Using Language for More than Conveying Environmental Information - Yuval Noah Harari Sapiens - Kevin Simler and Robin Hanson The Elephant in the Brain - Joe Abittan

Using Language for Conveying More Than Environmental Information

In the most basic utilitarian sense, our complex human languages evolved because they allowed us to convey information about the world from one individual to another. Language for early humans was incredibly important because it helped our ancestors tell each other when a predator was spotted nearby, when fruit was safe to eat, or if there was a dead water buffalo nearby that our ancestors could go scavenge some scraps from.  This idea is the simplest idea for the evolution of human language, but it doesn’t truly convey everything we have come to do with our language over a couple million years of evolution.
Yuval Noah Harari expands on this idea in his book Sapiens, “a second theory agrees that our unique language evolved as a means of sharing information about the world. But the most important information that needed to be conveyed was about humans, not lions and bison.” What Harari means in this quote is that human language allowed our ancestors to gossip. This is an idea that Kevin Simler and Robin Hanson share in their book The Elephant in the Brain. They argue that language is often more about showing off and gossiping than it is about utilitarian matters such as conveying environmental information. They also argue that the use of language for gossip and signaling was one of the key drivers of the evolution of the human brain, rewarding our ancestors for being smarter and more deceptive, hence rewarding larger and more complex brains.
In Sapiens, Harari explains that many species of monkeys are able to convey basic information through specific calls that are recognized among a species, such as when a predator is nearby or when there is ample food nearby. Playbacks of sounds identified as warnings will make monkeys in captivity hide. However, studies haven’t been able to show that other species are able to communicate and gossip about each other in the ways that humans do from a very young age. Our use of language to convey more than basic information about our environment allowed humans to develop into social tribes, and it has sine allowed us to develop massive populations of billions of people all cooperating and living together.
The Pursuit of Solid Answers

The Pursuit of Solid Answers

Human’s have egos, and that causes a lot of problems. To be clear, it is often not the ego itself that causes problems, but our feeling that we need to be right, that we need to be powerful, that we need to have important friends and connections that becomes problematic. Humans evolved in small tribes where survival often depended on being high status. Men had to be high status to pass their genes along and being high status meant that people would come to your aid if you needed help. Knowing useful things, being physically imposing, and having useful skills all contributed to make us higher status. Today, the drive for higher status is often understood as ego, and it is still with us, even if survival and evolutionary pressures toward super high status have declined.
One way in which this status and ego pursuit manifests to cause problems in our lives is in our intellectual discussions and debates. We often pursue our own ego rather than accurate knowledge and information when we are in debates. We are both signaling to our tribe and trying to dominate a conversation with our strong convictions rather than trying to have constructive discussions that help us get to correct answers.
Mary Roach writes about this phenomenon in her book Spook when discussing paranormal phenomena. She writes, “hasty assumptions serve no one. To make up one’s mind based on nothing beyond a simple summary of events – as believers and skeptics alike tend to do – does nothing to forward the pursuit of solid answers.” When we get into debates on religious topics, questions of psychic or paranormal phenomena, and complex social science questions, we often fall into reductive arguments that are mostly aimed at people who hold the same assumptions and beliefs that we already hold. We make hasty assumptions because our ego wants us to appear decisive and correct without spending time in ambiguity carefully considering the truth. The goal for us should be to become less wrong, but that is not a mindset that is generally rewarded by the ego, which for much of human evolution was rewarded by conviction and demonstrations of loyalty. Making changes so that more considerate thought is rewarded over ego-centric thought is crucial for us to move forward, but it runs against evolution, our self-interest, and what gets the most attention on social media today. Hasty assumptions may not be helpful, but they do get strong reactions and generate support among like-minded individuals.
A Sense of Danger

