Tag: Innovation

How culture explains our weak response to the coronavirus

The sneakiness of the novel coronavirus virus has wreaked havoc worldwide.

Although the coronavirus is a global pandemic, what’s striking is how the pathogen’s destruction has varied across regions.

Whilst East Asia has largely got a grip on the virus, Europe is still reeling. The United Kingdom recently pipped Italy to claim Europe’s highest death toll, with a tally that dwarfs all but a handful of nations. The United States has established itself as the world’s coronavirus leader— although not in the way President Trump would want us to believe. And Brazil appears to be the new epicentre of the pandemic, with growing fears that their healthcare system will not survive the oncoming onslaught.

This all begs the question: why has Europe and the Americas been hit so much harder by the pandemic?

If your eyes are glued to the news, you’ll be able to point your finger at the guilty culprits. For example, we can blame our politicians— who were quick to dismiss scientists’ warnings and too slow to act.

Whilst there’s truth to this claim, it isn’t a sufficient explanation. After all, it doesn’t explain why our politicians didn’t take the threat seriously in the first place, nor why whole continents struggled to contain the coronavirus.

To help make sense of this, Michele Gelfand and her colleagues have recently released a preprint which explores the role of culture in our response to the outbreak.

Rule makers, rule breakers

Michele Gelfand is an American cultural psychologist, and author of Rule Makers, Rule BreakersMichele has dedicated her life’s work to solving what has long been considered an enigma: why do cultures differ?

Having conducted painstaking research across the world’s diverse societies, Michele discovered that cultural differences essentially boil down to two dimensions: how ‘tight’ or ‘loose’ cultures are. That is, whether groups prioritise order and strictly abide by rules, or if they are more permissive and disorganised.

Tight countries have many rules in places, where punishments are strictly enforced (think of Singapore, where chewing gum is illegal). Citizens in tight countries are used to a high degree of monitoring aimed at curtailing bad behaviour. In contrast, loose societies have laxer rules— and are more tolerant and accepting of transgressions (think of Italy and Spain).

Crucially, Michele found that these cultural differences are not random. Rather, countries with the most draconian laws and harshest punishments are those that have historically faced a barrage of existential threats.

Throughout our evolutionary history, we humans have faced hostile forces of nature. These persistent foes include famine, natural disasters, invasions from rival tribes— and you guessed it— outbreaks of infectious disease.

Because these threats are present to varying degrees, our cultural practices and social norms have evolved accordingly— tightening up in the presence of existential threats, which provides protection against danger. In contrast, societies that have faced fewer threats have experienced the luxury of loosening— cultivating social norms that favour freedom and self-expression.

As with all things in life, there’s a clear trade-off. Tight cultures instil order and stability, at the cost of being less tolerant and creative. On the other hand, loose cultures are open and dynamic— with the drawback of being more chaotic and disorderly.

Despite overlap, Michele makes clear that tight and loose transcends political ideology and does not correspond with the ‘left-right’ political spectrum.

A failed response

This trade-off between tightness and looseness was clear for all to see during the coronavirus’ initial exponential explosion. Famously tight countries such as Singapore mobilised an effective response early on. Meanwhile, looser countries like Italy did not initially take the threat as seriously— and as a consequence are still suffering.

Armed with their knowledge of cultural evolution, Michele and her colleagues wondered how much tightness and looseness explained countries’ initial responses to the outbreak.

Specifically, the team predicted countries that are tight culturally and have highly efficient governments would respond most effectively to the pandemic. That is, they’d have less people infected and subsequently less people dying.

Why would the efficiency of governments matter? They suspected tightness may only provide protection when governments also have the expertise and resources necessary to respond in a timely manner.

Michele’s team used a couple of tools to test this.

First, they crunched government statistics on the coronavirus worldwide, and cross referenced this with their data on cultural differences. They also fed in key economic and demographic information, which give them the ability to predict both the amount of infections and deaths from the coronavirus disease.

Like forensic accountants, they also unearthed countries underreporting coronavirus cases— and corrected for this in their analysis.

To complement their slicing and dicing, they also created a computer simulation to model how people respond to infectious outbreaks (think of The Sims computer game. But instead of Sims spreading ‘poopy pants’, they’re catching coronavirus).

Tightness saves lives

So, what did Michele and her team find?

The team found that tightness and government efficiency interacted to predict infection rates— and that this relationship strengthened with more information fed into their equations.

