Data Modelling Quotes

Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide. We have to explicitly embed better values into our algorithms, creating Big Data models that follow our ethical lead. Sometimes that will mean putting fairness ahead of profit.

The point is, the brain talks to itself, and by talking to itself changes its perceptions. To make a new version of the not-entirely-false model, imagine the first interpreter as a foreign correspondent, reporting from the world. The world in this case means everything out- or inside our bodies, including serotonin levels in the brain. The second interpreter is a news analyst, who writes op-ed pieces. They read each other's work. One needs data, the other needs an overview; they influence each other. They get dialogues going. INTERPRETER ONE: Pain in the left foot, back of heel. INTERPRETER TWO: I believe that's because the shoe is too tight. INTERPRETER ONE: Checked that. Took off the shoe. Foot still hurts. INTERPRETER TWO: Did you look at it? INTERPRETER ONE: Looking. It's red. INTERPRETER TWO: No blood? INTERPRETER ONE: Nope. INTERPRETER TWO: Forget about it. INTERPRETER ONE: Okay. Mental illness seems to be a communication problem between interpreters one and two. An exemplary piece of confusion. INTERPRETER ONE: There's a tiger in the corner. INTERPRETER TWO: No, that's not a tiger- that's a bureau. INTERPRETER ONE: It's a tiger, it's a tiger! INTERPRETER TWO: Don't be ridiculous. Let's go look at it. Then all the dendrites and neurons and serotonin levels and interpreters collect themselves and trot over to the corner. If you are not crazy, the second interpreter's assertion, that this is a bureau, will be acceptable to the first interpreter. If you are crazy, the first interpreter's viewpoint, the tiger theory, will prevail. The trouble here is that the first interpreter actually sees a tiger. The messages sent between neurons are incorrect somehow. The chemicals triggered are the wrong chemicals, or the impulses are going to the wrong connections. Apparently, this happens often, but the second interpreter jumps in to straighten things out.

What does regression do? It finds a prediction based on the average of what has occurred in the past. For instance, if all you have to go on to determine whether it is going to rain tomorrow is what happened each day last week, your best guess might be an average. If it rained on two of the last seven days, you might predict that the probability of rain tomorrow is around two in seven, or 29 percent. Much of what we know about prediction has been making our calculations of the average better by building models that can take in more data about the context.

Here we see that models, despite their reputation for impartiality, reflect goals and ideology. When I removed the possibility of eating Pop-Tarts at every meal, I was imposing my ideology on the meals model. It’s something we do without a second thought. Our own values and desires influence our choices, from the data we choose to collect to the questions we ask. Models are opinions embedded in mathematics.

Companies can learn a lot from biological systems. The human immune system for example is adaptive, redundant, diverse, modular, data-driven and network collaborative. A company that desires not just short term profit but also long term resilience should apply these features of the human immune system to it's business models and company structure.

It’s important to remember that big data all comes from the same place – the past. A new campaigning style, a single rogue variable or a ‘black swan’ event can throw the most perfectly calibrated model into chaos.

However, when you create a model from proxies, it is far simpler for people to game it. This is because proxies are easier to manipulate than the complicated reality they represent.

Our own values and desires influence our choices, from the data we choose to collect to the questions we ask. Models are opinions embedded in mathematics.

Something as superfluous as "play" is also an essential feature of our consciousness. If you ask children why they like to play, they will say, "Because it's fun." But that invites the next question: What is fun? Actually, when children play, they are often trying to reenact complex human interactions in simplified form. Human society is extremely sophisticated, much too involved for the developing brains of young children, so children run simplified simulations of adult society, playing games such as doctor, cops and robber, and school. Each game is a model that allows children to experiment with a small segment of adult behavior and then run simulations into the future. (Similarly, when adults engage in play, such as a game of poker, the brain constantly creates a model of what cards the various players possess, and then projects that model into the future, using previous data about people's personality, ability to bluff, etc. The key to games like chess, cards, and gambling is the ability to simulate the future. Animals, which live largely in the present, are not as good at games as humans are, especially if they involve planning. Infant mammals do engage in a form of play, but this is more for exercise, testing one another, practicing future battles, and establishing the coming social pecking order rather than simulating the future.)

The lack of transparency regarding training data sources and the methods used can be problematic. For example, algorithmic filtering of training data can skew representations in subtle ways. Attempts to remove overt toxicity by keyword filtering can disproportionately exclude positive portrayals of marginalized groups. Responsible data curation requires first acknowledging and then addressing these complex tradeoffs through input from impacted communities.

So we pour in data from the past to fuel the decision-making mechanisms created by our models, be they linear or nonlinear. But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand.[...]It is in those outliers and imperfections that the wildness lurks.

I’ve said several times that the brain acts like a scientist. It forms hypotheses through prediction and tests them against the “data” of sensory input. It corrects its predictions by way of prediction error, like a scientist adjusts his or her hypotheses in the face of contrary evidence. When the brain’s predictions match the sensory input, this constitutes a model of the world in that instant, just like a scientist judges that a correct hypothesis is the path to scientific certainty.

Every piece of data ingested by a model plays a role in determining its behavior. The fairness, transparency, and representativeness of the data reflect directly in the LLMs' outputs. Ignoring ethical considerations in data sourcing can inadvertently perpetuate harmful stereotypes, misinformation, or gaps in knowledge. It can also infringe on the rights of data creators.

And in any case, while there is evidence that women can to a certain extent accept men as role models, men won’t do the same for women. Women will buy books by and about men, but men won’t buy books by and about women (or at least not many).

A computer model which manipulated data about itself and its “surroundings” in essentially the same way as an organic brain would have to possess essentially the same mental states. “Simulated consciousness” was as oxymoronic as “simulated addition.