A Sense of Danger

2020 was a unique year in many senses, and one worrying change in 2020 was an increase in violence that seems to be continuing through 2021. Crime rates have been falling across the United States since a peak in the 1990s, until a reversal in the trend in 2020. We have not yet seen whether it is an anomaly related to the COVID-19 Pandemic that will dissipate, or whether it reflects a new trajectory of violence that we need to be concerned about. Nevertheless, crime has recently been on an uptick after a long decline.
People may currently be aware of an increase in crime, but that likely doesn’t mean that the increase in crime feels new to them. Despite the recent falling crime rates, people’s general perception of crime is that it had been increasing before 2020. The perception of increasing crime did not match the continual drop in crime, at least not until 2020. Part of the misperception seems to come from the constant news reporting of crime and better measures of crime by police and the FBI. Christopher Jencks wrote about this in his book The Homeless, “police have spent billions of dollars computerizing their record keeping systems, so crimes that get reported are more likely to become part of the office record. Improved reporting and record-keeping plus highly selective news reporting have, in turn, helped convince the public that their neighborhoods are more dangerous.”
Having good information, data, and statistics for crime is a good thing. It is important that we have a good and accurate sense of how much crime and violence is taking place in our cities, who is committing the crime, and who tends to be the victims. However, new data reporting and collecting abilities can make it seem like there is more crime than there used to be, simply because we can better collect and report that information. Better collecting and reporting means that news stations can run more stories about crimes that previously would have gone unreported, increasing the prevalence of crime in the news, building the sense of danger that people feel. With broader news reporting and an online news system driven by clicks, we also see more crime that takes place outside our communities, even when browsing local news websites.
This can ultimately have negative effects for society. While it is good to have accurate information, that information can be misleading and misused. Increasing people’s sense of danger for political ends can erode social trust and lead to profiling and dangerous policing policies that have racial disparities. It can lead to disinvestment in areas that people deem dangerous and can limit the interactions that people are willing to have in their communities, furthering disinvestment and reinforcing a sense of danger. Context is the key and is easy to leave out when reporting crime and discussing individual crimes within larger trends. Our recent uptick in crime against a background of misperception could be especially dangerous, with extreme reactions against increases in crimes that may end up being driven by the peculiar circumstances of the Pandemic. We should work to make our cities and communities safer, but we should also work to make sure people have an accurate perception of the safety or danger of their communities.
On "The Media"

On “The Media”

“The media” is a  term that is frequently used to categorize journalists, newspapers, and broadcast news shows. We often use “the media” in a negative way, complaining about coverage of events in unfair and oversimplified ways. “The media” always seems to have an agenda, a narrative, and a specific concern plucked from the zeitgeist that will fade away without a real resolution. But this idea is a bit misleading. Categorizing only news sources as “the media” misses out on a lot of media consumption that we engage with every day. It also lumps together news organizations and sources that have vastly different ways of operating, different profit motives, and different general beliefs. Even within a single news or media source there can be things that are terrible, things that are marvelous, and things that we barely notice.
Challenges with “the media” have existed as long as news and media have existed. Books, even fiction books, have been burned and banned almost as long as books have existed. People expressing heretical views against churches or governments have also received the same fate across human history.  But “the media” has been a lens through which we have understood the world past and present. Expanding our view of media to include books, movies, podcasts, and even TikTok videos shows us how media consumption can be cultural cornerstones of our highest values and simultaneously cesspools of rot.
In the George Herriman biography Krazy, author Michael Tisserand includes a quote from a critique written by Gilbert Seldes in the Pittsburgh Sun in the 1920’s. Tisserand’s passage reads:
“In his initial appraisal of Krazy Kat [George Herriman’s celebrated comic strip], he wrote that the cult of the genius of the comic strip who has created the fantastic little monster is a growing one. He added if we have to condemn utterly the press which demoralizes all thought and makes ugly all things capable of beauty, we must still be gentle with it, because Krazy Kat, the invincible and joyous, is a creature of the press, inconceivable without its foundation of cheapness and stupidity. He is there to enliven and encourage and to give much delight.
I really like this quote when viewed through the lens of “the media” that I have been trying to lay out in this post, even though Seldes uses “the press” in the quote above. Categorizing “the media” as entirely worthless or negative or alternatively categorizing “the media” as a cornerstone of democracy is an overly broad brush with which to paint news and information ecosystems. There are things we may hate about “the media” but there are also things we may find invaluable and necessary. Thinking clearly about the media requires that we delve into the particulars, understand the profit motives, understand the competition, and understand the forces that drive the things we like and dislike.
Individually, we are probably powerless to change the course of “the media” or how we talk about “the media.” However, we can think about the choices we make in relation to “the media” and to our friends, family, and colleagues. We can engage in meaningful and deep topics, or we can become enraged over shallow and meaningless topics. We can enjoy the cultural reflections of the shallow or we can criticize them. Ultimately, “the media” is a product of our humanity, and we can project onto it what we want, but we shouldn’t categorize an entire institution as rotten or democracy saving as a whole. “The Media” is complex and has multiple layers running throughout each interconnected element.
Data Mining is a First Step

