Quadratic Payments: A Primer

how do we aggregate together people's preferences? Some private and public goods are worth producing, others are not. In the case of private goods, the question is easy, because we can just decompose it into a series of decisions for each individual. Whatever amount each person is willing to pay for, that much gets produced for them; the economics is not especially complex. In the case of public goods, however, you cannot "decompose", and so we need to add up people's preferences in a different way.

The author is addressing the fundamental challenge of collectively deciding which goods and projects deserve to be produced or funded based on people's preferences. For private goods (like apples), aggregation of preferences is straightforward because individual demands can be separated—each person decides independently how much to pay, and producers supply accordingly. This decomposability allows traditional market mechanisms to work effectively.

However, with public goods (such as public parks, clean air, or this article), the situation is different because the good is non-excludable and non-rivalrous: it benefits everyone simultaneously, and producing it for one person is effectively producing it for all. This means preferences cannot be simply broken down into individual, isolated decisions. Instead, preferences must be aggregated as a whole, since the provision decision is collective and indivisible—either the good is produced for all or for none. Consequently, a distinct approach is required to sum up and interpret people’s preferences over public goods in order to decide whether these goods should be produced. This sets the stage for exploring new mechanisms like quadratic voting and quadratic funding, which aim to aggregate preferences in ways that fairly balance individual intensity of support and collective benefit.

Q. In the context of private goods, what does 'decomposition' of economic decisions mean?
A. Breaking down production decisions into separate choices for each individual, where each person's consumption can be handled independently.

The prompt targets the core conceptual anchor of the user's highlight: the 'decomposability' of private-good production decisions, which is the foundational premise Vitalik uses to explain why public goods require novel aggregation mechanisms like quadratic voting/funding. The question is atomic (it tests exactly one concept), uses precise language drawn directly from the source ('in the context of private goods', 'decomposition'), and its answer is faithful to the highlighted text ('a series of decisions for each individual'). It remains clear and self-explanatory when reviewed in isolation months later, so it satisfies both the meaningfulness and stability criteria. Although it covers only the private-goods side of the private/public contrast, the concept tested is a substantive building block of the insight rather than a trivial fragment.


Summary: Vitalik Buterin introduces quadratic payments as a novel social technology that could transform public decision-making. He explains three variants: quadratic voting, quadratic funding, and quadratic attention payments. These mechanisms aim to balance influence in proportion to how much people care about an issue, avoiding the pitfalls of both one-person-one-vote systems (which ignore preference strength) and one-dollar-one-vote systems (which give too much power to the wealthy). Early implementations have shown promise in various contexts including hackathons, public goods funding, and political decision-making.

Quadratic Payments: A Primer

Q. What fundamental difference exists between private and public goods in terms of preference aggregation?
A. Private goods can be decomposed into individual decisions, while public goods require collective preference aggregation and cannot be decomposed.

This prompt directly targets the core insight the user highlighted: the decomposability asymmetry between private and public goods that motivates the entire quadratic mechanisms framework. It is atomic—testing a single, well-defined conceptual contrast—and uses precise, durable terminology ('decomposed into individual decisions') drawn straight from the source text, ensuring stable retrieval even months later in isolation. The question framing 'in terms of preference aggregation' provides built-in context, and the answer remains faithful to the highlighted passage without introducing trivial fragments or external details beyond the user's engagement.


Quadratic Payments: A Primer

Bob is producing apples, and Alice wants to buy apples, we can economically model the interaction between the two, and the results seem to make sense:

The highlighted passage—"Bob is producing apples, and Alice wants to buy apples, we can economically model the interaction between the two, and the results seem to make sense"—serves as a foundational example to introduce basic economic reasoning about private goods and market interactions.

In context, the author is contrasting the relatively straightforward economics of private goods with the more complex issues surrounding public goods. Here’s the underlying idea unpacked:

- Bob and Alice represent typical actors in a market for private goods (apples).
- The exchange between Bob (producer) and Alice (consumer) can be modeled economically by classical supply and demand theory: Alice buys apples up to the point where the marginal value she places on the next apple is just less than or equal to Bob’s cost of producing it.
- This kind of interaction leads to efficient allocation of private goods, as formalized by the “fundamental theorems of welfare economics,” meaning that markets can in principle reach optimal outcomes for private goods through decentralized buying and selling.
- This example sets up the contrast to later discussions on public goods, where such neat decompositions into one-to-one trades don’t work because public goods benefit many people simultaneously and aren’t rivalrous in consumption.
- The author uses the apple example to establish the strength and explanatory power of traditional economic thinking for private goods, to then highlight how these models fall short when dealing with public goods, motivating the need for new mechanisms like quadratic payments.

In short, the passage illustrates how traditional market interactions between a single buyer and seller over a private good can be understood and modeled clearly and successfully by existing economic theory, which sets the stage for exploring the limitations and challenges that arise with public goods and collective decision-making.

Q. In a scenario where Bob produces apples, and Alice wants to buy apples, when does Alice stop buying apples?
A. Alice keeps buying apples until the marginal value of the next apple is less than the cost of producing.

The prompt isolates the single core insight embedded in the highlighted apple example: Alice's purchasing decision follows the marginal rule—she keeps buying only while the marginal value of an apple meets or exceeds Bob's cost of producing it. The interpretation explicitly identifies this marginal-analysis logic as what makes the private-goods market interaction 'make sense,' so the prompt is faithful to what the user engaged with. It is atomic (one well-defined decision rule), uses precise terminology ('marginal value,' 'cost of producing'), and is anchored by a concrete, memorable scenario (Bob/Alice/apples) that stays self-explanatory even months later without the original document. The only minor weakness is slight ambiguity around the strict boundary (whether she stops exactly at equality), but this does not materially undermine retrievability or meaning.


Quadratic Payments: A Primer

Q. Why do simple market models 'make sense' from an economic perspective?
A. They demonstrate clear allocation of resources based on voluntary exchange between parties with defined roles and rights

This prompt fails on both stability and fidelity. The question 'Why do simple market models make sense?' is generic and abstract—without the original context, 'simple market models' and the quoted phrase 'make sense' become ambiguous anchors that a reviewer months later cannot reliably decode. Worse, the answer ('clear allocation of resources based on voluntary exchange between parties with defined roles and rights') is a vague paraphrase that introduces fuzzy terminology ('defined roles and rights') not present in the source's actual reasoning. The document's real insight—which the user's highlight anchors—is concrete: for private goods like apples, classical supply-and-demand modeling works because Alice buys until her marginal value meets Bob's marginal cost, yielding efficient allocation (per welfare economics), which then sets up the contrast with public goods. A pluckable prompt would target that specific mechanism or the private-vs-public-goods distinction atomically; instead, this prompt tests a diluted, hand-wavy generalization that would be difficult to grade consistently and drifts from the idea the user actually engaged with.


Quadratic Payments: A Primer

Q. What fundamental conditions must exist for a simple market exchange (like Bob's apples) to function?
A. Property rights, voluntary participation, and ability to transfer ownership

The prompt fails the fidelity criterion. The user's highlight anchors the apple example, whose core insight (per the interpretation) is that classical economic theory cleanly and successfully models private-goods exchange—Alice buys until marginal value meets Bob's marginal cost, an efficiency result formalized by the welfare theorems—and that this success sets up the contrast with public goods where such modeling breaks down. The prompt instead tests a generic list of institutional preconditions ('property rights, voluntary participation, ability to transfer ownership') that appears nowhere in the highlight or its interpretation, making it a tangential detail rather than the idea the user engaged with. It also raises stability concerns: the exact three-item triad is not clearly traceable to the source, and the modal framing ('what must exist') invites contestation, since plausible alternative lists (e.g., divisibility, information, medium of exchange) could be defended months later in isolation. A pluckable version would target the actual insight, e.g., asking what the Bob/Alice apple exchange illustrates about why markets work well for private goods.


Summary: Neil Postman’s address to the National Council of Teachers of English argues that the most important skill schools can teach is the ability to detect and critique “bullshit”—misleading, pompous, fanatical, inane, or superstitious language. He categorizes various forms of bullshit, critiques the educational system’s complicity in perpetuating them, and contends that effective “crap-detecting” is more a matter of values and attitudes than technical skill. Postman concludes that while schools may not be well-equipped to teach this art, it remains a vital goal for educators and citizens.

“Bullshit and the Art of Crap-Detecting”

or those of youwho do now know, it may be worth sayingthat the phrase, “crap-detecting,” originatedwith Mr. Ernest Hemingway who whenasked if there were one quality needed,above all others, to be a good writer, replied,“Yes, a built-in, shock-proof, crap detector.”

In this passage, the author is explaining the origin and significance of the term "crap-detecting," which is central to the talk. He attributes the phrase to Ernest Hemingway, who, when asked about the most essential quality for a good writer, famously answered that it was having a "built-in, shock-proof, crap detector." By invoking Hemingway, the author underscores the importance of being able to recognize and sift through worthless, deceptive, or insincere communication—what he calls "crap" or "bullshit." This ability to detect nonsense or insincerity is presented not just as a vital skill for writers, but more broadly as a crucial tool for navigating professional conferences, education, and everyday life. Thus, the author sets up "crap-detecting" as a metaphor and practical necessity for critical thinking and discernment in language use, especially relevant for English teachers who must help students differentiate meaningful communication from linguistic fluff or deception.

