Your Daily Tao

Holger Bösch, Fiona Steinkamp, and Emil Boller reviewed 380 studies of attempts to psychically influence randomly generated sequences of 0’s and 1’s to favor one or the other. Consistently with earlier analyses, they found slightly more results matching the value the volunteer was aiming for. Although the difference in proportions of 0’s and 1’s was only slight, the probability of obtaining such a difference purely by chance was very small. So it looked as if the effect was real: something was leading to an outcome favoring the volunteer’s target. The question was whether the difference was caused by the participants’ psychic powers, or by something else—analogous to the bent coin. One possibility Bösch and his colleagues suggested was that it could be due to something called publication bias. This is the very real phenomenon that editors of scientific journals are more likely to publish experiments that report positive results than those that report negative results. In random-number-generator experiments of the kind outlined above, a positive result is one that shows a difference in the proportions of 0’s and 1’s in the direction the volunteer was aiming for, while a negative result is one where no such difference was found. Publication bias isn’t due to dishonesty or malevolence on the part of journal editors, but is subconscious, probably arising from the fact that it’s much more exciting to show that something, instead of nothing, has happened. The fact that publication bias might explain the results doesn’t prove that genuine psychic powers didn’t play a role. But it is another way to account for the results. And then the onus is on those proposing the more unorthodox explanation to show why publication bias cannot account for the results—remember David Hume’s comment that he accepted an explanation only if the alternative seemed less likely.

An interesting book I picked up recently that shares some interesting statistical principles and knowledge to explain how things that are seemingly improbably or even impossible can actually happen. I highlighted quite a few of examples or passages that I found interesting, and this will be the 1st of 4 excerpts that I’ll be sharing.

As what is mentioned in this excerpt, do enough studies and experiments testing the same thing, and you’ll eventually find what you’re looking for :p . I remember reading somewhere in another book (sadly before I began to take notes), that if you do enough experiments for the thousands different kinds of drugs, you’ll eventually get 1 result that is positive that you can market, just by statistical probability.

Publication bias is real, and it actually obscures us from knowing what is the real “base rate”, i.e how many experiments that succeed over how many experiments that failed. That is why when you’re looking for studies to back up your daily argument on the internet, you should be looking across multiple studies and doing a literature review. Obviously that is too much work for internet points. This might also explain why everyone is able to find something that supports their statement.

This is partly the fault, as so much is these days, of “academizing” what used to be a trade. Rather than apprenticeships as part of a career track that includes writing obituaries and covering boring town meetings, journalism and communications are now undergraduate majors. These departments and programs crank out young people with little knowledge about the subjects of their correspondence. They are schooled in the structure of a story but not in the habits or norms of the profession. Many of them, accustomed to posting their deep thoughts online since high school, do not understand the difference between “journalism” and “blogging.” Veteran journalists, meanwhile, are being pushed out of newsrooms to make room for the youngsters who know how to generate clicks, as The Nation writer Dale Maharidge described in 2016. Old-school journalism was a trade, and legacy journalists find today’s brand of personality journalism, with its emphasis on churning out blog posts, aggregating the labor of others, and curating a constant social-media presence, to be simply foreign. And the higher-ups share the new bias. One editor of a major national publication, who himself is well over 40, confided to me that he’s reluctant to hire older journalists, that “they’re stuck in the mentality of doing one story a week” and not willing to use social media. The market’s focus on form rather than content, the need for speed, and the fashionable biases of the modern university combine to create a trifecta of misinformation. Little wonder that experienced writers like Joel Engel, an author and former New York Times and Los Angeles Times journalist, have lamented that America was better served “when ‘journalists’ were reporters who’d often barely graduated high school.”

Interesting excerpt I’ve read about journalism. This book was published in 2017 and the sentiment is probably even more true today. The way most of us acquire and consume news today is definitely very different than that of just 1 or 2 decades ago.

Headlines of many information are almost required to have certain key words to generate clicks. Opinion driven headlines are also a lot more appealing to people. This is just the reality of the market though. The internet has really democratised the tools of communication and press and news companies no longer hold the monopoly on information dissemination.

In this book, the author shares more about how we can combat misinformation. Ultimately, it is down to us, the consumers, to develop the interest in combating misinformation. We also have to be open-minded to new information, even if it goes against our beliefs. If we don’t change, then natural economic incentives will make it difficult for change to happen.

