Published February 21-28, 2023
I have been fascinated by prediction markets for some time. The idea of using the wisdom of crowds (or a subset) to predict future outcomes as opposed to relying on a few experts is quite compelling.
As part of IndiaWorld, I had built an Indian Political Stock Exchange in 1998-99 to attempt to use the wisdom of crowds to predict the outcome of the Indian elections. I had borrowed the idea from an early version of a prediction market for movies, HSX (Hollywood Stock Exchange). It worked well initially but once people realised that they could get free ‘virtual money’ by creating multiple accounts the concept collapsed. Since then I have followed the Iowa Electronic Markets for an alternate perspective on US elections. [More on IEM: “The faculty at the University of Iowa developed the IEM to be an Internet-based teaching and research tool. It allows students to invest real money ($5.00-$500.00) and to trade in a variety of contracts. You may be familiar with the best-known part of the IEM, the political markets. Here students can trade “shares” of political candidates or parties (the payoff depends on the election results). Students also have the opportunity to trade in contracts whose eventual payoff depends on a future event such as an economic indicator, a company’s quarterly earnings, a corporation’s stock price returns or a movie’s box office receipts.”]
While opinion polls and surveys are the norm globally for political predictions, prediction markets could provide an interesting alternative. Who will win India’s 2024 elections? Or even the many state elections in 2023? What will India’s GDP growth be in FY 24? Will India make it to the final of the cricket Test Championship? Who will win IPL 2023? What will the Sensex close at the end of 2023? Will there be a new Covid wave in 2023? What will be the highest temperature in India in the summer of 2023? How many cars and 2-wheelers will be sold in 2023? While we can rely on experts (or even the grey market – the satta bazaar) to get some answers, the “wisdom of crowds” can provide an alternate (and perhaps better) view on outcomes.
With prediction markets playing one person’s expectations versus another, current entities face two drawbacks because of their use of real money for participation: customers are limited, and the operating companies invite regulation. So, I started thinking about creating a prediction market using Mu as the token for participation. As long as Mu could be decoupled from real-world (fiat) money, it could stay away from regulation, and there could also be mass participation. Mu could be earned via participation in brand activities (for attention, data and referrals), and then could be spent in prediction markets (along with the other options I have discussed earlier: gamelets, shop, marketplace, and exchange). Could Mu crypto tokens power next-generation prediction markets? This is what I consider in this series. Before we get to the how, let us start by understanding prediction markets.
Background – 1
Wikipedia: “Prediction markets (also known as betting markets, information markets, decision markets, idea futures or event derivatives) are open markets where specific outcomes can be predicted using financial incentives. Essentially, they are exchange-traded markets created for the purpose of trading the outcome of events. The market prices can indicate what the crowd thinks the probability of the event is…Prediction markets can be thought of as belonging to the more general concept of crowdsourcing which is specially designed to aggregate information on particular topics of interest. The main purposes of prediction markets are eliciting aggregating beliefs over an unknown future outcome. Traders with different beliefs trade on contracts whose payoffs are related to the unknown future outcome and the market prices of the contracts are considered as the aggregated belief.”
ChatGPT in response to “What are Prediction Markets?”: “Prediction markets, also known as “information markets” or “event futures,” are exchange-traded markets created for the purpose of trading the outcome of events. The market prices can be interpreted as predictions of the probability of the event occurring. Participants in the market can buy and sell contracts that represent the possible outcomes of an event, and the prices of these contracts fluctuate based on supply and demand. The market prices can be used to forecast the likelihood of future events, and can be used by organizations to make better decisions by aggregating the collective wisdom of market participants.”