Q. According to Hemingway, what is the single most important quality for a good writer?
A. A built-in, shock-proof, crap detector

The prompt directly targets the core of the user's highlighted text: Hemingway's famous quote defining the essential quality of a good writer as 'a built-in, shock-proof, crap detector.' This is the anchor insight of the passage—the origin story of 'crap-detecting' that Postman builds his whole talk around—so it is meaningful rather than trivial. The prompt is also atomic and stable: it asks exactly one well-defined question, embeds the necessary context ('According to Hemingway') in the question stem, uses precise language mirroring the source text, and remains fully self-explanatory when reviewed months or years later in isolation.


Q. Who originated the term "crap-detecting" and in what context?
A. Ernest Hemingway, when discussing essential qualities for good writing

This prompt is pluckable. It directly targets the core insight of the highlighted passage—the attribution of 'crap-detecting' to Ernest Hemingway in the context of identifying the essential quality of a good writer—which is exactly what the user chose to highlight. The knowledge tested forms one coherent, self-contained unit (the famous origin of the term), so despite having two components ('who' and 'in what context'), it remains effectively atomic rather than a vague compound question. The language is specific and stable: both 'crap-detecting' and 'Hemingway' are distinctive anchors that remain unambiguous even when reviewed in isolation months later, without needing the original document. The answer faithfully reflects the source material without introducing external information or trivial fragments.


re Iwant to provide an example of Eichmannismso that you will see why I think it isessentially fanatical. The example alsopoints to, I think, some singularcharacteristics of Eichmannism.Some months ago a young man presentedhimself to me requesting to be admitted to aMasters Degree program in communica-tions offered by my university. He is theauthor of an intriguing book on the subjectof media and cybernation. He has written ahalf-dozen articles on the subject, haslectured at major universities in this countryand abroad, and was the principalinvestigator of an extensive research effortinto the relationship of television andsensory bias. There was one difficulty. Hedoes not have what is called a BachelorsDegree. I was not entirely sure why hewanted a Masters Degree, but it seemedperfectly clear that he was “intellectuallycapable” of pursuing such studies. I will notreport on the various episodes that followedmy request that he be accepted into the M.A.program. They are both boring and hideous.Here was the result: His application wasdenied because, and I quote, “by definition,one cannot be qualified for an M.A. programunless he holds a Bachelors degree.” Andthere you have the essence of Eichmannism.Eichmannism is that form of bullshit whichaccepts as its starting and ending pointofficial definitions, rules, and categorieswithout regard for the realities of particularsituations. It is also important to say that thelanguage of Eichmannism, unlike othervarieties of fanaticism, is almost alwayspolite, subdued, and seemingly neutral. Afriend of mine actually received a letter froma mini-Eichmann which began– “We arepleased to inform you that your scholarshipfor the academic year 1968-69 has beencancelled.”

The highlighted passage uses a concrete example to illustrate the author's concept of "Eichmannism," which he describes as a particular kind of fanatical adherence to bureaucratic rules and official definitions, regardless of their practicality or fairness in specific contexts. The story of the young man—who, despite demonstrating clear intellectual capability and significant scholarly contributions in media and communications, is denied entry into a master's program solely because he lacks the formal prerequisite of a bachelor's degree—demonstrates the rigid and unthinking application of bureaucratic rules. The refusal does not consider the man's actual qualifications or the realities of his individual case, embodying the "Eichmannism" the author warns about.

Further, the author contrasts this form of fanaticism with others by noting that Eichmannism is characterized by polite, neutral, and subdued language, which masks its rigidity and can make it more insidious. The mention of a friend's experience receiving a similarly polite but devastating letter about a scholarship cancellation reinforces this point. Overall, this passage conveys that Eichmannism is a form of bullshit grounded in inflexible bureaucracy—following rules blindly and impersonally—without regard for human nuance or context, thus illustrating the dangers when systems prioritize formality over substance and humanity.

Q. What is the defining characteristic of Eichmannism as a form of fanaticism?
A. The rigid adherence to official definitions and rules without regard for practical realities

This prompt targets the core insight the user highlighted: Postman's definition of Eichmannism as blind adherence to official definitions and rules without regard for real situations. It is atomic (tests one clearly defined concept), faithful to the source (the answer closely mirrors Postman's own formulation), and stable—the named concept 'Eichmannism' plus the framing 'form of fanaticism' keeps it self-explanatory even when reviewed months later in isolation. While it omits the secondary detail about polite, neutral language, that omission serves atomicity rather than distortion, as the rule-adherence definition is the essential anchor of the passage.


Q. What tone characterizes the language of Eichmannism compared to other forms of fanaticism?
A. It is polite, subdued, and neutral rather than aggressive or emotional.

This prompt is pluckable. It isolates a single, atomic detail that Postman himself explicitly flags as important ('It is also important to say that the language of Eichmannism, unlike other varieties of fanaticism, is almost always polite, subdued, and seemingly neutral'), so it targets a genuinely emphasized part of the user's highlight rather than a trivial fragment. The user's interpretation devotes an entire paragraph to this contrastive trait, confirming it was part of the core engagement. The question is specific and self-contained—naming 'Eichmannism' and framing it against 'other forms of fanaticism'—which keeps it clear even when reviewed months later without the source text. The answer stays faithful to the source; the added gloss 'rather than aggressive or emotional' is a reasonable unpacking of the implied contrast ('unlike other varieties of fanaticism') and does not introduce foreign information. Overall, it satisfies both criteria: meaningful (captures a distinctive, durable insight about why Eichmannism is insidious) and stable (precise wording that remains unambiguous in isolation).


Summary: The authors argue that prediction markets are not as promising as many advocates believe. While they are largely legal in the US and other countries, prediction markets remain small and illiquid because they lack natural demand from key market participants: savers (who avoid zero-sum investments), gamblers (who prefer quick-resolving bets), and sharps (who need large markets to profit from their expertise). Without subsidies, prediction markets will likely remain niche and no more accurate than other forecasting methods.

The Limits of Prediction Markets

Certain prediction markets do exist. Americans bet over $330 billion on sports last year. Sportsbooks are markets on the outcomes of sporting events and you can reliably get an accurate probability of sporting events outcomes by checking them.

The author is explaining that, although broad prediction markets on a wide range of topics are rare and limited, certain prediction markets do actively exist and are quite substantial—specifically, sports betting markets. In the U.S., last year over $330 billion was wagered on sports outcomes, which are essentially prediction markets focused on sporting events. These sportsbooks function by allowing participants to bet on the results of games, and the odds or prices set by these markets can be interpreted as reliable indicators of the probability of different outcomes occurring. Thus, while the envisioned "prediction markets on everything" are scarce, the sports betting markets serve as a real-world example where prediction markets operate at scale and provide reasonably accurate probabilistic forecasts of future events. This point grounds the discussion by demonstrating that prediction markets are not entirely theoretical or non-existent; they do work in certain contexts where there is substantial demand and active participation.

Q. What was the total amount wagered on sports betting in the US (2023)?
A. $330 billion

This prompt targets a trivial surface detail—a specific dollar figure—rather than the core insight the user highlighted. The user's interest was conceptual: sportsbooks are a real-world example of prediction markets operating at scale, whose prices reliably encode probabilities of outcomes. A durable, meaningful prompt would ask something like 'What existing large-scale market functions as a de facto prediction market, and why?' Testing an exact statistic like '$330 billion' fails both criteria: it is not the meaningful unit of knowledge the user cared about, and exact figures are notoriously unstable for spaced-repetition retrieval over months or years, especially since the source only said 'over $330 billion last year' (making the '2023' framing itself an added assumption).


The Limits of Prediction Markets

Q. What was the total volume of sports betting in the US [2023]?
A. $330 billion

This prompt fails the 'meaningful' criterion: it isolates a transient statistic ($330B) rather than the core insight behind the user's highlight—that sports betting markets are a real-world, large-scale example of prediction markets producing reliable probabilities. The dollar figure is decorative support for that argument, and it risks feeling like disconnected trivia on a months-or-years review horizon. There is also a mild stability problem: the source says 'last year,' so the '[2023]' bracket flags uncertain anchoring, and a volume statistic naturally decays in relevance over time. A stronger, pluckable version would target the durable concept, e.g., 'What existing market demonstrates that prediction markets can operate accurately at scale?' (US sports betting/sportsbooks).


The Limits of Prediction Markets

Q. What was the total volume of sports betting in the US in the referenced year?
A. $330 billion

This prompt targets a surface-level statistic rather than the core insight of the highlighted passage. What the user likely found interesting is the conceptual point that sportsbooks already constitute large-scale, functioning prediction markets whose odds reliably encode outcome probabilities—the $330 billion figure merely supports that argument. Testing an arbitrary dollar amount is a trivial fragment that fails atomicity in spirit: it isolates a decorative detail while leaving the durable insight (sports betting as a real-world example of prediction markets at scale) untested. Additionally, the prompt has stability problems: 'the referenced year' is vague and meaningless in isolation months later, and precise dollar figures like '$330 billion' are notoriously fragile to recall accurately over long review intervals. A better prompt would ask why sportsbooks demonstrate that prediction markets can work at scale.