To create a model, then, we make choices about what’s important enough to include, simplifying the world into a toy version that can be easily understood and from which we can infer important facts and actions. We expect it to handle only one job and accept that it will occasionally act like a clueless machine, one with enormous blind spots.

At the same time, we have fallen for the idea that these services are ‘free’. In reality, we pay with our data into a business model of extracting human attention.

Your job isn’t to build a product; it’s to de-risk a business model.

as data becomes the new oil, many business models will be transformed from hardware to software to services.

A flexible mind changes itself and builds a better model as it gathers more data about reality.

Big Tech firms, however, have no need to raise prices, because they have a business model by which they are not paid in money; they are paid in data, via a system of barter

His previous triangulation data informed him that they were come nigh unto the Kingdom of Heaven. Which Nietzschean compass indicated that God was dead ahead.

The philosophical implications of "predictive coding" are deep and strange. The model suggests that our perceptions of the world offer us not a literal transcription of reality but rather a seamless illusion woven from both the data of our senses and the models in our memories.

Whoever has the best algorithms and the most data wins. A new type of network effect takes hold: whoever has the most customers accumulates the most data, learns the best models, wins the most new customers, and so on in a virtuous circle (or a vicious one, if you’re the competition).

Racism, at the individual level, can be seen as a predictive model whirring away in billions of human minds around the world. It is built from faulty, incomplete, or generalized data. Whether it comes from experience or hearsay, the data indicates that certain types of people have behaved badly. That generates a binary prediction that all people of that race will behave that same way. Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias.

So why does the world appear stable to you when you’re looking at it? Why doesn’t it appear as jerky and nauseating as the poorly filmed video? Here’s why: your internal model operates under the assumption that the world outside is stable. Your eyes are not like video cameras – they simply venture out to find more details to feed into the internal model. They’re not like camera lenses that you’re seeing through; they’re gathering bits of data to feed the world inside your skull." The Brain: The Story of You - David Eagleman

The economist Robin Hanson estimates, based on historical economic and population data, a characteristic world economy doubling time for Pleistocene hunter–gatherer society of 224,000 years; for farming society, 909 years; and for industrial society, 6.3 years.3 (In Hanson’s model, the present epoch is a mixture of the farming and the industrial growth modes—the world economy as a whole is not yet growing at the 6.3-year doubling rate.) If

A forecaster should almost never ignore data, especially when she is studying rare events like recessions or presidential elections, about which there isn’t very much data to begin with. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model—that she is interested in showing off rather than trying to be accurate.

This is a point I’ll be returning to in future chapters: we’ve seen time and again that mathematical models can sift through data to locate people who are likely to face great challenges, whether from crime, poverty, or education. It’s up to society whether to use that intelligence to reject and punish them—or to reach out to them with the resources they need.

employees more space for more critical, high-level work. Employees will have a virtual assistant, almost like a brilliant intern with near-perfect memory, capable of instantly recalling any piece of knowledge stored on computers and the internet. Instead of simple file retrieval, the models can generate smarter insights drawn from the entire pool of a company’s internal data.

The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.

Turns out, while all light pollution is bad for astrophysics, the low-pressure sodium lamps are least bad because their contamination can be easily subtracted from telescope data. In a model of cooperation, the entire city of Tucson, Arizona, the nearest large municipality to the Kitt Peak National Observatory, has, by agreement with the local astrophysicists, converted all its streetlights to low-pressure sodium lamps.

We’ve seen time and again that mathematical models can sift through data to locate people who are likely to face great challenges, whether from crime, poverty, or educations. It’s up to society whether to use that intelligence to reject and punish them—or to reach out to them with the resources they need. We can use the scale and efficiency that make WMDs so pernicious in order to help people. It all depends on the objective we choose.

The Internet of Things (IoT) devoid of comprehensive security management is tantamount to the Internet of Threats. Apply open collaborative innovation, systems thinking & zero-trust security models to design IoT ecosystems that generate and capture value in value chains of the Internet of Things.

Opaque and invisible models are the rule, and clear ones very much the exception. We’re modeled as shoppers and couch potatoes, as patients and loan applicants, and very little of this do we see—even in applications we happily sign up for. Even when such models behave themselves, opacity can lead to a feeling of unfairness.

So we pour in data from the past to fuel the decision-making mechanisms created by our models, be they linear or nonlinear. But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand.[...]Even though many economic and financial variables fall into distributions that approximate a bell curve, the picture is never perfect.[...]It is in those outliers and imperfections that the wildness lurks.

Now he was…dust. To an outside observer, these ten seconds had been ground up into ten thousand uncorrelated moments and scattered throughout real time - and in model time, the outside world had suffered an equivalent fate. Yet the pattern of his awareness remained perfectly intact: somehow he found himself, “assembled himself” from these scrambled fragments. He’d been taken apart like a jigsaw puzzle - but his dissection and shuffling were transparent to him. Somehow - on their own terms - the pieces remained connected. Imagine a universe entirely without structure, without shape, without connections. A cloud of microscopic events, like fragments of space-time … except that there is no space or time. What characterizes one point in space, for one instant? Just the values of the fundamental particle fields, just a handful of numbers. Now, take away all notions of position, arrangement, order, and what’s left? A cloud of random numbers. But if the pattern that is me could pick itself out from all the other events taking place on this planet, why shouldn’t the pattern we think of as ‘the universe’ assemble itself, find itself, in exactly the same way? If I can piece together my own coherent space and time from data scattered so widely that it might as well be part of some giant cloud of random numbers, then what makes you think that you’re not doing the very same thing?

All models are wrong, but some are useful.’ CHAPTER 6 Algorithms, Analytics and Prediction

In an optimization-based model, preferences or payoffs are fundamental. In a rule-based model, the behavior is fundamental. Behavioral rules can be fixed or adapt.

