The other day I was listening to a recent episode of Sam Harris’s podcast (in which he’s in conversation with Jonathan Haidt). Something Harris said got my attention. In reference to the current coronavirus pandemic he stated that “this has been an interesting ordeal of epistemology, really”, and Haidt quickly concurred with this view.
Harris went on to mention things like the prevalence of unreliable information and rumours, politically-motivated suppression of information, various tribal “spins” being given to the available evidence, and what he interpreted as actors’ economic concerns “trimming down […] [their] ability to think clearly about what the data is suggesting”. He also referred to the social amplification of “errant ideas”, such as some promoted by US President Donald Trump, along with lingering uncertainties such as about exactly how lethal COVID-19 is. All of this can make the pandemic an exhausting never-ending ordeal of claim evaluation, and it can create challenges for people seeking to get at the truth of such matters.
Harris appeared to mainly have in mind other actors who are spreading or believing misinformation about the current pandemic, and didn’t appear to consider whether he might also be influenced by various biases and/or guilty of misinterpretations.
Harris’s reflections were articulated in response to Haidt’s suggestion that “the attitude we have to take into the 2020s is a lot more humility” and his related arguments. That is, Haidt argued that we often “simply don’t know what the truth is no matter how fervently we believe we do” and stated “I’m hopeful that this virus, this pandemic, has humbled everyone because pretty much everyone was wrong about a lot of things [regarding this pandemic]. We’re still wrong about most things, well many things – probably […]”.
Haidt’s basic conviction regarding the tenuous nature of many truth claims (e.g. about the pandemic) combined with his apparent uncertainty regarding how wrong we could be about some aspects conveys some of the difficulty of the present moment. Six months into this pandemic basic questions like “So, how deadly is COVID-19?” are still being asked, though people differ in their judgments regarding the importance of such questions.
I found myself mentally ‘nodding’ throughout much of their interesting exchange. I’ve certainly found the current pandemic to be humbling in the ways Haidt suggests, and the idea of pandemics as ‘epistemological ordeals’ has stayed with me.
After listening to it I read an interesting editorial in the journal WIREs Climate Change authored by Hulme et al considering ‘Social scientific knowledge in times of crisis’. This editorial addresses the roles of knowledge along with the “pleas to “listen to the experts” [which] have emerged in response to the avalanche of misinformation regarding both the COVID-19 pandemic and climate change”. However, they argue that “there is widespread misunderstanding on the part of publics, propagated by dominant political (and scientific) discourses, that scientific and technical knowledge can provide clear and unequivocal answers to policy problems”. They further ask “which experts and what advice?”.
The following passage from this editorial is especially provocative:
Technical and scientific knowledge is always partial, uncertain and often contradictory — as we see particularly well in the case of mathematical modeling. That is not to say that such knowledge is not valuable. It is rather to say that to effectively deal with crises, multiple forms of knowledge and expertise are required and political judgment is then necessary to sort, select and present it to publics”.
During the pandemic we have seen related challenges and I’ve sometimes found myself dealing with related epistemological difficulties. A general key challenge during such an event is taking stock of evolving research findings. More specifically, we’ve seen:
- Continuing debates about the lethality of the virus, for whom, etc;
- Debates about whether ‘COVID-19’ is a brand new disease (or, alternatively, a variant of familiar forms of respiratory illnesses), or a single disease (with doctors reporting multiple, qualitative distinct patient presentations);
- Debates about the necessity of universal mask-wearing (versus, say, just sick people wearing them to minimise the risk of infecting others, and/or simply emphasising and practicing physical distancing to prevent transmission of the virus);
- Debates about whether it’s safe to keep schools open, with some public health authorities and experts arguing it’s unnecessary to close schools (e.g. link)
- Debates about how the virus spreads, along with the extent to which pre-symptomatic and asymptomatic spread are major transmission routes (see, for example, recent contention regarding statements made by World Health Organisation staff about the extent of asymptomatic spread);
- Debates about whether blanket ‘lockdowns’ and strict prohibitions are necessary or whether, alternatively, more targeted measures can be combined with the provision of guidance for at-risk groups/populations (with some public health experts arguing there is no alternative to lockdowns and others expressing contrary views);
- Debates about whether unanticipated secondary effects of public health interventions may produce subsequent crises and/or even greater health problems than what was being mitigated;
- Debates about the reliability of basic case and death statistics being presented in the media, including the extent to which the excess deaths in particular countries should be attributed to COVID-19 or other factors associated with the pandemic;
- Debates about whether countries should expect a ‘second wave’ of infection;
- Debates about the viability of alternative policy goals such as achieving ‘herd immunity’ (e.g. the potential or likely loss of life), and when it might be achieved;
- And so on…
Depending on the question there has been more or less consensus (often shifting over-time), and more or less variation regarding whether scientists and health experts can provide the ‘clear and unequivocal answers to policy problems’ referred to by Hulme et al. The basic call to “follow the science” and/or “listen to the experts” can be complicated by this.
