Signals, Speculation, and Power: The Polymarket Case as a Window into Prediction Markets, Geopolitical Conflict, and Digital Institutional Change

Prediction markets have returned to the center of public debate because they increasingly sit at the intersection of finance, politics, platform technology, and global media. The recent Polymarket case involving six newly created wallets, reportedly earning about $1.2 million by positioning ahead of strikes linked to Iran before February 28, 2026, offers a powerful entry point for examining how digital markets transform uncertainty into tradeable signals. At one level, the case appears to confirm a classic claim in market theory: dispersed participants, each holding fragments of information, can aggregate expectations more efficiently than traditional commentary or polling. At another level, the same case raises serious questions about market integrity, information asymmetry, moral boundaries, and the social consequences of monetizing forecasts related to war.

This article analyzes the Polymarket case as more than an isolated episode. It treats the event as a sociotechnical moment that reveals how digital prediction markets operate as institutions of interpretation. The article uses a qualitative analytical method grounded in recent reporting, platform developments, and scholarly literature on market efficiency, platform governance, and geopolitical risk. It applies three theoretical frameworks: Bourdieu’s theory of fields and capital, world-systems analysis, and institutional isomorphism. Through these lenses, the article argues that prediction markets do not merely reflect reality. They produce hierarchies of credibility, concentrate symbolic power, and extend financial logics into domains once considered ethically exceptional.

The analysis shows five central findings. First, prediction markets can aggregate expectations under conditions of uncertainty, but their performance depends heavily on access, timing, contract clarity, and governance. Second, in geopolitically sensitive settings, unusual profits may signal either superior interpretation or unequal access to information, and the distinction is often difficult to establish empirically. Third, war-related contracts convert political violence into speculative infrastructure, producing ethical tensions that cannot be solved by efficiency arguments alone. Fourth, as these platforms expand, they increasingly resemble established financial institutions, even while presenting themselves as alternative knowledge systems. Fifth, their growth reflects broader transformations in the world economy, where data, attention, and credibility function as strategic assets.

The article concludes that the Polymarket case should be understood as an early indicator of a wider institutional shift. Prediction markets are becoming part of the architecture through which contemporary societies price uncertainty, debate truth, and organize expectations. Their future legitimacy will depend not only on technical accuracy, but also on governance, transparency, and normative limits.

Introduction

During the last two decades, prediction markets moved from academic experiments and niche platforms into mainstream public visibility. They became attractive because they promised a simple and powerful idea: that prices generated through trading could reveal collective expectations about future events. Supporters argued that markets can synthesize dispersed information more effectively than experts, journalists, or bureaucratic forecasting systems. Critics replied that such confidence ignores inequality in information access, the possibility of manipulation, and the moral discomfort created when human suffering becomes a speculative opportunity.

The Polymarket case involving six newly created wallets and forecasts linked to Iran before February 28, 2026, matters precisely because it concentrates these tensions into one visible episode. Public discussion of the case did not focus only on whether the traders were “right.” It focused on why they were right, how early they were right, what kind of information may have informed their positions, and whether such profits can be treated as a neutral market outcome. In this sense, the case is important not because it provides a final answer about prediction markets, but because it reveals the questions that prediction markets now force societies to confront.

This article takes that case as a starting point for a broader academic inquiry. The central research question is: What does the Polymarket case reveal about the economic, institutional, and ethical character of prediction markets when they are used to trade on geopolitical conflict? Several sub-questions follow from this. Do prediction markets improve informational efficiency in high-uncertainty contexts? Under what conditions do they amplify information asymmetry? How do they reshape the symbolic meaning of conflict by turning it into priceable probability? Why do they increasingly resemble conventional financial institutions despite their claims of novelty or decentralization?

The article speaks to management, technology, and institutional studies at the same time. From a management perspective, prediction markets are organizational tools for decision support, risk assessment, and signal extraction. From a technology perspective, they are digital platforms built on data infrastructures, incentives, and governance protocols. From an institutional perspective, they are social arrangements that claim authority over the future. Their relevance is therefore not limited to crypto communities or speculative traders. They matter for firms, regulators, media organizations, political actors, and researchers studying how modern societies coordinate uncertainty.