All models are wrong, but some are useful.” In other words, models intentionally simplify our complex world.

With limited training data, a more constrained model tends to perform better.

Surveillance is the business model of the Internet for two primary reasons: people like free, and people like convenient.

The purpose of models is not to fit the data but to sharpen the questions.

If we want the future to be better than the past, moral imagination is required, and that’s something only humans can provide [87]. Data and models should be our tools, not our masters.

If you look at this development from the perspective of a university president, it’s actually quite sad. Most of these people no doubt cherished their own college experience—that’s part of what motivated them to climb the academic ladder. Yet here they were at the summit of their careers dedicating enormous energy toward boosting performance in fifteen areas defined by a group of journalists at a second-tier newsmagazine. They were almost like students again, angling for good grades from a taskmaster. In fact, they were trapped by a rigid model, a WMD.

The basic training procedure for the perceptron, as well as its many contemporary progeny, has a technical-sounding name—“stochastic gradient descent”—but the principle is utterly straightforward. Pick one of the training data at random (“stochastic”) and input it to the model. If the output is exactly what you want, do nothing. If there is a difference between what you wanted and what you got, then figure out in which direction (“gradient”) to adjust each weight—whether by literal turning of physical knobs or simply the changing of numbers in software—to lower the error for this particular example. Move each of them a little bit in the appropriate direction (“descent”). Pick a new example at random, and start again. Repeat as many times as necessary.

Were we dealing with a spectrum-based system that described male and female sexuality with equal accuracy, data taken from gay males would look similar to data taken from straight females—and yet this is not what we see in practice. Instead, the data associated with gay male sexuality presents a mirror image of data associated with straight males: Most gay men are as likely to find the female form aversive as straight men are likely to find the male form aversive. In gay females we observe a similar phenomenon, in which they mirror straight females instead of appearing in the same position on the spectrum as straight men—in other words, gay women are just as unlikely to find the male form aversive as straight females are to find the female form aversive. Some of the research highlighting these trends has been conducted with technology like laser doppler imaging (LDI), which measures genital blood flow when individuals are presented with pornographic images. The findings can, therefore, not be written off as a product of men lying to hide middling positions on the Kinsey scale due to a higher social stigma against what is thought of in the vernacular as male bisexuality/pansexuality. We should, however, note that laser Doppler imaging systems are hardly perfect, especially when measuring arousal in females. It is difficult to attribute these patterns to socialization, as they are observed across cultures and even within the earliest of gay communities that emerged in America, which had to overcome a huge amount of systemic oppression to exist. It’s a little crazy to argue that the socially oppressed sexuality of the early American gay community was largely a product of socialization given how much they had overcome just to come out. If, however, one works off the assumptions of our model, this pattern makes perfect sense. There must be a stage in male brain development that determines which set of gendered stimuli is dominant, then applies a negative modifier to stimuli associated with other genders. This stage does not apparently take place during female sexual development. 

If every time new data comes along we have to add complexity to our model in order to accommodate it, this should be a hint that the model is fundamentally a failure. It becomes a blob of ‘silly putty’ that is malleable enough to fit any new data. This sort of model is not a proper basis for a hypothesis; it is merely a blank check to claim we understand something when we really do not.

It’s not in keeping with the scientific model to investigate a purportedly haunted location with the intent to prove that ghosts exist. The paranormal researcher should remain neutral and unbiased throughout the investigation and let the data prove a definitive conclusion, whether that’s the one they wanted or not. They should walk into an investigation thinking, “I will document what happens and then examine the data for conclusions.

while there is evidence that women can to a certain extent accept men as role models, men won’t do the same for women. Women will buy books by and about men, but men won’t buy books by and about women (or at least not many).

Two months in Shanghai, and what does she have to show for herself? She had been full of plans on the plane ride over, had studied her phrase book as if cramming for an exam, had been determined to refine her computational model with a new set of data, expecting insights and breakthroughs, plotting notes for a new article. Only the time has trickled away so quickly. She has meandered through the days chatting with James instead of gathering data. At night, she has gone out to dinners and bars. [James'] Chinese has not improved; her computational model has barely been touched. She does not know what she has been doing with herself, and now an airplane six days away is waiting for her.

Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.

The data on organised abuse has been simplified or distorted in an attempt force it to conform to mechanical psychological models of dissociative obedience or else to the psychiatric framework of ‘paedophilia’. Psychopathology alone is an inadequate explanation for environments in which sexual abuse has a social and symbolic function for groups of adults. Abusive groups do not emerge in a vacuum but rather they are formed within pre-existing social arrangements such as families, churches and schools.

Thanks in part to the resulting high score on the evaluation, he gets a longer sentence, locking him away for more years in a prison where he’s surrounded by fellow criminals—which raises the likelihood that he’ll return to prison. He is finally released into the same poor neighborhood, this time with a criminal record, which makes it that much harder to find a job. If he commits another crime, the recidivism model can claim another success. But in fact the model itself contributes to a toxic cycle and helps to sustain it. That’s a signature quality of a WMD.

finance. What’s the expected amount of data? What’s the expected signal in the data? How much data do we have? What are the opportunities to use this model? What is the payoff for those opportunities? What’s the scale of this model if it works? What’s the probability that the idea is valid?

Research by media scholars Daniel Kreiss and Philip Howard indicates that the 2008 Obama campaign compiled significant data on more than 250 million Americans, including “a vast array of online behavioral and relational data collected from use of the campaign’s web site and third-party social media sites such as Facebook.…”96 Journalist Sasha Issenberg, who documented these developments in his book The Victory Lab, quotes one of Obama’s 2008 political consultants who likened predictive modeling to the tools of a fortune-teller: “We knew who… people were going to vote for before they decided.