Data Mining is a First Step

From big tech companies, sci-fi movies, and policy entrepreneurs data mining is presented as a solution to many of our problems. With traffic apps collecting mountains of movement data, governments collecting vast amounts of tax data, and heath-tech companies collecting data for every step we take, the promise of data mining is that our sci-fi fantasies will be realized here on earth in the coming years. However, data mining is only a first step on a long road to the development of real knowledge that will make our world a better place. The data alone is interesting and our computing power to work with big data is astounding, but data mining can’t give us answers, only interesting correlations and statistics.
In The Book of Why Judea Pearl writes:
“It’s easy to understand why some people would see data mining as the finish rather than the first step. It promises a solution using available technology. It saves us, as well as future machines, the work of having to consider and articulate substantive assumptions about how the world operates. In some fields our knowledge may be in such an embryonic state that we have no clue how to begin drawing a model of the world. But big data will not solve this problem. The most important part of the answer must come from such a model, whether sketched by us or hypothesized and fine-tuned by machines.”
Big data can give us insights and help us identify unexpected correlations and associations, but identifying unexpected correlations and associations doesn’t actually tell us what is causing the observations we make. The messaging of massive data mining is that we will suddenly understand the world and make it a better place. The reality is that we have to develop hypotheses about how the world works based on causal understandings of the interactions between various factors of reality. This is crucial or we won’t be able to take meaningful action based what comes from our data mining. Without developing causal hypotheses we cannot experiment with associations and continue to learn, we can only observe what correlations come from big data. Using the vast amounts of data we are collecting is important, but we have to have a goal to work toward and a causal hypothesis of how we can reach that goal in order for data mining to be meaningful.
Stories from Bid Data

Stories from Big Data

Dictionary.com describes datum (the singular of data) as “a single piece of information; any fact assumed to be a matter of direct observation.” So when we think about big data, we are thinking about massive amounts of individual pieces of information or individual facts from direct observation. Data simply are what they are, facts and individual observations in isolation.
On the other hand Dictionary.com defines information as “knowledge communicated or received concerning a particular fact or circumstance.” Information is the knowledge, story, and ideas we have about the data. These two definitions are important for thinking about big data. We never talk about big information, but the reality is that big data is less important than the knowledge we generate from the data, and that isn’t as objective as the individual datum.
In The Book of Why Judea Pearl writes, “a generation ago, a marine biologist might have spent months doing a census of his or her favorite species. Now the same biologist has immediate access online to millions of data points on fish, eggs, stomach contents, or anything else he or she wants. Instead of just doing a census, the biologist can tell a story.” Science has become contentious and polarizing recently, and part of the reason has to do with the stories that we are generating based on the big data we are collecting. We can see new patterns, new associations, new correlations, and new trends in data from across the globe. As we have collected this information, our impact on the planet, our understanding of reality, and how we think about ourselves in the universe has changed. Science is not simply facts, that is to say it is not just data. Science is information, it is knowledge and stories that have continued to challenge the narratives we have held onto as a species for thousands of years.
Judea Pearl thinks it is important to recognize the story aspect of big data. He thinks it is crucial that we understand the difference between data and information, because without doing so we turn to the data blindly and can generate an inaccurate story based on what we see. He writes,
“In certain circles there is an almost religious faith that we can find the answers to … questions in the data itself, if only we are sufficiently clever at data mining. However, readers of this book will know that this hype is likely to be misguided. The questions I have just asked are all causal, and causal questions can never be answered from data alone.”
Big data presents us with huge numbers of observations and facts, but those facts alone don’t represent causal structures or deeper interactions within reality. We have to generate information from the data and combine that new knowledge with existing knowledge and causal hypothesis to truly learn something new from big data. If we don’t then we will simply be identifying meaningless correlations without truly understanding what they mean or imply.
Regression Coefficients

