Category: Judea Pearl – The Book Of Why

The Illusion of Free Will & Computer Software

The Illusion of Free Will & Computer Software

Posted on by jabittan
Judea Pearl uses soccer as an analogy to demonstrate the usefulness of freewill, even if it is only an illusion, in The Book of Why. Pearl argues that believing we have free will, even if it doesn’t exist as we believe it does, has been helpful for humans throughout our evolutionary history. He argues that being able to communicate about our intentions, desires, and actions through a lens of free will has helped us develop agency to improve our existence as a species and survive.
Pearl also views the illusion of free will as a two tiered system that helps our species survive through agency by attributing responsibility to individuals. He communicates this idea through the language of computers by writing, “when we start to adjust our own software, that is when we begin to take moral responsibility for our actions. This responsibility may be an illusion at the level of neural activation but not at the level of the self-awareness software.”
Pearl is arguing that our consciousness (software) is different from our neural activity (the computer hardware equivalent of the brain). In this sense, Pearl is viewing consciousness and free will as a dualist. There is the electrical activity of the brain, and the software (our thinking and self-awareness) running on top of that electrical activity. While we might not be able to directly change the neural activity and while it may be automatic and deterministic, the software packages it runs are not, they are in a way revisable, and we are responsible for those revisions. That is the view that Pearl is advancing in this argument.
I think this idea is wrong. I understand the dualist view of consciousness and use that model most of the time when thinking about my thinking, but I don’t think it reflects reality. Additionally, throughout human history we have used technological analogies to explain the brain. Always equating the brain and thinking to the best technologies of the day, we have viewed the brain as having some sort of duality about it. The brain was once viewed as hydraulic pumps and levers, and today it is compared to computerized hardware and software.
I don’t have a full rebuttal for Pearl. I recognize that our experience feels as though it is not deterministic, that there seems to be some role for free will and individual agency, but I can’t go as far as Pearl and actually assign revision responsibility to our consciousness. I agree with him that the illusion can be and has been useful, but I can’t help but feel that it is a mistake to equate the brain to a computer. I don’t truly feel that even within the illusion of free will we are entirely revision responsible for our consciousness (the software/operating system). I think that comparing us to a computer is misleading and gives people the wrong impression about the mind, and I’m sure that in the future we will replace the hardware/software distinction and thoughts with different and more complex technologies in our analogies.
The Illusion of Free Will & Soccer

The Illusion of Free Will & Soccer

Posted on by jabittan
My previous post was about post-action rationalization, the idea that we often do things at an instinctual level and then apply a rationalization to them upon reflection, after the action has been completed. Our rationalization sounds logical and supports the idea that we have free will, that our decision was based on specific factors we identified, and that we consciously chose to do something. An understanding of post-action rationalization helps reveal the illusion of free will.
In The Book of Why Judea Pearl uses the example of a soccer player to demonstrate how post-action rationalization works. The soccer player reacts to situations in the game as they develop. They don’t do complex math to calculate the best angle to kick a ball, they don’t paus to work out the probability of successfully scoring a goal based on passing to one player over another, and they don’t pause to think about all the alternatives available to them in any given moment. Their minds pick up on angles, speeds, past experiences, and other unknown factors unique to each situation and players respond instinctively, without conscious thought guiding how they move and what choices they make. According to Pearl, this instinctive and intuitive processing should challenge the idea that we have free will. It should challenge the idea that we consciously chose our actions and behaviors and cause us to think that we respond to situations without a real knowledge of why we are responding. Nevertheless, we all feel that we have free will, even if we know the feeling described in the sporting event example.
This illusion of free will has some benefits. Pearl writes, “the illusion of free will gives us the ability to speak about our intents and to subject them to rational thinking, possibly using counterfactual logic.” Free will helps us talk about the stimuli around us and how we respond to them, and it helps us by providing reinforcements for outcomes that go well and admonishment for outcomes that should be avoided in the future. It is useful by creating a sense of agency and feedback between us.
Pearl continues, “I would conjecture, then, that a team of robots would play better soccer if they were programmed to communicate as if they had free will. No matter how technically proficient the individual robots are at soccer, their team’s performance will improve when they can speak to each other as if they are not preprogrammed robots but autonomous agents believing they have options.”
As artificial intelligence and robotic capabilities progress Pearl may come to regret this quote. However, it is a helpful lens to apply to human evolution and how we arrived at our current mental states. Matter arranged itself to become self-reproducing and eventually became self-observant. By being able to attribute agency and free will to its own actions, matter became even better at self-replicating and self-preserving. This is the argument that Pearl ultimately makes through his soccer analogy. Robots might in the future be the most proficient at soccer without a sense of self, but at least at times in human history we have been served well by our illusion of free will. It has helped us organize and collaborate in complex social and political societies, and it has helped us work together to create the world we now inhabit. Free will may not truly exist, but the illusion of free will has helped us do everything from play soccer to launch satellites so that we can watch other people play soccer from the other side of the planet.
Post-Action Rationalization

