OOur new constitution is now established and has an aspect which promises permanence; but in this world nothing can be said to be certain except death and taxes.
The human brain evolved to predict.
Born into a world of uncertainty, early humans had to manage threats, weather and food sources to survive. Evolution made humans good at it and we benefited more from it.
Today we face similar problems. Will monkeypox be a serious pandemic? Will Russia and the North Atlantic Treaty Organization engage directly? China-Taiwan? Climate change? Global food supply?
It is impossible to answer these questions in advance. The future is uncertain and the systems these issues deal with are too complex and interdependent for full forecasting. But that does not preclude models. And therefore, we can generate a reliable and partial forecast. You can’t tell for sure if a parent is angry or disappointed when they call you, but hearing your full name is a big signal. By studying patterns based on the accumulation of experience and knowledge, humans have developed frameworks for quantifying uncertainty and predicting.
Clay Graubard and Andrew Eaddy are the founders of Baserate.iothe editor of Global riddlesa geopolitical forecast site, and Crowd Money, a newsletter and a podcast on prediction markets. This article is an overview of a lecture they will give next week on the Big Ideas stage at Consensus 2022 in Austin, Texas.
Weather forecasts, once considered futile despite milenias of practice, are erroneous enough to be reliable. You won’t know at 8 a.m. whether it will rain at 2 p.m., but advances in meteorology have allowed the Dark Sky app to predict a 24% chance. Of course, unlikely. But in 2016, Nate Silver gave former US President Donald Trump a 28.2% chance of being elected president, so it’s probably wise to grab an umbrella.
In the social sciences, we have identified that two democracies are less likely to go to war than other combinations of states, that crimes are more likely to occur in neighborhoods with “broken windows”, that civil wars are almost twice as frequent. likely in countries with a gross domestic product per capita of $250 as well as in those of $500 (15% versus 8%).
Unless we believe in foreordination, we live in a probabilistic world. Forecasting is something we all do, whether we know it or not. It’s a tool, it’s a process and above all, it’s a skill to generate information. Luckily, it’s a skill we can hone and improve.
This is, in essence, the heart of the findings of research by Philip E. Tetlock, Barbara Mellers and many others. Their research showed that if we quantify, record, update, note, and practice, we can make accurate predictions about complex questions. We can see at least some of the “fog of war”.
That’s why we have to be careful when forecasters say there’s a 28% chance the World Health Organization will declare monkeypox a public health emergency of international concern. That is a 10% chance that there will be a direct Russia-NATO conflict before July 2023 and a 15% chance that there will be a China-Taiwan war before 2024. Etc. Etc.
This is also why we should be surprised, if not challenge, how little attention and funding is given to forecasting.
Read more: 2022 Consensus Speaker Profile: Clay Graubard
Forecasts produce good information, demand accountability (you can’t ignore a balance sheet!), and reject fallacies, sensationalism, and biased thinking. It’s a perfect check on mainstream media and a potential antidote to polarization. One cannot predict by ignoring an important prospect. Otherwise, accuracy suffers. This is perhaps why forecasters have proven to be less politically polarized than non-forecasters.
Unfortunately, having value does not guarantee immediate, or even relatively timely, widespread use. History is full of such examples, including even more obvious things like seat belts. Forecasting is an inherently disruptive practice, making it difficult for businesses to adopt.
Forecasting is also difficult, time-consuming, and (for now) underpaid (trust us). Adoption will require many approaches, and we believe prediction markets hold one of the most promise in bringing monetary and psychological incentives and market efficiencies into the prediction process.
Prediction markets are nothing new. Individuals have made bets on the future outcome of events since ancient times. And the coined phrase “the wisdom of crowds” dates back to early 20th century England.
In 1906-1907, Sir Francis Galton, a scientist and mathematician from Birmingham and a relative of Darwin, witnessed a competition in which around 800 villagers from Plymouth, England, were challenged to estimate the weight of ‘a beef. While each individual estimate was either too high or too low, Galton observed that the median was within 0.8% of the weight measured by the contest judges. The average estimate was quite correct.
