1. Introduction
In the summer of 2013, Ozge Sigirci reached out to Brian Wansink about spending a summer in his lab as a visiting student. For Sigirci it was a savvy move. Wansink was an academic superstar: The Cornell professor (and former head of the US Department of Agriculture Center for Nutrition Policy) regularly published highly cited work and received national press coverage. But it was also a beneficial relationship for Wansink. Prior to Sigirici’s arrival, he sent her a “failed study” and instructed her to comb through the data for statistically significant results. For example, he told her to “see if there are any weird outliers … [and] if there seems to be a reason they are different, pull them out” and to “think of all of the different ways that you can cut the data and analyze subsets of it to see where the relationship holds.” He closed saying, “Work hard, squeeze some blood out of this rock, and we’ll see you soon.”Footnote 1 Following his instructions, Sigirci was able to produce five papers, all coauthored with Wansink.
When these practices came to light, Wansink (Reference Wansink2016) defended himself saying that,
hypotheses usually don’t “come out” on the first data run. But instead of dropping the study, a person contributes more to science by figuring out when the hypo worked and when it didn’t. This is Plan B. Perhaps your hypo worked during lunches but not dinners, or with small groups but not large groups. You don’t change your hypothesis, but you figure out where it worked and where it didn’t. Cool data contains cool discoveries.
While under certain circumstances post-hoc analyses can be helpful, by publishing the results as if they were the result of a single preplanned analysis, Wansink engaged data dredging—a practice that has long been known to generate spurious results (Selvin and Stuart Reference Selvin and Stuart1966; Erasmus et al. Reference Erasmus, Holman and Ioannidis2022).
Subsequent investigation showed that such procedures were standard practice in the lab. A graduate student, who left the lab after becoming uncomfortable with Wansink’s research practices, revealed that he would intentionally facilitate dredging by creating studies with as many variables as possible (Newburger Reference Newburger2018) and use one-sided hypothesis tests (Brown Reference Brown2017b). Moreover, he sought out results that would capture media attention (Lee Reference Lee2018). In short, Wansink was a walking embodiment of the abuse of statistics that have led to the replication crisis.
Yet what often gets overlooked is that his travails began with a blog post intended to help aspiring academics. In his telling, Sigirci’s data dredging is a model for an enterprising researcher to emulate. He contrasts her success with a paid post-doc who was now leaving academia and had turned down the same project because it was “low quality.” Wansink’s message to young academics was publish prolifically even at the cost of sacrificing work-life balance. Yet, what he ultimately displayed in his routine practice is that his prolific publication stemmed from more than just forgoing a few episodes of Game of Thrones; in addition to his abuse of statistical methodology, Wansink also was found to have self-plagiarized, republished work without acknowledgment, and published the same work in multiple venues (Brown Reference Brown2017a).
Aside from his individual stature, the reason why Wansink is worthy of discussing is that he openly described research practices (e.g., fishing, p-hacking) that are presumed to be pervasive. The explosive attention to Wansink came on the heels of a controversy prompted by prominent psychologist Daryl Bem (Reference Bem2011) purporting to show evidence for paranormal abilities. The experiments failed to replicate (Ritchie et al. Reference Ritchie, Wiseman and French2012), but the fact that standard statistical methodology could produce apparent evidence for paranormal abilities drew increased scrutiny to more prominent and central findings in psychology.
For example, Roy Baumister’s theory of self-control posited that volitional actions depend on an exhaustible inner resource and that if, as in the original study, you have just spent five minutes resisting the urge to eat chocolate chip cookies, you will be less able to persist in attempting to solve an unsolvable puzzle (Baumeister et al. Reference Baumeister, Bratslavsky, Muraven and Tice1998). A meta-analysis of 83 studies of ego-depletion found that the effect was robust across a wide range of experimental paradigms (Hagger et al. Reference Hagger, Wood, Stiff and Chatzisarantis2010). Yet when a large multinational attempt was made to replicate the finding with a preregistered plan of analysis, the effect size could not be distinguished from zero (Hagger et al. Reference Hagger, Chatzisarantis, Hugo Alberts, Anggono, Birt and Brand2016).
