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Singularly Complex. The Catch with Hybrid Finance
BY ABHIJNAN REJ
A Jurassic Park in the Canary Wharf?
On the 6th of May, 2010, at around 2:45 pm, the Dow fell unusually rapidly losing over 9% of its total value in a couple of minutes without any perceptible external information input. After a circuit-breaker in the Chicago Mercantile Exchange was switched on for a few seconds, the markets started climbing back up and by 3:00 pm most of the 600 points or so that were lost was regained as traders recalibrated their mental and computational models. There are many conflicting accounts around what might have caused this event—jazzily named the Flash Crash—but almost everybody agrees that high-frequency algorithmic traders had a large role to play. Since algorithms have a much faster reaction time to market movements than humans, at first sight of the unexpectedly hectic buying and selling amidst a large sell-order by Proctor & Gamble, a large number of algorithmic traders decided to exit the market in a classic display of herding behavior (“the herders”) while others continued selling vigorously. At the final reckoning, most of the temporary losses during the couple of hours since the Flash Crash have been attributed to them.
Over the last few years, a small group of experts have built a parallel description of financial markets that go much beyond the orthodox dogma taught in a typical MBA finance course. These researchers have uncovered a very rich set of analogies with ecological and other biological systems which have helped us understand the mechanism behind financial crashes—both Flash and the more prolonged purges—and given us a comprehensive notion of the global human/machine hybrid financial system which is, to use a very bold word, living. Building models and theories that cut across disciplinary boundaries, and through extensive computational modeling, these researchers, including early pioneers of chaos theory such as Robert May and Doyne Farmer, have presented us with a narrative about the future of finance that is, again to use a bold word, alarming. Recognizing that complex systems display unexpected and un-programmed features, these researchers (much like the fictional Dr. Ian Malcolm of Jurassic Park) have argued that introducing financial innovations of increasing complexity (much of which is machine- or high-frequency algorithmically driven) is, more than ever before, making the global financial network fragile and prone to fractures both big and small.
The Need for Speed
More than 70% of equity trades in the US are executed through algorithmic trading. For example, the mathematician-turned-financier James Simons’ Renaissance Technologies, a hedge fund mostly driven by automated trading, manages about 23 billion US dollars--their Nova Fund is completely electronic and potent enough to invite the apocryphal story that on a certain day their trades accounted for 14% of all trades in NASDAQ.
Basically for an algorithmic trader to make a profit, one needs three technical tools- imaginative trading strategies, extremely fast computer programs that can execute those strategies and finally for international funds, cutting-edge communication tools—all three taken together form a formidable weapon to hunt for market mispriced assets, i.e., identical assets selling for two different prices at the same time in two different markets. This is known as an arbitrage opportunity in the finance jargon. The idea is actually quite simple: suppose I know that a certain stock is selling for more in London than in New York at the same time. I’d immediately buy a certain amount of this stock in New York and simultaneously sell it in London pocketing the risk-free difference.
The catch to such a sweet deal is the following: you have to be the first to know of the arbitrage opportunity before all others. Once this information is available to all other traders, they will quickly move in with a similar strategy with the inevitable result that the two prices will converge to one. This is the sacred Efficient Market Hypothesis (EMH) around which much of contemporary finance is built around. It assumes all traders possess the same amount of information at any given point in time, something which is routinely invalidated in the real-world. In practice, the amount of risk-free money you will make through arbitrage is a function of how fast your information channels are (that is, how much more you know about the state of the markets) and how quickly your algorithms can trade. (Often arbitrage opportunities exist only for milliseconds, much smaller than the average human response times.) Increasingly, large funds are beginning to invest in newer technologies that will optimize arbitrage opportunities, including a specially designed chip that prepares and executes trades at 740 nanoseconds and a new transatlantic cable that reduces communication time between Canary Wharf and Wall Street by five milliseconds. These new techniques don’t come cheap, with some estimates of building the transatlantic cable alone running close to a billion dollars. However given the number of arbitrage opportunities, for large banks and funds the price is just right.
For regulators such as the US Securities and Exchange Commission (SEC), such technologies are frightening for many reasons. One, they are simply not equipped to monitor such high speed/high frequency/high-volume trades. Two, they are not sure if such technologies can be used for insider trading, and finally, a recent paper by Neil Johnson and others demonstrate that such ultrafast technologies are in fact making and driving financial black swans such as the Flash Crash and, even more scarily, making large investment banks susceptible to contagion exposure. Simply put, what Johnson and collaborators are arguing is that flash crashes are increasingly becoming more and more common and, in analogy to engineering, these Flash Crash-type micro-fractures are building up to a much larger and violent fractures in the global financial market.
