market maker types has accumulated in economics for over 20 years, and it is known that Google and Hewlett-Packard once adopted internal prediction markets of the market maker type.

As an application of prediction markets, Professor Sakai particularly emphasized their use within companies. He proposed that prediction markets could function as an alert mechanism for the universal problem that "bad news tends not to rise to the top in organizations."
As a specific use case, he illustrated selling a ticket internally asking, "Will important project A be completed within this year?" If an employee familiar with the on-site circumstances judges that it "will not be completed," the ticket price will drop. If the price indicates an extremely low level (e.g., 0.15 points), the board of directors can recognize the crisis of the project. This can also be utilized in choosing measures. By establishing prediction markets for both Plan A and Plan B, the decision-making mechanism would adopt the one with the higher price.
"Management really wants to know about bad news. I believe such tools will spread relatively quickly within Japanese companies."
However, Professor Sakai cautioned that the design of participants is crucial. He stated that operational know-how is necessary to design eligibility to prevent participants who would bet heavily on the "losing" side and actually cause project failures.

Board trading type prediction markets are likely to fall under online gambling according to current laws in Japan. Professor Sakai acknowledged this point directly, stating, "It is truly regrettable and a waste," suggesting that using low liquidity points as a legal implementation method is a realistic option. However, he stressed that even in that case, legal checks are indispensable.
As mechanisms for collective intelligence to function, Professor Sakai cited two factors. The first is the diversity of the group: the biases of optimistic and pessimistic individuals cancel each other out, converging towards neutral judgment, akin to the Condorcet jury theorem effect. The second is the mechanism by which a small number of knowledgeable participants have a strong influence on prices through many trades. Regarding insider trading, he mentioned the difficulty of drawing the line, stating, "The more people with information participate, the higher the predictive accuracy, but insiders who can manipulate the results should not participate."
"The way industries advance will change between countries where predictions are easy and those where they are difficult. I believe a regulatory framework that allows flexible use of prediction markets as a new genre of invention is necessary."
Finally, Professor Sakai pointed out that the resistance to gambling in Japan could be a barrier to the spread of prediction markets. "If society becomes a little more familiar with the game of betting, such mechanisms will become easier to introduce," he concluded, expressing expectations for social implementation as he wrapped up the session.

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