Investopedia: “Prediction markets are similar to futures markets for commodities or other financial asset prices. In futures markets, traders bid up or down the price of a future contract based on their expectation of what the future price of the underlying asset will be. Prediction markets are just futures markets where the future event being traded upon is something other than the price of an asset at some point in the future. Prediction markets involve a collection of people speculating on a variety of events—exchange averages, election results, quarterly sales results, or even gross movie receipts. Robin Hanson, a professor at George Mason University, is an advocate of prediction markets. He makes the case for prediction markets by emphasizing the removal of reliance on self-interested punditry by so-called experts. “Instead, let us create betting markets on most controversial questions, and treat the current market odds as our best expert consensus. The real experts (maybe you), would then be rewarded for their contributions, while clueless pundits would learn to stay away,” Hanson says.”
Tyler Cowen (Mar 2021): “Prediction markets [are] contracts with payoffs contingent on some real-world event…In essence, [they] let people “bet” on some feature of the economy, thereby creating a new financial derivative. A prediction market in gross domestic product, or perhaps in local rates of unemployment, could be a useful means of hedging risk. If you are afraid that GDP will fall, you could “short” GDP in a prediction market and thus protect your overall economic position, because your bet would pay out if GDP came in lower than expected. Prediction markets are also a useful means of discovering information about what is likely to happen next. If you want to know who is likely to win the Super Bowl, is there any better place to look than the published betting odds? By the same reasoning, various interest rate futures markets offer clues about what the Federal Reserve might be planning. The value of having more and better public information is another reason to encourage prediction markets…For a prediction market to take off, it probably has to satisfy a few criteria: general enough to attract widespread interest; important enough to matter; and unusual enough not to be replicable by trading in existing assets. The outcomes also need to be sufficiently well-defined that contract settlement is not in dispute.”
Daniel E. O’Leary writes in a brief survey of prediction markets: “In prediction markets, participants buy and sell stocks. Each stock’s price is tied to a different event happening in the future. Information about the future is captured in the stock prices…The prices reflect the traders’ aggregated beliefs about the probability of their winning – a higher price means a higher perceived likelihood of winning…Although using play money makes it possible for many people to participate, one potential challenge for prediction markets that don’t use real money is gaining and maintaining interested participants. Despite using different devices to keep up engagement, such as leader boards indicating who has accumulated the biggest portfolio, there is literally no money on the table to keep participants interested in the market.”
Background – 2
Vitalik Buterin (Feb 2021): “Prediction markets are a subject that has interested me for many years. The idea of allowing anyone in the public to make bets about future events, and using the odds at which these bets are made as a credibly neutral source of predicted probabilities of these events, is a fascinating application of mechanism design.”
Liam Vaughan (May 2022): “The potential of prediction markets is well known to anyone who’s read James Surowiecki’s bestseller, “The Wisdom of Crowds.” Well-designed markets can help draw out knowledge contained within disparate groups, and research shows that when people have money on the line, they make better forecasts…Google, Microsoft Corp., and even the US Department of Defense have used prediction markets internally to guide decisions… The size of these markets had been limited because regulators worried that Wall Street-scale trading could create incentives for investors to meddle with reality.”
Wikipedia: “The ability of the prediction market to aggregate information and make accurate predictions is based on the efficient-market hypothesis, which states that asset prices are fully reflecting all available information. For instance, existing share prices always include all the relevant related information for the stock market to make accurate predictions. James Surowiecki raises three necessary conditions for collective wisdom: diversity of information, independence of decision, and decentralization of organization. In the case of predictive markets, each participant normally has diversified information from others and makes their decision independently. The market itself has a character of decentralization compared to expertise decisions. Because of these reasons, predictive market is generally a valuable source to capture collective wisdom and make accurate predictions. Prediction markets have an advantage over other forms of forecasts due to the following characteristics. Firstly, they can efficiently aggregate a plethora of information, beliefs, and data. Next, they obtain truthful and relevant information through financial and other forms of incentives. Prediction markets can incorporate new information quickly and are difficult to manipulate.”