The Limits of Prediction Markets

We disagree. Instead, our view is that prediction markets on everything – liquid markets over a wide range of important topics – will not work without subsidies. These subsidies would be expensive, so other forms of information aggregation are usually more attractive. The scarcity of prediction markets in the world today is not a failure of regulation, but a sign that they are much less promising than many advocates, including the authors of this piece, once hoped.

The authors challenge the common belief that the lack of widespread, liquid prediction markets across many topics is mainly due to regulatory barriers. Instead, they argue that such "prediction markets on everything" inherently require costly subsidies to function effectively. Because these subsidies would be expensive, alternative methods for aggregating information tend to be more practical and appealing. Therefore, the limited presence of prediction markets is less about legal restrictions and more a reflection of their intrinsic limitations and lower promise compared to earlier expectations—even from proponents like the authors themselves. This suggests that the core issue is demand and economic viability rather than just regulation.

Q. What fundamental constraint limits the widespread adoption of prediction markets?
A. The need for expensive subsidies, not regulatory restrictions

The prompt captures the core insight of the highlight: the authors' central claim that prediction markets fail to scale because they require costly subsidies—not because of regulation. This is precisely the counterintuitive reframing the user highlighted. The question is atomic, testing a single well-defined causal claim, and the answer is faithful to the source material ('will not work without subsidies' and 'not a failure of regulation'). The phrasing 'fundamental constraint' and 'widespread adoption' remains clear and self-explanatory even when reviewed in isolation months later, since the subject matter (prediction markets) provides built-in context. The answer's two-part structure (need for subsidies + not regulation) forms one coherent unit of knowledge matching the user's interpretation that the core issue is economic viability rather than legal restrictions.


The Limits of Prediction Markets

There are currently around twelve million pounds in play on the US presidential election – about the same as typically gets bet on a single cricket match.5 Entrepreneurs have not created ‘markets on everything’ even where it is legal to do so.

The highlighted passage emphasizes that despite legality and market potential, prediction markets have not developed into large, diverse platforms covering a wide range of topics as some advocates envisioned. It illustrates this by comparing the volume of money wagered on the U.S. presidential election—approximately twelve million pounds—with the amount typically bet on just one single cricket match, indicating that the scale of political prediction markets is relatively modest. This comparison serves to highlight that entrepreneurs have not succeeded in—or perhaps have not aggressively pursued—the creation of expansive "markets on everything" even in environments where such markets are legally permitted. Implicitly, this example supports the broader argument that the scarcity and small size of prediction markets result not primarily from regulatory constraints, but rather from limited demand and participation incentives in these markets.

Q. What does the similar betting volume between presidential elections and cricket matches reveal about prediction markets?
A. It demonstrates the relatively low adoption of political prediction markets, even for major events.

The memory prompt successfully isolates the core insight the user engaged with: the striking equivalence between total wagering on the US presidential election (~twelve million pounds) and a single cricket match, which signals surprisingly low adoption of political prediction markets despite major-event salience. The question is atomic, testing one coherent concept, and it embeds its own factual anchor (the election-vs-cricket comparison), so it remains self-explanatory and stable when reviewed months later without the source article. The answer ('low adoption of political prediction markets, even for major events') faithfully reflects the user's interpretation without drifting into trivial details or external claims beyond the highlight. Minor quibbles—such as not explicitly mentioning that these markets were legal—are acceptable since they don't undermine the durability or clarity of the core recall target.


The Limits of Prediction Markets

Markets become efficient when making them efficient is profitable. Large markets and markets where people will ‘pay’ expected return for access create those conditions. In our view, in prediction markets, no type of market participant – savers, gamblers, or sharps – is clamoring to be in the market, so there is no strong incentive pushing the market toward efficiency.

The highlighted passage explains a key economic principle about market efficiency: markets tend to become efficient (accurately pricing information) only when it is profitable for participants to make them so. This generally happens in large markets or markets where participants—such as investors or gamblers—are willing to pay a cost (or accept some expected loss) for access because they want to either save money or enjoy the betting experience.

The authors argue that prediction markets lack this dynamic. Unlike typical financial or gambling markets, prediction markets do not strongly attract the three main types of market participants:

1. **Savers**—those seeking long-term returns—avoid prediction markets because these markets are zero-sum and negative-sum (due to fees), so they don’t produce positive returns over time like stocks or bonds.
2. **Gamblers**—those seeking thrill and quick resolution—are not drawn to prediction markets because many prediction contracts resolve slowly and often involve niche or esoteric events, which lack the excitement or community engagement that sports betting, for example, provides.
3. **Sharps** (skilled traders)—who might benefit from exploiting inefficiencies—are reluctant to enter because there are few savers or gamblers to provide liquidity and profit opportunities. Trading solely against other sharps reduces market size, liquidity, and the incentive to invest effort in pricing accuracy.

Because none of these participant groups has a strong natural demand to be active in prediction markets, the markets remain small, illiquid, and inefficient. Without sufficient participant incentives and volume, prediction markets lack the self-reinforcing profit motive that drives efficiency in other types of markets. Thus, the absence of eager participants undermines the fundamental mechanism that would normally push prediction markets toward accurate, efficient price discovery.

Q. Why do prediction markets often have weak incentives for participation compared to traditional markets?
A. Because no main participant group (such as savers, gamblers, or informed traders) has strong motivation to enter the market, reducing market efficiency.

The prompt targets the right topic but fails on stability and atomicity grounds because it is essentially circular: asking 'Why do prediction markets have weak incentives for participation?' and answering 'Because no participant group has strong motivation to enter' merely restates the question in different words. The substantive insight the user highlighted—the causal mechanisms—is stripped out: savers avoid zero-/negative-sum payoffs, gamblers dislike slow resolution and esoteric events, and sharps stay out because there are few savers/gamblers to profit against. The core economic principle ('markets become efficient when making them efficient is profitable') is also absent. Additionally, the answer appends 'reducing market efficiency,' introducing a second element the question never asked about, making it slightly compound. Reviewed in isolation months later, this prompt yields little retrievable knowledge beyond what is already embedded in the question itself.


The Limits of Prediction Markets

Q. Why do prediction markets often lack efficiency compared to traditional markets?
A. No participant group (savers, gamblers, or sharps) has strong incentives to enter the market

The prompt targets the core insight the user highlighted: prediction markets fail to become efficient because no participant type (savers, gamblers, or sharps) has a strong natural incentive to join, so there is no profit motive pushing prices toward accuracy. The question is clear and self-contained, and the answer faithfully reproduces the specific three-way taxonomy from the source rather than a vague generalization. It is atomic (one causal claim), meaningful (captures the article's central argument about missing participant incentives), and stable enough for isolated review months later—the terms 'savers,' 'gamblers,' and 'sharps' are concrete anchors tied to the source material.


The Limits of Prediction Markets

Q. Why do prediction markets often lack efficiency compared to traditional markets?
A. No participant group (savers, gamblers, or sharps) has strong incentives to enter the market, reducing pressure toward efficiency.

The prompt targets the core insight of the highlighted passage: prediction markets fail to become efficient because none of the three participant types (savers, gamblers, sharps) has strong incentives to join, so no profit motive pushes prices toward accuracy. It is atomic—testing one clear causal claim—and uses precise, source-grounded terminology ('prediction markets', 'savers, gamblers, or sharps') that remains self-explanatory when reviewed in isolation months later. The comparative framing against 'traditional markets' provides built-in context without introducing external information, and the answer faithfully captures the complete unit of knowledge the user engaged with rather than a trivial fragment.


The Limits of Prediction Markets

Quick resolutions are so valued that live, in-game betting is becoming the most popular type of sports betting, despite the fact that the house tends to widen spreads on live bets, hurting bettors’ expected returns.10

In the highlighted passage, the authors emphasize that the appeal of quick outcomes significantly drives the popularity of live, in-game sports betting. Even though sportsbooks often increase the betting spreads in these live scenarios—meaning the odds are less favorable and bettors’ expected returns are reduced—bettors still prefer these fast-paced bets. This reflects a strong preference among gamblers for immediacy and excitement, valuing the quick resolution of their wagers over potentially better value or returns. The passage suggests that the thrill and rapid conclusion of live bets outweigh the downside of less advantageous odds, highlighting how the temporal aspect of betting outcomes is a key factor in gambler behavior. This insight is part of the broader argument explaining why prediction markets struggle to attract the typical gambler: prediction markets often involve longer time horizons and less immediate payoffs, making them less exciting and less appealing compared to the fast-paced betting options favored by gamblers.

Q. How do betting houses adjust the point spreads for live, in-game bets compared to traditional bets?
A. They widen the point spreads to manage risk.

The prompt fails on both meaning and fidelity. The user's highlight centers on a behavioral insight: quick resolutions are valued so highly that bettors accept live bets even though widened spreads reduce their expected returns—a preference for immediacy that underpins the article's broader argument about why prediction markets struggle against fast-paced gambling. The prompt instead isolates a surface detail (that houses widen spreads) and strips away the essential context about bettor behavior and the immediacy-vs-returns tradeoff. Worse, it introduces an external rationale ('to manage risk') that is not stated in the highlighted text, which only notes the effect on bettors' expected returns. On long review horizons, recalling 'houses widen spreads' without the surrounding insight about gambler preference for quick resolution loses the durable, meaningful idea the user actually engaged with.