Learning new things requires patience and the ability to listen to other people. The Internet and social media, however, are making us less social and more confrontational. Online, as in life, people are clustering into small echo chambers, preferring only to talk to those with whom they already agree. The writer Bill Bishop called this “the big sort” in a 2008 book, noting that Americans now choose to live, work, and socialize more with people like themselves in every way. The same thing happens on the Internet. We’re not just associating with people more like ourselves, we’re actively breaking ties with everyone else, especially on social media. A 2014 Pew research study found that liberals are more likely than conservatives to block or unfriend people with whom they disagreed, but mostly because conservatives already tended to have fewer people with whom they disagreed in their online social circles in the first place. (Or as a Washington Post review of the study put it, conservatives have “lower levels of ideological diversity in their online ecosystem.”) 18 Liberals were also somewhat more likely to end a friendship over politics in real life, but the overall trend is one of ideological segregation enabled by the ability to end a friendship with a click instead of a face-to-face discussion. This unwillingness to hear out others not only makes us all more unpleasant with each other in general, but also makes us less able to think, to argue persuasively, and to accept correction when we’re wrong. When we are incapable of sustaining a chain of reasoning past a few mouse clicks, we cannot tolerate even the smallest challenge to our beliefs or ideas. This is dangerous because it both undermines the role of knowledge and expertise in a modern society and corrodes the basic ability of people to get along with each other in a democracy.

While the internet was supposed to be able to better connect people of all types, ideologies and countries, it has instead helped us connect with people whom we already agree with. This can be dangerous, as we slowly lose the ability to peacefully disagree with people and accept differences. It corrodes democracy, and moves the political landscape into extremes.

Whether we should be blaming the internet companies for it, I do not know or think its fair. Ultimately, most algorithms were built based on our very own human psychology. The fact that we tend to group ourselves in silos and break ties with others is a common behaviour and the internet is just a catalyst of that. While society might need more thoughtful internet products and platforms, we the users have to be more careful about entering into echo chambers.

At its best, college should aim to produce graduates with a reasonable background in a subject, a willingness to continue learning for the rest of their lives, and an ability to assume roles as capable citizens. Instead, for many people college has become, in the words of a graduate of a well-known party school in California, “those magical seven years between high school and your first warehouse job.” College is no longer a passage to educated maturity and instead is only a delaying tactic against the onset of adulthood—in some cases, for the faculty as well as for the students. Part of the problem is that there are too many students, a fair number of whom simply don’t belong in college. The new culture of education in the United States is that everyone should, and must, go to college. This cultural change is important to the death of expertise, because as programs proliferate to meet demand, schools become diploma mills whose actual degrees are indicative less of education than of training, two distinctly different concepts that are increasingly conflated in the public mind. In the worst cases, degrees affirm neither education nor training, but attendance. At the barest minimum, they certify only the timely payment of tuition. This is one of those things professors are not supposed to say in polite company, but it’s true. Young people who might have done better in a trade sign up for college without a lot of thought given to how to graduate, or what they’ll do when it all ends. Four years turns into five, and increasingly six or more. A limited course of study eventually turns into repeated visits to an expensive educational buffet laden mostly with intellectual junk food, with very little adult supervision to ensure that the students choose nutrition over nonsense. The most competitive and elite colleges and universities have fewer concerns in this regard, as they can pick and choose from applicants as they wish and fill their incoming classes with generally excellent students. Their students will get a full education, or close to it, and then usually go on to profitable employment. Other institutions, however, end up in a race to the bottom. All these children, after all, are going to go to college somewhere, and so schools that are otherwise indistinguishable on the level of intellectual quality compete to offer better pizza in the food court, plushier dorms, and more activities besides the boring grind of actually going to class.

College is probably seen as the “magic pill” that would create a group of highly educated and critical thinkers with a passion for lifelong learning. Instead, it has failed to accomplish this lofty objective and has not helped stem the tide in people unnecessarily going against experts without having a good grasp on the issues.

For me, its always a thin line between having no mind of your own (“being a sheep”) or learning to accept that there are others who see a broader perspective. It has always been a delicate balancing act. Having more education might also just get us to double down on our confirmation biases as well.

