Thinking in algorithms. How to combine computer analysis and human creativity for better problem solving and decision making. Strategic thinking skills book two written by Albert Rutherford narrated by Russell Newton.

Humans are strange creatures We often do things that don’t make sense, sometimes even to ourselves What makes us more willing to purchase a product for $4.99 than for $5.00? Why do we get items for 50% off that we would never buy at full price? And what makes us so eager to use products celebrities use when we have nothing in common?

We think of using logic and using emotion to make decisions as separate ideas when they go hand-in-hand As behavioral economics and psychology have discovered, it’s next to impossible to decide without using our feelings and biases Our heads often defer to our hearts to help make quick choices. Modern research and technology have looked into the brain and found it comprises a messy network of overlapping emotional and rational sections Whether we like it or not, our rationality has been tainted by our feelings where the two are impossible to extricate When comparing properties, making pros and cons lists may be the logical way of looking at things, but a feeling of home will usurp them every time We may crunch the numbers to see if we can afford those new shoes we’ve been eyeing, but if we believe they’ll bring us enough happiness, our minds will be made up no matter what our calculations say Even when we think we’re making a logical choice, emotional impulses will seize control of situations and steer us in illogical directions Our “gut” often hot-wires our decisions and takes them on a joyride to buy things for a rush of dopamine despite our empty wallets or to go on a date with an attractive person we know isn’t good for us When we leap to conclusions and grab for the nearest solution or craving, we’re not cutting out our brains completely There’s no way to be that carefree Instead, our minds recognize that they must find a quick solution and speed up the process of decision-making to the degree that we might not even be able to follow This isn’t some miraculous hyper-speed thinking function but a simple process of shortcuts We’ve developed these shortcuts, known as “heuristics,” as a survival mechanism that enables us to act more efficiently during a life-or-death situation But, as we’ve evolved, it has become a less beneficial part of our everyday lives Now we use quick thinking to make decisions about things that have no dire consequences or any consequences at all, like choosing whether we should get lettuce or spinach at the grocery store or picking out the next book we want to read from our shelf Although heuristics speed up our thinking processes and work fine for inconsequential choices, they do so by creating corner-cutting habits that can be too simplistic for our own good. Our Brains in Efficiency Mode The more our society has grown and flourished, the more we have available to us—more entertainment, relationships, connectivity, knowledge...and more options In fact, we have too many options From the time we get up to the moment we fall asleep, we ask ourselves to decide on almost every minute of the day (sometimes more) But, for the most part, we don’t notice these choices taking place Our brains have gotten used to finding ways to function efficiently without interfering with the flow of our day Like a server running in the background of a computer setup, our brains store, sort, process, and draw on information from previous experiences This Rolodex of information allows our minds to be ready with conclusions to our questions before or quickly after they arise This way, our day’s flow isn’t disturbed.

Our brains are like great warehouses of information And when the boss (us) asks for a file, it’s just too much work to run to the other end Instead, the poor employee grabs for a nearer drawer It might not have the exact or most correct answer the boss was looking for, but it’s satisfactory enough that the job is considered finished Maybe it’s a B+ or a C+ type of answer, but it’s good enough, and the employee is let off the hook Our brains don’t just do this to make us happy; they do it to save energy We only have so much brain power to give to decision making every day, so we have to make sure that we save it for the important stuff There’s no need to waste our fuel deciding what shoe goes on which foot or how to drive a car when we can put those actions on autopilot If we gave our all to everything we had to choose in life, it would be like taking every possible road on how to work; we’d run out of gas long before we reached our destination. We think of many of these skills and habits as naturally occurring instincts that we’ve always had But that’s not true Once upon a time, we had to learn them Some were intrinsic, like breathing, while others were difficult to discover and build, like riding a bike But over time, they became second nature We didn’t need to think about them anymore These processes that allow us to do our routine activities run in the background of our brain so we can focus on other things. The more we can put skills on autopilot, the more complex ideas and complicated actions we can take on When we save our energy on minor problems, we can put that extra fuel towards more important uses and questions, like figuring out the steps we need to take to further our career, deciding which relationships are worth holding onto or pondering our purpose and motivations in life.