The Limits of Prediction Markets

Q. How do betting houses adjust their pricing for live, in-game bets compared to traditional bets?
A. They widen the spreads, increasing their profit margins and reducing bettor returns.

The prompt is precise and atomic, but it targets the wrong layer of the insight. The user's highlight—and their interpretation—centers on the behavioral point: bettors value quick resolutions so much that they flock to live, in-game betting even though it worsens their expected returns. That trade-off between immediacy and value is the durable, meaningful idea anchoring the passage (and the article's broader argument about prediction markets' unappealing long time horizons). The prompt instead isolates a supporting mechanism—houses widening spreads—stripped of the context that makes it interesting. Reviewed in isolation months later, 'houses widen spreads on live bets' is a trivial bookkeeping fact that fails to capture what the user actually found compelling. A pluckable version would ask why live betting is popular despite worse odds, making the preference-for-quick-resolution insight the focus of recall.


The Limits of Prediction Markets

Beyond their exciting unpredictability and quick conclusions, sports matches are communal events of general interest. People are already fans of sports teams, even before the betting starts, and the predictable pace of seasons creates an ongoing community. The same cannot be said of most prospective prediction markets, which are often one-off events of little general interest.

The author is explaining why sports betting markets tend to attract more participants compared to most other prediction markets. They argue that sports matches are not only exciting because their outcomes are unpredictable and quickly resolved, but also because they are communal events that people care about collectively. Many people are already fans of sports teams, which creates a built-in audience with emotional investment and social connection. Additionally, regular sports seasons provide a predictable cadence that sustains ongoing interest and community engagement.

In contrast, most prediction markets focus on isolated, one-off events—such as specific political decisions, scientific outcomes, or economic questions—that lack general public interest or community followings. These events do not evoke the same kind of widespread fandom or recurring engagement. This difference in the social and emotional context is a key reason why prediction markets outside of sports and a few high-profile events struggle to attract participants and liquidity. Essentially, prediction markets do not benefit from the same foundational appeal and structure that make sports betting vibrant and popular.

Q. How does the 'predictable pace of seasons' contribute to sports betting communities?
A. It creates regular, anticipated events that allow community development over time

The prompt is pluckable. It takes a phrase the user directly highlighted ('the predictable pace of seasons creates an ongoing community') and converts it into an atomic, single-concept question with a clear, faithful answer ('creates regular, anticipated events that allow community development over time'). The question embeds its own context by referencing sports betting communities, so it remains self-explanatory and consistently retrievable months later without the original article. While it captures only one facet of the broader insight (that sports betting thrives due to communal, pre-existing fandom versus one-off prediction market events), it targets a genuinely meaningful mechanism in the user's highlighted material rather than a triviality, and its precise, concrete framing ensures stability over long review intervals.


The Limits of Prediction Markets

Q. How does the 'predictable pace of seasons' contribute to sports betting communities?
A. It creates regular, anticipated events that maintain continuous engagement and allow community development over time

The prompt is pluckable. It isolates a single, well-defined causal mechanism that appears verbatim in the user's highlight ('the predictable pace of seasons creates an ongoing community'), making it faithful to the exact idea the user engaged with. The question is atomic—it tests one concept (how seasonal cadence sustains sports betting communities)—and its framing provides enough built-in context ('sports betting communities') that it remains self-explanatory when reviewed months later without the original article. The answer paraphrases the source accurately ('regular, anticipated events... continuous engagement... community development over time'), so retrieval can be judged consistently across spaced reviews. While the broader document-level insight is the contrast between communal sports and one-off prediction market events, this prompt targets a substantive, non-trivial component of that insight that the user explicitly highlighted, rather than a decorative fragment or external detail.


The Limits of Prediction Markets

Yet even in these cases, gamblers’ preference for quick resolution bites: 42 percent of the volume on the 2020 election was traded in the last week before the vote, excluding the additional trading in the period between the election and the resolution of the market.

The highlighted passage emphasizes that, even in the relatively popular prediction markets for major events like the 2020 US presidential election, gamblers strongly prefer bets that resolve quickly. Specifically, it points out that 42% of the betting volume occurred in the last week before the election—meaning that most traders waited until close to the event's outcome before placing their bets. This pattern illustrates a broader insight in the article: the timing and speed at which prediction markets resolve heavily influence trader participation and market liquidity.

In context, this passage supports the authors’ argument that prediction markets struggle to gain widespread traction partly because many potential participants—especially gamblers—favor short-term, fast-resolving bets. Even for such high-profile, recurring events that naturally attract attention and community interest, a significant portion of activity clusters near the event horizon. This behavior parallels bettors’ preferences in sports betting, where quick outcomes generate more excitement and engagement.

Thus, the core idea conveyed here is that even in the best-case scenarios for prediction markets, the demand for long-duration or distant-future bets is limited. This demand-side limitation contributes to the relatively small size and liquidity of prediction markets at large, as many potential users avoid markets on events that take a long time to resolve or lack immediacy. The passage highlights a key behavioral factor restraining prediction markets’ growth and underpins the broader claim that certain structural features—like quick resolution—play a critical role in market participation patterns.

Q. What percentage of trading volume in the 2020 presidential election prediction market occurred in the final week before the vote?
A. 42% (excluding post-election trading)

The prompt isolates the exact statistic the user chose to highlight, framed with sufficient embedded context (the 2020 election prediction market, the final week before the vote) to remain self-explanatory during isolated reviews months or years later. It satisfies the key pluckability criteria: it is atomic (tests one well-defined fact), precise and stable (unambiguous wording drawn verbatim from the highlight), and faithful to what engaged the user—a concrete empirical datum showing that even the most popular prediction markets exhibit heavy late-stage volume clustering, which anchors the broader insight about gamblers' preference for quick resolution. While it recalls a number rather than restating the full causal principle, the question's framing inherently evokes the clustering phenomenon, so retrieving '42%' reinforces the meaningful pattern rather than degenerating into decontextualized trivia.


The Limits of Prediction Markets

But since prediction markets lack savers – who flood security markets with capital and create profit opportunities – this never happens. Prediction markets are orders of magnitude smaller than other financial markets. This makes the markets much less appealing to traders who want to profit off of time spent figuring out the ‘right’ price. That is because potential profits scale directly with market size. A one percent edge on a one billion dollar market is better than a ten percent edge on a $50 million market. It’s hard to imagine how prediction markets would ever find the size and liquidity necessary to pay the salaries of top sharps without savers.

The author argues that prediction markets do not attract enough "savers"—investors who provide large amounts of capital into markets such as stocks or bonds—because these markets are relatively small and zero-sum. This lack of large capital pools results in prediction markets being orders of magnitude smaller than traditional financial markets, like equity markets, which strongly limits their scale and liquidity.

Because the size of a market directly influences the potential profits available to skilled traders ("sharps") who try to find and exploit pricing inefficiencies, smaller prediction markets offer less financial incentive to attract these savvy participants. For example, gaining a 1% advantage on a $1 billion market yields far greater absolute returns than a 10% advantage on a $50 million market, so larger markets naturally draw more expert traders.

Without savers contributing substantial capital, prediction markets cannot achieve the scale or liquidity necessary to support sufficient sharp trading activity that would promote market efficiency and accurate pricing. This creates a self-reinforcing cycle: the markets stay small and unattractive to sharps because of limited capital and potential returns, while the absence of sharps keeps the market from growing more liquid and efficient. Thus, the lack of savers is a critical bottleneck preventing prediction markets from reaching the size and sophistication seen in other financial arenas.

Q. How does market size affect professional trader participation in prediction markets?
A. Smaller markets offer insufficient absolute returns to justify professional research and trading costs.

The prompt isolates the single core causal insight from the highlighted passage: potential profits scale with market size, so small prediction markets cannot generate enough absolute returns to attract professional traders ('sharps'). It is atomic, testing one well-defined relationship rather than compounding multiple ideas. It remains faithful to the highlight, using the source's own framing (the 1% edge on $1B vs. 10% edge on $50M logic) without importing external details like the broader 'savers' argument, which the interpretation treats as background context. The language is concrete and stable — 'market size,' 'professional traders,' 'absolute returns' — so the prompt stays self-explanatory when reviewed in isolation months or years later. The answer precisely captures why smallness deters expert participation, making it durable and meaningful for long-term retention.


The Limits of Prediction Markets

This situation, of sharps trading against sharps, is analogous to the no-trade theorem in financial economics. The theorem gives conditions for when no trading will occur in a market. If all traders in a market are rational, and it is common knowledge among all traders that they are all rational, then no trades happen. Existing prices incorporate all previously available information, so in proposing a trade, a trader reveals that they have private knowledge that the other traders don’t have. The other traders surmise that they would lose if they accepted any trade that was offered. So no trades occur.

This is an unrealistic limit case. Even in highly professionalized markets, not all traders are rational, and most do not assume that any price is a perfect aggregation of all available information.

The highlighted passage explains a key economic concept that helps illuminate a core challenge faced by prediction markets, especially those dominated by "sharps"—traders using superior analysis to profit from market inefficiencies.

The passage draws a parallel between the scenario where only sharps trade with each other and the "no-trade theorem" from financial economics. According to this theorem, if every market participant is rational and it is commonly known that everyone is rational, then no trades would actually take place. This is because current prices in the market would already reflect all available information. Any trader proposing a trade would thereby reveal that they hold private information unknown to others. Rational traders would anticipate losing money by accepting such a trade, so trades would be avoided.

