Principles and practices of good corporate governance offer a potentially powerful way to manage uncertainty that is often unrealized. Boards allocate capital based on probabilistic assessments of outcomes they often cannot control. Risk committees price scenarios that may never materialize. Institutional investors build social impact (e.g., ESG, CSR, and GRC) mandates around commitments whose fulfillment remains unverifiable in real-time. These and other concerns remain in some if not most of the trillion-dollar socially responsible investing (SRI) industry. The emergence and ascendance of prediction markets over the past year suggest a new social impact architecture that might serve both the informational needs of corporate actors and the accountability demands of the stakeholders they affect. These needs and demands, we argue, may be satisfied by offering a market architecture for betting on positive, aspirational, and socially impactful time-bound event outcomes.

Every MNC managing a global supply chain, every institutional investor operating under a social-impact mandate, and every executive-education program purporting to prepare leaders for the coming decade confronts an identical structural deficit. There is no objective, continuously updated signal of how the public actually expects corporate social commitments to unfold. Disclosure regimes have produced an extraordinary volume of data. What they have not produced, though, is a functioning market in beliefs about the future.

The deficiency is not simply informational. Sustainability reports, third-party ESG ratings, regulatory filings, and NGO scorecards are, without exception, retrospective; they can only describe what has already occurred. None assigns a probability to what has yet to occur. Yet it is precisely that forward-looking signal, the crowd’s best real-time estimate of whether a given social or environmental commitment will actually be met, that matters most for strategic decision-making, capital allocation, and any accountability regime.

Social impact prediction (SIP) markets are our proposed answer. A SIP platform runs on the same logic as any conventional prediction market, and the participants stake positions on the probability of a verifiable future event, with payouts calibrated to the difficulty and precision of that event. The menu is reoriented from tradeable events to outcomes tied to the sustainable development goals, climate commitments, biodiversity thresholds, supply-chain governance milestones, and related corporate accountability metrics.

Building such a market need not be complicated. Firms can begin simply by sponsoring a curated slate of prediction questions tied to verifiable outcomes within their own sector, supply chain, or public commitments. Each question then does triple duty (market signal, statement of belief, and data point in a continuously updated probability map of corporate social performance) and, because payouts are contingent on accuracy rather than sentiment, the architecture rewards participants for surfacing genuine beliefs rather than performative ones. The resulting information is distinct from (and in important respects superior to) conventional social-impact disclosure, precisely because it reflects what informed participants collectively expect to happen.

The value of a SIP platform is not confined to trading returns, nor to whatever share of proceeds is earmarked for social allocation. Aggregating thousands of informed lay predictions about social and environmental outcomes yields a dataset of genuine analytical power, with no real counterpart anywhere in the policy-research toolkit or the corporate intelligence function.

The distinction is worth dwelling on. A SIP platform is built to capture what the public expects to be true of the future, not what has already happened, and that is categorically different information. It captures collective anticipation, risk perception, and the weight lay stakeholders assign to competing causal narratives. For governance practitioners, that signal does something third-party ESG ratings cannot: It is forward-looking, continuously updated, and drawn from precisely the audience whose perceptions ultimately determine reputational and regulatory exposure.

For instance, our case study of the Amazon Rainforest (a measurable, satellite-monitored ecosystem whose future depends, in substantial part, on the governance decisions of corporations with supply-chain exposure) illustrates both the practical potential and the theoretical stakes of this design. A SIP platform operating on Amazon-linked questions would generate a publicly visible, continuously updated probability estimate of deforestation trajectories that ESG analysts, institutional investors, export creditors, and trade partners can read in real time, introducing a form of soft accountability that complements regulatory policy without displacing it. The private sector’s leverage over such outcomes is a function of exposure.

SIP offers something the conventional wisdom rarely can: real-time, stakes-bearing engagement with the causal complexity of social outcomes. An executive who wagers on whether a supply-chain deforestation commitment will actually be met is reasoning about that commitment in a fundamentally different register than one who merely reads about it in a quarterly sustainability report; the act of prediction compels genuine deliberation rather than performative assent. That same logic, we think, should extend to the regulators who will ultimately decide whether SIP markets are permitted to scale. The existing regulatory environment for prediction markets is in rapid flux, and it is far from obvious that categories built for commodity derivatives are the right frame for an instrument that is, in equal measure, financial contract, civic-participation mechanism, and information good. Within this sui generis regulatory framework of its own, SIP might be giving the CFTC the affirmative category it has been missing.

William S. Laufer is the Julian Aresty Endowed Professor of Legal Studies and Business Ethics, Sociology and Criminology, and co-director of the Zicklin Center for Governance and Business Ethics, at the Wharton School, University of Pennsylvania. Eduardo Saad-Diniz is a senior fellow at the Zicklin Center for Governance and Business Ethics, Wharton School, and a researcher at the ARC Foundation (Manaus, Brazil). This post is based on their forthcoming essay, “Going Long on Social Impact Prediction (SIP) Markets.”