For the countries with inefficient governments, tightness was actually associated with slightly more infection rates. However, countries with tight cultures and highly efficient governments had significantly less infections and overall deaths.

Their algorithms revealed several other important factors that predict infections. Specifically, they discovered that developed countries with high levels of wealth inequality and older populations had the highest number of infections and subsequent deaths (which in not surprising, as we know COVID-19 is a disease that mainly kills the elderly).

To model an infectious outbreak, the team tailored the Prisoner’s Dilemma (no, this isn’t the dilemma governments faced when releasing prisoners early to prevent the pathogen’s spread. Rather, Prisoner’s Dilemma is one of game theory’s iconic strategic games).

During the early stages of the simulation, tight and loose cultures exhibited similar levels of cooperation. However, as time passed and The Sims zombie apocalypse was in full swing, big differences emerged. Automatons in tight cultures found it easier to copy each other’s cooperative behaviour— and therefore had higher rates of survival. In contrast, those in loose cultures didn’t fair so well.

Their simulation suggests that tight cultures may mount a more effective response to epidemics because people in tight cultures are more likely to conform and copy people’s survival strategies. If this is correct, tightness may only be effective when social norms championing cooperation are established early on in a pandemic. If they aren’t, tightness may not provide any additional protection.

Surviving the pandemic

As this paper yet to be published, one needs to be careful commenting on it. However, appreciating both the rigour of the research and the extraordinary circumstances we now face, drawing practical implications from their paper seems justified.

Reflecting on Michele’s grand theory, what screams out is the need for Western democracies to tighten up accordingly.

Several European countries have experienced intolerable suffering from the avalanche of coronavirus cases, and had no choice other than imposing draconian measures. Conversely, countries such as the United Kingdom have adopted a more hands-off approach— where the rules that have been put in place are more lax and less strictly enforced. Coincidently, the United Kingdom is now one of the world’s worst affected countries.

Bar a miracle, we’ll be living with the coronavirus for some time to come. For nations such as the UK to overcome the pandemic, we’ll need to tighten up our cultural practices to minimize disruption and protect vulnerable people from future outbreaks.

To dispel any misconceptions, I am not advocating for our governments to become more autocratic— far from it. Authoritarianism was controlled for in their study, which didn’t actually slow the rate of infections. While it’s important for governments to promote practices that stop the virus spreading, Michele’s team argue that heavy handed responses to the pandemic may cause irreparable harm. Also, the excessive use of force can hamper innovation— which becomes increasingly important when devising long-term solutions.

Rather, we should aspire to what Michele has coined ‘cultural ambidexterity’. That is, we should retain the positive aspects of our loose cultures— such as tolerance for diversity and greater creativity— whilst also having the flexibility to tighten up when necessary.

Think this can’t be done? Look south to Australasia.

New Zealand is one of the loosest countries in the world. Yet under Jacinda Ardern’s leadership, Middle Earth mobilised an effective response to the coronavirus early on. New Zealand now has one of the lowest death rates among Western nations, and Kiwis are even bracing themselves for coronavirus ‘elimination day’.

The tight-loose seesaw

Whether it’s business partners or family members squabbling, Michele has found clashes between people leaning tight or loose is a major source of conflict. Noticeably, ‘tight-loose’ clashes have become defining stories of the coronavirus in the UK.

Days after Boris Johnson ordered Britain to “stay at home, protect the NHS, save lives”, Derbyshire Police received a stiff telling off for using drones to shame people for visiting the Peak District (you could call this ‘meta-shaming’). On the other hand, a steady stream of social media posts complained about people flouting the rules— and the reticence of the police to enforce them.

More recently, the justifications provided for Dominic Cummings’ coronavirus road trips were frankly absurd— and have scorched political capital and damaged the public’s trust in the UK Government. Although Boris Johnson is betting this saga will blow over, this breach may undermine the restrictions in place and the next phase of the government’s strategy.

To successfully navigate the pandemic, we must ensure that the rules in place are properly and consistently enforced. However, we also need to calibrate our tightness to reflect the actual level of risk— tightening the rules when cases flare up, and relaxing them once the threat from the virus wanes.

Reopening for business

As Europe and the United States begin easing restrictions and reopening for business, risks abound.