The authors acknowledge that this "no-trade" scenario is an idealized and unrealistic extreme, since in reality not all traders are fully rational or believe that prices perfectly reflect all information. Nevertheless, the analogy illustrates the practical difficulty prediction markets face when dominated solely by sharps: trading activity diminishes because each trader suspects the others possess equal or superior knowledge, making profitable trades scarce and the market illiquid or inefficient.

Thus, the passage serves to clarify why prediction markets struggle to thrive without a diverse mix of participants, like savers and gamblers, who provide liquidity and incentives for sharps to engage actively. Without them, the market risks stagnation as sharp traders hesitate to take positions against equally informed rivals, mirroring the no-trade outcome predicted by the theorem.

Q. What is the no-trade theorem in financial economics?
A. A theoretical principle stating that no trading will occur in a market when all traders are rational and this rationality is common knowledge.

The prompt targets the exact core insight the user highlighted: the definition and conditions of the no-trade theorem (all traders rational + rationality being common knowledge → no trades occur). It is atomic, testing a single well-defined economic concept rather than a compound claim. It is faithful to the source material, using the article's own framing ('sharps trading against sharps' context aside, the theorem statement itself is reproduced accurately without external additions). It is also stable: naming the specific theorem and its field ('financial economics') provides built-in context so the prompt remains clear and unambiguous even months later in isolation. While the answer omits the intuitive mechanism behind the theorem (that proposing a trade reveals private information others would exploit), the theorem's formal statement is a complete, self-contained unit of knowledge suitable for long-term retention, and the mechanism is secondary supporting detail rather than the anchor of the highlight.


The Limits of Prediction Markets

Q. What are the two key conditions required for the no-trade theorem to apply?
A. 1. All traders must be rational
2. It must be common knowledge among traders that everyone is rational

This memory prompt is pluckable. It targets the core insight the user highlighted—the defining conditions of the no-trade theorem—rather than surface details or decorative examples. The question is atomic, testing exactly one well-defined concept (the theorem's two required conditions), and it uses precise, specific language ('no-trade theorem', 'common knowledge') drawn directly from the source text, ensuring stability when reviewed months later without original context. The answer is faithfully grounded in the highlight itself ('If all traders in a market are rational, and it is common knowledge among all traders that they are all rational, then no trades happen'), introduces no external information, and captures a complete, self-contained unit of knowledge the user demonstrably cared about given their extensive interpretation connecting it to prediction markets.


The Limits of Prediction Markets

Q. Why does proposing a trade reveal private information under the no-trade theorem?
A. It signals that the trader possesses private knowledge not reflected in current prices, prompting others to decline the trade.

The memory prompt isolates the single most important mechanism from the highlighted passage—why a proposed trade acts as a signal of private information—and frames it atomically around the precise term 'no-trade theorem,' which provides built-in context for isolated review months later. The question is faithful to the source: the answer correctly captures the causal chain that prices already incorporate available information, so a trade proposal signals unincorporated private knowledge, leading others to decline. It avoids trivial fragments (e.g., mere definitions) and avoids introducing external content beyond the user's engaged insight. The language is specific, concise, and stable, making it consistently retrievable without the original article.


The Limits of Prediction Markets

Q. Why does proposing a trade reveal information under the no-trade theorem?
A. It signals that the trader has private knowledge not reflected in current prices, causing others the decline the trace

The prompt isolates the central causal mechanism of the no-trade theorem—the very idea the user highlighted—namely that proposing a trade signals private knowledge not reflected in prices, which deters counterparties from accepting. It is atomic (tests one concept), anchored to a precise named term ('the no-trade theorem') that keeps it self-explanatory during isolated reviews months later, and faithful to the source material rather than introducing external details. Minor typographical errors in the answer ('the decline the trace') slightly weaken polish, but they do not obscure the underlying insight or undermine long-term retrievability.


The Limits of Prediction Markets

So while prediction markets’ probabilities are worth considering, the limitations in size and liquidity of the markets greatly diminish their power. There is no evidence that they are better than other comparable mechanisms for information aggregation, let alone decisive.

The author is emphasizing that although prediction markets can provide useful probability estimates about future events, their practical impact is significantly weakened by the small scale and low liquidity of these markets. Because the markets are not large or active enough, they do not produce price signals that are substantially more accurate or reliable than other well-established methods of forecasting or information aggregation, such as expert forecasters, forecasting platforms like Metaculus, or poll aggregators like FiveThirtyEight. The author therefore argues that prediction markets' effectiveness in producing superior or “decisive” insights is not supported by current evidence. This passage underscores a central claim of the article: despite theoretical appeal, the real-world limitations of prediction markets constrain their ability to outperform alternative forecasting mechanisms.

Q. How does a limited market size reduce prediction markets' ability to aggregate information effectively?
A. It causes experts to participate less because they can earn better returns elsewhere, weakening the market’s information aggregation.

The prompt is pluckable. It isolates a single, well-defined causal mechanism—limited market size reduces expert participation because skilled forecasters can earn better returns elsewhere—that directly underlies the highlighted claim that size and liquidity constraints 'greatly diminish' prediction markets' power. Rather than restating the surface-level conclusion verbatim, it brings the highlighted idea to life by testing the explanatory 'why,' which is the durable, meaningful unit of knowledge behind the user's interest. The question is atomic (one causal chain), uses precise terminology consistent with the source ('information aggregation'), and its specific answer remains self-explanatory and retrievable in isolation months later, satisfying the stability criterion. While it does not also cover the comparative claim about Metaculus/FiveThirtyEight, a good prompt need not exhaust the entire highlight—it must faithfully anchor one coherent insight, which this does.


The Limits of Prediction Markets

Q. How does limited market size affect the ability of prediction markets to aggregate information effectively?
A. Experts see better RoI elsewhere

The prompt fails fidelity to the highlighted insight. The highlight captures a specific causal claim: limited market size and liquidity diminish prediction markets' aggregative power, such that there is no evidence they outperform other comparable forecasting mechanisms. The provided answer ('Experts see better RoI elsewhere') does not address this at all—it describes a reason why experts might not participate in these markets (a separate point about market popularity), not how limited size affects information aggregation or comparative accuracy versus alternatives like Metaculus or poll aggregators. The question-answer pairing is also internally mismatched: the question asks about an effect on aggregation effectiveness, while the answer offers a cause of limited participation, making the prompt confusing when reviewed in isolation months later. A pluckable version would target the core claim directly, e.g., asking what the evidence shows about prediction markets' accuracy relative to other information-aggregation mechanisms.


The Limits of Prediction Markets

Q. How does limited market size affect the ability of prediction markets to aggregate information effectively?
A. It reduces incentives for experts to correct mispriced predictions, as potential profits are too small to justify the effort.

This prompt is pluckable. It targets the core causal mechanism underlying the user's highlighted insight—that limited market size and liquidity diminish prediction markets' power—by asking precisely how small market size undermines information aggregation. The answer isolates a single, atomic concept (insufficient profit incentives deter experts from correcting mispriced predictions), which faithfully completes the highlighted idea rather than testing a trivial fragment or importing unrelated material. The language is specific and self-explanatory ('prediction markets,' 'mispriced predictions,' 'profit incentives'), so the prompt remains clear and reliably retrievable in isolation months or years later, satisfying both the meaningfulness and stability criteria.


The Limits of Prediction Markets

We suspect there is simply very little demand for hedging events like whether a certain law gets passed; there is only demand for hedging the market outcomes those events affect, like what price the S&P 500 ends the month at. Hedging market outcomes already implicitly hedges for not just one event but all the events that could impact financial outcomes.

The highlighted passage explains a key reason why prediction markets on specific nonfinancial events (e.g., whether a law passes) are not widely demanded or developed. The authors argue that there is minimal interest in using prediction markets to hedge directly against uncertain events like legislation outcomes. Instead, market participants focus on hedging the financial consequences those events might cause—such as fluctuations in stock indices like the S&P 500.

In other words, rather than seeking to hedge individual events, investors and traders prefer to hedge their portfolios against broader market outcomes that already incorporate the combined effect of multiple possible events. Since financial markets aggregate and reflect many underlying risks, hedging instruments based on market indices effectively cover the risks posed by a variety of events without requiring bets or contracts on each discrete occurrence.

This reasoning suggests that the lack of demand for event-specific hedging contracts limits the development and liquidity of typical prediction markets. It also supports the authors’ broader argument that prediction markets fail to attract ‘sharps’ (professional traders), since the existing financial instruments already serve important hedging functions more efficiently. The passage thus highlights a fundamental economic and practical reason why prediction markets on individual nonfinancial events have not flourished: market participants prefer managing risk through broader financial instruments that implicitly cover many events at once.

Q. Why do market participants typically prefer hedging overall market outcomes rather than individual specific events?
A. Because overall market outcomes automatically reflect the combined impact of all relevant events.

The prompt directly targets the core insight of the highlighted passage: the causal link between preferring market-outcome hedging over event-specific hedging and the fact that market outcomes aggregate the impact of many events into a single hedge. It is atomic (tests one coherent concept), faithful to the source material (the answer closely mirrors 'hedging market outcomes already implicitly hedges for not just one event but all the events'), and stable—the question is self-contained and unambiguous even when reviewed months later without the original article context. While it drops the specific examples (law passing, S&P 500), these were illustrative rather than essential, and the generalized framing remains precise and meaningful.