Economist (Feb 2022): “The line between investing and gambling has always been thin. This is especially true for prediction markets, where punters bet on events ranging from the banal (“will average gas prices be higher this week than last week?”) to the light-hearted (“who will win best actress at the Oscars?”). Prediction markets have something of a cult following among finance types who rave about the value of putting a price on any event, anywhere in the world. Such prices capture insights into the likelihood of something happening by forcing betters to put money where their mouths are. But critics argue such markets will fail to grow beyond a niche group, reducing the value of their predictions in the process.”
John Holden (Dec 2022): “Prediction markets are based upon the Efficient Market Hypothesis, which in the prediction market context is the idea that the price represents the likelihood of an event taking place based on all the relevant information. In the context of a presidential election market, if a contract for Joe Biden to be president in 2024 were trading at $.52, then the market would be suggesting that there is a .52 probability that Biden would be elected. As the election passes, someone will go to $1.00, or at least $.99. Prediction markets are effectively a market of binary option contracts: either an event happens or it does not. The contracts settle on the date of an election (or another specified date for other events.) This is not dissimilar from other types of options contracts, which are purchased for a designated date in the future, reflecting a likelihood that a company’s share price or commodity will be at a certain level.”
Background – 3
Clay Graubard and Andrew Eaddy (Jun 2022): “Barring belief in preordination, we live in a probabilistic world. Forecasting is something we all do, whether or not we know it. It is a tool, it is a process, and critically, it is a skill to generate information. Thankfully, it is a skill we can improve and get good at. That, in essence, is the core takeaway from the research of Philip E. Tetlock, Barbara Mellers and many others. Their research has shown that if we quantify, record, update, score and practice, we can make accurate predictions on complex questions. We can see at least part way through the “fog of war.”… Prediction markets are not new. Individuals have made bets on the future outcome of events since ancient times. And the coined phrase “the wisdom of crowds” can be traced back to early 20th-century England. Prediction markets are marketplaces where participants trade on future outcomes about particular topics. Think of the stock or crypto markets, but dealing with events. Prediction markets are typically binary, offering two fungible assets for a given market (think “Yes” or “No”). These assets trade between 0% and 100% (think $0 to $1), with the current market price representing the crowd consensus. When a forecasted event occurs, traders who purchased shares of the correct outcome are paid $1 for each share that they owned. Similar to long-established public equities markets, the primary incentive for participants in prediction markets is profit, while the by-product of their forecasting activity is information…Prediction markets have the potential to transform today’s political process by involving people in the conversation and delivering more reliable information about the future.”
Alex Tabarrok (Sep 2022): “Political election markets have proven themselves to be a powerful tool for forecasting elections and are typically more accurate, timely and complete than alternative methods such as polls…Political election markets are also useful to hedgers, traders and other market participants to help them predict and incorporate information about risks into asset prices. Markets similar to political election markets have been used to predict other important events such as the prospects for war or scientific breakthroughs and have been adopted by firms to better estimate sales forecasts and other relevant events.”
Economist (Aug 2018): “Legal barriers have long hampered such attempts at crowdsourcing. In America many prediction markets are considered a form of illegal gambling, or akin to trading in commodities futures that requires a licence. Regulators have allowed such services to operate if they are structured as non-profit “research” initiatives and limit bet sizes and numbers of traders, as IEM and PredictIt do. But because of the legal risk, private investors are reluctant to finance prediction markets.”
Walter Frick (Dec 2022): “Prediction markets have been around in one form or another for decades and have already made inroads into journalism during elections. 2023 will be the year they become a source for other types of stories, simply because there’s now too much activity in the crowd forecasting world to ignore. For almost any question you can think of, there are online crowds making predictions. And if journalists do think of a question that isn’t yet being forecasted, there are platforms where they can pose it themselves…As the economists Justin Wolfers and Eric Zitzewitz explain, prediction markets work because they provide: “1) incentives to seek information; 2) incentives for truthful information revelation; and 3) an algorithm for aggregating diverse opinions…Prediction markets aren’t perfect. They’re only as good as the wisdom of their participants and the information those participants have access to. And, like any market, they can be vulnerable to manipulation without oversight.”