This excerpt also reminds me of how the main purpose for most of us going into higher education is about getting ahead in the job market. For most of us, signalling that we are intelligent and capable members for the workforce via academic results comes before the actual passion of learning. Its not wrong, and its also probably asking too much to expect some 19-20 year olds to figure out their existential purpose for entering higher education.

P.s I have been super busy in my day life and its been tough trying to keep churning out content on a daily basis. I have also been trying to be more thoughtful about the excerpts I share, and the time taken for each post has increased. Alternative day postings seem to be much more likely going forward.

Also, feel free to share any interesting books that you might have read and I’ll add it into my upcoming list.

With that said, there’s still the problem of at least some people thinking they’re bright when in fact they’re not very bright at all. We’ve all been trapped at a party or a dinner when the least-informed person in the room holds court, never doubting his or her own intelligence and confidently lecturing the rest of us with a cascade of mistakes and misinformation. It’s not your imagination: people spooling off on subjects about which they know very little and with completely unfounded confidence really happens, and science has finally figured it out. This phenomenon is called “the Dunning-Kruger Effect,” named for David Dunning and Justin Kruger, the research psychologists at Cornell University who identified it in a landmark 1999 study. The Dunning-Kruger Effect, in sum, means that the dumber you are, the more confident you are that you’re not actually dumb. Dunning and Kruger more gently label such people as “unskilled” or “incompetent.” But that doesn’t change their central finding: “Not only do they reach erroneous conclusions and make unfortunate choices, but their incompetence robs them of the ability to realize it.” In fairness to the “unskilled,” we all tend to overestimate ourselves. Ask people where they think they rate on any number of talents, and you will encounter the “above average effect,” in which everyone thinks they’re … well, above average. This, as Dunning and Kruger dryly note, is “a result that defies the logic of descriptive statistics.” It is nonetheless so recognizable a human failing that the humorist Garrison Keillor famously created an entire town dedicated to this principle, the mythical Lake Woebegone, where “all the children are above average” in his radio show A Prairie Home Companion.

The Dunning-Kruger Effect. Probably something that pretty much all of us observe on a day-to-day basis whether at work, online or with our social circles. With complex topics, it is likely that the more you know, the more reserved you’ll be in making conclusions as you began to weigh a higher variance of factors in your head when making a judgement.

It is akin to starting a project in new domain for your boss and you overconfidently guaranteeing that you’ll be able to deliver it in a week. Come to day 5, and you’ve realised that you’re still only 20% through and just spent a good 2 hours fixing that irritating formatting bug on your document.

It is indeed good to beware those who shout the loudest and simplify problems with a 3-step solution. It seems like this describes almost all politicians. But just a word of fairness, it is their job to be loud and to simplify problems for the comprehension of us average folk.

What if the Hype Machine’s design wasn’t geared toward likes that give us a fleeting dopamine rush to induce us to produce more of the most popular content, but instead incentivized us to produce the most valuable, uplifting, motivating, or thought-provoking content? In a competitive market, platforms may be more likely to move from information-poor to information-rich designs, with metadata about the provenance of the content they offer, the veracity of its sources, and the context in which it is produced. Such information could go a long way toward informing our choices of what to believe and share. This is just one hypothetical example, but it stresses the importance of thinking about what we want to promote in the world. Do we really want a world dominated by popularity? (Such worlds tip toward madness and away from wisdom, as we learned.) Or would we rather promote those who uplift our spirit, enhance our knowledge, and deepen our emotional stability? The “time well spent” movement is admirable, but design alone cannot achieve its goals. Yes, we need social software designed to support the values we want to promote, but we need to advocate for those values through our own collective behavior as well. The #deletefacebook movement is an expression of that desire. Even without real alternatives, society is pushing back on the Hype Machine’s current designs. We need to lean into that feeling and back it up with action. Software code design is only one of the four levers we have at our disposal. If regulators can create and enforce competition and mitigate market failures, in privacy and speech, through laws, the environment will enable realistic choices that can lead us away from the Hype Machine’s current design. If designers think carefully about the software code that can support the values we espouse rather than the ones we are forced into today, we will have real alternatives to choose from. If we develop and enforce the norms that transform human agency into collective action, we will make those choices a reality at a societal level. If all these levers are pulled in unison to create the future we want, it will force the business models that direct the money in today’s social media economy to change, because the money follows our attention. In this way, we are the architects of our own future. We control the Hype Machine’s destiny because it depends on us for its survival. Social media is not going to be cleaned up with a simple slogan or a three-step action plan. It’s a complex system. Improving it will require a coordinated set of approaches. And because it is so new, there’s a great deal of uncertainty. One path may seem like the right approach, only to backfire and create the very outcomes we’re trying to avoid. But with a coordinated campaign of money, code, norms, and laws, I believe we can successfully adapt the Hype Machine to a brighter future that achieves its thrilling promise while avoiding its perils. As we attempt to steer social media in the right direction, we’ll need to test different approaches, guided by theory and validated by experiment. The social media platforms, the policy makers, and the people will need to work together, drawing on the data and analyses of the scientists who study social media. With the right goals, experimentation, and a little determination we can start to move in a positive direction, creating incremental victories and building something that will promote the best values of human civilization. I, for one, look forward to collaborating with the brilliant, conscientious engineers, executives, policy makers, and scientists who are working on changing the Hype Machine’s destiny. Our path to a brighter Social Age starts now.