To give just one thought provoking example, some recent studies suggest that a herd immunity threshold may be reached when a lower percent of the population has been infected (discussed in link, link, link) – perhaps as low as a 20% infection rate – as compared with the 60-70% infection rate that is generally assumed to be necessary based on estimated R0 values (for this novel coronavirus) and theoretical modelling. If these recent studies are correct it may herald a shift in understanding, with large policy implications for countries which have more advanced epidemics like the UK or Sweden.
In the country where I live, Australia, this is so far largely only ‘academic’ as the available case data suggests very low levels of infection and strict public health measures that have been enforced to ‘flatten the curve’ (initially) and contain and suppress the epidemic (more recently). However, depending on the success (or otherwise) with vaccine development – and/or the potential size and extent of future epidemic waves – we too may eventually have to face this virus and learn to ‘live with it’, rather than have the core goal of suppressing the epidemic. [UPDATE 02/08/20: a major second wave has developed in the city of Melbourne where I live which ever-stricter policies and regulations are seeking to contain].
More relevant to Australia is the argument that both the benefits and costs of lockdown need to be tallied to evaluate their effectiveness. Though this is sometimes framed as ‘health vs the economy’ analysis, lockdowns can have significant health costs (e.g. see link) which may also need to be weighed against their benefits. However, it’s likely to be years until we have a good picture of all the consequences and any likelihood of having access to a full assessment. Any such analysis is also likely to be debated for years.
Coming back to this idea of pandemics as epistemological ordeal, for me there have been a few key aspects to reflect upon and of interest:
Firstly, an aspect of the unfolding debates that has gotten my attention is the intolerance shown towards different views and policies. By this I don’t mean fringe conspiracy theories and the like; rather I mean the intolerance shown towards scientists and public health experts expressing different views and intolerance of societies adopting different polices. Some public health experts have also noted this and wrote a commentary arguing ‘Scientists who express different views on Covid-19 should be heard, not demonized’.
To the extent that scientific inquiry depends on constructive disputes – and particular forms of argumentation and related collective evaluation of truth and falsity – to move towards the truth, as I believe it often does (see the emphasis on dialogue in Mercier & Sperber, 2017), this is worrying aspect of present discourses. My own experiences of getting caught up in disputes about COVID-19 have at times been an ordeal and felt unproductive.
At the moment it feels difficult to have nuanced conversations about this coronavirus and this seems to hold back inquiry, both in public debates and potentially also scientifically. If scientists (or others) find it difficult to voice a different opinion this can make it harder to move towards the truth and may lead to a false sense of consensus.
Second, I have found myself trying (and struggling) to adopt an attitude of scientific skepticism but also worried about the potential for this attitude to veer towards dangerous forms of denialism. Whilst many people (and experts) felt we knew enough to enter blanket lockdowns in a bid to suppress or potentially eradicate the disease, others cautioned against taking drastic actions on the basis of partial and likely unreliable evidence. I worried that we didn’t know how lethal the virus is, and for whom, and that it was hard to know whether the harms of interventions might outweigh their benefits (and, moreover, that the proponents of these interventions were downplaying or ignoring potential harms).