These examples should be models for communication, precisely because they inspire curiosity. “How does money influence politics?” is not an especially engaging question, but “If I were running for president, how would I raise lots of money with few conditions and no scrutiny?” is much more intriguing.

Avoid succumbing to the gambler’s fallacy or the base rate fallacy. Anecdotal evidence and correlations you see in data are good hypothesis generators, but correlation does not imply causation—you still need to rely on well-designed experiments to draw strong conclusions. Look for tried-and-true experimental designs, such as randomized controlled experiments or A/B testing, that show statistical significance. The normal distribution is particularly useful in experimental analysis due to the central limit theorem. Recall that in a normal distribution, about 68 percent of values fall within one standard deviation, and 95 percent within two. Any isolated experiment can result in a false positive or a false negative and can also be biased by myriad factors, most commonly selection bias, response bias, and survivorship bias. Replication increases confidence in results, so start by looking for a systematic review and/or meta-analysis when researching an area.

I advise you to look for a chance to break away, to find a subject you can make your own. That is where the quickest advances are likely to occur, as measured by discoveries per investigator per year. Therein you have the best chance to become a leader and, as time passes, to gain growing freedom to set your own course. If a subject is already receiving a great deal of attention, if it has a glamorous aura, if its practitioners are prizewinners who receive large grants, stay away from that subject. Listen to the news coming from the hubbub, learn how and why the subject became prominent, but in making your own long-term plans be aware it is already crowded with talented people. You would be a newcomer, a private amid bemedaled first sergeants and generals. Take a subject instead that interests you and looks promising, and where established experts are not yet conspicuously competing with one another, where few if any prizes and academy memberships have been given, and where the annals of research are not yet layered with superfluous data and mathematical models.

The [Value at Risk model] was like a faulty speedometer, which is arguably worse than no speedometer at all. If you place too much faith in the broken speedometer, you will be oblivious to other signs that your speed is unsafe. In contrast, if there is no speedometer at all, you have no choice but to look around for clues as to how fast you are really going.

In the same way, hybrid models of blended learning are not noticeably simpler for teachers than the existing system. On the contrary, in many cases they appear to require all the expertise of the traditional model plus new expertise in managing digital devices and in integrating data across all the supplemental online experiences in the teacher-directed rotation.

Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And

The difference between human dynamics and data mining boils down to this: Data mining predicts our behaviors based on records of our patterns of activity; we don't even have to understand the origins of the patterns exploited by the algorithm. Students of human dynamics, on the other hand, seek to develop models and theories to explain why, when, and where we do the things we do with some regularity.

As should be obvious by now, surveillance is the business model of the Internet. You create “free” accounts on Web sites such as Snapchat, Facebook, Google, LinkedIn, Foursquare, and PatientsLikeMe and download free apps like Angry Birds, Candy Crush Saga, Words with Friends, and Fruit Ninja, and in return you, wittingly or not, agree to allow these companies to track all your moves, aggregate them, correlate them, and sell them to as many people as possible at the highest price, unencumbered by regulation, decency, or ethical limitation. Yet so few stop and ask who else has access to all these data detritus and how it might be used against us. Dataveillance is the “new black,” and its uses, capabilities, and powers are about to mushroom in ways few consumers, governments, or technologists might have imagined.

Science begins with the world we have to live in, accepting its data and trying to explain its laws. From there, it moves toward the imagination: it becomes a mental construct, a model of a possible way of interpreting experience. The further it goes in this direction, the more it tends to speak the languages of mathematics, which is really one of the languages of the imagination, along with literature and music.

In a provocative article in Wired, editor-in-chief Chris Anderson argued that huge databases render scientific theory itself obsolete. Why spend time formulating human-language hypotheses, after all, when you can quickly analyze trillions of bits of data and find the clusters and correlations? He quotes Peter Norvig, Google’s research director: “All models are wrong, and increasingly you can succeed without them.

But even when Facebook isn't deliberately exploiting its users, it is exploiting its users—its business model requires it. Even if you distance yourself from Facebook, you still live in the world that Facebook is shaping. Facebook, using our native narcissism and our desire to connect with other people, captured our attention and our behavioral data; it used this attention and data to manipulate our behavior, to the point that nearly half of America began relying on Facebook for news. Then, with the media both reliant on Facebook as a way of reaching readers and powerless against the platform's ability to suck up digital advertising revenue—it was like a paperboy who pocketed all the subscription money—Facebook bent the media's economic model to match its own practices: publications needed to capture attention quickly and consistently trigger high emotional responses to be seen at all. The result, in 2016, was an unending stream of Trump stories, both from the mainstream news and from the fringe outlets that were buoyed by Facebook's algorithm. What began as a way for Zuckerberg to harness collegiate misogyny and self-interest has become the fuel for our whole contemporary nightmare, for a world that fundamentally and systematically misrepresents human needs.

we’ve seen time and again that mathematical models can sift through data to locate people who are likely to face great challenges, whether from crime, poverty, or education. It’s up to society whether to use that intelligence to reject and punish them—or to reach out to them with the resources they need. We can use the scale and efficiency that make WMDs so pernicious in order to help people. It all depends on the objective we choose.

This creates a pernicious feedback loop. The policing itself spawns new data, which justifies more policing. And our prisons fill up with hundreds of thousands of people found guilty of victimless crimes. Most of them come from impoverished neighborhoods, and most are black or Hispanic. So even if a model is color blind, the result of it is anything but. In our largely segregated cities, geography is a highly effective proxy for race.