George Calhoun (Nov 2022): “It may be that guessing the weight of an ox, and forecasting the winner of a close political race, present different challenges. It is likely that “the wisdom of crowds” is not a universal principle of human decision-making. It is also likely that the error of group judgments increases with the value-at-risk and the volume of trading, contrary to what standard statistical reasoning would suggest. In fact, the failures of prediction markets in the recent [US mid-term] elections were so consistently wrong that they begin to resemble more traditional sentiment metrics, useful perhaps as contrarian indicators, but dangerous if taken at face value.”
I asked ChatGPT about how prediction markets work. The response:
A company creates a prediction market for a specific event, such as the outcome of an election or the success of a product launch.
The company issues contracts representing the possible outcomes of the event. For example, in an election prediction market, there might be contracts representing the victory of each candidate.
Participants in the market can buy and sell these contracts. The prices of the contracts fluctuate based on supply and demand, and can be interpreted as the market’s prediction of the probability of each outcome occurring.
As the event approaches, the prices of the contracts may change based on new information or changing market conditions.
After the event occurs, the contracts are settled based on the actual outcome. Those who hold contracts representing the winning outcome are paid the full value of the contract, while those holding contracts for the losing outcomes receive nothing.
From the introduction of the 2011 book “Prediction Markets: Fundamentals, Designs, and Applications” by Stefan Luckner, Jan Schröder, Christian Slamka, et al: “The basic idea of prediction markets is to trade contracts whose payoff depends on the outcome of uncertain future events. Although the final payoffs of the contracts are unknown during the trading period, rational traders should sell contracts if they consider them to be overvalued and buy contracts if they consider them to be undervalued. Until the outcome is finally known, the trading prices reflect the traders’ aggregated beliefs about the likelihood of the future events. In efficient markets, all the available information is reflected in the trading prices at any time.”
Here is a graphic from the book which explains the working:
There are different types of contracts in prediction markets as this table from a paper on “Prediction Markets for Economic Forecasting” by Erik Snowberg, Justin Wolfers and Eric Zitzewitz shows:
Cultivate Labs writes about the two primary mechanisms to enable trading:
Continuous Double Auction (CDA): A continuous double auction (often abbreviated as CDA) is a mechanism for matching buyers and sellers of a stock. In a CDA, the market maker keeps an order book that tracks bids and asks. If I come along and say that I’d like to buy a share stock A for $5, that is recorded in the order book as a bid for 1 share at $5. On the flip side, if you own a share of stock A and are willing to sell that share for $5, that is recorded as an ask. If the bid & ask for two traders match, like in our example (I want to buy stock A for $5, you want to sell it for $5), then the trade is executed. A continuous double auction is also used in traditional stock markets like the NYSE.
Automated Market Makers: One issue with using a continuous double auction in a prediction market is that liquidity can be a problem. Most prediction markets have far fewer participants than an exchange like the NYSE. If I make a bid for $5 and there is no one out there selling the same stock for $5, then I can’t make my trade. If there’s no one to take the other side of my trade, the market would be said to have low or poor liquidity. To alleviate this problem, platforms use what’s known as an automated market maker. In this setup, the platform acts as the “house,” taking the opposite side of all trades. Doing so ensures that participants are always able to make a trade, effectively creating or “making” the market.
The Logarithmic Market Scoring Rule (LMSR), proposed by Robin Hanson in 2002, has become the de facto market-maker mechanism for prediction markets. As the abstract puts it: “In practice, scoring rules elicit good probability estimates from individuals, while betting markets elicit good consensus estimates from groups. Market scoring rules combine these features, eliciting estimates from individuals or groups, with groups costing no more than individuals. Regarding a bet on one event given another event, only logarithmic versions preserve the probability of the given event. Logarithmic versions also preserve the conditional probabilities of other events, and so preserve conditional independence relations. Given logarithmic rules that elicit relative probabilities of base event pairs, it costs no more to elicit estimates on all combinations of these base events.”