Usually, this ability to save energy serves us well It enables us to go about our daily activities with little effort or strain But sometimes, the habit simplifies decision making too much It leads us to make choices that aren’t as logical as our brains might like us to believe Thanks to the research of behavioral economics, psychology, and neuroscience, we've come to understand many biases and shortcuts our brains use to make these leaps And the better we understand them, the better we can combat them and move beyond them to more complex and rational forms of thinking Relative Advantage and Absolute Terms When we make decisions, we often struggle to compare unlike or abstract things Despite our irrational natures, our brains try their best to produce logical and critical thinking They prefer concepts that can be nailed down, like comparing the cost of a $50 item to a $45 item or a score of 95% on a test versus a score of 86% One is better (or cheaper) than the other Their relativity to one another is clear and tangible But choices in life are rarely so uncomplicated. Imagine you’re looking for a gift for your friend’s wedding You find the perfect item at a store nearby for around $50 It’s more than you’d like to spend, but you decide that it’s worth it because it’s something you know they’ll love Before you get in the car, you remember that a similar store is having a sale one town over You check and see that the item is in stock for $35 Would you go to the further store or the nearer store? Chances are, you’d drive the few extra miles The numerical values of $35 and $50 are easy to compare One is cheaper than the other But this considers only one variable: price You haven’t factored in the time spent driving or the gas needed to get there These elements make the decision too complicated From an easily comparable sum, the problem turns into a three-part formula, some aspects of which are abstract in value, such as the worth of your time.

We usually make choices about whether an item is “worth it” in one of two ways When we see that a product has increased or decreased in value, such as by going on sale, and decide whether to buy it based on this, we are judging it based on “absolute terms." We are comparing it only with itself However, this method is flawed We have no true assessment of its worth except for the happenstance of its cost when we first encountered it The product's value may be its sale price, its original price, or something else altogether But we can’t know that value for sure, so we base it off what information we have However, more often, we use another form of reasoning When we compare an item to like items, such as a name brand and store brand, we are assessing its “relative advantage” by comparing it to similar or substitute products When we get suckered into buying a “new and improved” version of something, we decide that the higher-priced version of the same item is of higher quality If we choose the cheap knockoff, we assess the product’s relative advantage in terms of cost and benefit Although this suggests more data than absolute terms, it doesn’t tell us the product's true value But it does give context. Our brain prefers and therefore seeks easier comparisons and calculations We’re more likely to buy something for $4.99 than $5.00 We’re more ready to get something on sale Numerical values make decisions easier to understand and explain We can see the difference between two numbers, their relative value, and assess which one is ‘better’ based on our chosen comparison variable (i.e., price) Sometimes using relative advantage will help us to see what’s best in our spread of options We won’t know whether the first car we test drive is the best until we test a few others and see how they compare But sellers and marketers can also take advantage of the way we compare rather than calculate Take, for instance, a showroom with three cars The salesperson walks you through the display to see the three options, which are all mid-size vehicles fit your needs One is listed at $30,000, another at $50,000, and the third at $80,000 Which do you go for?

Most of the time, the buyer will go for the middle option The lower option seems too cheap compared to the other two, but $80,000 seems like it’s too much to pay when you could get something similar for less The salesperson knows this and will place a car they’re looking to sell with two other options of lesser and higher values on purpose to make it seem like the best deal based on relative advantage (Ariely, p 3). Imprinting or the Anchoring Effect Another issue we have with our reasoning is the undue importance we place on prior experiences Too often, when we believe we’re being logical and basing our choices on facts, we’re just using our memories and biases to conclude Konrad Lorenz discovered this phenomenon while raising geese When the goslings hatched, Lorenz realized that baby birds would attach themselves, or “imprint,” on the closest figure available if the duck mother was absent But this wasn’t a temporary arrangement; it was an initial reaction that led to a permanent connection to the figure as a parent, no matter what it may be, Lorenz included We create similar relationships to products, using our first impressions and experiences with objects to deem their worth In behavioral economics terms, this is called “anchoring” (Ariely, p 28) If you see an item for the first time at $500 and, the next time, see that it’s $400, you’ll think the newer price must be a good deal It doesn’t matter whether the item’s actual worth is $1,000 or $40 This is also why you’ll hear older generations bemoaning the cost of gas and milk prices As inflation caused prices to rise, their perception of the worth of a product stayed the same If they grew up with gas being $1 a gallon, its price would always seem high when it’s over $1 One day, we’ll also be saying things like, “back in my day, iPhones cost $1,000” because it's how the technology was introduced to us Herd Mentality Society influences our decision making Making choices because “everyone else is doing it” isn’t the most logical But when we see something is popular, we want to hop on the trend Over 20,000 five-star reviews can’t be wrong, and a long line of people can’t be waiting around for nothing, right? And besides, it’s much easier to go with the decision making of other people than it is to make choices for ourselves This is called “herd mentality,” and it’s a troublesome phenomenon that has only become more difficult to avoid with social media and the internet.

Our emotions are the most straightforward and most accessible part of our brains to latch onto with decision making Emotions are the first line of defense when a question arises To make the best decisions would require going deeper into our mental capacities and using our more critical facilities It would mean putting in much effort. Emotional thinking comes to us naturally while rational decision making is learned It requires intention and skill Critical thinking only comes to us when we practice it repeatedly, making it a habit. Emotions aren’t the enemy, but they aren’t reliable when used alone We can take back control of our seemingly irrational behaviors by improving our rational thinking and questioning our decisions Do I think about this logically or through the lens of past experiences?