The Limits of Prediction Markets

Q. Why are event-specific prediction markets less favored for hedging?
A. Because they cover single outcomes (like a law passing), while instruments like the S&P 500 hedge many risks at once.

The memory prompt successfully targets the core insight of the highlighted passage: the demand-side explanation for why event-specific prediction markets fail to flourish—namely, that hedging broad market outcomes (e.g., the S&P 500) implicitly hedges all underlying events at once, making discrete event contracts redundant. The question is atomic, testing a single causal relationship, and the answer faithfully reproduces the highlighted idea with concrete anchors ('a law passing' vs. 'S&P 500') drawn directly from the source material. The framing remains self-explanatory in isolation months later, since both the question and answer use specific terms rather than vague abstractions. It neither trivializes the insight into a decorative fragment nor imports external claims (e.g., about sharps or liquidity) beyond what the user engaged with.


The Limits of Prediction Markets

Q. Why do market participants generally prefer hedging market outcomes over specific events?
A. Market outcomes automatically capture the impact of all relevant events.

This prompt is pluckable. It isolates the core insight the user highlighted—the demand-side explanation for why event-specific prediction markets fail—and tests it as a single, atomic causal claim. The question directly mirrors the highlighted passage ('very little demand for hedging events... only demand for hedging the market outcomes'), and the answer faithfully captures the key mechanism: hedging a market outcome implicitly covers all the events that could affect it, since market outcomes aggregate multiple underlying risks. The terminology ('hedging,' 'market outcomes,' 'specific events') is drawn precisely from the source, so the prompt remains self-explanatory when reviewed in isolation months later. It avoids testing trivial fragments or introducing external information beyond the user's engagement, making it both meaningful and stable for long-term retention.


The Limits of Prediction Markets

Q. What is the key difference between hedging specific events (like law passages) and hedging market outcomes (like S&P 500 prices)?
A. Market outcomes implicitly hedge against multiple underlying events

The prompt targets the core insight of the highlighted passage: the asymmetry between hedging discrete events versus hedging market outcomes, where the latter implicitly covers all events affecting financial results. It is atomic (tests one well-defined distinction), uses concrete anchors from the source ('law passages', 'S&P 500 prices') that keep it self-explanatory during isolated reviews months later, and its answer faithfully reproduces the highlighted claim ('implicitly hedges... not just one event but all the events'). While the answer states only one side of the contrast (the implicit-hedge property of market outcomes) without explicitly restating the low demand for event-specific hedging, the question's framing supplies that context, so the pair together recovers the full insight. No trivial fragments or external information are introduced.


The Limits of Prediction Markets

Q. What is the key difference between hedging specific events (like law passages) and hedging market outcomes (like S&P 500 prices)?
A. Market outcomes implicitly hedge against multiple underlying events, while specific event hedging only covers single isolated risks.

This prompt is pluckable. It targets the core insight of the highlighted passage: the distinction between hedging discrete events versus hedging market outcomes, where the latter implicitly aggregates risk across many underlying events. The question is atomic, testing a single well-defined conceptual distinction rather than a compound or trivial detail. It maintains fidelity to the source by using the exact examples given (law passages, S&P 500 prices), which anchor the abstract idea in concrete terms. Stability is strong because both the question and answer are self-explanatory in isolation—the concepts 'event-specific hedging' and 'market outcome hedging' remain meaningful without access to the original article, and the answer precisely captures the causal mechanism ('implicit hedging of multiple events') that the authors emphasized. While the answer slightly compresses the source's claim ('all the events' becomes 'multiple underlying events'), this simplification preserves the essential insight and improves memorability.


The Limits of Prediction Markets

Q. Why do market participants generally prefer hedging market outcomes over specific events?
A. Market outcomes automatically capture the impact of all relevant events, providing more comprehensive risk protection with a single instrument.

This prompt is pluckable. It targets the core insight of the highlighted passage—the causal explanation for why demand concentrates on hedging market outcomes rather than discrete events—and does so atomically, testing one clear concept. The question is self-contained and stable: 'Why do market participants generally prefer hedging market outcomes over specific events?' remains unambiguous even months later without the original article context. The answer faithfully preserves the highlighted idea ('Hedging market outcomes already implicitly hedges for not just one event but all the events that could impact financial outcomes') by stating that market outcomes automatically capture all relevant events in a single instrument. It avoids trivial fragments or external embellishments, and the concrete framing (hedging, market outcomes, specific events) gives built-in context that supports long-term retention.


The Limits of Prediction Markets

If a single high-quality forecasting team is relatively accurate, those who might subsidize a market may not find it to be worth it to eke out additional accuracy. In most cases, high degrees of precision aren’t even actionable – people are likely to take the same course of action if they judge something is 60 percent likely as if they judge it 63 percent (and often even 75 percent) likely.

The highlighted passage argues that once a forecasting team achieves a reasonably high level of accuracy, the potential benefits of further improving prediction precision through subsidized prediction markets diminish substantially. This is because, in practical decision-making, such fine-grained differences in estimated probabilities (for example, between 60% and 63%, or even 75%) typically do not change the decisions people make. In other words, there is a threshold beyond which more precise probability estimates yield little to no additional actionable insight. Therefore, investing in subsidies to advance prediction markets and squeeze out incremental accuracy gains might not be justified, as the marginal utility of that improved precision is low in real-world contexts. This reflects a key economic consideration about diminishing returns on accuracy enhancements in forecasting and the practical limits on the value of ultra-precise predictions for influencing behavior.

Q. Why might a single digit increase in prediction accuracy (ie. from 60% to 65%) not affect decision-making?
A. Because decision-makers typically take the same action within broader probability ranges

The prompt successfully isolates the core insight the user highlighted: fine-grained improvements in forecast accuracy have low marginal value because real-world decisions operate on coarse probability thresholds, so people act identically across small probability differences. It is atomic (tests one concept), faithful to the highlighted passage's point about non-actionable precision, and uses concrete anchor numbers (60% to 65%) that keep it self-explanatory during isolated reviews months later. Its main weakness is that the answer is somewhat thin and borders on restating the phenomenon ('decisions don't change because decisions don't change within ranges'), but since the source itself presents this as a direct claim about actionability rather than offering a deeper mechanism, the prompt remains a durable, retrievable unit of knowledge tied directly to what the user found interesting.


The Limits of Prediction Markets

Q. Why might a 3% increase in prediction accuracy (from 60% to 63%) not affect decision-making?
A. Because decision-makers typically take the same action within broader probability ranges, making small accuracy improvements practically irrelevant.

The prompt is pluckable. It isolates the core insight the user highlighted—that incremental gains in forecast precision (e.g., 60% to 63%) rarely change real-world decisions—making it both meaningful and faithful to the source material. It is atomic, testing a single causal relationship rather than compounding multiple ideas, and its concrete anchoring (the specific 60%→63% example) gives it stability: even months later, reviewed in isolation, the question remains self-explanatory and unambiguous. The answer cleanly expresses the durable principle (decisions are invariant across broad probability ranges) without introducing external information or trivial fragments.


The Limits of Prediction Markets

But these markets are very few and far between compared to the ‘prediction markets on everything’ visions of their advocates. In this vision, we would not just see liquid markets on high-profile events like elections, but all sorts of niche political, scientific, economic, and technological questions.1

The author is emphasizing a significant gap between the actual presence of prediction markets today and the expansive, idealized vision held by many proponents. While some prediction markets do exist—especially for high-profile events like elections—the number and variety of these markets are very limited. This contrasts sharply with advocates' ambitions, who imagine a world where prediction markets cover a vast spectrum of subjects, including highly specialized or niche political, scientific, economic, and technological issues.

Essentially, the author is pointing out that despite enthusiasm for prediction markets as universal tools for forecasting, their practical implementation so far has been narrow and sparse. This passage sets up the broader argument that real-world demand and usage of prediction markets are much more constrained than the hopeful vision suggests. It highlights the distinction between current reality and future aspirations in the domain of prediction markets.

Q. What domains have the largest volume on current prediction markets (2025)?
A. Sports, elections.

The prompt fails fidelity to the highlighted concept. The user's highlight captures the core insight of the passage: the striking gap between today's sparse prediction markets and advocates' expansive 'markets on everything' vision. Instead of targeting this durable, central idea, the prompt tests a tangential factoid about which domains currently have the largest trading volume. Moreover, the answer ('sports') introduces a detail not present in the highlighted text—only 'elections' appears there as an example of a high-profile event—so the prompt is partly unanchored from what the user engaged with. A pluckable prompt would ask something like how the actual prevalence of prediction markets compares to their advocates' vision, keeping the insight atomic, faithful, and stable.