Management gurus are purporting that the work office ‘is now dead’. Although there’ll certainly be long lasting changes to the way we work, declaring the end of the office is not clear-cut. Although the pandemic has demonstrated that whole companies can successfully work from home, there are several reasons why people will want to meet their colleagues and clients in person (at the end of the day, we are social primates).

By understanding the hidden forces of social norms, business leaders can tilt their companies towards the ideal tight-loose balance in the age of the coronavirus.

I’ll provide a couple of examples.

Before the pandemic, people who came into work sick were frequently deemed more loyal and dedicated employees (particularly in tight corporate cultures, where taking time off was seen as slacking). However, this is nonsensical. Not only does coming into work sick jeopardise your recovery and therefore productivity, it also risks spreading the illness to other employees. In the wake of the coronavirus, this social norm needs to be flipped: no more brownie points for coming into work sick, but rather ostracism for putting other people’s lives at risk.

Whilst we need to tighten up our hygiene standards, we also need looseness to foster innovative working practices. If we cannot resume business without causing a resurgence of new cases, we face a bleak future of continuously stalling and restarting our economy.

A team of Israeli scientists have proposed a rather ingenious solution to this dilemma, by exploiting a key property of the coronavirus: its ‘latent period’. On average, there is a three-day window between someone being infected with the virus and actually being able to spread it to others.

The scientists’ solution is to work in two-week cycles, in a system dubbed ‘10:4’. In this arrangement, people work on the job as normal for four days straight. Once they’ve passed this latency period and are therefore possibly infectious, they then work from home in isolation for ten days. The scientists’ models suggest that this two-week working cycle can drastically reduce infection rates, causing cases to drop off a cliff.

Time will tell whether this working arrangement is actually effective. But it precisely this kind of innovative thinking that’ll help us overcome the coronavirus.

So far, the coronavirus’ s sneaky strategy has paid off handsomely. However, if we can adapt our social norms and become culturally ambidextrous— tightening up our hygiene standards whilst retaining our creativity and innovativeness— we can play the virus against itself and resume some normality.


Written by Max Beilby for Darwinian Business

Article updated on the 4th June 2020.

We don’t need to understand how technology works for it to evolve

We modern humans live in a world surrounded by ever evolving technology. Whether it’s the combustion engine or the modern computer, these technologies are ubiquitous and have radically altered the world we live in.

What’s no so obvious is how complex the technologies of traditional societies are too. Bow and arrows and clothing are just a couple of sophisticated technologies that pre-industrial humans created, and used to venture into new, challenging environments.

How is it that we humans have managed to produce such impressive technology, when our closest living primate relatives have produced nothing of the sort?

Many believe this comes down to our superior cognitive abilities.  That is, our intelligence and our ability to reason.

However, some scientists argue that the inherent complexity of certain technologies make them very hard to understand. Instead, they argue that complex technologies result from many small improvements made over generations which are culturally transmitted– without people understanding how these technologies actually work.

To help settle the debate, Maxime Derex and his colleagues Jean-François Bonnefon, Robert Boyd and Alex Mesoudi conducted a rather ingenious experiment, involving a technology which changed the face of our planet: the wheel.

Note that at the time of writing this post, the paper is a preprint and yet to be peer-reviewed, and is therefore subject to further to scrutiny. Despite the amendments that may be made to the paper, the significance of this study should become apparent.

Spinning wheels

The experiment boiled down to getting participants to increase the speed of a wheel down a meter long, inclined track. The wheel had 4 radial spokes, and a single weight could be moved along each spoke.

Participants were organised into ‘chains’ of 5 individuals. Each participant had 5 trials  to minimize the time it took for the wheel to reach the end of the track. All participants  were provided with the last two choices and  scores of the previous participant in their chain (except those who went first). 14 chains were run, with each containing different people.

In total, 140 people took part in the study (with two versions of the experiment conducted). Each person received money for participating in the experiments. The money they received ranged from €3 to €29, depending on their performance and that of their peers.

Derex and his colleagues provide sound reasons for choosing a wheel for their experiment on causal understanding.  First, existing studies suggest Westerners generally have poor understanding of how wheels work, which means most participants didn’t know what was required of them (this is not meant to be insulting). Secondly, the speed of the wheel depends solely on the laws of physics, and not on irrelevant factors which could compromise the validity of their findings. And thirdly, the wheel systems doesn’t involve many dimensions, which made it well suited for hypothesis testing.

So what were the researchers actually evaluating? They were essentially testing whether wheel speeds would increase after several generations of trails, and if people’s understanding of the underlying physics would do too.