Why am I doing this? And what factors are guiding my hand? To make sure that we’re taking both aspects of thinking into account and start making rational thinking the norm rather than the exception, we can produce and use formulas for thinking and decision making to replace those we have that rely on biases and shortcuts Exercise Take a day to reflect on your own decision making Sit down and think about what choices you made on a given day, whether it was a decision to buy something, eat something, talk to someone, or some big life-altering choice Then, think about these questions: Do you feel regret about the choice you made or wish you could do it over?

What factors helped determine your choice, such as other people’s opinions, price, previous experiences, etc.? Now that you’re warmed up, think about some bigger decision making you’ve done in the past and answer the questions below, whether in your head or on a piece of paper: 1 Take a moment to reflect on your own economic decision making by writing down your five biggest purchases in life and asking yourself these questions about each: a Do you think they were worth it? b Did they bring you the desired result, whether it was happiness, change, forward movement, etc.?

c What factors helped determine your choice? Did you compare it to other options to assess its value? d Would you still buy the item today were you to have the option?

Why or why not? 2 What are your five biggest regrets in life? a Why do you regret those decisions?

b What made you make those decisions in the first place? c Why did you believe they were a good idea? 3 What are five traits you dislike about yourself (e.g., stubbornness, procrastination, etc.)?

a Why do you dislike these traits? Chapter Two: Machina Sapiens In our world of modern technology, many decisions we make are weighed heavily by the algorithms of machines, although we may not always know it Whenever we Google a question, scroll through social media or click on an ad, we’re setting off complex calculations Take, for instance, a simple search for a car repair service You type into the Google search bar “car repair service” and get the answer you need in a few seconds But in those seconds, numerous formulas are running Google uses specific algorithms to select pages from millions of options to find the most helpful you, considering your location and your previous inquiries about cars and car repairs It factors in similar pages you’ve looked at before and which ones you’ve liked and clicked on or not liked and quickly exited out of or scrolled past Depending on what you’ve searched before, Google may even have a good idea of what car you have and recommend car repair services specifically for those vehicles The search engine weighs all these elements based on importance and relevance while also using its standards to assess the page itself The algorithm looks through text and images to see if the selections are ‘good’ sites or not, meaning that they’re comprehensive and match applicable search terms And it does all this in the blink of an eye. We don’t think about how complicated this underground search process is when we type our question We’re just happy to find a result that will help us get our car fixed But if we consider how much headache and time it can shave off, Google’s search engine is an incredible invention Of course, a lot of this digital digging is a marketing tactic used by companies and Google itself to show us ads and other sources of revenue But a large part of it is also to better display the worlds of knowledge at our fingertips in a digestible way and make decision making easier for us Sometimes, our technology even tries to provide us with solutions before we ask questions Have you ever been scrolling through Facebook or Instagram and stumbled on an ad for something that seems just perfect for you?

And you wonder: ‘How did it know?’ It may seem like a coincidence, but it's precise algorithms using your data These sites take your information, such as your location, age, gender, likes and dislikes, and search history, to understand your habits and predict what products you like or need And, usually, they do a good job. Machine Learning Algorithms are always learning and evolving based on the information we give them The more we search and use them, the better they become at knowing what we like and want Just like humans, they pick up on cues and fix mistakes to do things better each time This is called machine learning Often, machine learning is lumped with AI algorithms Although they’re often used together, they’re not the same An algorithm decides what to show you or produce, while machine learning is a subfield of AI that tracks your online presence and interactions to understand your tendencies and habits AI algorithms are the more logical, process-driven side of technology, like the directions for baking a cake Meanwhile, machine learning is the more abstract component, the human-like factor dipping its finger into the batter to see whether it’s sweet enough The two parts must work together to find success; algorithms have to learn to know a change must be made, and machine learning has to have an algorithm to solve issues that arise Although algorithms are powerful and at the core of many technologies we lean on today, machine learning has quickly become more influential Yet, machine learning is based on human nature People don’t just rely on what they learn initially but grow and evolve to better understand themselves and the world around them Machine learning imitates this through a more scientific method that generates and tests new ideas to see the viability of hypotheses With the power of technology and AI, machine learning is usually much faster at this process than we are. AI Algorithms and Advertising Good marketers have tapped into the potential of both AI algorithms and machine learning And it’s almost scary how accurate it can be But humans are more predictable than we might think.