Towards an Ecology of Hybrid Finance
The quest to understand the global human/machine hybrid financial market as an ecological system goes back to the early days of chaos and complexity theory. Indeed both financial markets and ecological systems share many common features, such as spontaneous emergence of unexpected patterns without any exogenous input,in-built fragilities and instabilities driven by increasing complexity. Over the recent years, Didier Sornette of Swiss Federal Institute of Technology Zurich (ETHZ) has become a vocal advocate of this point of view, arguing that crashes and extreme financial events can be best modeled through local interactions and positive feedback loops, and one of the biggest drivers of these crashes are herders and noise traders.
Very recently in January, along with Vladimir Filimonov, Sornette had put forward a very interesting model for flash crashes that draw as much from the study of evolution of human societies as to nuclear engineering. Building on an idea of George Soros about market prices being self-fulfilling prophecies, they have created a model in which a given price data stream can be linearly decomposed into two parts—a part that depends on the “exogenous”, such as “game-changing” input information of geopolitical or economic nature, and an “endogenous” part which arises from the self-excitation of the market due to such input information which triggers a branched cascade of price movements. According to Filimonov and Sornette, it is this endogenous part of the price movement that is responsible for flash crashes. In fact, they go on to suggest a single parameter, n, called the “branching ratio”, in analogy to nuclear processes, whose value determines whether or not we are going into a flash crash regime simply by looking at the price stream over a 10 minutes interval.
The branching ratio measures the internal system dynamics directly. For example, n>0.9 indicates that more than 90% of the price movements can be attributed to the system’s intrinsic dynamics alone; this was precisely the case during the Flash Crash of 2010. Interestingly enough, through the analysis of historical data, Filimonov and Sornette show that the growth of n directly coincides with the rise of high-frequency algorithmic trading. Extrapolating their analysis in light of the news of the upcoming ultra-fast electronic trading devices and accessories such as the transatlantic cable as well as the analysis of Johnson and others presents us with a very worrying picture of finance of the future.
In the past few years, another related set of analysis on the “ecology” of the financial markets addressing similar problems have come from A.G. Haldane of the Bank of England in collaboration with the mathematical biologist and chaos theorist Robert May. In a model published in the science journal Nature last year, they show how, given a sudden external shock, the liquidity crunch cascades through the financial network which is viewed as a random graph (that is, a random collection of nodes representing banks and line segments between nodes that represent inter-banking transactions.) Building on earlier studies in ecology, they conclude that instability and fragility are a direct consequence of increasing complexity and connectivity of the financial network with a few banks (nodes) controlling a large fraction of the total capital in the market. Their view of a financial crash is one of an information cascade where, in the event of external shock, a big failing bank passes its liquidity crunch onto slightly smaller banks that, through all the way down, pass the crunch to the smallest banking/quasi-banking entity and ultimately to individuals--at each step “amplifying the liquidity shock”. A worrying part of the story is that there has been a steady decline in the amount of actual physical or monetary assets that banks have held over the years, severely degenerating their capacity to absorb an external shock or an endogenous perturbation; simply put, a bank might only hold as assets instruments that derive their value from instruments generated by other banks equally susceptible to any crisis! In this era of “post-modern finance”, such destructive closed loops should be the first ones regulators need to break into.
Haldane and May’s research contain several insights. First, they argue that adding each layer of complexity in terms of the introduction of highly-leveraged exotic financial instruments erodes systemic stability by a significant amount and after reaching a critical level of liquidity crunch, the whole network is likely to collapse in one go. Haldane and May (echoing the famous words of Warren Buffet about derivatives being weapons of mass financial destruction) also, understandably enough, hold credit derivatives such as the infamous Credit Default Swap (CDS) responsible for much of the fragility of the financial system; as they aptly note, “CDSs have outpaced Moore’s law of growth”. Second, given that values of derivatives are highly correlated, shocks can propagate smoothly across the entire financial sector making the system very unstable even with large fluctuations in the value of a single derivative. Third, they argue that banks too taken as wholes are highly susceptible to “herding”, which experts such as Didier Sornette have time and again identified as drivers of crashes.
