Books for decision makers: Vol 6-Elastic
Flexible Thinking in a Constantly Changing World
The world is changing at an inconceivable speed. Every day we have new options, new ways, new tools; we meet new people and we have to reach our path to success between all of this noise. One cause for this continuous changing process is human nature. For generations we have sought to develop new tools or to find new ways in order to help us in our activities and make our lives easier.
Taking these factors into account, and what sparked Leonard Mlodinow to write Elastic, is the exponentially growing rate at which technology changes, impulsing a globalization process and giving more information to the brain to process. The rate of change is often so great that our brain cannot process all of this information by itself. For this reason, Mlodinow, building on complex cognitive science and other medical/scientific areas, studied the brain, its structure and how it works. He takes the reader on a journey through understanding the decision making process and how the brain manages it.
The main, and most important takeaway from Mlodinow’s book, is that we can divide our information process in three modes: scripted, analytical, and the most important (characteristic to mammals, especially humans), elastic. Let's start from the beginning and discover this for ourselves.
Remember the Netflix vs. Blockbuster’s case?; or possible Yahoo & Google? History has taught us that we should adapt (not only our mindset but also our actions), and be attracted to novelty if we want to succeed. These cases may make us think that humans are averse to novelties and changes, but you would be wrong. The way our brain works as humans, is that we are always seeking new ways and technologies for making our lives easier. From the wheel being invented thousands of years ago to language, and more recently, the Internet, cloud computing and blockchain. What history really shows us is that we, as a species, seek these novelty technologies and that the people or companies against this novelty changes are the ones who will be left behind and fail.
Understanding this is essential for achieving success in this constantly changing world. One of the most recent examples for this is Niantic (Pokemon-Go creators). Thinking about a business idea or a new project, while ignoring held assumptions in your environment and asking yourself “What if?” might be the key to success. This is exactly what Niantic did with Pokemon-Go. They broke an accepted, and apparently “true” assumption in the video game industry and developed their game from the idea, “What if people are willing to move while playing a video game?”. Sounds crazy, but before Pokemon-Go, the video game industry widely accepted the assumption that people don't want to move a lot while playing a video game. This may have been true some years ago, but it is not true anymore. Niantic created a new dynamic, which led to a wildly popular and profitable video game. No longer are video games only meant to be enjoyed on the couch.
The success behind Pokemon-Go is the result of a collective decision process originated through a collective thinking process. So, we are going to focus on our individual thinking process and understand how the Pokemon-Go success case had a cognitive science justification and how this might be applied to our lives. To begin, we need to understand how we think.
If you look for the definition of think, you will find a very superficial definition; nevertheless, the definition that Mlodinow uses (and we do now too) is as follows:
Thinking is evaluating circumstances and making a meaningful response by generating ideas.
Using this definition, most animals don’t think all that often, because they are not constantly generating ideas. Neither do humans. Most of the time we don’t think, not even when something valuable is at stake because we don't always generate ideas as a result of analyzing a situation we face. This scripted behavior is the most common across species.
Then, what should we do? We act as automatons (humans in autopilot) most of the time, and it's not bad; but we need to control how and when we should act scripted and when we should think in order to better dictate our situation. Psychologists call this process mindfulness. In order to improve your mindfulness, these are some suggested exercises: The Body Scan, Mindfulness of Thoughts and Mindful Eating. Once we have gone through our mindfulness process and improved it, we can go through our second mode of thinking and facing problems, or analytical thought.
Analytical thought is driven by logical rules and shows how our brain can be, at least partially, similar to computers. We can understand it as our objective thinking; how we think without the noise or human emotions and find the most accurate solution. We see that society gives a very high value to this analytical thought. The GRE and GMAT exams, and even some job interviews try to identify how competent we are at solving problems based on the information that we have learned and how we are able to apply it. This affects the way we weigh different thoughts in the sense that we have been trying to learn a lot of things and remain logical, but even this is starting to change. It’s becoming an obsolete way to determine a person's potential or suitability for a scholarship, a job, etc. Even if our society is starting to change its mindset, most of the people still place a lot of importance on this analytical thinking and try to be human-computers that can solve problems quickly and logically. Based on this, we should be mindful and begin to change our mindset as soon as possible.
Our brain already trashes/ignores non-logical ideas; and as for the human-computers we wish to be, well, most non-logical ideas get trashed, but these are the ideas that that could actually lead to something new or ground breaking, maybe even a gigantic success as in the case of Niantic. This last kind of thinking in we which analyze all the “what ifs,” is what will allow us to not only survive, but continuously reach success and push for greater heights in this constantly changing world.