The argument developed here is straightforward. Prediction markets are not merely neutral mechanisms for collecting forecasts. They are fields of struggle in which different forms of capital interact; they are embedded in unequal world structures that determine who benefits from information; and they are undergoing institutional normalization by borrowing legitimacy from established finance. The Polymarket case is therefore best read as a small but revealing event inside a much larger transformation: the financialization of expectation itself.

To develop this argument, the article proceeds in six steps. First, it outlines the background literature on prediction markets and their relation to uncertainty, market efficiency, and ethics. Second, it develops a theoretical framework using Bourdieu, world-systems theory, and institutional isomorphism. Third, it explains the qualitative method used in the study. Fourth, it analyzes the Polymarket case through these theories. Fifth, it presents the main findings. Finally, it concludes with implications for research, governance, and the future of digital markets.

Background and Theoretical Framework

Prediction markets as information systems

Prediction markets are organized exchanges where participants buy and sell contracts tied to future outcomes. In theory, the price of a contract represents the market’s collective estimate of the probability that an event will occur. This logic draws strength from older ideas associated with the “wisdom of crowds” and the efficient market hypothesis. If many people possess partial information, and if they have incentives to reveal that information through trading, then a market price may summarize dispersed knowledge more effectively than an individual forecast.

The practical appeal of prediction markets has been strong. Firms have experimented with them for sales forecasting, project completion estimates, and product launch expectations. Political observers have used them for election forecasting. Media outlets increasingly cite them because they offer continuous, numerical, and seemingly objective interpretations of uncertainty. In a digital environment saturated with opinion, prediction markets present themselves as disciplined alternatives: not what people say, but what people are willing to risk money on.

Yet this appeal rests on demanding assumptions. Prediction markets work well only when participants can trade freely, understand contract terms, respond to new information, and trust the integrity of settlement procedures. They also depend on some distribution of relevant knowledge across the participant base. When information is heavily unequal, the market may aggregate less than it exposes. Prices may then reveal not a collective judgment, but the advantage of a few better-positioned actors. In high-stakes geopolitical settings, this becomes especially sensitive because privileged information may be politically, militarily, or ethically loaded.

Bourdieu: field, capital, and symbolic power

Pierre Bourdieu’s sociology helps explain why prediction markets cannot be understood only as neutral economic tools. For Bourdieu, social life consists of fields: structured arenas in which actors struggle over resources, positions, and legitimacy. Different forms of capital matter inside these fields, including economic capital, social capital, cultural capital, and symbolic capital. Actors do not compete only over money. They also compete over recognition, credibility, and authority.

Applied to prediction markets, this approach shifts the analysis from price alone to the structure behind price formation. A trader’s success may depend on more than analytical skill. It may also depend on network access, technical fluency, political proximity, reputation, or the ability to interpret elite discourse. What appears as a market signal is thus shaped by the unequal distribution of capitals. Prediction markets become fields in which technologically literate traders, platform operators, data analysts, political insiders, and media amplifiers struggle to define what counts as credible foresight.

Bourdieu also helps illuminate symbolic power. When a prediction market price is cited by journalists or institutions, the market acquires authority not simply because it exists, but because others recognize it as meaningful. This recognition transforms the platform into a legitimate interpreter of the future. The market’s number begins to function as a public fact, even when it is only a contingent outcome of strategic trades. Symbolic power therefore matters as much as informational accuracy.

World-systems theory: unequal geographies of uncertainty

World-systems theory, associated above all with Immanuel Wallerstein, places markets within global hierarchies of power. The modern world economy is structured by unequal relations among core, semi-peripheral, and peripheral zones. These inequalities shape flows of capital, labor, information, and coercion. Applied to the study of prediction markets, world-systems analysis reveals that forecasts about geopolitical conflict are not socially neutral. They emerge within a global order where some regions become objects of speculation more than subjects of interpretation.

The Polymarket case illustrates this point sharply. Conflict involving Iran becomes a tradeable asset for participants whose lives may be geographically distant from the violence under consideration. The profits are privatized, but the risks and losses of war remain social, territorial, and unequal. This is not simply a moral objection. It is a structural one. The world economy repeatedly converts instability in certain regions into opportunity elsewhere. Prediction markets can deepen this pattern by providing rapid financial channels for monetizing geopolitical asymmetry.

World-systems analysis also clarifies the role of digital infrastructure. Platforms headquartered or culturally centered in powerful economies can define the global terms through which conflict is priced and discussed. Even when users are geographically dispersed, the epistemic center often remains concentrated. This means that the act of forecasting war is embedded in a hierarchy of voice, visibility, and gain.