Along the way I’ve gotten various things wrong and often struggled to know whether I’m getting it ‘right’ in terms of a healthy skepticism (rather than dangerous denialism). Other people I know adopted a more ‘panicked’ stance towards the pandemic and it’s sometimes been hard to know who’s been right in their judgements and approach.
Philosopher of science Jonathan Fuller’s comment that “institutionalized skepticism is important in science and policymaking. Too much of it is paralyzing, but it can provide a check on the pragmatic ethos of public health epidemiology” is relevant here. The skepticism I’ve been entrenched in of late does have the potential to paralyse timely action.
Third, shifting away from personal ordeals, wider epistemological issues have concerned the extensive use and roles of models (e.g. the theoretical modelling initially relied upon by the Australian government) and what should count as policy-relevant evidence.
The reliance of governments and their advisers on models has been especially interesting and is a major target of criticism. Increasingly vocal critics have been scathing in their assessments. For example, Matt Ridley has sharply critiqued what he sees as “a strange obsession with mathematical modelling”. In his most recent newspaper column in The Telegraph Ridley goes so far as to suggest that “the British scientific establishment [may have ] made its biggest error in history”. Wow – those are fighting words.
Of particular interest to me, given my work on prospective knowledge practices (e.g. link), is Ridley’s broader assertion that “science needs to rethink its affair with models rather than data”. He is clearly of the view that the use of mathematical modelling has compromised the UK’s response to COVID-19. This is a very interesting argument, which potentially points to broader epistemological issues and partially competing philosophies.
At the broader social level, what Ridley seems to be calling for is placing much less emphasis on model-based prediction. Depending on how the current pandemic plays out (and I don’t claim any special insight into this), it will be very interesting to see if it does have wider implications for scientific practice and/or the relations between science and policy-making. If it does this, this may be its most significant societal consequence.
A final aspect concerns the potential for significant biases which can seem fairly immutable to evidence. Such prolonged biases can go in either direction, e.g. towards either an exaggerated sense of threat or the opposite bias of understating risk.
The fact that we’re dealing with a new virus and, related to this, potentially also a novel disease (COVID-19) adds significantly to the epistemological challenges that we face and the potential for biases. For example, clinical pathologist Dr John Lee emphasises the potential importance of ‘ascertainment bias’ when encountering the new:
When a new disease becomes apparent where do you first come across it? You don’t come across it in the community with people having asymptomatic infection. You come across it in hospitals where people who have particularly nasty versions of the disease get concentrated. So, inevitably it means that the first cases you find are nasty ones and that’s what ascertainment bias is. It makes the disease look worse than it’s going to turn out to be. It’s an inevitable consequence of the way that we discover a new disease” (link).
On the other hand, scientists and others committed to contrasting views, such as that the virus and disease have a very low fatality rate and are generally mild for the vast majority of people, have been alleged to have the opposite biases in their assessments and practices.
Perhaps the key lesson here is the need to be aware of, and try to compensate for, potential biases. Beyond general biases like those mentioned by Dr Lee, there is a need to consider whether we have cognitive biases that shape how we interpret evidence and claims.
Indeed, coming back to my own ordeals, I’ve been wary about the extent to which we may be seeing exaggerated claims-making and about the potential for misplaced panic, but this may have led me to gravitate too much towards the contrarians in COVID-19 debates. This is something I intend to reflect on. Like Haidt suggests, I have found the pandemic to be humbling as I hoped my research training and experience would have provided a stronger foundation for navigating it and interrogating claims. Some preliminary conclusion are that I should further develop my knowledge of statistical analysis and quantitative causal research methods, and that I need to work on further bias mitigation techniques.
Overall, at times I have personally found the pandemic to be a bit like a jolting rollercoaster ride we’ve all been strapped in to without our consent. In real-time we’re constantly being exposed to new data and debates and the potential need to re-calibrate our assessments. New data or analysis emerge and seems to have significance, only to later not be so significant (or sometimes it actually is). Analysts rush to finalise their analysis hoping to inform public policy, potentially making more errors as a result. Getting caught up in all this – and in the debates that have regularly flared up – can indeed be an ordeal.