A standard dogma in financial theory is that prices of derivatives of stocks, by definition, depend on the price of the underlying stock in some predictable way and not the other way around (this is the famous Black-Scholes-Merton model), something that has, in practice, turned out to be false. In reality, there are positive feedback loops by which stock prices are influenced by the prices of options that derive their value from these very stocks. (EMH holds that stock prices are random in nature and, if large informational shocks from the outside are absent, immune to tinkering by traders. Even though using derivatives to maneuver stock or other assets such as commodities prices is illegal in almost all Western countries, it is nevertheless, as many traders in their private conversations with the author have confided, practiced on a day-to-day basis. Part of the problem in analyzing such positive feedback loops is their mathematical intractability, and given that almost all profitable trading strategies are proprietary, it is unavailable for scrutiny of regulators and investors alike. All of these, taken together, have given us a financial system that is opaque not because of the number of entities involved but by the very fact that as a network it is far from being “linear”. (A more mathematical way of saying this would be that when viewed as a graph, the financial network is not a “tree” but instead has very many loops.)
Haldane and May go on to suggest several policy measures through an ecological lens. First they suggest that regulators stop looking at problems with individual funds and banks alone and start looking at systemic risks, a point also advocated by Sornette. Haldane and May compare this current attitude of regulators to the attitude of public health practitioners who may look at the contagion of infectious diseases as a matter of only isolating a couple of individuals without understanding the terrain in which these diseases may propagate. Haldane and May advocate much higher capital and liquidity requirements on banks that pose the greater threat to the system by the virtue of their higher connectivity and therefore larger “liability” to smaller entities that they are connected to. Second, they push for ``systemic diversity” by means of which shocks do not propagate so evenly in times of crisis. Finally, they call for partitioning the financial markets into modules with the idea in mind that in times of trouble, disturbances can be contained inside a given module and not be allowed to spread beyond that. It must be noted that modularity, especially in the hybrid world, brings with it its own set of problems. For example, a recent study published by Miguel Fortuna and others show non-trivial predator-prey and exclusionary relationships between different modular packages of the Debian Linux operating system. It is not clear whether a similar “life-like” phenomenon will not arise in a modular financial network increasingly driven by algorithms.
It is also important to note that there are dissidents who would ascribe to the picture described above, and yet have a very different view of how it informs regulations. For example, they may claim that in a complex system, too much interference actually increases volatility than decreases it, so the whole business of regulation without understanding the system as a whole might actually make things worse. (Nassim Nicholas Taleb and Mark Blythe also make this excellent point in the realm of geopolitics in their May 2011, Foreign Affairs article.)
Till then, our best bet still is to remember former Defense Secretary Donald Rumsfeld’s charming reformulation of Knightian uncertainty when he asked us to differentiate between the domains of “known unknowns” (where we know what can go wrong) and the “unknown unknowns” (where we do not even know where to look). If ours is to be the true hybrid era, it is the second domain that we ought to pay much more attention to, if not to predict then to simply hedge.
Abhijnan Rej is a Researcher at the Hybrid Reality Institute and on the faculty of the Institute of Mathematics and Applications, Bhubaneswar.
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
"You dream about these kinds of moments when you're a kid," said lead paleontologist David Schmidt.
- The triceratops skull was first discovered in 2019, but was excavated over the summer of 2020.
- It was discovered in the South Dakota Badlands, an area where the Triceratops roamed some 66 million years ago.
- Studying dinosaurs helps scientists better understand the evolution of all life on Earth.
Credit: David Schmidt / Westminster College<p style="margin-left: 20px;">"We had to be really careful," Schmidt told St. Louis Public Radio. "We couldn't disturb anything at all, because at that point, it was under law enforcement investigation. They were telling us, 'Don't even make footprints,' and I was thinking, 'How are we supposed to do that?'"</p><p>Another difficulty was the mammoth size of the skull: about 7 feet long and more than 3,000 pounds. (For context, the largest triceratops skull ever unearthed was about <a href="https://www.tandfonline.com/doi/abs/10.1080/02724634.2010.483632" target="_blank">8.2 feet long</a>.) The skull of Schmidt's dinosaur was likely a <em>Triceratops prorsus, </em>one of two species of triceratops that roamed what's now North America about 66 million years ago.</p>
Credit: David Schmidt / Westminster College<p>The triceratops was an herbivore, but it was also a favorite meal of the T<em>yrannosaurus rex</em>. That probably explains why the Dakotas contain many scattered triceratops bone fragments, and, less commonly, complete bones and skulls. In summer 2019, for example, a separate team on a dig in North Dakota made <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">headlines</a> after unearthing a complete triceratops skull that measured five feet in length.</p><p>Michael Kjelland, a biology professor who participated in that excavation, said digging up the dinosaur was like completing a "multi-piece, 3-D jigsaw puzzle" that required "engineering that rivaled SpaceX," he jokingly told the <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">New York Times</a>.</p>
Morrison Formation in Colorado
James St. John via Flickr
|Credit: Nobu Tamura/Wikimedia Commons|
A new study proposes mysterious axions may be found in X-rays coming from a cluster of neutron stars.