Eventually, the performance of a classifier, computational power as well as predictive power, depends heavily on the underlying data that are available for learning. The five main steps that are involved in training a machine learning algorithm can be summarized as follows: Selection of features. Choosing a performance metric. Choosing a classifier and optimization algorithm. Evaluating the performance of the model. Tuning the algorithm.

The fact that the information platform requires an extension of sensors means that it is countering the tendency towards a lean platform. These are not asset-less companies – far from it; they spend billions of dollars to purchase fixed capital and take other companies over. Importantly, ‘once we understand this [tendency], it becomes clear that demanding privacy from surveillance capitalists or lobbying for an end to commercial surveillance on the Internet is like asking Henry Ford to make each Model T by hand’.15 Calls for privacy miss how the suppression of privacy is at the heart of this business model. This tendency involves constantly pressing against the limits of what is socially and legally acceptable in terms of data collection. For the most part, the strategy has been to collect data, then apologise and roll back programs if there is an uproar, rather than consulting with users beforehand.16 This is why we will continue to see frequent uproars over the collection of data by these companies.

Most of us didn’t feel too enthusiastic about making a collapsar jump, either. We’d been assured that we wouldn’t even feel it happen, just free fall all the way. I wasn’t convinced. As a physics student, I’d had the usual courses in general relativity and theories of gravitation. We only had a little direct data at that time — Stargate was discovered when I was in grade school — but the mathematical model seemed clear enough. The collapsar Stargate was a perfect sphere about three kilometers in radius. It was suspended forever in a state of gravitational collapse that should have meant its surface was dropping toward its center at nearly the speed of light. Relativity propped it up, at least gave it the illusion of being there … the way all reality becomes illusory and observer-oriented when you study general relativity. Or Buddhism. Or get drafted. At any rate, there would be a theoretical point in space-time when one end of our ship was just above the surface of the collapsar, and the other end was a kilometer away (in our frame of reference). In any sane universe, this would set up tidal stresses and tear the ship apart, and we would be just another million kilograms of degenerate matter on the theoretical surface, rushing headlong to nowhere for the rest of eternity or dropping to the center in the next trillionth of a second. You pays your money and you takes your frame of reference. But they were right. We blasted away from Stargate 1, made a few course corrections and then just dropped, for about an hour.

If a model did anything too obviously bizarre—flooded the Sahara or tripled interest rates—the programmers would revise the equations to bring the output back in line with expectation. In practice, econometric models proved dismally blind to what the future would bring, but many people who should have known better acted as though they believed in the results. Forecasts of economic growth or unemployment were put forward with an implied precision of two or three decimal places. Governments and financial institutions paid for such predictions and acted on them, perhaps out of necessity or for want of anything better. Presumably they knew that such variables as “consumer optimism” were not as nicely measurable as “humidity” and that the perfect differential equations had not yet been written for the movement of politics and fashion. But few realized how fragile was the very process of modeling flows on computers, even when the data was reasonably trustworthy and the laws were purely physical, as in weather forecasting.

From a mathematical point of view, however, trust is hard to quantify. That's a challenge for people building models. Sadly, it's far easier to keep counting arrests, to build models that assume we're birds of a feather and treat us as such. Innocent people surrounded by criminals get treated badly, and criminals surrounded by law-abiding public get a pass. And because of the strong correlation between poverty and reported crime, the poor continue to get caught up in the digital dragnets. The rest of us barely have to think about them.

Faced with this disparity, Netflix stopped asking people to tell them what they wanted to see in the future and started building a model based on millions of clicks and views from similar customers. The company began greeting its users with suggested lists of films based not on what they claimed to like but on what the data said they were likely to view. The result: customers visited Netflix more frequently and watched more movies. “The algorithms know you better than you know yourself,” says Xavier Amatriain, a former data scientist at Netflix.

model’s blind spots reflect the judgments and priorities of its creators. While the choices in Google Maps and avionics software appear cut and dried, others are far more problematic. The value-added model in Washington, D.C., schools, to return to that example, evaluates teachers largely on the basis of students’ test scores, while ignoring how much the teachers engage the students, work on specific skills, deal with classroom management, or help students with personal and family problems. It’s overly simple, sacrificing accuracy and insight for efficiency.

Hey Pete. So why the leave from social media? You are an activist, right? It seems like this decision is counterproductive to your message and work." A: The short answer is I’m tired of the endless narcissism inherent to the medium. In the commercial society we have, coupled with the consequential sense of insecurity people feel, as they impulsively “package themselves” for public consumption, the expression most dominant in all of this - is vanity. And I find that disheartening, annoying and dangerous. It is a form of cultural violence in many respects. However, please note the difference - that I work to promote just that – a message/idea – not myself… and I honestly loath people who today just promote themselves for the sake of themselves. A sea of humans who have been conditioned into viewing who they are – as how they are seen online. Think about that for a moment. Social identity theory run amok. People have been conditioned to think “they are” how “others see them”. We live in an increasing fictional reality where people are now not only people – they are digital symbols. And those symbols become more important as a matter of “marketing” than people’s true personality. Now, one could argue that social perception has always had a communicative symbolism, even before the computer age. But nooooooothing like today. Social media has become a social prison and a strong means of social control, in fact. Beyond that, as most know, social media is literally designed like a drug. And it acts like it as people get more and more addicted to being seen and addicted to molding the way they want the world to view them – no matter how false the image (If there is any word that defines peoples’ behavior here – it is pretention). Dopamine fires upon recognition and, coupled with cell phone culture, we now have a sea of people in zombie like trances looking at their phones (literally) thousands of times a day, merging their direct, true interpersonal social reality with a virtual “social media” one. No one can read anymore... they just swipe a stream of 200 character headlines/posts/tweets. understanding the world as an aggregate of those fragmented sentences. Massive loss of comprehension happening, replaced by usually agreeable, "in-bubble" views - hence an actual loss of variety. So again, this isn’t to say non-commercial focused social media doesn’t have positive purposes, such as with activism at times. But, on the whole, it merely amplifies a general value system disorder of a “LOOK AT ME! LOOK AT HOW GREAT I AM!” – rooted in systemic insecurity. People lying to themselves, drawing meaningless satisfaction from superficial responses from a sea of avatars. And it’s no surprise. Market economics demands people self promote shamelessly, coupled with the arbitrary constructs of beauty and success that have also resulted. People see status in certain things and, directly or pathologically, use those things for their own narcissistic advantage. Think of those endless status pics of people rock climbing, or hanging out on a stunning beach or showing off their new trophy girl-friend, etc. It goes on and on and worse the general public generally likes it, seeking to imitate those images/symbols to amplify their own false status. Hence the endless feedback loop of superficiality. And people wonder why youth suicides have risen… a young woman looking at a model of perfection set by her peers, without proper knowledge of the medium, can be made to feel inferior far more dramatically than the typical body image problems associated to traditional advertising. That is just one example of the cultural violence inherent. The entire industry of social media is BASED on narcissistic status promotion and narrow self-interest. That is the emotion/intent that creates the billions and billions in revenue these platforms experience, as they in turn sell off people’s personal data to advertisers and governments. You are the product, of course.

