The Search for Why

A Revolutionary New Model for Understanding Others, Improving Communication, and Healing Division

Bob Raleigh

• Editorial: S&S/Simon Element
• Año: 2021
• ISBN:
  9781982130565
• Categoría: Psychology
• Categoría: Personality in Psychology

EVER WONDERED WHY PEOPLE REALLY DO WHAT THEY DO? (AND WHAT WE COULD ACCOMPLISH IF WE ONLY KNEW?) We need a clear-eyed look at what's happening in society right now. Misinformation, fake news, and politicization is affecting how we as a society come to grips with a global pandemic, economic inequality, and racial injustice. If we are to mend the divides between us and grapple with the challenges before us, we need, first, to understand the why.In The Search for Why, Bob Raleigh provides a new model for how to understand human behavior, the fundamentals of why we do what we do. He draws on his experience in market research and public communication strategy and combines that with research in the social sciences, like psychology, cognitive and behavioral sciences, and anthropology. The Search for Why covers topics like:
-Why so frequently people seem to act against their own best interests, both in politics and their personal lives
-How to better communicate with one another across political and cultural divides
-How to craft persuasive messages that meet people where they are, and listen to what they are saying back
-Ways you can apply this model to help build a better world, at a personal, social, and global level
-What influences our decisions, even when we don't realize itFor anyone looking to persuade people, heal divisions, or build better relationships, The Search for Why is a crucial step in the right direction.

CHAPTER 1 WHY ASK WHY?

Why do we do what we do? Let that question sink in. Over time, the search for an answer has inspired journeys both mundane and profound. Since the dawn of civilization, the greatest thinkers in the world have debated the origins of choice and motivation, and the tug-of-war between reason and emotion.

I’m not sure at what point in my childhood I realized that not everything was knowable. I was also surprised to learn my parents and my teachers didn’t have all the answers. But as much as possible, I was determined to understand why people behaved a certain way and why they made the choices they made, both good and bad. How did we decide between “right” and “wrong”? I poured myself into the philosophies of Aristotle, Plato, Locke, Kierkegaard, Confucius, and Kant and explored the scientific theories of Darwin and Einstein. I read Marx, Freud, Thoreau, and even the debates of the Founding Fathers. History proved instructive as I read story after story about wars, peace, civilizations being built and destroyed, from pre-civilization through ancient and medieval times and into modern times. The more I read, the more I appreciated the complexity of human motivation and morality, and I had a strong desire to translate whatever answers I could find into action, even if the pursuit was a work in progress.

There are countless theories and approaches that underpin the fields of psychology, sociology, anthropology, economics, biology, and neuroscience. In the past sixteen years, I’ve read about many of them in hopes of understanding human behavior. I have sought to synthesize all the information we have—and can act upon—to make sense of our ever-changing world and benefit from its many possibilities. Ultimately, I believe no single discipline owns all of the answers. But I do think we are entering an era of breakthroughs, as we increasingly bring concepts from one discipline into another, to innovate and enhance each other’s work. I’ve embarked on this journey with one eye on the past and studying history and another on the future of humankind, including understanding the impact of great technological changes brought on by data science and, in particular, artificial intelligence, which is getting better at revealing how our minds and emotions work.

Understanding why people do what they do is not a straightforward business. I agree with neurobiologist and primatologist Robert Sapolsky when he says: “If you were interested in the biology of, say, how migrating birds navigate, or in the mating reflex that occurs in female hamsters when they’re ovulating, this would be an easier task. But that’s not what we’re interested in. Instead, it’s human behavior, human social behavior, and in many cases abnormal human social behavior. And it is indeed a mess, a subject involving brain chemistry, hormones, sensory cues, prenatal environment, early experience, genes, both biological and cultural evolution, and ecological pressures, among other things.”¹ In short—understanding human behavior, is a complex endeavor.

There is a significant difference between a complex and a complicated problem, and we turn to the science of complexity not only to explain the difference, but also to provide a necessary lens in our pursuit of why; otherwise, any answers we arrive at are little more than guesses, or extensions of our individual biases. I first learned about complexity science while working with the Institute for Scientific Interchange (ISI), one of the world’s foremost data science laboratories, headquartered in Turin, Italy. Since their founding in 1983, the ISI has participated in several of the greatest breakthroughs in data science—chaos theory, quantum computing, and complex networks, to mention a few. They continue to break new ground with their research every day.

In 2014, I was hired as an advisor to help them prepare for the next chapter of their growth. It was very important to them that they preserve their organizational culture during this time of expansion. The ISI was founded on the principle of “curiosity-inspired science”—defined as an interdisciplinary pursuit of the answers to really hard questions, without preconception.