Prediction markets are markets where participants trade on future outcomes on particular topics. Think stock or crypto markets, but deal with events.
Prediction markets are typically binary, offering two fungible assets for a given market (think “Yes” or “No”). These assets are trading between 0% and 100% (think $0 to $1), with the current market price representing the consensus of the crowd.
When a predicted event occurs, traders who bought shares of the correct outcome are paid $1 for each share they owned. Similar to long-established public equity markets, the main incentive for participants in prediction markets is profit, while the by-product of their prediction activity is information.
Read more: 2022 Consensus Speaker Profile: Andrew Eaddy
But while traditional prediction markets often operate as described above, it is important to contextualize this technology within the larger ecosystem of prediction platforms that use different methods to obtain predictions. There are prediction markets that use real or play money. (Among real-money platforms, some place bets in fiat, others in crypto.)
Other platforms like Metaculus use a survey-based method where people continually give their subjective ratings of a question’s likelihood. Many of these platforms leverage reputation, community, and other factors to generate more high-quality information.
One of the advantages of prediction markets over other methods is that, all things being equal, they generate more accurate forecasts than traditional prediction platforms (or polls) such as Good Judgment Open. The forecasts they produce are less biased, less vocal and more informed.
Another benefit is that betting makes things more exciting and often increases engagement, not only on hot topics like sports, but on other important yet overlooked topics like the risks to humanity from the artificial intelligence. And given that individuals can produce consistently accurate and well-calibrated forecasts, there is an opportunity for smart traders and elite forecasters to generate reliable returns, especially because returns for accuracy can get worse with time.
Large modern prediction markets continue to build on this discovery, creating more efficient and repeatable ways to aggregate predictions to produce accurate insights. Within the ecosystem, however, there are different categories and classifications for prediction market platforms.
Some prediction markets, like Kalshi, are centralized and use US dollars to support trading. Others, like Polymarket, are partially decentralized and trade using the USDC stablecoin. And still others, like Augur, are fully decentralized, with decentralized oracles to facilitate decentralized issue resolution.
There are also differences between prediction markets related to their stated purpose. For example, Google has been using prediction marketplaces internally to improve business decisions and understand employee sentiment since 2008. The incentive structure and functionality of a prediction marketplace like Google’s will naturally diverge from that of a publicly available prediction market.
Finally, there are also differences in the technical functionality of the prediction markets. Some prediction market platforms, namely those on blockchains, use automated market makers (AMMs) to provide liquidity. Other platforms use a more traditional order book-based trading system.
MAs provide consistent liquidity to a market and often result in a more positive trading experience for market participants, but if liquidity runs out, slippage can occur, which means order sizes can affect order prices, which is not good. Trading the order book, on the other hand, is much simpler. A trading screen displays a selection of buy orders and sell orders for an asset at different prices. As a trader, you can execute any order in the book.
In addition to improving prediction accuracy and driving engagement, prediction markets can help reform the media, boost community on social media, and inform key decision makers in the private and public sectors through systems such as Robin Hanson’s Futarchy described below.
Such a system would reward thoughtful and involved citizens. It would also encourage good decision-making, because even if you choose the “correct” and implemented policy, to make money, it must work. Over time, bad forecasters would be weeded out. Through Futarchy, prediction markets can support fundamental and vital values within a healthy society, namely information (through prediction), governance (through Futarchy) and institutions (prediction markets).
Read more: Polymarket’s CFTC Fine Advice on DeFi Regulatory Roadmap
Even outside of Futarchy, prediction markets have the potential to transform the current political process by involving people in the conversation and providing more reliable information about the future.
We live in uncertain and complex worlds. Critical thinking and reliable information are needed to make good decisions. Quantified forecasting is a valuable yet underutilized tool, and forecasting markets are emerging as an indispensable tool for its adoption. At baserate.io, we believe this needs to happen sooner rather than later, because even certainties like death can now be uncertain.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.