The future for ancient DNA laboratories that I find appealing is based on a model that has emerged among radiocarbon dating laboratories. For example, the Oxford Radiocarbon Accelerator Unit processes large numbers of samples for a fee, and uses this income stream to support a factory that churns out routine dates and produces data more cheaply, efficiently, and at higher quality than would be possible if its scientists limited themselves to their own questions. But its scientists then piggyback on the juggernaut of the radiocarbon dating factory they have built to do cutting-edge science,

Wild animals enjoying one another and taking pleasure in their world is so immediate and so real, yet this reality is utterly absent from textbooks and academic papers about animals and ecology. There is a truth revealed here, absurd in its simplicity. This insight is not that science is wrong or bad. On the contrary: science, done well, deepens our intimacy with the world. But there is a danger in an exclusively scientific way of thinking. The forest is turned into a diagram; animals become mere mechanisms; nature's workings become clever graphs. Today's conviviality of squirrels seems a refutation of such narrowness. Nature is not a machine. These animals feel. They are alive; they are our cousins, with the shared experience kinship implies. And they appear to enjoy the sun, a phenomenon that occurs nowhere in the curriculum of modern biology. Sadly, modern science is too often unable or unwilling to visualize or feel what others experience. Certainly science's "objective" gambit can be helpful in understanding parts of nature and in freeing us from some cultural preconceptions. Our modern scientific taste for dispassion when analyzing animal behaviour formed in reaction to the Victorian naturalists and their predecessors who saw all nature as an allegory confirming their cultural values. But a gambit is just an opening move, not a coherent vision of the whole game. Science's objectivity sheds some assumptions but takes on others that, dressed up in academic rigor, can produce hubris and callousness about the world. The danger comes when we confuse the limited scope of our scientific methods with the true scope of the world. It may be useful or expedient to describe nature as a flow diagram or an animal as a machine, but such utility should not be confused with a confirmation that our limited assumptions reflect the shape of the world. Not coincidentally, the hubris of narrowly applied science serves the needs of the industrial economy. Machines are bought, sold, and discarded; joyful cousins are not. Two days ago, on Christmas Eve, the U.S. Forest Service opened to commercial logging three hundred thousand acres of old growth in the Tongass National Forest, more than a billion square-meter mandalas. Arrows moved on a flowchart, graphs of quantified timber shifted. Modern forest science integrated seamlessly with global commodity markets—language and values needed no translation. Scientific models and metaphors of machines are helpful but limited. They cannot tell us all that we need to know. What lies beyond the theories we impose on nature? This year I have tried to put down scientific tools and to listen: to come to nature without a hypothesis, without a scheme for data extraction, without a lesson plan to convey answers to students, without machines or probes. I have glimpsed how rich science is but simultaneously how limited in scope and in spirit. It is unfortunate that the practice of listening generally has no place in the formal training of scientists. In this absence science needlessly fails. We are poorer for this, and possibly more hurtful. What Christmas Eve gifts might a listening culture give its forests? What was the insight that brushed past me as the squirrels basked? It was not to turn away from science. My experience of animals is richer for knowing their stories, and science is a powerful way to deepen this understanding. Rather, I realized that all stories are partly wrapped in fiction—the fiction of simplifying assumptions, of cultural myopia and of storytellers' pride. I learned to revel in the stories but not to mistake them for the bright, ineffable nature of the world.

Of course, there can be clear indications that a teacher is not worth paying attention to. A history as a fabulist or a con artist should be considered fatal; thus, the spiritual opinions of Joseph Smith, Gurdjieff, and L. Ron Hubbard can be safely ignored. A fetish for numbers is also an ominous sign. Math is magical, but math approached like magic is just superstition—and numerology is where the intellect goes to die. Prophecy is also a very strong indication of chicanery or madness on the part of a teacher, and of stupidity among his students. One can extrapolate from scientific data or technological trends (climate models, Moore’s law), but most detailed predictions about the future lead to embarrassment right on schedule.

Taking least squares is no longer optimal, and the very idea of ‘accuracy’ has to be rethought. This simple fact is as important as it is neglected. This problem is easily illustrated in the Logistic Map: given the correct mathematical formula and all the details of the noise model – random numbers with a bell-shaped distribution – using least squares to estimate α leads to systematic errors. This is not a question of too few data or insufficient computer power, it is the method that fails. We can compute the optimal least squares solution: its value for α is too small at all noise levels. This principled approach just does not apply to nonlinear models because the theorems behind the principle of least squares repeatedly assume bell-shaped distributions.