Similar results have been obtained for several of John Bargh’s classic experiments on social priming (e.g., Bargh, Chen, and Burrows Reference Bargh, Chen and Burrows1996; cf., Chivers Reference Chivers2019). Likewise, the much-vaunted finding that striking a “power pose” can cause people to behave more assertively (Carney et al. Reference Carney, Cuddy and Yap2010) failed to stand up to scrutiny (e.g., Simmons and Simonsohn Reference Simmons and Simonsohn2017). Indeed, in a massive collaboration which replicated 100 prominent psychology experiments, only 40 percent successfully replicated (Open Science Collaboration 2015).
In diagnosing this issue, a number of philosophers of science have faulted the reward structure of science (Bright Reference Bright2017; Romero Reference Romero2017; Zollman Reference Zollman2019; Heesen Reference Heesen2021). Indeed, Remco Heesen (Reference Heesen2018) has gone so far as to claim that the reward structure of science makes reproducibility problems inevitable. While agreeing that the reward structure of science incentivizes unreliable work, I argue that reliable judgments can nonetheless be derived.
In section 2, I review these concerns and begin my argument that they are overly pessimistic. In particular, I argue that they tacitly assume that our best estimate of the truth will be determined by individual incentives and/or that our best judgment relies on a blind amalgamation of all the available evidence. However, if there are means of discriminating reliable studies, it undercuts the inevitability the reproducibility crisis. Indeed, such work is already now underway by the Open Science Collaboration (Alipourfard et al. Reference Alipourfard, Beatrix Arendt, Noam Benkler, Burstein and Bush2021). However, because such work is inchoate, I first turn in section 3 to a proof of principle by reviewing the election forecasting model assembled by FiveThirtyEight. Here I show that despite misaligned incentive structures in polling, the FiveThirtyEight election model can generate remarkably accurate projections. In section 4, I use this case study to illustrate how the concerns raised by philosophers are addressed in election forecasting and anticipate some objections to my proposal.
2. Incentive structures and the division of cognitive labor
Recent concerns about the incentive structure in science stand in stark contrast to the views offered by earlier philosophers of science. Most notably, both Phillip Kitcher (Reference Kitcher1990) and Michael Strevens (Reference Strevens2003) argued that credit incentives within science permits a community of selfish fame-seeking scientists to distribute themselves in a way that would promote truth. These “invisible hand” type arguments begin from the premise that there are some puzzles the scientific community is attempting to solve and that there are numerous approaches that might be successful in providing a solution. If scientists were only concerned with the truth, then every scientist would pursue the project most likely to solve the puzzle. In contrast, in a community where scientists are selfish credit seekers, when highly promising projects become saturated it will be rational to attempt a less promising project. Accordingly, Kitcher and Strevens argue, the scientific community will discover more truths if individual scientists selfishly try to maximize credit.
Recent reflections have cast doubt on this idealization of scientific inquiry. As Felipe Romero (Reference Romero2017) argues, these arguments depend on assumptions that often do not hold. First, invisible-hand arguments assume that scientific races for priority have a “winner-contributes-all” character such that once a discovery is made there is no additional epistemic benefit for a researcher to demonstrate the same effect. Romero points out that this depends on the assumption that discoveries are genuine and replicable. However, given that a substantial portion of studies are spurious and would fail to replicate if tested, the winner-takes-all priority rule fails to adequately value replication work. Accordingly, a group of selfish credit-seeking scientists will focus on the discovery of novel effects to the exclusion of the replication work that needs to be done to ensure that previous discoveries are genuine.
Moreover, it may well be that the reward structure incentivizes researchers to “rush to print” and publish specious results (Heesen Reference Heesen2018). Specifically, Heesen argues that as long as (1) there is a trade-off between speed and reproducibility; (2) that scientists get rewarded for publishing; and (3) that peer review has to assess long-term merit of the paper, then (4) the reward structure of science inevitably incentivizes scientists to be less rigorous than would be optimal from a community perspective. Furthermore, if scientists can turn published work into more resources (e.g., better jobs, grants) before the scientific community can show that their work is unreliable, then scientists are doubly incentivized to crank out unreliable research (Heesen Reference Heesen2021). In such cases, researchers can run a “scientific Ponzi scheme” in which apparent successes in the short term are leveraged to attract enough additional resources and prestige that the researcher can absorb the eventual reputation hit when some of their old research fails to replicate because they have produced so many new novel findings (Zollman Reference Zollman2019). This can come crashing down, as it did for Brian Wansink, but it need not.