Regression Coefficients

Statistical regression is a great thing. We can generate a scatter plot, generate a line of best fit, and measure how well that line describes the relationship between the individual points within the data. The better the line fits (the more that individual points stick close to the line) the better the line describes the relationships and trends in our data. However, this doesn’t mean that the regression coefficients tell us anything about causality. It is tempting to say that a causal relationship exists when we see a trend line with lots of tight fitting dots around and two different variables on an X and Y axis, but this can be misleading.
In The Book of Why Judea Pearl writes, “Regression coefficients, whether adjusted or not, are only statistical trends, conveying no causal information in themselves.” It is easy to forget this, even if you have had a statistics class and know that correlation does not imply causation. Humans are pattern recognition machines, but we go a step beyond simply recognizing a pattern, we instantly set about trying to understand what is causing the pattern. However, our regression coefficients and scatter plots don’t always hold clear causal information. Quite often there is a third hidden variable that cannot be measured directly that is influencing the relationship we discover in our regression coefficients.
Pearl continues, “sometimes a regression coefficient represents a causal effect, and sometimes it does not – and you can’t rely on the data alone to tell you the difference.” Imagine a graph with a regression line running through a plot of force applied by a hydraulic press and fracture rates for ceramic mugs. One axis may be pressure, and the other axis may be thickness of the ceramic mug. The individual points represent the point at which individual mugs fractured We would be able to generate a regression line by testing the fracture strength of mugs of different thickness, and from this line we would be able to develop pretty solid causal inferences about thickness and fracture rates. A clear causal link could be identified by the regression coefficients in this scenario.
However, we could also imagine a graph that plotted murder rates in European cities and the spread of Christianity. With one axis being the number of years a city has had a Catholic bishop and the other axis being the number of murders, we may find that murders decrease the longer a city has had a bishop.  From this, we might be tempted to say that Christianity (particularly the location of a Bishop in a town) reduces murder. But what would we point to as the causal mechanism? Would it be religious beliefs adopted by people interacting with the church? Would it be that marriage rules that limited polygamy ensured more men found wives and became less murderous as a result? Would it be that some divinity smiled upon the praying people and made them to be less murderous? A regression like the one I described above wouldn’t tell us anything about the causal mechanism in effect in this instance. Our causal-thinking minds, however, would still generate causal hypothesis, some of which would be reasonable but others less so (this example comes from the wonderful The WEIRDest People in the World by Joseph Henrich).
Regression coefficients can be helpful, but they are less helpful when we cannot understand the causal mechanisms at play. Understanding the causal mechanisms can help us better understand the relationship represented by the regression coefficients, but the coefficient itself only represents a relationship, not a causal structure. Approaching data and looking for trends doesn’t help us generate useful information. We must first have a sense of a potential causal mechanism, then examine the data to see if our proposed causal mechanism has support or not. This is how we can use data and find support for causal hypothesis within regression coefficients.
The Screening-Off Effect

The Screening-Off Effect

Sometimes to our great benefit, and sometimes to our detriment, humans like to put things into categories – at least Western, Educated, Industrialized, Rich, Democratic (WEIRD) people do. We break things into component parts and categorize each part as belonging to a category of thing. We do this with things like planets, animals, and players within sports. We like established categories and dislike when our categorization changes. This ability has greatly helped us in science and strategic planning, allowing our species to do incredible things and learn crucial lessons about the world. What is remarkable about this ability is how natural and easy it is for us, but how hard it is to explain or program into a machine.
One component of this remarkable ability is referred to as the screening-off effect by Judea Pearl in The Book of Why. Pearl writes, “how do we decide which information to disregard, when every new piece of information changes the boundary between the relevant and the irrelevant? For humans, this understanding comes naturally. Even three-year-old toddlers understand the screening-off effect, though they don’t have a name for it. … But machines do not have this instinct, which is one reason that we equip them with causal diagrams.”
From a young age we know what information is the most important and what information we can ignore. We intuitively have a good sense for when we should seek out more information and when we have enough to make a decision (although sometimes we don’t follow this intuitive sense). We know there is always more information out there, but don’t have time to seek out every piece of information possible. Luckily, the screening-off effect helps us know when to stop and makes decision-making possible for us.
Beyond knowing when to stop, the screening-off effect helps us know when to ignore irrelevant information. The price of tea in China isn’t a relevant factor for us when deciding what time to wake up the next morning. We recognize that there are no meaningful causal pathways between the price of tea and the best time for us to wake up. This causal insight, however, doesn’t exist for machines that are only programmed with the specific statistics we build into them. We specifically have to code a causal pathway that doesn’t include the price of tea in China for a machine to know that it can ignore that information. The screening-off effect, Pearl explains, is part of what allows humans to think causally. In cutting edge science there are many factors we wouldn’t think to screen out that may impact the results of scientific experiments, but for the most part, we know what can be ignored and can look at the world around us through a causal lens because we know what is and is not important.
A Vaccine for Lies and Falsehoods