Institutional isomorphism: why new platforms become old institutions

Institutional theory, especially the concept of isomorphism developed by DiMaggio and Powell, explains why organizations in uncertain environments tend to become more similar over time. They imitate successful models, conform to regulatory expectations, and professionalize their operations. Although prediction markets often describe themselves as disruptive or decentralized, their growth increasingly pushes them toward the forms and practices of conventional finance.

This isomorphism occurs in three major ways. First, coercive isomorphism arises through regulation, legal pressure, and state oversight. As prediction markets attract political attention, they must speak the language of compliance, surveillance, and formal rules. Second, mimetic isomorphism emerges when platforms adopt the design, terminology, or reputational strategies of more established financial institutions. Third, normative isomorphism develops as lawyers, economists, technologists, and policy professionals shape the field according to shared standards of expertise.

The result is paradoxical. Platforms that once celebrated radical openness or anti-institutional innovation increasingly seek legitimacy by resembling institutions they originally differentiated themselves from. This does not necessarily reduce risk. It may instead broaden their authority, making their outputs appear more objective and professionally grounded than they actually are.

Ethics and the monetization of conflict

Theoretical discussion of prediction markets often privileges informational efficiency. But the Polymarket case forces a different question: even if a market is efficient, what should it be allowed to price? This is not a purely philosophical issue. It concerns the social meaning of turning military action, death, or political violence into speculative contracts.

Three ethical tensions stand out. First, there is the question of incentive distortion. Even if traders cannot influence geopolitical outcomes directly, the act of profiting from violence may alter public discourse by rewarding attention to escalation. Second, there is the question of dignity and moral boundary. Some events may be socially inappropriate as objects of trade, regardless of predictive value. Third, there is the question of legitimacy spillover. Once such contracts exist, their prices may be cited as authoritative indicators, thereby normalizing the commodification of conflict.

These tensions do not disappear because a platform is technologically innovative. On the contrary, digital scale and visibility can intensify them.

Method

This article uses a qualitative analytical method. It is not an econometric test of prediction market accuracy, nor an investigation claiming proof of insider trading. Instead, it is a theoretically informed case analysis. The study draws on three sources of material: recent reporting on the Polymarket wallets and related regulatory scrutiny; scholarly literature on prediction markets, market efficiency, insider information, and geopolitical risk; and classic social theory relevant to fields, institutions, and global inequality.

The choice of a qualitative method is deliberate. The significance of the Polymarket case lies not only in numerical profit or contract pricing, but in the meanings attached to those numbers. Public controversies about prediction markets are interpretive controversies. They concern trust, legitimacy, governance, and moral classification. These issues are often better explored through conceptual analysis than through narrow statistical measurement.

The method proceeds in four stages. First, the case is reconstructed at a descriptive level: what happened, what was reported, and what questions emerged. Second, the event is situated within the broader development of prediction markets as digital institutions. Third, the three theoretical frameworks are applied to identify the logics operating beneath the surface of the case. Fourth, the analysis synthesizes these insights into broader findings about management, technology, and society.

The article does not attempt to judge the legal culpability of particular actors. Reports about unusual profits and suspicious timing raise questions, but questions are not identical to proof. This distinction is essential for scholarly caution. The aim here is not to resolve an enforcement matter. It is to understand what kinds of institutional and ethical problems such a case makes visible.

Analysis

1. The case as a problem of information aggregation

At first glance, the Polymarket episode seems to validate a strong market argument. A small number of wallets placed positions that later proved highly profitable. In classic prediction-market logic, this may suggest that traders processed available signals faster or more accurately than the wider public. Markets are supposed to reward such superior interpretation. If the event had been foreseeable from open-source information, diplomatic patterns, military posture, or narrative signals, then the profits could be read as evidence that the market aggregated weak signals into a sharp forecast.

This interpretation has real analytical value. Modern geopolitical events are often preceded by fragmented indicators: troop movement, political rhetoric, unusual silence, media framing, sanctions language, and energy-market behavior. Skilled actors may synthesize such clues better than traditional news commentary. In that sense, prediction markets can act as rapid-response knowledge systems.