A natural response to the preceding analysis is to recommend aligning the researcher’s incentive structure with the production of reliable science. However, two additional results warrant caution. First, merely aligning individual incentives with truth doesn’t necessarily lead to individuals who act in truth-promoting ways. On some occasions, it is possible that truth-motivated scientists will be inspired to tell a “noble lie”. That is, they will commit scientific fraud because, from their perspective, truthfully reporting their results would be misleading (Bright Reference Bright2017). Second, on the basis of his analysis, Heesen (Reference Heesen2018) argues that “no ‘nearby’ reward structure fully solves this problem” (664). In particular, Heesen argues that the solutions to prevent publication bias (e.g., publication of negative results) and p-hacking (e.g., preplanned analysis) would still fail to prevent the incentive for individual scientists to rush to publish. In section 4, I argue that reliability weighting in meta-analyses provides just such a solution, but before I do so, it will be helpful to examine a working example of the procedure: the election modeling published by FiveThirtyEight.
3. The signal and the noise
Polling as a means to predict an election has a great deal in common with many of the problems tackled in the sciences.Footnote 2 Polls are surveys of a relatively small sample that attempt to estimate a population value (i.e., the margin of victory in an upcoming election). Just as in science, polling agencies must make a series of methodological decisions to derive their estimate. For example, a polling agency must first determine how to generate their sample. One might employ a call center staffed with people, utilize robopolls (i.e., automated voice polling), or survey people online. There are also issues with survey design. For example, some pollsters recommend asking the participant to predict the outcome of the election rather than ask for whom they plan to vote (Graefe Reference Graefe2014; cf. Gelman and Azari Reference Gelman and Azari2017). Beyond sampling techniques and constructing survey questions, polling agencies must also make choices about how to weight their samples, which might have oversampled some groups and undersampled others. To compensate for this, agencies must employ a “turn out model” and then weight their sample accordingly.
Importantly, as in science, the incentives for polling agencies are often not truth conducive. At the extreme, some polls are used as a means to exploit betting markets (Yeargain Reference Yeargain2020). Similarly, “push polls,” in which pollsters divulge beneficial or critical information before soliciting the respondent’s opinion, can be used to manipulate public opinion (Streb and Pinkus Reference Streb, Pinkus, Genovese and Streb2004). Less egregiously, campaigns conduct internal polls but generally only release results that reflect favorably on their candidate (Freiss Reference Freiss2015). Even professional firms can be subject to misaligned incentives. As Gelman et al. (Reference Gelman, Hullman, Wlezien and Elliott Morris2020) note, polling firms that have a house effect that consistently favor one political party may be more likely to be hired by partisan actors and receive coverage on partisan media outlets. Similarly, close to the election, pollsters tend to “herd.” This occurs when an agency produces a poll that is out of step with other results and decides to alter their assumptions post-hoc to produce a poll that is better in line with other polls.
All these factors impede accurate forecasts of the election; however, over the past two decades FiveThirtyEight’s forecast has been remarkably accurate in predicting American elections. Most recently, while not forecasting the election for Donald Trump in 2016, they gave Trump a 29 percent chance of winning the electoral college, in comparison to other models that gave Trump 15 percent, 8 percent, 2 percent, or less than 1 percent chance of winning (Silver Reference Silver2016). Moreover, the reasons why FiveThirtyEight’s model gave Trump a higher chance of winning were borne out by the election—namely polling errors were correlated.Footnote 3 Similarly, though the 2020 election forecast gave Biden a 90 percent chance of winning, Silver cautioned that with a standard-sized polling error in Trump’s favor:
Biden would probably hold on, but he’d… narrowly win some states that she [Clinton] narrowly lost… Biden would probably be reliant on Pennsylvania in this scenario—a state that is expected to take some time to count its vote—the election might take longer to call…. So while Biden isn’t a normal-sized polling error away from losing, he is a normal-sized polling error away from having a messy win that might not come with control of Congress. (Silver Reference Silver2020b, emphasis in original)
After a normal-sized polling error in Trump’s favor, the election outcome remained uncertain for four days and was only resolved when a Biden victory in Pennsylvania became definitive. Democrats narrowly retained the Senate by virtue of two improbable and close runoff elections in Georgia. As illustrated here, a good forecast does not merely generate a prediction, but articulates the range of uncertainty, a task which FiveThirtyEight has been remarkably successful at.