A Vaccine for Lies and Falsehoods

Vaccines are on everyone’s mind this year as we hope to move forward from the Coronavirus Pandemic, and I cannot help but think about today’s quote from Quassim Cassam’s book Vices of the Mind through a vaccine lens. While writing about ways to build and maintain epistemic virtues Cassam writes, “only the inculcation and cultivation of the ability to distinguish truth from lies can prevent our knowledge from being undermined by malevolent individuals and organizations that peddle falsehoods for their own political or economic ends.” In other words, there is no vaccine for lies and falsehoods, only the hard work of building the skills to recognize truth, narrative, and outright lies.
I am also reminded of a saying that Steven Pinker included in his book Enlightenment Now, “any jackass can knock down a barn, but it takes a carpenter to build one.” This quote comes to mind when I think about Cassam’s quote because building knowledge is hard, but spreading falsehoods is easy. Epistemic vices are easy, but epistemic virtues are hard.
Anyone can be closed-minded, anyone can use lies to try to better their own position, and anyone can be tricked by wishful thinking. It takes effort and concentration to be open-minded yet not gullible, to identify and counter lies, and to create and transmit knowledge for use by other people. The vast knowledge bases that humanity has built has taken years to develop, to weed out the inaccuracies, and to painstakingly hone in on ever more precise and accurate understandings of the universe. All this knowledge and information has taken incredible amounts of hard work by people dedicated to building such knowledge.
But any jackass can knock it all down. Anyone can come along and attack science, attack knowledge, spread misinformation and deliberately use disinformation to confuse and mislead people. Being an epistemic carpenter and building knowledge is hard, but being a conman and acting epistemically malevolent is easy.
The task for all of us is to think critically about our knowledge, about the systems and structures that have facilitated our knowledge growth and development as a species over time, and to do what we can to be more epistemically virtuous. Only by working hard to identify truth, to improve systems for creating accurate information, and to enhance knowledge highways to help people learn and transmit knowledge effectively can we continue to move forward. At any point we can chose to throw sand in the gears of knowledge, bringing the whole system down, or we can find ways to make it harder to gum up the knowledge machinery we have built. We must do the latter if we want to continue to grow, develop, and live peacefully rather than at the mercy of the epistemically malevolent. After all, there is no vaccine to cure us from lies and falsehoods.
Improve Your Posture - Joe Abittan - Vices Of The Mind - Cassam

Improve Your Posture

In the book Vices of  the Mind, Quassim Cassam compares our thinking to our physical posture. Parents, physical therapists, and human resources departments all know the importance of good physical posture. Strengthening your core, lifting from your legs and not your back, and having your computer monitor at an appropriate height is important if you are going to avoid physical injuries and costly medical care to relive your pain. But have you ever thought about your epistemic posture?
Your epistemic posture can be thought of in a similar manner as your physical posture. Are you paying attention to the right things, are you practicing good focus, and are you working on being open-minded? Having good epistemic posture will mean that you are thinking in a way that is the most conducive to knowledge generation. Just as poor physical posture can result in injuries, poor epistemic posture can result in knowledge injuries (at least if you want to consider a lack of knowledge and information an injury).
Cassam writes, “The importance of one’s physical posture in doing physical work is widely recognized. The importance of one’s epistemic posture in doing epistemic work is not. Poor physical posture causes all manner of physical problems, and a poor epistemic posture causes all manner of intellectual problems. So the best advice to the epistemically insouciant and intellectually arrogant is: improve your posture.”
Improving our epistemic posture is not easy. Its not something we just wake up and decide we can do on our own, just as we can’t improve our walking form, the way we lift boxes, or easily adjust our workspace to be the most ergonomic all on our own. We need coaches, teachers, and therapists to help us see where we are going through dangerous, harmful, or imbalanced motions, and we need them to help correct us. These are skills that should be taught from a young age (both physically and epistemically) to help us understand how to adopt good habits maintain a healthy posture throughout life.
Thinking in ways that build and enhance our knowledge is important. It is important that we learn to be open-minded, that we learn how not to be arrogant, and that we learn that our opinions and perspectives are limited. The more we practice good epistemic posture the better we can be at recognizing when we have enough information to make important decisions and when we are making decisions without sufficient information. It can help us avoid spreading misinformation and disinformation, and can help us avoid harmful conspiracy theories or motivated reasoning. Good epistemic posture will help us have strong and resilient minds, just as good physical posture will help us have strong and resilient bodies.