But the same case also shows the limits of celebratory efficiency claims. Markets do not tell us why a price moved or a profit was earned. A profitable position may reflect excellent inference, privileged access, imitation of another informed actor, or opportunistic concentration in a thin market. Prices reveal outcomes, not epistemic biographies. This ambiguity matters greatly in geopolitical contexts because the stakes are higher and the possibility of unequal access is more consequential.

From a management perspective, the lesson is clear: prediction markets may generate useful signals, but those signals should not be treated as self-validating truth. Organizations that use such markets for risk monitoring must distinguish between signal value and signal legitimacy. A forecast can be accurate for troubling reasons.

2. Bourdieu and the unequal distribution of foresight

Bourdieu’s framework reveals the social structure hidden behind the appearance of market objectivity. Not everyone enters a prediction market with the same resources. Economic capital matters because larger or more timely positions can shape visible price trends. Cultural capital matters because interpreting contract language, platform mechanics, geopolitical signals, and crypto-based transaction systems requires specialized competence. Social capital matters because access to informed networks, influential accounts, or elite discourse may improve timing. Symbolic capital matters because some actors, by reputation or visibility, can influence how others read the market.

The six-wallet case is especially interesting because the accounts were reportedly newly created. Newness can mean many things. It can indicate fresh entrants acting on genuine conviction. It can indicate strategic anonymity. It can indicate efforts to separate positions from prior histories. In Bourdieusian terms, the apparent anonymity of digital wallets does not eliminate capital; it often masks it. The social resources enabling profitable action may be hidden rather than absent.

This perspective helps explain a recurring paradox in digital markets. Platforms often present themselves as flattening hierarchies because participation is open and interfaces are public. Yet field inequalities remain. Some actors know how to read weak signals. Some know how to move quickly across technical infrastructures. Some are socially closer to decision-making circles than others. A market price, therefore, is not simply the voice of “the crowd.” It is the temporary outcome of a structured struggle among unequally endowed participants.

Bourdieu also sharpens the role of media. Once journalists, commentators, and influencers cite a market probability, they convert a trading outcome into symbolic authority. The platform becomes a credible oracle. This can create reflexive loops: visibility attracts liquidity; liquidity attracts credibility; credibility attracts more visibility. In this cycle, symbolic capital accumulates around the market itself. The market becomes not only a place where expectations are traded, but a place where public legitimacy is manufactured.

3. World-systems theory and the geography of speculative conflict

World-systems analysis pushes the discussion beyond platform design to global inequality. Prediction markets on geopolitical conflict often involve asymmetrical relationships between those exposed to violence and those able to profit from forecasting it. When a potential strike, coup, crisis, or sanctions escalation becomes a contract, distant traders may gain from volatility that local populations experience as danger, displacement, or death.

The Polymarket case illustrates this structural asymmetry. A conflict linked to Iran became, in part, a financial object in a digital marketplace. This does not mean traders caused the event. The issue is different: the event entered an economic circuit in which uncertainty itself became monetizable. Such monetization is characteristic of a world economy that extracts value from instability as well as from production.

This matters for technology studies because digital platforms accelerate the conversion of distant events into immediate speculative opportunities. The core no longer needs to wait for formal market instruments to price peripheral risk. Platformized finance can create ad hoc instruments rapidly, circulate them globally, and embed them in media discussion. The result is a compressed chain between geopolitical danger and speculative gain.

A world-systems perspective also highlights whose conflicts become market topics. Not all forms of uncertainty are equally tradable. Events occurring in geopolitically charged regions often attract speculative attention precisely because they are already positioned within global narratives of risk, security, and intervention. These narratives are unevenly distributed. They reflect historical power. Thus, prediction markets do not merely price events. They reproduce a map of which lives, territories, and crises are treated as globally legible objects of probability.

4. Institutional isomorphism and the normalization of prediction platforms

As prediction markets grow, they are increasingly subject to the pressures described by institutional theory. Public controversy, regulatory interest, and user demand all push platforms toward organizational stabilization. They must clarify contract rules, improve dispute resolution, standardize compliance practices, and cultivate reputational trust. These are classic moments of institutionalization.

The Polymarket case accelerates these pressures because unusual profits in war-related markets raise questions that platforms cannot solve through interface design alone. They need governance. They need procedures for suspicious activity. They need legitimacy in the eyes of regulators, media, and possibly institutional users. In response, they begin to resemble more established exchanges and financial intermediaries.