The wheel’s speed depends on just two variables: its moment of inertia (how mass is distributed around the axis), and its initial potential energy (the distance between the wheel centre of mass and the ground).

If the weights are located closer to the centre of the wheel, and if one of the weights at the top or to the right of wheel are further away from the axis before its descent, then the wheel will cover the track faster. Note that there’s a trade-off here between the two forces, and some experimentation is required to work out the optimal configuration.

The simplicity of the system meant the researchers could measure participants’ understanding of the wheel after they completed their trials. The research team evaluated their understanding by presenting them with a few options, and asking them to predict which wheels would cover the track faster.

Causal understanding_image 2
Illustration of the experimental set up (Derex et al, preprint)

So what did Derex and his team find having conducted the experiment?

After the 5 generations, the average wheel speed increased significantly. However, participants’ actual understanding of the physics did not.

The average wheel speed produced by the first participants on their last trial was 123.6 meters per hour, and their average understanding score was 4.60. After 5 generations, the average wheel speed increased to 145.7 meters per hour, while participants’ understanding didn’t significantly change.

With a maximum possible speed of 154 m/h, the team found remarkable improvements in just a few generations.

Stifling exploration

The authors were particularly interested in whether or not the sharing of lay theories to one and another would increase people’s understanding.

To further explore how individuals gain their understanding, Derex and his colleagues ran another version of the experiment.

The set up was largely the same, with 5 trials per participant and 14 chains. However, the difference was that participants could now also write their own theory about the wheel, and share this with the next participant in their chain.

All participants were provided with the previous participant’s theory, except those who were starting.

What did they find? The average wheel speed increased at a similar rate to the first experiment, and the participants’ understanding also barely changed across the generations (see the graph below).

Counter-intuitively, the authors also found that the sharing of theories had a negative  effect on participant’s actual understanding of the underlying physics.

Causal understanding_Graph
Participants produced faster wheels across generations, but their understanding of the system did not (Derex et al, preprint)

Although little differences were observed between the experimental conditions overall,  further digging found “striking” differences in participant’s exploration and independent learning.

The researchers found that if a participant had received a theory about either inertia or potential energy, then their configurations would be constrained to one of these forces. In other words, inheriting an inertia theory increased their understanding of this dynamic, but reduced participant’s understanding of energy (and vice versa).

The main explanation presented is that receiving a theory mostly constrained participants’ focus, and blinded them to the dynamics beyond the theory they received.

Derex and his colleagues argue that these results support the theory that small improvements occur over generations via cultural transmission, in the absence of people’s actual understanding of the technology.

As stated by the authors:

These results indicate that highly optimized technologies do not necessarily result from evolved reasoning abilities but instead can emerge from the blind accumulation of many small improvements made across generations linked by cultural transmission, and demand a focus on the cultural dynamics underlying technological change as well as individual cognition.

Implications

With  the paper yet to be peer reviewed, it does seem a bit premature drawing lessons from the study at this stage. However, a wealth of research demonstrates the role of cultural evolution in driving technological advancement, which means we can have some confidence in the research findings.

The authors also note that these experiment were conducted on ‘WEIRD’ people. That is,  those who are Western, educated, industrialised, rich and democratic. Further experiments would need to be conducted cross-culturally to confirm whether or not this finding is universal.

These points aside, one key take away I took from these experiments are the roles groups and demographics play in fostering technological advancements, rather than the contributions of individuals.

In business and society more broadly, a widespread belief is that the most significant innovations come from geniuses and their novel ideas. However, such experimental findings from the field of cultural evolution reveal how overly simplistic these beliefs are; these beliefs ignore the wider environmental factors and culturally acquired knowledge that facilitate novel insights in the first place.

Another potential lesson concerns exploration and independent learning. If it is the case that receiving incomplete theories can compromise people’s understanding of technology, then this has implications for research and development professionals (or anyone fostering innovation for that matter). Working around this effect and encouraging independent learning may lead to insights which may have otherwise been missed.

Ultimately, such findings illustrate the importance of experimentation in driving technological advancements. Whether one is trying to improve a process or create new products, continuous small-scale experimentation may lead to new technologies being developed- although you may not understand how they actually work.

Written by Max Beilby for Darwinian Business

Note: Derex et al’s paper has since been published in the journal Nature Human Behaviour (1st April 2019)