The final passage I’ll be sharing from this book. At its essence, what the author is trying to say is that there is no quick solution to dealing with the negative effects of social media. A simple political slogan or a 3 step plan won’t cut it, due to the various incentives and stakeholders involved.

Whats needed is a series of different approaches, all proceeding at the same time where we can test and continually improve how it works. As we are constantly breaking new ground with technology, we’ll need to be cognisant that current structures and regulations in society will  be left behind and be unable to cover all negative impacts of social media. What is important is that we remain open to collaboration and not get grid-locked in the argument of pursuing purist solutions like “breaking big tech” or “shutting them down”.

Pennycook, Rand, and Eckles recently teamed up with Ziv Epstein, Mohsen Mosleh, and Antonio Arechar to put this approach to the test in a series of experiments reducing the spread of misinformation online. They found that subtly nudging people to think about the accuracy of what they read can increase the quality of the information they share. A separate experiment by Brendan Nyhan and his colleagues showed that fake news labels reduced the perceived accuracy of false headlines. Taken together, these results suggest that subtle cognitive nudges to think about accuracy and veracity can dampen the spread of untrustworthy information in social media. That is good news, because labeling and nudges to consider accuracy are unobtrusive solutions that scale. But the solution is not perfect. In the study by Nyhan and colleagues, fake news labels also decreased the belief in true news, suggesting the labels created a general distrust in news, which mirrors what happened when the SEC outed the fake news circulating on stock market news sites (as I described in Chapter 2). Furthermore, labeling fake news can create an “implied truth effect” whereby consumers assume news that isn’t labeled must be true simply because it has avoided being debunked. As we pursue labeling, we must ensure that it counters fake news effectively while avoiding known difficulties in its implementation. I advocated strongly for labeling fake news in my 2018 TEDx Talk at CERN in Geneva. Since then, the major platforms have adopted labeling as a proactive approach to routing out misinformation. Twitter began labeling “manipulated media” in March 2020, including sophisticated deepfakes and simple, deceptively edited video and audio that is either fabricated or altered to the point that it changes the meaning of the content. While Facebook moved to label false posts more clearly in October 2019, they have so far refused to do so for political advertising or content. When Twitter applied its new manipulated media label to a video of Joe Biden edited to make him look like he was conceding that he couldn’t win the presidency, Facebook was blasted by the Biden campaign for failing to label the manipulated video. These judgement calls and the details of misinformation labeling policies will be the front lines of the fight to transparently distinguish truth from falsity. It’s important that we make these policies as effective as possible while avoiding some of their documented shortcomings.

Labelling fake news as misleading and nudging people to think about the accuracy of what they read definitely works. However, this has always been a solution that I have been uncomfortable with because as a principle, I do believe that everyone should always have the right to come to their own conclusions.

Yet, we all know that as humans, we are prone to biases, anchor ourselves to our initial view points and tend to change our viewpoints to match the “tribes” we identify with. Naturally, this leads to more conflict and it becomes difficult to move a nation together especially in times of crisis (see our vaccination debate today).

I’m not sure if there will ever be a clear solution that can balance both the risk of over censorship and individual rights to come to their own conclusion. Benevolent leaders, are within their rights to implement such initiatives for the common good. Trusting malevolent leaders with such power though will be a slippery slope. Unfortunately this is a debate in my head that doesn’t seem like it will ever be resolved.