Post-Action Rationalization

Posted on by jabittan
I have heard people write about a split brain experiment where a participant whose corpus collosum was severed was instructed in one ear, through a pair of headphones, to leave the room they were in because the experiment was over. As the participant stood to leave the room, a researcher asked them why they had gotten up. The participant said they wanted to get something to drink.
This experiment is pretty famous and demonstrates the human ability to rationalize our behaviors even when we really don’t know what prompted us to behave in one way or another. If you have ever been surprised that you had an angry outburst at another person, if you have ever had a gut feeling in an athletic competition, and if you ever forgot something important in a report and been bewildered by your omission, then you have probably engaged in post-action rationalization. You have probably thought back over the event, the mental state you were in, and tried to figure out exactly why you did what you did and not something else.
However, Judea Pearl in The Book of Why would argue that your answer is nothing more than an illusion. Writing about this phenomenon he says:
“Rationalization of actions may be a reconstructive, post-action process. For example, a soccer player may explain why he decided to pass the ball to Joe instead of Charlie, but it is rarely the case that those reasons consciously triggered the action. In the heat of the game, thousands of input signals compete for the player’s attention. The crucial decision is which signals to prioritize, and the reasons can hardly be recalled and articulated.”
Your angry traffic outburst was brought on by a huge number of factors. Your in game decision was not something you paused, thought about, and worked out the physics to perfect before hand. Similarly, your omission on a report was a barely conscious lapse of information. Each of these situations we can rationalize and explain based on several salient factors that come to mind post-action, but that hardly describes how our brain was actually working in the moment.
The brain has to figure out what signals to prioritize and what signals to consciously respond to in order for each of the examples I mentioned to come about. These notions should challenge our ideas of free-will, our beliefs that we can ever truly know ourselves, and our confidence in learning from experience. Pearl explains that he is a determinist who compromises by accepting an illusion of free will. He argues that the illusion I have described with my examples and his quote helps us to experience and navigate the world. We feel that there is something that it is like to be us, that we make our decisions, and we can justify our behaviors, but this is all merely an illusion.
If Pearl is right, then it is a helpful illusion. We can still understand it better, still understand how this illusion is created, sustained, and can be put to the best uses. We might not have a true and authentic self under the illusion. We might not be in control of what the illusion is. But nevertheless, we can shape and mold it, and have a responsibility to do the best with our illusion, even if much of it is post-action rationalization.
Data Mining is a First Step

Data Mining is a First Step

Posted on by jabittan
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.
Causal Illusions - The Book of Why

Causal Illusions

Posted on by jabittan
In The Book of Why Judea Pearl writes, “our brains are not wired to do probability problems, but they are wired to do causal problems. And this causal wiring produces systematic probabilistic mistakes, like optical illusions.” This can create problems for us when no causal link exists and when data correlate without any causal connections between outcomes.  According to Pearl, our causal thinking, “neglects to account for the process by which observations are selected.”  We don’t always realize that we are taking a sample, that our sample could be biased, and that structural factors independent of the phenomenon we are trying to observe could greatly impact the observations we actually make.
Pearl continues, “We live our lives as if the common cause principle were true. Whenever we see patterns, we look for a causal explanation. In fact, we hunger for an explanation, in terms of stable mechanisms that lie outside the data.” When we see a correlation our brains instantly start looking for a causal mechanism that can explain the correlation and the data we see. We don’t often look at the data itself to ask if there was some type of process in the data collection that lead to the outcomes we observed. Instead, we assume the data is correct and  that the data reflects an outside, real-world phenomenon. This is the cause of many causal illusions that Pearl describes in the book. Our minds are wired for causal thinking, and we will invent causality when we see patterns, even if there truly isn’t a causal structure linking the patterns we see.
It is in this spirit that we attribute negative personality traits to people who cut us off on the freeway. We assume they don’t like us, that they are terrible people, or that they are rushing to the hospital with a sick child so that our being cut off has a satisfying causal explanation. When a particular type of car stands out and we start seeing that car everywhere, we misattribute our increased attention to the type of car and assume that there really are more of those cars on the road now. We assume that people find them more reliable or more appealing and that people purposely bought those cars as a causal mechanism to explain why we now see them everywhere. In both of these cases we are creating causal pathways in our mind that in reality are little more than causal illusions, but we want to find a cause to everything and we don’t always realize that we are doing so. It is important that we be aware of these causal illusions when making important decisions, that we think about how the data came to mind, and whether there is a possibility of a causal illusion or cognitive error at play.
Stories from Bid Data

