Archives For epistemic methods

Author Information: Saana Jukola and Henrik Roeland Visser, Bielefeld University, and

Jukola, Saana; and Henrik Roland Visser. “On ‘Prediction Markets for Science,’ A Reply to Thicke” Social Epistemology Review and Reply Collective 6, no. 11 (2017): 1-5.

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In his paper, Michael Thicke critically evaluates the potential of using prediction markets to answer scientific questions. In prediction markets, people trade contracts that pay out if a certain prediction comes true or not. If such a market functions efficiently and thus incorporates the information of all market participants, the resulting market price provides a valuable indication of the likelihood that the prediction comes true.

Prediction markets have a variety of potential applications in science; they could provide a reliable measure of how large the consensus on a controversial finding truly is, or tell us how likely a research project is to deliver the promised results if it is granted the required funding. Prediction markets could thus serve the same function as peer review or consensus measures.

Thicke identifies two potential obstacles for the use of prediction markets in science. Namely, the risk of inaccurate results and of potentially harmful unintended consequences to the organization and incentive structure of science. We largely agree on the worry about inaccuracy. In this comment we will therefore only discuss the second objection; it is unclear to us what really follows from the risk of harmful unintended consequences. Furthermore, we consider another worry one might have about the use of prediction markets in science, which Thicke does not discuss: peer review is not only a quality control measure to uphold scientific standards, but also serves a deliberative function, both within science and to legitimize the use of scientific knowledge in politics.

Reasoning about imperfect methods

Prediction markets work best for questions for which a clearly identifiable answer is produced in the not too distant future. Scientific research on the other hand often produces very unexpected results on an uncertain time scale. As a result, there is no objective way of choosing when and how to evaluate predictions on scientific research. Thicke identifies two ways in which this can create harmful unintended effects on the organization of science.

Firstly, projects that have clear short-term answers may erroneously be regarded as epistemically superior to basic research which might have better long-term potential. Secondly, science prediction markets create a financial incentive to steer resources towards research with easily identifiable short-term consequences, even if more basic research would have a better epistemic pay-off in the long-run.

Based on their low expected accuracy and the potential of harmful effects on the organization of science, Thicke concludes that science prediction markets might be a worse ‘cure’ than the ‘disease’ of bias in peer review and consensus measures. We are skeptical of this conclusion for the same reasons as offered by Robin Hanson. While the worry about the promise of science prediction markets is justified, it is unclear how this makes them worse than the traditional alternatives.

Nevertheless, Thicke’s conclusion points in the right direction: instead of looking for a more perfect method, which may not become available in the foreseeable future, we need to judge which of the imperfect methods is more palatable to us. Doing that would, however, require a more sophisticated evaluation of the different strengths and weakness of the different available methods and how to trade those off, which goes beyond the scope of Thicke’s paper.

Deliberation in Science

An alternative worry, which Thicke does not elaborate on, is the fact that peer review is not only expected to accurately determine the quality of submissions and conclude what scientific work deserves to be funded or published, but it is also valued for its deliberative nature, which allows it to provide reasons to those affected by the decisions made in research funding or the use of scientific knowledge in politics. Given that prediction markets function through market forces rather than deliberative procedure, and produce probabilistic predictions rather than qualitative explanations, this might be (another) aspect on which the traditional alternative of peer review outperforms science prediction markets.

Within science, peer review serves two different purposes. First, it functions as a gatekeeping mechanism for deciding which projects deserve to be carried out or disseminated – an aim of peer review is to make sure that good work is being funded or published and undeserving projects are rejected. Second, peer review is often taken to embody the critical mechanism that is central to the scientific method. By pointing out defects and weaknesses in manuscripts or proposals, and by suggesting new ways of approaching the phenomena of interest, peer reviewers are expected to help authors improve the quality of their work. At least in an ideal case, authors know why their manuscripts were rejected or accepted after receiving peer review reports and can take the feedback into consideration in their future work.

In this sense, peer review represents an intersubjective mechanism that guards against the biases and blind spots that individual researchers may have. Criticism of evidence, methods and reasoning is essential to science, and necessary for arriving at trustworthy results.[1] Such critical interaction thus ensures that a wide variety of perspectives in represented in science, which is both epistemically and socially valuable. If prediction markets were to replace peer review, could they serve this second, critical, function? It seems that the answer is No. Prediction markets do not provide reasons in the way that peer review does, and if the only information that is available are probabilistic predictions, something essential to science is lost.

To illustrate this point in a more intuitive way: imagine that instead of writing this comment in which we review Thicke’s paper, there is a prediction market on which we, Thicke and other authors would invest in bets regarding the likelihood of science prediction markets being an adequate replacement of the traditional method of peer review. From the resulting price signal we would infer whether predictions markets are indeed an adequate replacement or not. Would that allow for the same kind of interaction in which we now engage with Thicke and others by writing this comment? At least intuitively, it seems to us that the answer is No.

Deliberation About Science in Politics

Such a lack of reasons that justify why certain views have been accepted or rejected is not only a problem for researchers who strive towards getting their work published, but could also be detrimental to public trust in science. When scientists give answers to questions that are politically or socially sensitive, or when controversial science-based recommendations are given, it is important to explain the underlying reasons to ensure that those affected can – at least try to – understand them.