This isomorphism is important because it changes how society interprets these platforms. A market that appears as a playful or fringe experiment can be dismissed. A market that looks procedurally disciplined and professionally managed can gain authority. This authority may be useful when the platform genuinely improves transparency. But it can also mask unresolved conceptual problems. A well-governed market can still be morally troubling if the underlying contracts commodify violence.

Institutional isomorphism also explains why prediction markets now attract interest beyond crypto culture. Businesses, media organizations, and policymakers increasingly encounter them as legitimate tools. This expansion makes the ethical and informational questions more urgent, not less.

5. Ambiguity, contract design, and epistemic fragility

Prediction markets depend on contract clarity. Ambiguous terms produce contested outcomes, reduce trust, and create openings for strategic interpretation. In conflict-related markets, ambiguity can be especially damaging because concepts such as “strike,” “entry,” “escalation,” or “regime change” may carry military, legal, and symbolic meanings that differ across observers.

The Polymarket case should therefore be read not only as a question of who traded well, but as a reminder that forecasting markets rest on linguistic infrastructures. A contract is never a pure mirror of reality. It is an operational definition imposed on reality. When political events are complex, that definition can become unstable.

From a knowledge perspective, this means prediction markets are epistemically fragile. Their outputs appear precise, but they depend on human choices about wording, source determination, settlement, and arbitration. The precision of a probability can hide the softness of the underlying categories. This is another reason why the authority of such markets must be treated carefully.

6. The ethics of monetizing war forecasts

The strongest defense of prediction markets is usually consequentialist: if they improve forecasting, then they generate useful knowledge for society. But this defense is incomplete in the case of war-related markets. Ethical evaluation must consider more than informational performance.

One issue is moral distance. A trader may see a conflict contract as just another instrument. But the contract concerns events that for others are existential. The distance between speculative action and lived consequence creates a legitimacy gap. Another issue is public culture. When military escalation is represented as a tradable probability, the language of crisis can become absorbed into a language of opportunity. This may not change policy directly, but it changes the social atmosphere in which policy is discussed.

There is also the question of institutional boundary. Modern societies place limits on what may be bought, sold, or wagered upon without reputational cost. Those limits vary across cultures and over time, but they remain socially meaningful. Prediction markets challenge such boundaries by reframing sensitive events as neutral information products. The Polymarket case shows that efficiency alone cannot resolve whether this reframing is acceptable.

Findings

Five major findings emerge from the analysis.

Finding 1: Prediction markets can aggregate expectations, but not all accuracy is institutionally equal

The case supports the idea that prediction markets may detect or synthesize meaningful signals before broader public recognition. However, accuracy in outcome does not settle the question of institutional legitimacy. A correct forecast may arise from open-source synthesis, privileged information, imitation, or structural advantage. Therefore, organizations and observers should avoid treating profitable market outcomes as automatic proof of healthy information aggregation.

Finding 2: Information asymmetry is not an exception to market logic; it is often built into digital market fields

The Bourdieusian analysis shows that unequal access to capital is central, not accidental. Technical fluency, network access, symbolic credibility, and financial capacity shape who can act decisively and who can influence price discovery. The appearance of openness in digital platforms may conceal deep asymmetries in practical capacity.

Finding 3: Geopolitical prediction markets extend the financialization of instability

World-systems theory demonstrates that conflict-related contracts convert geopolitical risk into speculative value. This process is not merely individual opportunism. It reflects a broader world-economic pattern in which instability in some regions becomes monetizable elsewhere. Prediction markets compress this relationship and make it more visible.

Finding 4: Prediction platforms are moving toward institutional normalization

Institutional isomorphism helps explain why prediction markets increasingly resemble formal financial institutions. Regulatory pressure, reputational competition, and professional expertise are pushing platforms toward standardized governance. This may improve operational reliability, but it also increases the public authority of these markets, which means their ethical and conceptual weaknesses carry larger social consequences.

Finding 5: The central issue is not only whether prediction markets are accurate, but what kind of public knowledge order they create

Prediction markets do more than forecast events. They structure attention, define credibility, and influence how uncertainty is publicly understood. In this sense, they are emerging institutions of epistemic governance. The Polymarket case reveals that the future of these markets depends on the kind of knowledge order societies are willing to legitimize.