Stories from Big Data

Posted on by jabittan
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.
Data Driven Methods

Data Driven Methods

Posted on by jabittan
In the world of big data scientists today have a real opportunity to push the limits scientific inquiry in ways that were never before possible. We have the collection methods and computing power available to analyze huge datasets and make observations in minutes that would have taken decades just a few years ago. However, many areas of science are not being strategic with this new power. Instead, many areas of science simply seem to be plugging variables into huge data sets and haphazardly looking for correlations and associations. Judea Pearl is critical of this approach to science in The Book of Why and uses the Genome-wide association study (GWAS) to demonstrate the shortcomings of this approach.
 
 
Pearl writes, “It is important to notice the word association in the term GWAS. This method does not prove causality; it only identifies genes associated with a certain disease in the given sample. It is a data-driven rather than hypothesis-driven method, and this presents problems for causal inference.”
 
 
In the 1950s and 1960s, Pearl explains, R. A. Fisher was skeptical that smoking caused cancer and argued that the correlation between smoking and cancer could have simply been the result of a hidden variable. He suggested it was possible for a gene to exist that both predisposed people to smoke and predisposed people to develop lung cancer. Pearl writes that such a smoking gene was indeed discovered in 2008 through the GWAS, but Pearl also notes that the existence of such a gene doesn’t actually provide us with any causal mechanism between people’s genes and smoking behavior or cancer development.  The smoking gene was not discovered by a hypothesis driven method but rather by data driven methods. Researchers simply looked at massive genomic datasets to see if any genes correlated between people who smoke and people who develop lung cancer. The smoking gene stood out in that study.
 
 
Pearl continues to say that causal investigations have shown that the gene in question is important for nicotine receptors  in lung cells, positing a causal pathway to smoking predispositions and the gene. However, causal studies also indicate that the gene increases your chance of developing lung cancer by less than doubling the chance of cancer. “This is serious business, no doubt, but it does not compare to the danger you face if you are a regular smoker,” writes Pearl. Smoking is associated with a 10 times increase in the risk of developing lung cancer, while the smoking gene only accounts for a less than double risk increase. The GWAS tells us that the gene is involved in cancer, but we can’t make any causal conclusions from just an association. We have to go deeper to understand its causality and to relate that to other factors that we can study. This helps us contextualize the information from the GWAS.
 
 
Much of science is still like the GWAS, looking for associations and hoping to be able to identify a causal pathway as was done with the smoking gene. In some cases these data driven methods can pay off by pointing the way for researchers to start looking for hypothesis driven methods, but we should recognize that data driven methods themselves don’t answer our questions and only represent correlations, not underlying causal structures. This is important because studies and findings based on just associations can be misleading. Discovering a smoking gene and not explaining the actual causal relationship or impact could harm people’s health, especially if they decided that they would surely develop cancer because they had the gene. Association studies ultimately can be misleading, misused, misunderstood, and dangerous, and that is part of why Pearl suggests a need to move beyond simple association studies. 

Words and Formulas

Words and Formulas

Posted on by jabittan
Scientific journal articles today are all about formulas, and in The Book of Why Judea Pearl suggests that there is a clear reason why formulas have come to dominate the world of academic studies. In his book he writes, “to a mathematician, or a person who is adequately training in the mathematical way of thinking …. a formula reveals everything: it leaves nothing to doubt or ambiguity. When reading a scientific article, I often catch myself jumping from formula to formula, skipping the words altogether. To me, a formula is a baked idea. Words are ideas in the oven.”
Formulas are scary and hard to sort out. They use Greek letters and even in fields like education, political science, or hospitality management formulas make their way into academic study. Nevertheless, if you can understand what a formula is saying, then you can understand the model that the researcher is trying to demonstrate. If you can understand the numbers that come out of a formula, you can understand something about the relationship between the variables measured in the study.
Once you write a formula, you are defining the factors that you are going to use in an analysis. You are expressing your hypothesis in concrete terms, and establishing specific values that can be analyzed in the forms of percentages, totals, ratios, or statistical coefficients.
Words, on the other hand, can be fuzzy. We can debate all day long about specific words, their definitions, registers, and implications in ways that we cannot argue over a formula. The data that goes into a formula and information that comes out is less subjective than the language and words we use to describe the data and the conclusions we draw from the information.
I like the metaphor that Pearl uses, comparing formulas to baked ideas and words to ideas within an oven. Words allow us to work our way through what we know, to tease apart small factors and attempt to attach significance to each factor. A formula requires that we cut through the potentialities and possibilities to make specific definitions that can be proven false. Words help us work our way toward a specific idea and a formula either repudiates that idea or lets it live on to face another more specific and nuanced formula in the future, with our ideas becoming more crisp over time.
Complex Causation Continued