The shopkeeper is very efficient, has an efficient home delivery system and knows the tastes and price considerations of his customers. But he is labelled ‘unorganized’ by our experts and national income data and his contribution thereby diminished. The footfalls in his shop cannot be measured using Western models [since there is no place to keep anybody’s foot inside his shop!] and so he is derided and abused. It is like clubbing housewives along with prostitutes in our Census data to show them that they are involved in ‘unproductive’ activities. These are economic constructs imposed by the west on the rest and it is a form of terminological terrorism which is mouthed ad-nauseam by our economists and policy planners without understanding their implications.

The human brain runs first-class simulation software. Our eyes don’t present to our brains a faithful photograph of what is out there, or an accurate movie of what is going on through time. Our brains construct a continuously updated model: updated by coded pulses chattering along the optic nerve, but constructed nevertheless. Optical illusions are vivid reminders of this.47 A major class of illusions, of which the Necker Cube is an example, arise because the sense data that the brain receives are compatible with two alternative models of reality. The brain, having no basis for choosing between them, alternates, and we experience a series of flips from one internal model to the other. The picture we are looking at appears, almost literally, to flip over and become something else.

Scalable Social Network Analysis. The SSNA would monitor telephone calls, conference calls, and ATM withdrawals, but it also sought to develop a far more invasive surveillance technology, one that could “capture human activities in surveillance environments.” The Activity Recognition and Monitoring program, or ARM, was modeled after England’s CCTV camera. Surveillance cameras would be set up across the nation, and through the ARM program, they would capture images of people as they went about their daily lives, then save these images to massive data storage banks for computers to examine. Using state-of-the-art facial recognition software, ARM would seek to identify who was behaving outside the computer’s pre-programmed threshold for “ordinary.” The parameters for “ordinary” remain classified.

Even working within the laws of physics, researchers with an anti-God bias often make blind leaps of faith to escape any evidence of God’s involvement in the universe. For centuries Christians were criticized for their God-of-the-gaps arguments. Sometimes that criticism was deserved. Christians tended to use gaps in understanding or data to build a case for God’s miraculous intervention. Then, when scientific discoveries uncovered a natural explanation for the “divine phenomenon,” ridicule was heaped not only on those proposing the divine explanation but also on belief in God’s existence. In the twenty-first century we see the reverse of the God-of-the-gaps arguments. Nontheists, confronted with problems when ample research leads to no natural explanations and instead points to the supernatural, utterly reject the possibility of the supernatural and insist on a natural explanation even if it means resorting to absurdity. For example, steady state models were supported by an imagined force of physics for which there was not one shred of observational or experimental evidence. The oscillating universe model depended on an imagined bounce mechanism for which there was likewise not one shred of observational or experimental evidence. Similar appeals to imagined forces and phenomena have been the basis for all the cosmological models proposed to avoid the big bang implications about God (see chs. 8 and 9). The disproof of these models and the ongoing appeal by nontheists to more and more bizarre unknowns and unknowables seem to reflect the growing strength of the case for theism (see chs. 8, 9, 13, and 16).

In the longer term, by bringing together enough data and enough computing power, the data giants could hack the deepest secrets of life, and then use this knowledge not just to make choices for us or manipulate us but also to reengineer organic life and create inorganic life-forms. Selling advertisements may be necessary to sustain the giants in the short term, but tech companies often evaluate apps, products, and other companies according to the data they harvest rather than according to the money they generate. A popular app may lack a business model and may even lose money in the short term, but as long as it sucks data, it could be worth billions.4 Even if you don’t know how to cash in on the data today, it is worth having it because it might hold the key to controlling and shaping life in the future. I don’t know for certain that the data giants explicitly think about this in such terms, but their actions indicate that they value the accumulation of data in terms beyond those of mere dollars and cents. Ordinary humans will find it very difficult to resist this process. At present, people are happy to give away their most valuable asset—their personal data—in exchange for free email services and funny cat videos. It’s a bit like African and Native American tribes who unwittingly sold entire countries to European imperialists in exchange for colorful beads and cheap trinkets. If, later on, ordinary people decide to try to block the flow of data, they might find it increasingly difficult, especially as they might come to rely on the network for all their decisions, and even for their healthcare and physical survival.

VaR has been called “potentially catastrophic,” “a fraud,” and many other things not fit for a family book about statistics like this one. In particular, the model has been blamed for the onset and severity of the financial crisis. The primary critique of VaR is that the underlying risks associated with financial markets are not as predictable as a coin flip or even a blind taste test between two beers. The false precision embedded in the models created a false sense of security. The VaR was like a faulty speedometer, which is arguably worse than no speedometer at all. If you place too much faith in the broken speedometer, you will be oblivious to other signs that your speed is unsafe. In contrast, if there is no speedometer at all, you have no choice but to look around for clues as to how fast you are really going.