Before we discuss Mu and Prediction Markets, we will take a detour to discuss Superforecasting.
Dan Gardner and Philip E. Tetlock write in their book “Superforecasting: The Art and Science of Prediction”: “We are all forecasters. When we think about changing jobs, getting married, buying a home, making an investment, launching a product, or retiring, we decide based on how we expect the future will unfold. These expectations are forecasts. Often we do our own forecasting. But when big events happen—markets crash, wars loom, leaders tremble—we turn to the experts, those in the know…Forecasting is not a “you have it or you don’t” talent. It is a skill that can be cultivated.”
Stephen Desmyter writes in Forbes: “Superforecasting is an addictive thing: it requires deep thought, sending your brain from geopolitics to economics to sociology. It asks you to throw yourself into vastly different situations and to take on the role of states-person, Central Bank chairperson, military strategist. More than anything, though, I find as I leap from one forecast to the next, it requires a profound understanding of human behavior. And maybe this is where the real skill of the Superforecasters lies. Take away the superstructures of state and financial systems, take away received ideas about how people will vote or the likely direction of rates, and you have humans making decisions under pressure. That’s why, I think, the Superforecasters are so often able to get their predictions right. It’s fascinating to read through the comments that the forecasters make below their predictions and to see this same theme emerging again and again – they reduce situations down to their essentials, and then ask how they would behave if they were in the same position.”
ChatGPT in response to “How do prediction markets compare with superforecasters?”: “Superforecasters are individuals who are particularly skilled at making accurate predictions about future events. They tend to be more accurate than the average person, and may use a variety of techniques and strategies to improve their forecasting abilities. Prediction markets, on the other hand, are exchange-traded markets that allow people to buy and sell contracts representing the possible outcomes of future events. The prices of these contracts fluctuate based on supply and demand, and can be used to forecast the likelihood of future events. Both prediction markets and superforecasters can be useful for making predictions about future events, and may be used in different contexts to help organizations make better decisions. However, prediction markets rely on the collective wisdom of many people, while superforecasters are individual experts.”
From the Gartner-Tetlock book: “Teams of ordinary forecasters beat the wisdom of the crowd by about 10%. Prediction markets beat ordinary teams by about 20%. And superteams beat prediction markets by 15% to 30%… How did superteams do so well? By avoiding the extremes of groupthink and Internet flame wars. And by fostering minicultures that encouraged people to challenge each other respectfully, admit ignorance, and request help.”
Just as social media platforms have given rise to influencers, prediction markets could help identify the superforecasters. Money and regulation have limited the spread of prediction markets. How can Mu change the game and enable wider reach for prediction markets?
People love to gamble. NYTimes had a story on the popularity of sports betting recently: “One in five Americans has bet on sports in the past year, according to research from the Pew Research Center. During the first half of 2022, Americans placed an average of nearly $8 billion a month in legal sports bets, compared with under $1 billion a month three years earlier, according to SportsHandle, a trade publication. Some analysts have predicted that figure could climb to $20 billion a month by 2026.” It is estimated that more than 140 million Indians use sports betting sites, with the number more than doubling during cricket tournaments like the Indian Premier League. Sports betting provides instant gratification and outcomes are known in minutes or hours.
There is a vast world of events beyond sports – politics, economic, financial, weather, and more. While the stock markets offer many sophisticated instruments for traders to bet on global and company news, there are very limited options for betting or predicting future events – and then being able to boast “I told you so.” This is where prediction markets with ‘play money’ (as opposed to real money) can come in.