Though the FiveThirtyEight model has several features that contribute to its success, the most pertinent for our purposes is how it handles polls. For reasons outlined in the preceding text, FiveThirtyEight does not treat all polls equally, but instead factors in a polling agency’s reliability score before letting it influence the model. The first step in calculating the reliability score is collecting every poll an agency has conducted that meets certain inclusion parameters (e.g., conducted within three weeks of the election). Next, these polls are compared against the election result to calculate the polling agency’s average error. Then, a regression analysis is run to control for known factors of polling error (e.g., sample size, race type, days from the election), and a score is derived from the difference between the polling firms observed error and its predicted error. Finally, the score is adjusted by the pollster’s relative performance in the field (i.e., how well did they do compared to other agencies that polled the same race). Further details are available (Silver Reference Silver2014, Reference Silver2020a), but what is relevant for our purposes is that when generating predictions of future elections, a pollster’s past track record is taken into consideration. Agencies with better track records are given more influence in subsequent predictions.
4. How to mitigate the effects of misaligned incentives
To recap the argument thus far, I first surveyed arguments that the incentive structure of science produces epistemically deleterious outcomes. In the previous section, I reviewed how FiveThirtyEight is able to generate a high-quality forecast despite a series of underlying incentives that distort the underlying polls. In this section, I will show that such “reliability weighting” is particularly adept at confronting the very issues that have been at the root of the pessimism expressed by philosophers of science. Again, how such a procedure would be translated into various areas of scientific inquiry is a nontrivial challenge. While such efforts are underway (Alipourfard et al. Reference Alipourfard, Beatrix Arendt, Noam Benkler, Burstein and Bush2021), the particulars of how a system would be implemented do not matter for the argument here, at least to refute arguments that the incentive structure of science makes these problems inevitable. The fact that such problems have proved tractable in political polling undercuts the inevitability of such issues and suggests promising avenues for future work. Thus, in this section I will consider the specific concerns expressed in the context of science and show that they have been mitigated in the field of political polling.
Recall that Romero’s (Reference Romero2017) primary concern was that the priority rule in science leads to an undervaluation of replication work. As has frequently occurred, the first study of a phenomena finds a strong effect and garners a significant amount of attention, but replications of the study find that the true effect is substantially smaller (Schooler Reference Schooler2011). Indeed, this is almost inevitable if studies are conducted with small samples and only publish statistically significant results (Gelman and Carlan Reference Gelman and Carlin2014). Even when subsequent studies show the effect to be significantly smaller than previously described, the original authors still earn most of the credit for the discovery. In contrast, note that this is not the case for FiveThirtyEight’s credibility rating. Though the first poll in some contest might garner outsized media attention, what matters is accuracy not priority. If a similar reliability weighting practice were adopted in science, early movers who produced inflated estimates of effects would be judged as less reliable than careful attempts to replicate that produced a more accurate estimate of the true effect. Moreover, in the FiveThirtyEight reliability ratings, polling firms are incentivized to seek out places where there are a small number of inflated polls because a firm’s weighting is partially determined by relative performance (Silver Reference Silver2014).
This same feature also addresses Heesen’s (Reference Heesen2018, Reference Heesen2021) concerns, though the broad strokes of how such a system is implemented matter here. If it were the case that promotion and granting agencies began using reliability ratings in making their decisions, then accuracy rather than publication quantity drives the incentive structures and the preconditions for Heesen’s proofs (authors are rewarded for publication) no longer obtain. Yet a system in which scientists are rewarded for accuracy is a trivial case in which the incentive structure no longer produces unreliable science. Far more interesting are cases in which the incentive structures for personal advancement remain in place. Note that, as discussed in section 3, numerous pressures to perform unreliable polls exist and do still in fact lead to the production of unreliable information. What occurs is not a curbing of these incentive structures, but an iterative process of separating the signal from the noise.