Complex Causation Continued

Posted on by jabittan
Our brains are good at interpreting and detecting causal structures, but often, the real causal structures at play are more complicated than what we can easily see. A causal chain may include a mediator, such as citrus fruit providing vitamin C to prevent scurvy. A causal chain may have a complex mediator interaction, as in the example of my last post where a drug leads to the body creating an enzyme that then works with the drug to be effective. Additionally, causal chains can be long-term affairs.
In The Book of Why Judea Pearl discusses long-term causal chains writing, “how can you sort out the causal effect of treatment when it may occur in many stages and the intermediate variables (which you might want to use as controls) depend on earlier stages of treatment?”
This is an important question within medicine and occupational safety. Pearl writes about the fact that factory workers are often exposed to chemicals over a long period, not just in a single instance. If it was repeated exposure to chemicals that caused cancer or another disease, how do you pin that on the individual exposures themselves? Was the individual safe with 50 exposures but as soon as a 51st exposure occurred the individual developed a cancer? Long-term exposure to chemicals and an increased cancer risk seems pretty obvious to us, but the actual causal mechanism in this situation is a bit hazy.
The same can apply in the other direction within the field of medicine. Some cancer drugs or immune therapy treatments work for a long time, stop working, or require changes in combinations based on how disease has progressed or how other side effects have manifested. Additionally, as we have all learned over the past year with vaccines, some medical combinations work better with boosters or time delayed components. Thinking about causality in these kinds of situations is difficult because the differing time scopes and combinations make it hard to understand exactly what is affecting what and when. I don’t have any deep answers or insights into these questions, but simply highlight them to again demonstrate complex causation and how much work our minds must do to fully understand a causal chain.
Complex Causation

Complex Causation

Posted on by jabittan
In linear causal models the total effect of an action is equal to the direct effect of that action and its indirect effect. We can think of an oversimplified anti-tobacco public health campaign to conceptualize this equation. A campaign could be developed to use famous celebrities in advertisements against smoking. This approach may have a direct effect on teen smoking rates if teens see the advertisements and decide not to smoke as a result of the influential messaging from their favorite celebrity. This approach may also have indirect effects. Imagine a teen who didn’t see the advertising, but their best friend did see it. If their best friend was influenced, then they may adopt their friend’s anti-smoking stance. This would be an indirect effect of the advertising campaign in the positive direction. The total effect of the campaign would then be the kids who were directly deterred from smoking combined with those who didn’t smoke because their friends were deterred.
However, linear causal models don’t capture all of the complexity that can exist within causal models. As Judea Pearl explains in The book of Why, there can be complex causal models where the equation that I started this post with doesn’t hold. Pearl uses a drug used to treat a disease as an example of a situation where the direct effect and indirect effect of a drug don’t equal the total effect. He says that in situations where a drug causes the body to release an enzyme that then combines with the drug to treat a disease, we have to think beyond the equation above. In this case he writes, “the total effect is positive but the direct and indirect effects are zero.”
The drug itself doesn’t do anything to combat the disease. It stimulates the release of an enzyme and without that enzyme the drug is ineffective against the disease. The enzyme also doesn’t have a direct effect on the disease. The enzyme is only useful when combined with the drug, so there is no indirect effect that can be measured as a result of the original drug being introduced. The effect is mediated between the interaction of both the drug and enzyme together. In the model Pearl shows us, there is only the mediating effect, not a direct or indirect effect.
This model helps us see just how complicated ideas and conceptions of causation are. Most of the time we think about direct effects, and we don’t always get to thinking about indirect effects combined with direct effects. Good scientific studies are able to capture the direct and indirect effects, but to truly understand causation today, we have to be able to include mediating effects in complex causation models like the one Pearl describes.