Three forces create unequal access to the opportunities created by the Hype Machine. First, there are disparities in access to the Hype Machine across geography, socioeconomic status, and gender. Developing countries lag behind advanced economies in Internet, social media, and smartphone access. But beyond the digital divide in access to social media, there is a digital divide between what my friend and colleague Eszter Hargittai calls “capacity enhancing” and recreational uses of social media. The economically advantaged tend to use social media in ways that offer “opportunities for upward mobility,” including relationship and reputation building, information seeking, collaboration, mobilization, and other “activities that may lead to more informed political participation, career advancement, or information seeking about financial and health services.” Although research has found positive effects of activities that lead to self-improvement for those from less advantaged backgrounds, Hargittai found they tend to engage in these activities less, exacerbating inequality in the distribution of social media’s benefits. Second, the Hype Machine’s network helps the rich get richer. As people make connections on social media, they connect to other people like themselves (that’s homophily). So new connections reinforce existing disparities. Friend-recommendation algorithms are based in part on the user’s current connections. Since mutual friends guide “people you may know” recommendations on social media, the tendency to connect with people like ourselves keeps social media networks segregated and divided among the rich and the poor. Finally, the Hype Machine provides greater returns for highly skilled workers whose jobs depend more on acquiring and processing the information, knowledge, and skills that social media provides, exacerbating inequality.*2 The Hype Machine holds the potential for tremendous promise and significant peril. It enables broad, rapid collective action, but action that is fragile. It spreads both positive and harmful content and behavior. When it is programmed for privacy and security, it forgoes transparency. It supports broad increases in economic welfare and costly harms that are not priced in. It creates social and economic opportunity, but with unequal access. To achieve the promise and avoid the peril we will need scalpels, not broadswords.

Several things to distill in this excerpt. One key insight is that the Hype Machine (social media and its impact) is basically a catalyst that can be used to accentuate its impacts. This effect basically widens the economic gap between the advantaged and disadvantaged due to access and better networks. Your network on social media and its recommendation algorithm can have a huge impact on how it benefits you economically.

If we accept the above as true, then it means that there can be huge societal benefit and we should not be too quick to ban or severely curtail social media just because of the negative impacts. Rather, what we need are “scalpels”, dealing with the deleterious impacts of social media while leaving the good.

One thing Damon Centola and his team learned while studying networked crowds was that crowd wisdom can be improved by social influence when the most influential individuals are also the most accurate. Networks that put more weight on the opinions of those with the greatest accuracy, reliability, or access to the truth can perform even better than independent crowds. (The call to “listen to the scientists” on climate change and pandemic responses comes to mind.) But how can we engineer the Hype Machine to put more emphasis or weight on these peers? The Hype Machine is already replete with feedback mechanisms—they’re just designed to feed back the wrong signals. Take likes, for example. The “like” button is the engine of the attention economy. It is designed to capture our attention, to elicit our approval or disapproval of the content we see, and to incentivize us to produce more content by giving us a dopamine rush. The more we like social media content, the more engaged we are and the more opportunity there is to serve us ads. Likes serve another purpose, however, because as we like more content, we signal our preferences to the Hype Machine, which enables the ads served in those impressions to be targeted at the right people. Now imagine a world in which we went back to the invention of the “like” button and replaced it with a “truth” button (for content we think is true), or a “reliability” button (for content we think is from a reliable source), or a “wholesomeness” button (for content that is good for us), or an “educational” button (for content that taught us something). The thought exercise forces us to rethink the feedback we see on social media and to consider how code changes could reengineer the Hype Machine toward positivity. In fact, we already use norms to participate in this reengineering effort. For example, we have, as a society, largely accepted that on Twitter “retweets do not necessarily mean endorsements,” because we have adopted the ubiquitous “RT ≠ Endorsement” tag to reengineer the meaning of a retweet. Research shows that feedback is essential to our ability to process social information in collectively useful ways. So how we formally and informally design that feedback will help shape how the Hype Machine shapes us. What if every time we posted content to social media we were given the option to relate how “confident” we were in the material, or if we were asked whether we thought other people’s posts were true? How long would it take before all Americans knew the correct capitals of all fifty states? How long would it take before everyone in the United States knew their Miranda rights? Feedback is not just about weighting the information we receive in socially beneficial ways. It also allows us to adapt the network itself. Would we change who we are following on Twitter if their profiles displayed a “veracity” score that recorded the percentage of their posts that were fact-checked to be true or false? If decisions on who to follow were affected by how truthful people were, and if truth tellers amassed larger followings, would everyone be inspired to be more truthful? Would that limit the number of reshares of false information and the followers of false-news-peddling accounts?