The final concern is that incentive structures can reward fraud and that this can occur even when scientists are truth-seeking (Bright Reference Bright2017). Cases of malicious fraud do occur in polling. For example, in July 2017, Delphi Analytic released a poll indicating that Kid Rock was leading the race for a Michigan Senate seat over incumbent Debbie Stabenow. The poll quickly ricocheted through the political media, but the team at FiveThirtyEight was able to determine the poll was fraudulent (Enten Reference Enten2017). Firms that have been found to conduct such polls or are suspected of engaging in fraud are excluded from FiveThirtyEight’s election forecast (Silver Reference Silver2020a). More interesting is the type of fraud engaged in by truth-seeking pollsters. Bright’s concern is that a truth-motivated actor may tell a “noble lie” when they believe that their results are out of line with the truth. Though Bright raises the case as a hypothetical, this is exactly what happens when pollsters “herd” as the election draws near. As Silver (Reference Silver2014) notes, “[P]aradoxically while herding may make an individual polling firm’s results more accurate, it can make polling averages worse.” This is because the individual pollster is using additional information to inform their poll (which is good for the agency), but does so at the cost of sacrificing the independence of each observation (which is bad for the group [and the forecaster]). Accordingly, FiveThirtyEight includes a “herding penalty” when pollsters fail to produce results with the expected amount of variability (Silver Reference Silver2019).
In short, the very issues with incentive structures that philosophers of science claim make the reproducibility crisis inevitable in science have been successfully mitigated in polling. Of course, whether such procedures could be translated into other disciplines is a matter of conjecture. While I do not have space to address all the possible complications, perhaps the most salient dissimilarity appears to be that at the end of a polling period there is a definitive result that can be used as a benchmark, and there doesn’t seem to be any analogue in most areas of science. In response, I briefly consider two possible scenarios that might serve to allay these concerns.
In an optimistic scenario, the culmination of scientific work is sufficient to produce a reliable estimate of the relevant effect size. As a matter of empirical fact, scientific communities frequently come to consensus and I have previously argued that there is reason to believe that meta-analysis combined with more advanced tools should converge on the truth even in the face of distorted incentives (Bruner and Holman Reference Bruner and Holman2019; Holman Reference Holman2019). If the long-term results of inquiry are sufficiently definitive and reliable, then they can serve as a benchmark for reliability scores (perhaps iteratively as new data emerges). However, suppose a less happy situation in which consensus is unreliable. In this situation it may yet be possible to create benchmarks by conducting large, rigorous multinational experiments for important findings (e.g., Hagger et al. Reference Hagger, Chatzisarantis, Hugo Alberts, Anggono, Birt and Brand2016). Indeed, it might well be in the interest of governmental funding bodies to set aside grants for just such projects. Obviously, this is far from a worked-out proposal for how such benchmarks would be created, but hopefully it serves to illustrate that the creation of benchmarks is not foreclosed a priori. That, in turn, is enough to undercut our confidence in the pessimistic pronouncements of philosophers of science.
5. Conclusion
It is quite right to raise concerns that the incentive structures of science may be problematic. Contemporary work in philosophy of science is a step in the right direction from the optimistic invisible-hand-type models that sought to reassure philosophers that departures from rationality need not necessarily lead to postmodern relativism (Kitcher Reference Kitcher1990; Strevens Reference Strevens2003). Yet, though the work Romero, Heesen, Zollman, and Bright represent an improvement over previous models, I contend that they have done so at the expense of the primary insight of older work: a principle that Mayo-Wilson, Zollman, and Danks (Reference Mayo-Wilson, Zollman and Danks2011) call “the independence thesis.” Specifically, the contention that the epistemic merits of a group can come apart from the epistemic merits of the individuals that comprise it and “that methodological prescriptions for scientific communities and those for individual scientists are logically independent” (653).
In this article I have shown that some of the very same issues that philosophers claim are an inevitable consequence of scientists’ current incentive structure have been mitigated in political polling. While this falls short of a proposal for precisely how to achieve the same result within science, I contend that FiveThirtyEight’s forecast model is deserving of further study by those interested in taking the next step forward in addressing the replication crisis. With such an approach, individual studies would not necessarily be more replicable, but it would cease to be a crisis if the community could tell—with sufficient time—which findings were reliable. Even though such an approach may leave distorted incentive structures in place, it may nevertheless embody the primary insight of social epistemology: Science does not need fully reliable individuals to produce reliable knowledge.
Acknowledgments
I am grateful to Dan Singer, Patrick Grim, Remco Heesen, and Felipe Romero for their comments on an earlier draft of this paper.
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