My work with ISI taught me to recognize the balance between data and theory. Inspired by their “borderless attitude,” which they assert “allows them to draw an endless arc through time, space, disciplines, and the research domain,”² I learned that answers can come from any arena. The institute shares its perspective as follows: “Within the overarching domain of Complexity Science, the ISI Foundation leverages the competing contributions of Data and Theory to avoid the silos of science too prevalent elsewhere. The combination of data, theory and impact is the founding essence of all the ISI research domains.”³ I have often wondered whether striking this balance between data and theory is just a sneaky way to apply self-analysis via the scientific method. If either data or theory wins the battle, we risk losing a little bit of our humanity.

For those of us not steeped in complexity science, it is helpful to learn from the experts how to differentiate between a task that is complicated and one that is complex. Dr. Mario Rasetti of the ISI has often illustrated the difference with this anecdote: If one were to take all the parts of a Boeing 777 and spread them across a football field, the job of reassembling the plane and its millions of parts would make for a very complicated, difficult (and tedious!) task. But that assembly itself, especially if it came with a user’s manual, would not be considered complex. The solution is a linear process. By contrast, predicting the global migration pattern of an infectious disease is a complex problem: It requires understanding the intersection of networks (e.g., weather, transportation, wind, disease transmission rates and incubation times) across an extremely large number of variables to create a set of predictions that include time and location as outputs. The contagion rate, whether or not the virus is airborne, and the population of the outburst are all examples of variables that one might consider. Another example of this contrast might be understanding the dynamics of weather (complicated) versus predicting the exact future of weather patterns (complex). Complexity has nothing to do with difficulty. It simply means that a system is driven by many forces and that causality is nearly impossible to prove.

In simple terms, dynamic tension is found between what we expect to be true and what real data say is true. That tension is what guides the pursuit of all knowledge, and no single discipline has a broad enough perspective to fully define all answers to our questions. We must take a multidisciplinary approach to our research in order to truly learn anything.

Sapolsky has something to say about this point, too, when he argues it doesn’t make “sense to distinguish between aspects of a behavior that are ‘biological’ and those that would be described as, say, ‘psychological’ or ‘cultural.’ Utterly intertwined.”⁴ In other words, we must look to biology in understanding human behavior, but not rely on it to give us the full picture.

Another hallmark of complexity is the acknowledgment of incomplete knowledge. That is, we assume that any solution to a complex problem will, in the long run, be wrong, or at least incomplete. As more knowledge is gained, it leads to new insights and a more complete understanding of the problem. Think of it as arriving at successive hills of increasing elevation. Each hill improves your perspective, but none actually supply complete knowledge of the future.

As an example, consider our desire to understand the invisible world of pathogens and their effect on our health. First, determining causality in human biology is a classic complex problem because there are many variables to consider in our intricate biological systems. Turns out, building a tool that allows scientists and researchers to observe, at the tiniest level, the world of bacteria, viruses, fungi, and parasites that cause illness is also a complex problem. To understand just how complex, let’s go back to about 1590 when Zacharias Janssen⁵ and his son Hans created the first microscope using a stack of lenses in a tube. The amplification wasn’t great, but it laid the groundwork for future iterations of the microscope, including advances in magnifying power. In 1665, the physicist Robert Hooke used a simple single-lens microscope; he was the first person to identify the construction of a cell. The science began to expand rapidly as microscopists had to learn the limitations of conventional optics, to better understand how light works, and to ultimately develop electromagnetic lenses capable of discerning the individual particles that make up our world. And the world took note. In 1986, the Nobel Prize in Physics was awarded for the scanning tunneling microscope. In 2014, again the Nobel Prize was awarded for a microscope, the super-resolved fluorescence microscope which allows microscopes to “see” matter smaller than 0.2 micrometers.⁶ Does anyone think that this journey is at its endpoint? I believe that the complexity of the human interface and consequent behavior is the most complex node on the most complex network ever, it might be wise to prepare for a long journey ahead.

A THREE-PART SYSTEMS APPROACH

Back when I started studying Psychology at Syracuse University, I was confronted by something I hadn’t anticipated. After the first few years of general study, the department engaged in the not so subtle process of attaching students to standard philosophical schools of thought with which to guide their next phase of training. Most students naturally gravitated to one philosophical point of view on how best to help people. There were Fritz Perls loyalists for Gestalt Therapy, Carl Rogers loyalists for Client-Centered Therapy, and Albert Ellis loyalists for Rational Emotive Behavior Therapy, and the Freudians were there for Analysis. I didn’t realize it at the time, but this was an act of self-classification. Students’ decisions were made, in part, by how they felt that these philosophies aligned with their own worldviews. In hindsight, I should have thought of this as a first step in my inquiry into why we do what we do. (For the record, I thought the Analysis model seemed to be the most complete, offering more ways to solve real human problems.) But I did not participate in that process. Instead, I joined a group of colleagues whose goal was to look at treatment as a prescriptive concept. In other words, adapt the therapy treatment to the unique needs and characteristics of an individual patient. It sounds obvious now, but it wasn’t then. We were asked to consider the following:

1. 1. PATIENTS: What specific characteristics is the patient bringing to their treatment?
2. 2. TREATMENT: What treatments would we recommend based on those characteristics?
3. 3. OUTCOME: What expectations could we anticipate that might signal success, or at least progress?