Elastic thinking is the opposite of analytical thinking, which uses reasoning and logical principles and rules to solve a problem. Elastic thinking is the kind of thinking that creates the rules and principles that analytical thinking uses to work. It is the thinking that adapts and examines what would happen if we didn't follow the rule, and instead tinkered with or adapted to changes that push us to think “outside the box.” This thinking was applied by Niantic with Pokemon-Go, but also with the wheel thousands of years ago.
Nevertheless, the most optimal way to understand these two last ways of thinking we have as humans, is to compare them side by side. The game of chess brings us a beautiful example of this, Deep Blue vs. Gary Kasparov (1996-1997). Before diving into this example, let’s examine an important part of, Why We Think?
Our brain has such complex structures that some chemicals looking to solve one problem (medicine for Parkinson) might, unexpectedly, affect structures it wasn't meant to affect, thus altering our brain’s reward system. Our reward system relates to communication between different structures in our brain and is the system that makes us feel joy for doing different things. This is also where our inspiration to do certain things comes from.
This is what differentiates us and computers. Computers solve problems, but all that they know is a result of a human input. You can ask a computer to create a song, but it doesn't create anything, it just takes the information it has been given, processes it, and tries to create something from it. These computers can solve very specific problems, but they don't feel joy or a real sense of reward from solving these problems, and they also don't create and learn new insights from the work they’ve done. For this reason, the way computers process information is called top-down. They produce step-by-step analytical thoughts. On the other hand, humans, have a bottom-up information process, where we produce original insights from interactions of millions of neurons and complex structures in our brain.
Another consideration before we get into the Kasparov vs. Deep Blue, is a very ancient idea from Aristotle in which he proposes that: “Human thoughts are based on internal representations of the world.” Like a computer, our thoughts are originated from inputs. In our case, these inputs are what we perceive from our senses; our processing unit is our cerebral cortex; and, with this whole thinking process, we reach some conclusions. The difference remains in how we process data. We encode the data received by our senses and our brain uses it to make a representation of the world. This marked the difference between Kasparov and Deep Blue in the tournament they played between 1996 and 1997.
In this tournament between the best chess player at that moment, Kasparov, and the best supercomputer, Deep Blue; each player had a different way to process the information on the chess board. Deep Blue was programed to play chess. A group of humans at IBM taught the supercomputer the rules of the game, the value of each piece and it analyzed this information by brute force, creating trees with variants of the game and finding the most logical option to do based on probability. This way of thinking by Deep Blue is top-down, as we described before, and, if we compare the capacity of any human, even Kasparov, to fight against this supercomputer in information processing and variant analysis, we have totally lost the war. If we can’t fight supercomputers in this top-down thinking, how did Kasparov win the first tournament against Deep Blue in 1996 by a score of 4 games to 2?
The answer can be found in the way Kasparov processed the information on the chess board. He recognized patterns, cluster features of the game, and analyzed the game as a whole. Contrarily, Deep Blue analyzed every single position and weighed the variants. This elastic thinking gave Kasparov the opportunity to adapt to the situation when he perceived the right pattern, even if he didn’t know which was the best move from an objective/logical perspective. He knew where he should concentrate his moves and recognized situations; not just one specific move, but a set of moves that would make lead to a victory. The difference between Deep Blue and Kasparov, is the difference between analytical thinking (probably the most valued nowadays) and elastic thinking (probably the most underrated and important). Kasparov created meaningful insights from his moves and adapted to context changes in order to create new ways to solve his problem.
Even if some companies like Google are investing deep into Artificial intelligence, and are able to give machines a more bottom-up thinking approach, these machines still don’t have human-level understanding, and they are far from the General Problem Solver goal, which is, basically, a computer that solves any general problem, from the most basic ones, to proving complex theorems.
The idea is clear. It’s backed by how our brain is composed and how it functions. We understand that, with this world, full of so much data and information to be processed, we should let the computers and machines do the analytical, step-by-step processing of information. They are just better at it, but we are still superior to machines; not by how much information we can process, but by how we actually process it (via interaction between complex structures and neuronal nests in our brain). Let us find and do a more interesting job-understand how the world is changing, understand the paradigm shifts. Abandon our framework when facing a novel problem because we are biased and framed by our society and context. We must recognize that when facing any problem. Sometimes, the key to solving a problem, is to ignore what you already know. Let ignorance talk. Relax your cognitive filters. This is how you can improve your elastic thinking.
Where there is a will, there's a way.
David Leonardo Gonzalez
Content Collaborator at Macrowise
October 11th, 2019