In a network, the most influential individuals can shape what the crowd thinks and have the most impact in increasing overall crowd wisdom. This dynamic is a double-edged sword, allowing for great harm or good.

One suggestion in this excerpt that stands out is simply how we can reframe the audience mindset towards posts by simply changing the “like” button to something else. Such feedback, will not only reframe the audience mindset and get them to think critically, but also might be a sign of feedback to the poster on their thoughts.

Being the cynical individual that I am though, such mechanisms probably wouldn’t work and people who are “wrong” might end up doubling down on their views when faced with contrasting evidence. Also, discussions online largely ignore context, and statistics devoid of context can be found to support almost any viewpoint you want.

First, seeing photographs with more likes was associated with more activity in brain regions responsible for social cognition, rewards (the dopamine system), and attention (the visual cortex). When participants saw photos with more likes, they experienced greater overall brain activity, and their visual cortex lit up. When the visual cortex lights up, we are concentrating more on what we are looking at, paying more attention to it, and zooming in to look at it in greater detail. To ensure that differences in the images were not driving the results, the researchers randomized the number of likes across images and controlled for photographs’ luminosity and content. The results held true whether participants were looking at their own photos or others’ photos. In short, when we see social media images with more likes, we zoom in and inspect them in greater detail. We pay more attention to online information when it is valued more highly by others. You might think, Well, the photos that get more likes are probably more interesting. But the researchers randomly assigned the likes, which means it was the likes themselves, not the photos, that were triggering the activation of the visual cortex. Second, having more likes on one’s own photos stimulated the mentalizing network—the social brain. When participants were looking at photos of themselves, they responded to those with more (randomly assigned) likes with significantly greater brain activity in regions associated with social skills. They also recorded greater neural activity in the inferior frontal gyrus, which is associated with imitation. When we view photos of ourselves, our brains activate regions responsible for thinking about how people view us and our similarities and differences with them. In other words, when we think about our own photos, we perceive them in their social context—we think about how other people are thinking about them. Last, more likes on one’s own photos activated the dopamine reward system, which controls pleasure, motivation, and Pavlovian responses. The dopamine system makes us crave rewards by stimulating feelings of joy, euphoria, and ecstasy. When psychologists James Olds and Peter Milner gave rats the ability to stimulate their own reward system by pushing a lever, they found the rats would drop everything, stop eating and sleeping, and push that little lever again and again until they died from exhaustion. Ivan Pavlov extended our understanding of rewards by proving he could condition dogs to associate a reward (like food) with an unrelated stimulus (like a bell) so that the stimulus alone would make the dogs salivate. This cognitive binding of stimulus and reward enabled Pavlov to stimulate the brain’s reward system with a symbol (a bell)—in the same way likes stimulate and reward us with social acceptance and digital praise. Seeing likes stimulates our dopamine system and encourages us to seek social approval online for the same basic reason that Olds and Milner’s rats kept pushing their levers, and Pavlov’s dogs salivated at the sound of a bell. So our brains are wired to process and be moved by the social signals that the Hype Machine curates. But was the Hype Machine really designed with that in mind? Sean Parker answered that question about Facebook’s design in an interview with Mike Allen in 2017: “The thought process was all about, ‘How do we consume as much of your time and conscious attention as possible?’ ” he said. “And that means that we need to sort of give you a little dopamine hit every once in a while, because someone liked or commented on a photo or a post or whatever, and that’s going to get you to contribute more content, and that’s going to get you more likes and comments. It’s a social validation feedback loop….You’re exploiting a vulnerability in human psychology.”

It seems that having social affirmation sometimes matters more than the actual quality of the content when catching eyeballs. It’s also why we pay more attention and are more likely to listen to people with outward displays of success, such as a fancy car, big titles or superficial accomplishments.

Ultimately, the above is just a manifestation of how social media is built or has evolved to exploit our human psychology. It is built to trigger positive stimuli and keep us hooked into the platform at every step.