Are Axions Dark Matter?<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="5e35ce24a5b17102bfce5ae6aecc7c14"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/e7yXqF32Yvw?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span>
New research suggests you can't fake your emotional state to improve your work life — you have to feel it.
What is deep acting?<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTQ1NDk2OS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxNTY5MzA0Nn0._s7aP25Es1CInq51pbzGrUj3GtOIRWBHZxCBFnbyXY8/img.jpg?width=1245&coordinates=333%2C-1%2C333%2C-1&height=700" id="ddf09" class="rm-shortcode" data-rm-shortcode-id="9dc42c4d6a8e372ad7b72907b46ecd3f" data-rm-shortcode-name="rebelmouse-image" data-width="1245" data-height="700" />
Arlie Russell Hochschild (pictured) laid out the concept of emotional labor in her 1983 book, "The Managed Heart."
Credit: Wikimedia Commons<p>Deep and surface acting are the principal components of emotional labor, a buzz phrase you have likely seen flitting about the Twittersphere. Today, "<a href="https://www.bbc.co.uk/bbcthree/article/5ea9f140-f722-4214-bb57-8b84f9418a7e" target="_blank">emotional labor</a>" has been adopted by groups as diverse as family counselors, academic feminists, and corporate CEOs, and each has redefined it with a patented spin. But while the phrase has splintered into a smorgasbord of pop-psychological arguments, its initial usage was more specific.</p><p>First coined by sociologist Arlie Russell Hochschild in her 1983 book, "<a href="https://www.ucpress.edu/book/9780520272941/the-managed-heart" target="_blank">The Managed Heart</a>," emotional labor describes the work we do to regulate our emotions on the job. Hochschild's go-to example is the flight attendant, who is tasked with being "nicer than natural" to enhance the customer experience. While at work, flight attendants are expected to smile and be exceedingly helpful even if they are wrestling with personal issues, the passengers are rude, and that one kid just upchucked down the center aisle. Hochschild's counterpart to the flight attendant is the bill collector, who must instead be "nastier than natural."</p><p>Such personas may serve an organization's mission or commercial interests, but if they cause emotional dissonance, they can potentially lead to high emotional costs for the employee—bringing us back to deep and surface acting.</p><p>Deep acting is the process by which people modify their emotions to match their expected role. Deep actors still encounter the negative emotions, but they devise ways to <a href="http://www.selfinjury.bctr.cornell.edu/perch/resources/what-is-emotion-regulationsinfo-brief.pdf" target="_blank">regulate those emotions</a> and return to the desired state. Flight attendants may modify their internal state by talking through harsh emotions (say, with a coworker), focusing on life's benefits (next stop Paris!), physically expressing their desired emotion (smiling and deep breaths), or recontextualizing an inauspicious situation (not the kid's fault he got sick).</p><p>Conversely, surface acting occurs when employees display ersatz emotions to match those expected by their role. These actors are the waiters who smile despite being crushed by the stress of a dinner rush. They are the CEOs who wear a confident swagger despite feelings of inauthenticity. And they are the bouncers who must maintain a steely edge despite humming show tunes in their heart of hearts.</p><p>As we'll see in the research, surface acting can degrade our mental well-being. This deterioration can be especially true of people who must contend with negative emotions or situations inside while displaying an elated mood outside. Hochschild argues such emotional labor can lead to exhaustion and self-estrangement—that is, surface actors erect a bulwark against anger, fear, and stress, but that disconnect estranges them from the emotions that allow them to connect with others and live fulfilling lives.</p>
Don't fake it till you make it<p>Most studies on emotional labor have focused on customer service for the obvious reason that such jobs prescribe emotional states—service with a smile or, if you're in the bouncing business, a scowl. But <a href="https://eller.arizona.edu/people/allison-s-gabriel" target="_blank">Allison Gabriel</a>, associate professor of management and organizations at the University of Arizona's Eller College of Management, wanted to explore how employees used emotional labor strategies in their intra-office interactions and which strategies proved most beneficial.</p><p>"What we wanted to know is whether people choose to engage in emotion regulation when interacting with their co-workers, why they choose to regulate their emotions if there is no formal rule requiring them to do so, and what benefits, if any, they get out of this effort," Gabriel said in <a href="https://www.sciencedaily.com/releases/2020/01/200117162703.htm" target="_blank">a press release</a>.