Conclusion

The Polymarket case involving six newly created wallets and profits linked to forecasts ahead of strikes before February 28, 2026, should not be read as a narrow story about clever trading alone. It should be understood as a revealing case of how digital platforms, financial incentives, and geopolitical uncertainty now interact. The episode shows both the strength and the danger of prediction markets. They can condense scattered expectations into visible prices. Yet those prices are produced inside unequal fields, within a stratified world economy, and through institutions still struggling to define their own legitimacy.

Three broader conclusions follow.

First, prediction markets are becoming central instruments for reading uncertainty in contemporary society. Their relevance extends beyond crypto speculation into management, journalism, risk analysis, and public discourse. This makes their governance a serious institutional question rather than a niche technical issue.

Second, the old debate between efficiency and ethics is no longer enough. A market can be efficient in limited informational terms and still be socially corrosive. War-related contracts expose this tension clearly. Efficiency tells us how a price may summarize information. Ethics asks whether the object being priced should enter speculative circulation at all.

Third, future research must move beyond simple accuracy comparisons between prediction markets and polls or expert forecasts. Scholars should investigate the social composition of traders, the governance of contract design, the media amplification of market probabilities, and the geopolitical distribution of harms and gains. Comparative studies across platforms, jurisdictions, and event types would be especially valuable. Management scholars should also examine whether organizations can use prediction markets responsibly without importing their hidden asymmetries into internal decision systems.

In the end, the deepest significance of the Polymarket case lies in what it says about our historical moment. We live in a time when probability has become a product, uncertainty has become infrastructure, and visibility itself can be monetized. Prediction markets promise a sharper reading of the future. The real question is what kind of social order is being built when that reading becomes tradable.

Hashtags

#PredictionMarkets #Polymarket #GeopoliticalRisk #DigitalPlatforms #InstitutionalTheory #MarketEthics #TechnologyAndSociety

References

Arrow, K. J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J. O., Levmore, S., Litan, R., Milgrom, P., Nelson, F. D., Neumann, G. R., Ottaviani, M., Schelling, T. C., Shiller, R. J., Smith, V. L., Snowberg, E., Sunstein, C. R., Tetlock, P. C., Tetlock, P. E., Varian, H. R., Wolfers, J., & Zitzewitz, E. (2008). The promise of prediction markets. Science, 320(5878), 877-878.

Bourdieu, P. (1984). Distinction: A Social Critique of the Judgement of Taste. Harvard University Press.

Bourdieu, P. (1993). The Field of Cultural Production. Columbia University Press.

Bourdieu, P. (1998). Practical Reason: On the Theory of Action. Stanford University Press.

Brenner, N. (2004). New State Spaces: Urban Governance and the Rescaling of Statehood. Oxford University Press.

DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147-160.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.

Forsythe, R., Nelson, F., Neumann, G., & Wright, J. (1992). Anatomy of an experimental political stock market. American Economic Review, 82(5), 1142-1161.

Galanis, S., Li, C., Mossel, E., Mueller-Frank, M., Pai, M., & Strack, P. (2024). Information aggregation under ambiguity. Review of Economic Studies, 91(6), 3423-3459.

Hayek, F. A. (1945). The use of knowledge in society. American Economic Review, 35(4), 519-530.

Hanson, R. (2003). Combinatorial information market design. Information Systems Frontiers, 5(1), 107-119.

Knight, F. H. (1921). Risk, Uncertainty and Profit. Houghton Mifflin.

Polanyi, K. (1944). The Great Transformation. Farrar & Rinehart.

Snowberg, E., Wolfers, J., & Zitzewitz, E. (2013). Prediction markets for economic forecasting. In G. Elliott & A. Timmermann (Eds.), Handbook of Economic Forecasting (Vol. 2, pp. 657-687). Elsevier.

Stiglitz, J. E. (2000). The contributions of the economics of information to twentieth century economics. Quarterly Journal of Economics, 115(4), 1441-1478.

Surowiecki, J. (2004). The Wisdom of Crowds. Doubleday.

Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown.

Wallerstein, I. (2004). World-Systems Analysis: An Introduction. Duke University Press.

Wolfers, J., & Zitzewitz, E. (2004). Prediction markets. Journal of Economic Perspectives, 18(2), 107-126.

Zhu, C., Yang, X., & Li, C. (2014). The predictability of aggregate insider trading on future market returns. Pacific-Basin Finance Journal, 29, 141-157.