The Limits of Prediction Markets

Prediction markets, unlike most asset markets, are zero-sum – in fact they are negative-sum, once you factor in platform fees. And if your money is in a prediction market, it can’t be invested in equities, or be earning interest in the bank, either. Every winner of a prediction market necessitates an equal and opposite loser. Securities investors with diversified portfolios can expect positive returns in the long term, because they are giving up their money for others to use to create output and wealth, in exchange for a share of what they create. That’s why responsible people have their pensions in stocks and bonds, rather than a diversified portfolio of sportsbooks. Positive-sum savings vehicles are far, far superior to zero-sum ones, for the simple reason that they will grow your savings in the long run.8

The highlighted passage explains a fundamental economic limitation of prediction markets that helps clarify why they do not attract typical investment capital (savers). Unlike most asset markets such as stocks and bonds—which are generally positive-sum, meaning that investors collectively can expect to earn a positive return over the long term—prediction markets are zero-sum or even negative-sum once platform fees are considered.

Being zero-sum means that for every winner in a prediction market, there must be a corresponding loser; the total monetary gains and losses net out to zero (before fees). When factoring in fees charged by the market platform, the overall expected return becomes negative, making prediction markets unattractive as a savings or investment vehicle.

Because money committed to prediction markets cannot simultaneously earn returns elsewhere (like in equities or interest-bearing accounts), rational investors seeking long-term growth choose more productive, positive-sum assets over prediction markets. This explains why savers, who provide a large base of capital in financial markets, avoid prediction markets—they do not offer the wealth-growing potential that traditional securities provide.

Thus, the passage underscores a key reason prediction markets remain niche: they fail to serve as effective long-term savings instruments. This investor aversion contributes to the persistent small size and limited liquidity of prediction markets, hampering their potential to become large, efficient mechanisms for aggregating information on future events.

Q. What fundamental distinguishes prediction markets from traditional investment markets in terms of returns?
A. Prediction markets are zero-sum (one participant's gain equals another's loss), while traditional markets are positive-sum (can generate wealth for all participants).

The prompt directly targets the core insight of the highlighted passage: the structural difference between prediction markets (zero-sum) and traditional securities (positive-sum). It tests a single, coherent conceptual unit—the sum-structure dichotomy—in precise, stable language that remains self-explanatory when reviewed in isolation months later. The phrasing 'in terms of returns' anchors the question clearly, and the contrast format matches how the idea is framed in the source. While it omits the secondary detail that fees make prediction markets strictly negative-sum, this omission does not distort the fundamental distinction the user engaged with; the prompt remains faithful to and complete at the level of the core insight.


The Limits of Prediction Markets

Our view is that, for gamblers, quick resolutions are one of the key things that make a bet attractive and exciting.9 Sports betting sites’ futures bets on longer-term outcomes are far less traded than bets on single games about to happen, even when the future event (like the winner of the Super Bowl) is far higher-profile than tonight’s game

The highlighted passage explains a key insight about the behavior and preferences of gamblers in the context of prediction markets and betting. The authors argue that a major appeal for gamblers is the quick resolution of bets—how soon after placing a bet they find out whether they won or lost. Bets on events that conclude rapidly, such as single sports games happening imminently, tend to attract more trading and engagement than bets on longer-term, future events that might conclude months or even years later—for example, betting on the eventual winner of the Super Bowl far in advance.

This preference for quick resolution helps explain why futures bets on long-term outcomes, despite involving high-profile events, experience significantly less trading volume and interest compared to bets on immediate or live games. The excitement, suspense, and immediacy of knowing the outcome shortly after betting make short-term bets more attractive and engaging for gamblers.

In the broader context of the article, this observation supports the authors' argument that prediction markets on longer-term or esoteric future events may struggle to gain traction because they lack the features that gamblers seek—namely, speed and excitement. It helps clarify one reason why prediction markets have not become as widespread or liquid as some proponents hoped: the natural demand from gamblers, a crucial group for market liquidity, is limited when the bets do not resolve quickly. This dynamic contrasts with sports betting's popularity and volume, which is driven in large part by bettors' desire for rapid and thrilling outcomes.

Q. What is the relationship between a bet's resolution time and its trading popularity?
A. Shorter resolution times generally correlate with MUCH higher trading popularity.

This prompt is pluckable. It isolates a single, well-defined relationship—resolution time versus trading popularity—that is directly and faithfully anchored in the highlighted text (futures bets on long-term outcomes trade far less than imminent single-game bets, even when the future event is higher-profile). The question is atomic, testing one coherent insight rather than a compound or trivial detail, and its phrasing is self-contained and unambiguous, so it remains retrievable months later without the original article context ('What is the relationship between a bet's resolution time and its trading popularity?' clearly implies the gambling/prediction-market domain). The answer preserves the correct directional claim and even signals magnitude ('MUCH higher'), matching the source's emphasis. While it omits the underlying psychological mechanism (gamblers' craving for quick, exciting resolutions), the correlation itself is the durable, testable core of the highlighted insight, making it well-suited for long-term spaced repetition.


The Limits of Prediction Markets

Q. What is the relationship between a bet's resolution time and its trading popularity?
A. Shorter resolution times generally correlate with higher trading popularity, while longer-term bets see less trading activity.

This prompt is pluckable. It targets the exact core insight of the user's highlight: quick bet resolution drives gambler engagement and trading volume, evidenced by heavily traded single-game bets versus lightly traded high-profile futures bets. It satisfies atomicity by testing a single, coherent relational claim (resolution time inversely correlates with trading activity), and it remains faithful to the highlighted concept rather than testing trivial fragments or importing outside information. Stability is strong: the question uses concrete, self-explanatory terms ('resolution time,' 'trading popularity') that stay clear and answerable in isolation months or years later, and the directional answer is unambiguous. While it omits the underlying mechanism (gamblers crave excitement/immediacy), the tested relationship is the durable, memorable takeaway from the passage.


The Limits of Prediction Markets

Market prices tend to be public information. Even if you found a way to gate prices to only paying participants, they could still easily be leaked or passed around. Thus, a free rider problem emerges: many people who value the information a market provides cannot be charged.

The highlighted passage addresses a core challenge in financing prediction markets through user fees or subscriptions. It emphasizes that market prices—that is, the aggregated information about the likelihood of future events derived from trades—are inherently public or easily shareable information. Even if a prediction market operator tried to restrict access to this price information so that only paying users could see it, the information could be quickly leaked or shared with others who do not pay. This creates a classic "free rider problem," where individuals can benefit from the information without contributing financially.

In other words, because the predictive price data is non-excludable and non-rivalrous (anyone who sees it can pass it on without losing it themselves), it is very difficult for the market or its operators to charge users effectively for access. This undermines the possibility of funding prediction markets purely through fees on information access, limiting the financial sustainability of these markets without external subsidies or alternative funding mechanisms.

In the broader context of the article, this free rider problem is one reason why prediction markets struggle to attract sustained investment and why subsidies might be necessary to support them, since the potential users who benefit from the information are hard to charge directly.

Q. What is the free rider problem in prediction markets?
A. When people benefit from market information without paying.

The prompt targets the exact core insight of the highlight—the free rider problem arising because prediction market prices are inherently public and leakable—rather than a trivial fragment or decorative detail. It is atomic, testing a single well-defined concept, and the framing 'in prediction markets' builds in sufficient context for stable retrieval months later, even without the original article. The answer ('people benefit from market information without paying') faithfully captures the essential mechanism the user engaged with: non-excludability of price information prevents operators from charging those who value it, undermining fee-based sustainability. While the answer could optionally include the mechanism (prices being public/leakable), the definition-level recall remains accurate, durable, and directly tied to the highlighted passage.


The Limits of Prediction Markets

Q. What is the free rider problem in prediction markets?
A. When people benefit from market information without paying for it, making it difficult to charge users and sustain the market financially.

The prompt is pluckable. It isolates a single, well-defined concept—the free rider problem specific to prediction markets—that directly corresponds to the user's highlighted text about prices being public/leakable information. The question is atomic and self-explanatory, remaining clear even months later without the original article context, and the answer faithfully captures the core insight: the non-excludability of market information makes it hard to charge users, undermining financial sustainability. While the answer could optionally mention the mechanism (prices are inherently public and easily leaked) more explicitly, the essential durable idea the user cared about is captured accurately and stably.


The Limits of Prediction Markets

Financial institutions have analysts; governments use intelligence agencies; companies use consultants; NGOs partner with economists and data scientists. Institutions employ these alternatives and virtually none employ subsidies.

Why would this be, if each of these groups can be beat when it comes to predicting the future? In many cases, individuals, firms, and governments do not just wish to know the probability of a future event. They would like to know the contingent probabilities around a cluster of events and actions and the reasoning behind those probabilities.

The author is explaining why major institutions—such as financial firms, governments, companies, and NGOs—favor relying on human experts like analysts, intelligence agencies, consultants, and economists rather than subsidizing prediction markets, even though these groups might occasionally be outperformed in forecasting future events.

The key insight is that these institutions are not only interested in a simple probability of a single future event but need more complex, nuanced information: they want to understand conditional or contingent probabilities that interrelate multiple events, and they want detailed reasoning and analysis behind those probabilities. In other words, institutions require richer, contextual forecasts that help them grasp a range of possible futures and the logic driving those assessments, rather than just a raw numeric prediction from a market price.

Thus, the passage argues that the preference for expert-driven forecasting over subsidized prediction markets stems from the need for depth, explanation, and interconnected scenarios in decision-making, which markets, as currently constructed, do not provide. This helps explain why, despite some evidence that prediction markets can be beaten, institutions continue to rely on traditional methods rather than creating or paying for subsidized prediction markets.