</p><p>Across three studies, she and her colleagues surveyed more than 2,500 full-time employees on their emotional regulation with coworkers. The survey asked participants to agree or disagree with statements such as "I try to experience the emotions that I show to my coworkers" or "I fake a good mood when interacting with my coworkers." Other statements gauged the outcomes of such strategies—for example, "I feel emotionally drained at work." Participants were drawn from industries as varied as education, engineering, and financial services.</p><p>The results, <a href="https://psycnet.apa.org/doiLanding?doi=10.1037%2Fapl0000473" target="_blank" rel="noopener noreferrer">published in the Journal of Applied Psychology</a>, revealed four different emotional strategies. "Deep actors" engaged in high levels of deep acting; "low actors" leaned more heavily on surface acting. Meanwhile, "non-actors" engaged in negligible amounts of emotional labor, while "regulators" switched between both. The survey also revealed two drivers for such strategies: prosocial and impression management motives. The former aimed to cultivate positive relationships, the latter to present a positive front.</p><p>The researchers found deep actors were driven by prosocial motives and enjoyed advantages from their strategy of choice. These actors reported lower levels of fatigue, fewer feelings of inauthenticity, improved coworker trust, and advanced progress toward career goals. </p><p>As Gabriel told <a href="https://www.psypost.org/2021/01/new-psychology-research-suggests-deep-acting-can-reduce-fatigue-and-improve-your-work-life-59081" target="_blank" rel="noopener noreferrer">PsyPost in an interview</a>: "So, it's a win-win-win in terms of feeling good, performing well, and having positive coworker interactions."</p><p>Non-actors did not report the emotional exhaustion of their low-actor peers, but they also didn't enjoy the social gains of the deep actors. Finally, the regulators showed that the flip-flopping between surface and deep acting drained emotional reserves and strained office relationships.</p><p>"I think the 'fake it until you make it' idea suggests a survival tactic at work," Gabriel noted. "Maybe plastering on a smile to simply get out of an interaction is easier in the short run, but long term, it will undermine efforts to improve your health and the relationships you have at work. </p><p>"It all boils down to, 'Let's be nice to each other.' Not only will people feel better, but people's performance and social relationships can also improve."</p>
You'll be glad ya' decided to smile<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="88a0a6a8d1c1abfcf7b1aca8e71247c6"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/QOSgpq9EGSw?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>But as with any research that relies on self-reported data, there are confounders here to untangle. Even during anonymous studies, participants may select socially acceptable answers over honest ones. They may further interpret their goal progress and coworker interactions more favorably than is accurate. And certain work conditions may not produce the same effects, such as toxic work environments or those that require employees to project negative emotions.</p><p>There also remains the question of the causal mechanism. If surface acting—or switching between surface and deep acting—is more mentally taxing than genuinely feeling an emotion, then what physiological process causes this fatigue? <a href="https://www.frontiersin.org/articles/10.3389/fnhum.2019.00151/full" target="_blank">One study published in the <em>Frontiers in Human Neuroscience</em></a><em> </em>measured hemoglobin density in participants' brains using an fNIRS while they expressed emotions facially. The researchers found no significant difference in energy consumed in the prefrontal cortex by those asked to deep act or surface act (though, this study too is limited by a lack of real-life task).<br></p><p>With that said, Gabriel's studies reinforce much of the current research on emotional labor. <a href="https://journals.sagepub.com/doi/abs/10.1177/2041386611417746" target="_blank">A 2011 meta-analysis</a> found that "discordant emotional labor states" (read: surface acting) were associated with harmful effects on well-being and performance. The analysis found no such consequences for deep acting. <a href="https://doi.apa.org/doiLanding?doi=10.1037%2Fa0022876" target="_blank" rel="noopener noreferrer">Another meta-analysis</a> found an association between surface acting and impaired well-being, job attitudes, and performance outcomes. Conversely, deep acting was associated with improved emotional performance.</p><p>So, although there's still much to learn on the emotional labor front, it seems Van Dyke's advice to a Leigh was half correct. We should put on a happy face, but it will <a href="https://bigthink.com/design-for-good/everything-you-should-know-about-happiness-in-one-infographic" target="_self">only help if we can feel it</a>.</p>
Archaeologists discover a cave painting of a wild pig that is now the world's oldest dated work of representational art.