In many ways, marketers and machine learning have made our lives much easier The algorithms advertisers use to sell us products and services help narrow down the millions of options available to us But they can also be misconstrued and are often used in ways we don’t fully understand or for purposes we don’t like To make social media more desirable and fun (i.e., addictive), algorithms are put in place to keep you invested by learning what you like and dislike and showing you only information you enjoy interacting with On the surface, this helps weed out uninspired or annoying posts you don’t want to see and helps to make the scope of what you’re looking at more manageable But this also narrows your field of vision Often, you’ll find you have posts only from certain people in your feed, usually those that already agree with you on topics, and never see what differing points of view have to say These echo chambers may feel comforting, but they create a false sense of security in which we’re led to believe more people agree with us than do It’s easy to feel like the majority when all you hear are your thoughts bouncing back at you But the more we become surrounded by “yes men” who agree with us, the less we’ll be able to practice healthy debate When we never run into opposition, we don’t know how to handle it when it arises and are more likely to pretend it doesn’t exist. This filtering can be even more dangerous in other sectors, such as Google News, which uses your search history and interactions to gauge what stories you want to see Such algorithms don’t consider what information you should be seeing or need to be seeing but what you most enjoy So, if you always click on videos of cute cats and never want to read about politics or foreign trade, you’ll be very informed about kittens and not very informed about world issues Having a curated feed may make us happy and sometimes be helpful, but remember that the main goal of these algorithms is self-serving Their primary aim is to keep us using the apps and programs they’re designed for Google News wants you to stay informed, but only through their application Therefore, it prioritizes ads and engagement over learning and knowledge. It’s nearly impossible to escape these algorithms and the ulterior motives that often go along with them But we can understand them And, by doing so, use them for our benefit.

What is an Algorithm, Anyways? Not all machine learning and AI technologies are used for advertising Algorithms are everywhere: from code on websites to the sensors in your car, your bank account, and the stoplight on the street Some algorithms are simple, calculating the time since the last light and the status of the other lights around them Others are more complicated, running millions of data points in moments to create a result Yet even the most complex processes follow the same set of simple parameters. When you break it down into its most basic parts, an algorithm is merely a sequence of steps to perform a task given an initial situation (Fisher, p 4) These sequences will be long and elaborate, like the millions of data points running simultaneously to create a Google search result Others will be so basic that they might not even be mathematical Recipes, directions, and sewing patterns are all forms of algorithms too We need not be coders or mathematicians to think about algorithms, use them, or even create our own At their core, good algorithms have four simple characteristics: Correct: The answer comes from previous knowledge and calculations and follows logic.

Efficient: The algorithm must use only the space and time necessary to get the best result. Comprehensible: The algorithm can be understood by others. Illustrative: The solution can be applied to create greater concepts, such as by sorting data (Fisher, p 7) When we see algorithms in a more simplistic way, we can use them to help us sort concepts and ideas into actionable steps Our Minds as Machines AI algorithms and machine learning may be more rational and logical (plus more efficient) than humans can ever hope to be But we have something machines and AI don’t The human mind is a powerful place...perhaps not when it comes to quickly solving formulas, but with emotion, abstraction, and imagination Our minds work in much broader and more interesting ways than machine learning and AI can Those systems know only input and output They know how to move from A to B But we understand, intrinsically, a lot more about abstract ideas.

The same traits that weigh down our critical thinking can also be some of our greatest strengths Although we may look at our rational side in high esteem, our irrational decision making has its importance too Sure, it may be illogical to choose what city to move to based on the feeling But being happy where you live is crucial Maybe it seems silly to buy a pair of shoes just because they put a smile on your face, but why shouldn’t you surround yourself with things that make you grin? These are concepts that AI and machine learning do not fully understand We may see the problems and solutions AI deals with as too complex for our simple minds, but if they could, they would probably say the same about many decisions we have to make As Christian and Griffiths point out in their book Algorithms to Live By, “Life is full of problems that are, quite simply, hard And the mistakes made by people often say more about the intrinsic difficulties of the problem than about the fallibility of human brains” (p 5) This is exactly why we need as many tools in our toolkit as we can get If we put the rational side of algorithms and our abilities of imagination and abstraction together, we can achieve incredible things When we use the abilities of algorithms to our advantage, we can become more efficient and rational decision makers We can make choices with more surety and have fewer regrets We can move forward more effectively and spend our time more wisely Exercise Test the powers of machine learning and AI algorithms in your life by switching up your routine For example, search for something you would never normally search for or look at and interact with the profile of someone you never engage with on social media Watch, in these few days, how the algorithms change their approach to your content Did you see an ad you’ve never seen before? Did you see posts you’d never normally have on your feed?

How did this affect your life or mood? This has been Thinking in Algorithms. How to combine computer analysis and human creativity for better problem solving and decision making.