Q. Why might an institution prefer the analysis of a traditional forecasting team over a prediction market, even if the latter provides more accurate probability estimates?
A. Contingent probabilities and reasoning often beats (just) single-point predictions.

This prompt is pluckable. It isolates the core causal insight from the highlighted passage—institutions favor expert forecasting because they need contingent probabilities across clusters of events plus the reasoning behind them, not just a single-point probability—and makes that exact distinction the object of recall. The question is atomic, testing one coherent unit of knowledge rather than trivia, and its framing ('even if the latter provides more accurate probability estimates') precisely mirrors the source's setup that prediction markets can be beaten on point forecasts yet remain underused. The answer retains the source's own key vocabulary ('contingent probabilities,' 'reasoning,' 'single-point predictions'), which keeps the prompt stable and self-explanatory when reviewed months later without the original article. While the answer is tersely worded, its compression aids memorability and does not distort or truncate the insight; the parenthetical '(just)' correctly conveys that markets supply only bare point estimates. No external information is introduced beyond the user's engagement with the text.


The Limits of Prediction Markets

Q. Why might an institution prefer a traditional forecasting team over a prediction market, even if the latter provides more accurate probability estimates?
A. Traditional teams provide comprehensive analysis including contingent probabilities and underlying reasoning, not just single-point predictions.

The prompt directly targets the core insight of the user's highlight: institutions favor expert-driven forecasting because they need contingent probabilities across clusters of events plus the reasoning behind them, not just raw point probabilities. It is faithful to the source, tests a single coherent causal claim, and its terms ('prediction market', 'contingent probabilities', 'underlying reasoning') remain self-explanatory even months later without the original context. The 'even if more accurate' framing cleanly preserves the puzzle posed in the article, making the retrieval cue both specific and stable.


The Limits of Prediction Markets

In our view, that information is not waiting to be unearthed. Short of occasionally bribing insiders to leak information through markets, gaining information about the future is difficult and time-consuming, and prediction markets – even where legal – cannot create incentives that inspire people to uncover it. It’s an appealing prospect – that we could have highly accurate prediction markets on all sorts of future outcomes – but the information simply isn’t lying there latent, ready to collect.

The highlighted passage conveys a key skeptical insight about prediction markets: the authors argue that the core problem impeding highly accurate and widely useful prediction markets is not merely regulatory or technological but fundamentally informational. They assert that valuable, precise knowledge about future events—information needed to make sharp, accurate predictions—is not passively available "lying there latent" for markets to discover. Instead, acquiring such information requires active, often difficult, effort and resources, and it cannot be reliably incentivized just by enabling a prediction market. Even where prediction markets are legal and operational, they do not generate sufficient incentive structures to motivate participants to invest the necessary effort to uncover or generate deep, actionable insights about the future. The passage thus challenges the optimistic assumption that simply establishing prediction markets would automatically surface rich, accurate foresight; rather, meaningful information is scarce, hard-earned, and must be actively produced or purchased—it does not spontaneously emerge through market trading alone. This underscores a fundamental limitation of prediction markets as purely information-aggregating mechanisms.

Q. What misconception about prediction markets is described by the "latent information" fallacy?
A. It falsely assumes that valuable predictive information already exists and only needs to be uncovered, rather than created through effort.

This prompt is pluckable. It targets the core insight of the highlighted passage—the skeptical claim that valuable predictive information is not 'lying there latent' but must be earned through difficult, active effort—and frames it as a single, atomic concept ('the latent information fallacy'). Although the exact phrase 'latent information fallacy' appears to be a synthesized label rather than a verbatim quote, it is grounded in the source's own language ('isn't lying there latent') and the question is self-explanatory in isolation, ensuring stable retrieval months later. The answer faithfully captures the misconception the user engaged with, avoiding trivial details like legal status or specific examples, and correctly elevates the informational limitation as the central point rather than regulatory ones.


The Limits of Prediction Markets

Q. What fundamental misconception about prediction markets does the 'latent information' fallacy describe?
A. Valuable predictive information already exists and just needs to be uncovered instead of it being created through work.

The prompt is pluckable. It isolates the single core insight of the highlighted passage—that valuable predictive information is not 'lying there latent' but must be actively created through difficult work—rather than testing surface details like regulatory status or market mechanics. It is atomic, targeting one well-defined concept (the 'latent information' fallacy), and uses precise terminology anchored in the source ('fallacy', 'prediction markets', 'uncovered vs. created'). The answer is concise, faithful to the authors' actual argument as captured in the interpretation, and remains self-explanatory in isolation months later: even without the article at hand, the question supplies enough context (a fundamental misconception about prediction markets) for stable retrieval, and the answer states the causal distinction clearly. It also avoids overgeneralization by framing the claim specifically as a misconception/fallacy rather than an absolute rule about all information.


The Limits of Prediction Markets

Q. What fundamental misconception about prediction markets does the 'latent information' fallacy describe?
A. The belief that valuable predictive information already exists and just needs to be uncovered, rather than recognizing that such information often doesn't exist yet and must be created through effort.

This prompt successfully isolates the central insight of the highlighted passage: that useful predictive information is not 'lying there latent' waiting to be aggregated, but must be actively created through difficult effort—challenging the assumption that simply establishing prediction markets would automatically yield accurate foresight. It is meaningful because it directly targets the core skeptical claim the user engaged with, rather than peripheral details like legality or market mechanics. It is atomic, testing a single coherent concept (the latent-information misconception versus information-as-created). It maintains fidelity to the highlight by capturing the complete unit of knowledge—the contrast between passive discovery and active production—even though the highlight only states part of it. Finally, it is stable: the question frames itself around prediction markets and uses concrete language ('already exists and just needs to be uncovered'), so it remains self-explanatory when reviewed in isolation months or years later.


Summary: Alan Kay recounts the development of Smalltalk, one of the first object-oriented programming languages, from its conceptual origins in the 1960s through its various implementations at Xerox PARC in the 1970s. The paper traces how ideas from earlier systems like Sketchpad, Simula, JOSS, and LISP influenced Smalltalk's design philosophy of treating everything as objects that communicate via messages. Kay describes how Smalltalk evolved alongside the vision of personal computing and the Dynabook concept, emphasizing that object-oriented programming was developed to address both large-scale modularity and small-scale flexibility in programming.

The Early History of Smalltalk

The highlighted phrase, "Why not divide it up into little computers," appears in a section where Alan Kay reflects on his evolving understanding of object-oriented programming (OOP) and system design during the 1960s. In context, Kay recounts the influence of previous systems like the Burroughs 220 file system, the Burroughs B5000, Sketchpad, and Simula, all of which involved concepts of encapsulating data and behavior and layering modularity.

The core idea Kay is expressing here is a fundamental shift in how one might conceptualize computer systems and programming vocabulary. Instead of decomposing a program or system into disparate, less capable components—such as separate data structures, functions, and procedures, each lacking the full power of the whole—he is proposing that these units should themselves be "little computers." This means every component or object embodies both state (data) and process (behavior), encapsulating the full capability of computation internally, interacting with other objects solely through message passing.

This insight is crucial for the foundation of OOP: treating every object as an autonomous computational entity (a "little computer") that manages its own internal operations and communicates with others via well-defined interfaces. It contrasts with traditional programming models that fragment the computer into simpler but less independent parts. Kay links this approach to philosophical concepts such as Leibniz’s monads and Platonic ideals, emphasizing the recursive and self-contained nature of these "little computers."

Thus, within this passage, the phrase captures the pivotal conceptual leap toward object-oriented design: modeling systems as networks of independent, fully capable agents (objects) rather than collections of isolated and weaker data and procedures. This idea laid the groundwork for Smalltalk’s design and the subsequent shift in programming paradigms.

Q. With his OOP epiphany, Kay saw what error in the way people divided up the "whole" of the computer into recursive parts?
A. Why divide it into weaker things called data structures and procedures, and not into little computers?

The prompt directly targets the core insight behind the user's highlight: Kay's pivotal realization that a system should be divided into fully capable 'little computers' (objects encapsulating state and behavior) rather than weaker components like data structures and procedures. It is atomic, testing a single coherent idea, and uses specific, concrete terms ('data structures,' 'procedures,' 'little computers,' Kay's OOP epiphany) that remain self-explanatory in isolation months or years later. The answer faithfully mirrors the highlighted text's own rhetorical framing, aiding long-term retrieval without introducing external details or trivial fragments.


The Early History of Smalltalk

Q. What alternative did the speaker consider instead of dividing into weaker components?
A. Dividing into little computers

This prompt is not pluckable. First, it fails stability: 'the speaker' is a vague, unanchored reference—with no mention of Alan Kay, Smalltalk, or object-oriented programming, the question becomes ambiguous and hard to retrieve consistently months later in isolation. Second, it fails fidelity and meaning: it merely asks for verbatim recall of the highlighted phrase ('little computers') rather than testing the actual insight the user engaged with—the foundational OOP concept that system components should be autonomous, fully capable entities encapsulating both state and behavior, communicating via message passing, rather than fragmented into weaker data structures and procedures. A stronger prompt would target that causal/conceptual leap (e.g., 'In Alan Kay's account of designing Smalltalk, why did he propose modeling program components as little computers instead of weaker parts?'), making the remembered knowledge whole, contextualized, and durable.