I have written extensively in the past about Atomic Rewards and Loyalty 2.0 with Mu as the token for attention and data and MuCo as the entity building the Muniverse. From “The MuCo Future”: “MuCo is thus running a 2-sided marketplace between brands and consumers. The challenge such marketplaces face is the ‘cold start’ problem – creating enough demand on both sides to get activity going. To overcome this problem, MuCo may need partners who can help accelerate the process of getting Mu in the hands of consumers. It will also need to create an attractive shop to get consumers to see the value and utility of Mu before brands start coming in with their own offerings. This is where MuCo will need initial capital to bootstrap itself.” From “Muniverse Monetisation”: “MuCo enables brands to build hotlines with existing customers in the upstream (attention and data) and downstream (network and voice) by offering a lubricant in the form of Atomic Rewards (Mu points and tokens), thus enabling exchanges which are not happening today. MuCo ends over-reliance by brands on new customers, and instead enables better and deeper relationships with the existing customers – whose acquisition costs have already been paid. MuCo gives marketers a lever in the form of micro-incentives to offer customers to influence their behaviour for non-monetary actions, just as traditional loyalty programs nudge repeat transactions. MuCo’s pitch to brands: pay customers, not Big Adtech.”
Mu is the incentive offered by brands to their customers for the upstream of a transaction (attention and data) and the downstream (ratings, reviews and referrals). For Mu to be attractive, it also needs multiple places where it can be used. In my essays, I have discussed four options: Gamelets, MuShop, MuMarket, and MuExchange. Prediction Markets can be the fifth and perhaps most interesting option in the Muniverse.
As consumers own (“mine”) Mu for their attention, data and other brand-incentivised actions, prediction markets can be a good potential redemption (and additional earning) mechanism. It brings out the latent wisdom each of us have in our interpretation of future events. By removing the need for real money, MuCo’s Prediction Markets also eliminate the need for regulation. The use of crypto tokens ensures that MuCo cannot debase or devalue the tokens, and there are rules that govern it. Google helps us search the past, Twitter the present, and Mu-powered Prediction Markets (MPM) can help us with the future.
So, how would such markets work?
Mu mined through actions could provide an entry into the world of MPM. The working of MPM would be very much like prediction markets today which are fuelled by real money. People could launch their own public or private bets and contracts, and invite others to compete against them. A contract needs two parties. MPM would, in the initial days, work as a market maker to ensure liquidity. There should be clearly defined outcomes to determine payoffs. For example, India has many important state elections coming up in 2023. As of now, the reliance for directionality of outcomes is either on journalists who have very limited information or pollsters. MPM could offer a third window to the possible electoral outcomes.
Every individual (who so desires) could be given a score on a dashboard. This “social proof” could help identify the superforecasters among the participants who could then monetise their status – much like influencers do on social media.
This Muconomy would be good for brands and consumers. It would create greater demand for Mu which would benefit the brands who are the dispensers of Mu for attention and data. It would also create value for Mu among consumers who could then monetise the Mu on other platforms. It would thus create a Mu flywheel and get a circular earn-and-redeem economy going. As brands get more attention and hopefully transactions from their existing customers, their AdWaste would be reduced even as loyalty and profitability increase.
MPM could thus take our native instincts of betting and boasting on the future and give an outlet to them.
(I want to thank my colleague, Chirag Patnaik, for some of the ideas discussed above.)
I want to end this series with a quote to think about from Vitalik Buterin: “The idea behind futarchy was originally proposed by economist Robin Hanson as a futuristic form of government, following the slogan: vote values, but bet beliefs. Under this system, individuals would vote not on whether or not to implement particular policies, but rather on a metric to determine how well their country (or charity or company) is doing, and then prediction markets would be used to pick the policies that best optimize the metric. Given a proposal to approve or reject, two prediction markets would be created each containing one asset, one market corresponding to acceptance of the measure and one to rejection. If the proposal is accepted, then all trades on the rejection market would be reverted, but on the acceptance market after some time everyone would be paid some amount per token based on the futarchy’s chosen success metric, and vice versa if the proposal is rejected. The market is allowed to run for some time, and then at the end the policy with the higher average token price is chosen.”