Only if people are offered reasons for decisions that affect them can they effectively contest such decisions. This is why many political theorists regard the ability of citizens to demand an explanation, and the corresponding duty of decision-makers to be responsive to such demands, as a necessary element of legitimate collective decisions.[2] Philosophers of science like Philip Kitcher[3] rely on very similar arguments to explain the importance of deliberative norms in justifying scientific conclusions and the use of scientific knowledge in politics.

Science prediction markets do not provide substantive reasons for their outcome. They only provide a procedural argument, which guarantees the quality of their outcome when certain conditions are fulfilled, such as the presence of a well-functioning market. Of course, one of those conditions is also that at least some of the market participants possess and rely on correct information to make their investment decisions, but that information is hidden in the price signal. This is especially problematic with respect to the kind of high-impact research that Thicke focuses on, i.e. climate change. There, the ability to justify why a certain theory or prediction is accepted as reliable, is at least as important for the public discourse as it is to have precise and accurate quantitative estimates.

Besides the legitimacy argument, there is another reason why quantitative predictions alone do not suffice. Policy-oriented sciences like climate science or economics are also expected to judge the effect and effectiveness of policy interventions. But in complex systems like the climate or the economy, there are many different plausible mechanisms simultaneously at play, which could justify competing policy interventions. Given the long-lasting controversies surrounding such policy-oriented sciences, different political camps have established preferences for particular theoretical interpretations that justify their desired policy interventions.

If scientists are to have any chance of resolving such controversies, they must therefore not only produce accurate predictions, but also communicate which of the possible underlying mechanisms they think best explains the predicted phenomena. It seems prediction markets alone could not do this. It might be useful to think of this particular problem as the ‘underdetermination of policy intervention by quantitative prediction’.

Science prediction markets as replacement or addition?

The severity of the potential obstacles that Thicke and we identify depends on whether science prediction markets would replace traditional methods such as peer review, or would rather serve as addition or even complement to traditional methods. Thicke provides examples of both: in the case of peer review for publication or funding decisions, prediction markets might replace traditional methods. But in the case of resolving controversies, for instance concerning climate change, it aggregates and evaluates already existing pieces of knowledge and peer review. In such a case the information that underlies the trading behavior on the prediction market would still be available and could be revisited if people distrust the reliability of the prediction market’s result.

We could also imagine that there are cases in which science prediction markets are used to select the right answer or at least narrow down the range of alternatives, after which a qualitative report is produced which provides a justification of the chosen answer(s). Perhaps it is possible to infer from trading behavior which investors possess the most reliable information, a possibility explored by Hanson. Contrary to Hanson, we are skeptical of the viability of this strategy. Firstly, the problem of the underdetermination of theory by data suggests that different competing justifications might be compatible with the observation trading behavior. Secondly, such justifications would be post-hoc rationalizations, which sound plausible but might lack power to discriminate among alternative predictions.


All in all, we are sympathetic to Michael Thicke’s critical analysis of the potential of prediction markets in science and share his skepticism. However, we point out another issue that speaks against prediction markets and in favor of peer review: Giving and receiving reasons for why a certain view should be accepted or rejected. Given that the strengths and weaknesses of these methods fall on different dimensions (prediction markets may fare better in accuracy, while in an ideal case peer review can help the involved parties understand the grounds why a position should be approved), it is important to reflect on what the appropriate aims in particular scientific and policy context are before making a decision on what method should be used to evaluate research.


Hanson, Robin. “Compare Institutions To Institutions, Not To Perfection,” Overcoming Bias (blog). August 5, 2017. Retrieved from:

Hanson, Robin. “Markets That Explain, Via Markets To Pick A Best,” Overcoming Bias (blog), October 14, 2017

[1] See, e.g., Karl Popper, The Open Society and Its Enemies. Vol 2. (Routledge, 1966) or Helen Longino, Science as Social Knowledge. Values and Objectivity in Scientific Inquiry (Princeton University Press, 1990).

[2] See Jürgen Habermas, A Theory of Communicative Action, Vols1 and 2. (Polity Press, 1984 & 1989) & Philip Pettit, “Deliberative democracy and the discursive dilemma.” Philosophical Issues, vol. 11, pp. 268-299, 2001.

[3] Philip Kitcher, Science, Truth, and Democracy (Oxford University Press, 2001) & Philip Kitcher, Science in a democratic society (Prometheus Books, 2011).

Author Information: Steven Bland, Huron University College,

Bland, Steven.“Pragmatic vs. Dialectical Strategies for Resisting Epistemic Relativism: A Reply to Richard Fumerton.” Social Epistemology Review and Reply Collective 5, no. 5 (2016): 37-40.

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Richard Fumerton has raised several important questions regarding my dialectical strategy for resisting epistemic relativism.[1] My hope is that by answering these questions, I can both clarify and bolster my position.

On Epistemic Circularity

My target in “Circularity, Scepticism and Epistemic Relativism” is the problem of epistemic circularity, according to which an argument for the truth-conduciveness of any epistemic framework must itself be an application of the framework at issue.[2] This problem presents both a sceptical and a relativist threat. The sceptic uses this fact to support her conclusion that no epistemic framework is trustworthy, while the relativist uses it to support her conclusion that no framework can be any more trustworthy than another. My overarching aim is to show that these are distinct threats that must be answered by means of distinct arguments. The argument I present is meant to disarm the relativist threat while leaving the sceptical threat unanswered (though not unanswerable).  Continue Reading…