When evaluating the success of any treatment, each one of these factors needs to be considered. This focus on the interaction effect of each of these variables certainly helped prepare me for the concept of intersectionality that is reviewed later in this book.

Because of my belief that no single theory could possibly answer all of my questions, my objective was to build an approach where we would evaluate how any foundational theory or model would work with the tools in use at the time. What other models could we learn from and integrate into the model we were building? If we applied a holistic systems model, how well would any other model integrate with ours?

DIGGING INTO THE BUSINESS OF DIGGING

Even with multiple perspectives to explain why people do what they do and an appreciation for the complexity of the endeavor, it’s not easy explaining human behavior. First, the challenge for any broad-base theory to supply holistic insights on any topic related to people is the tendency to regress to the situational. It is very difficult to find any trait not qualified by at least some subset of the population. For example, often a theory will we presented as a cure-all for the habits that derail your career. What looks like a universal cure may turn out to be only appropriate for people who think of the world with very clear-cut stimulus-and-reward motivations. Those that don’t need not apply. This is not to say that this cure is not effective, just that it does not reflect something that is universal.

FAULTY HEURISTICS OR WHY WE’RE HARDWIRED TO DELUDE OURSELVES

One area where the research does hold up as a universal trait is in the area of cognitive bias. Extensive research on how people make decisions has proven to address this important but self-contained area of work: People do not always make rational decisions or act in their own best interest, as classical economists have believed. Our cognitive biases, including selective memory, attention limitations, interests, dislikes, etc.—all attempts to simplify information processing—affect our thinking, interpretations, judgments, and decision-making. The Rational Choice Theory assumes, in fact, that people are predictably illogical and incapable of making good decisions. Economist and professor Richard Thaler built upon the work of psychologists Daniel Kahneman and Amos Tversky to launch the science behind the field of Behavioral Economics (I highly recommend his book, written with legal scholar Cass R. Sunstein, Nudge: Improving Decisions About Health, Wealth, and Happiness).^I It answers a lot of questions about human economic behavior and goes into depth about the effects of cognitive bias on decision-making, challenging the belief that the market knows best. If we want to make better decisions in our personal lives, we need to be aware of our biases and false reasoning. One example is the negativity bias, where people fear loss more than they appreciate gain, or they focus on negative more than on positive experience. Just think about a time when someone complimented you on a job well done, but then gave you one tiny bit of feedback to help you improve. Guess what you focused on? The negative comment more than the positive one, so next time you perform you may not do so as confidently. In Kahneman’s book, Thinking, Fast and Slow, he talks of our innate tendency to be risk averse as we are hardwired to “treat threats as more urgent than opportunities,”⁷ thereby revealing the crucial link between economics and psychology. Historically, this particular bias has increased our odds of survival, and we’ve passed those genes along to our descendants so they, too, can win the “survival of the fittest” contest.

Another type of cognitive bias, confabulation, has humans pulling together a justification and rationale for the decisions they make, after they’ve made them, not before. In neuroscientist Michael Gazzaniga’s Who’s in Charge?: Free Will and the Science of the Brain, he writes, “When we set out to explain our actions, they are all post-hoc explanations using post-hoc observations with no access to non-conscious processing. Not only that, but our left brain also fudges things a bit to fit into a makes-sense story.”⁸ Psychologists would call this an act of inference. These rationales feel totally reasonable to u...

Índice

• Cover
• Title Page
• Dedication
• Introduction
• Chapter 1: Why Ask Why?
• Chapter 2: Laying the Foundation: Biologic Instincts
• Chapter 3: Building the Model: The Five Meta Worldviews
• Chapter 4: To Each Their Own: The Individuist Worldview
• Chapter 5: Better Together: The Social Binder Worldview
• Chapter 6: A Fine Balance: The Three Centrist Worldviews
• Chapter 7: Breaking It Down: The Five Instinctual Patterns, Deconstructed
• Chapter 8: The Language of Why
• Chapter 9: The Reach of Why
• Chapter 10: How to Use the Model
• Chapter 11: Flipping the Script: Instinctual Problem-Solving
• Chapter 12: Facing the Future
• About the Author
• Notes
• Index
• Copyright