Dr. Kary Mullis, who won the Nobel Prize in Chemistry for inventing PCR, stated publicly numerous times that his invention should never be used for the diagnosis of infectious diseases. In July of 1997, during an event called Corporate Greed and AIDS in Santa Monica CA, Dr. Mullis explained on video, “With PCR you can find almost anything in anybody. It starts making you believe in the sort of Buddhist notion that everything is contained in everything else, right? I mean, because if you can model amplify one single molecule up to something that you can really measure, which PCR can do, then there’s just very few molecules that you don’t have at least one single one of them in your body. Okay? So that could be thought of as a misuse of it, just to claim that it’s meaningful.” Mikki explained, “The major issue with PCR is that it’s easily manipulated. It functions through a cyclical process whereby each revolution amplifies magnification. On a molecular level, most of us already have trace amounts of genetic fragments similar to coronavirus within us. By simply over-cycling the process, a negative result can be flipped to a positive. Governing bodies such as the CDC and the WHO can control the number of cases by simply advising the medical industry to increase or decrease the cycle threshold (CT).” In August of 2020, the New York Times reported that “a CT beyond 34 revolutions very rarely detect live virus, but most often, dead nucleotides that are not even contagious. In compliance with guidance from the CDC and the WHO, many top US labs have been conducting tests at cycle thresholds of 40 or more. NYT examined data from Massachusetts, New York, and Nevada and determined that up to 90 percent of the individuals who tested positive carried barely any virus.”17 90 percent! In May of 2021, CDC changed the PCR cycle threshold from 40 to 28 or lower for those who have been vaccinated. This one adjustment of the numbers allowed the vaccine pushers to praise the vaccines as a big success.

In the twenty-first century it sounds childish to compare the human psyche to a steam engine. Today we know of a far more sophisticated technology – the computer – so we explain the human psyche as if it were a computer processing data rather than a steam engine regulating pressure. But this new analogy may turn out to be just as naïve. After all, computers have no minds. They don’t crave anything even when they have a bug, and the Internet doesn’t feel pain even when authoritarian regimes sever entire countries from the Web. So why use computers as a model for understanding the mind? Well, are we really sure that computers have no sensations or desires? And even if they haven’t got any at present, perhaps once they become complex enough they might develop consciousness? If that were to happen, how could we ascertain it? When computers replace our bus driver, our teacher and our shrink, how could we determine whether they have feelings or whether they are just a collection of mindless algorithms? When

The tragedy of Central Appalachia is that it is becoming more marginalized in American life just when the country needs more than ever what it has to offer. At a time when the bonds of community and family are visibly failing and people feel more alone than ever, and as they are bombarded from all sides with more demands, and with more "data" that they can possibly digest, Appalachia offers a model for a less frenetic and more measured way of life. People of Appalachian descent elsewhere in the nation-and they number many millions-still feel deep ties to some Appalachian hamlet or hollow as to an ancestral homeland, though they may never have even visited it. As they make their way in the big world of getting and spending they know that something valuable has been lost for all they may have gained. That less frenetic way of life is deeply embedded in Appalachian culture, which has proved incredibly tough and enduring. Yet Appalachia has now been so thoroughly bypassed and forgotten that it cannot give, because the rest of America will not take, what could be it's greatest gift.

qualifies as a WMD. The people putting it together in the 1990s no doubt saw it as a tool to bring evenhandedness and efficiency to the criminal justice system. It could also help nonthreatening criminals land lighter sentences. This would translate into more years of freedom for them and enormous savings for American taxpayers, who are footing a $70 billion annual prison bill. However, because the questionnaire judges the prisoner by details that would not be admissible in court, it is unfair. While many may benefit from it, it leads to suffering for others. A key component of this suffering is the pernicious feedback loop. As we’ve seen, sentencing models that profile a person by his or her circumstances help to create the environment that justifies their assumptions. This destructive loop goes round and round, and in the process the model becomes more and more unfair. The third question is whether a model has the capacity to grow exponentially. As a statistician would put it, can it scale? This might sound like the nerdy quibble of a mathematician. But scale is what turns WMDs from local nuisances into tsunami forces, ones that define and

Will those insights be tested,or simply used to justify the status quo and reinforce prejudices? When I consider the sloppy and self-serving ways that companies use data, I'm often reminded of phrenology, a pseudoscience that was briefly the rage in the nineteenth century. Phrenologists would run their fingers over the patient's skull, probing for bumps and indentations. Each one, they thought, was linked to personality traits that existed in twenty-seven regions of the brain. Usually the conclusion of the phrenologist jibed with the observations he made. If the patient was morbidly anxious or suffering from alcoholism, the skull probe would usually find bumps and dips that correlated with that observation - which, in turn, bolstered faith in the science of phrenology. Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. Models like the ones that red-lighted Kyle Behm and black-balled foreign medical students and St. George's can lock people out, even when the "science" inside them is little more than a bundle of untested assumptions.

Will those insights be tested, or simply used to justify the status quo and reinforce prejudices? When I consider the sloppy and self-serving ways that companies use data, I'm often reminded of phrenology, a pseudoscience that was briefly the rage in the nineteenth century. Phrenologists would run their fingers over the patient's skull, probing for bumps and indentations. Each one, they thought, was linked to personality traits that existed in twenty-seven regions of the brain. Usually the conclusion of the phrenologist jibed with the observations he made. If the patient was morbidly anxious or suffering from alcoholism, the skull probe would usually find bumps and dips that correlated with that observation - which, in turn, bolstered faith in the science of phrenology. Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. Models like the ones that red-lighted Kyle Behm and black-balled foreign medical students and St. George's can lock people out, even when the "science" inside them is little more than a bundle of untested assumptions.

As we’ve seen, one of the most frequently pursued paths for achievement-minded college seniors is to spend several years advancing professionally and getting trained and paid by an investment bank, consulting firm, or law firm. Then, the thought process goes, they can set out to do something else with some exposure and experience under their belts. People are generally not making lifelong commitments to the field in their own minds. They’re “getting some skills” and making some connections before figuring out what they really want to do. I subscribed to a version of this mind-set when I graduated from Brown. In my case, I went to law school thinking I’d practice for a few years (and pay down my law school debt) before lining up another opportunity. It’s clear why this is such an attractive approach. There are some immensely constructive things about spending several years in professional services after graduating from college. Professional service firms are designed to train large groups of recruits annually, and they do so very successfully. After even just a year or two in a high-level bank or consulting firm, you emerge with a set of skills that can be applied in other contexts (financial modeling in Excel if you’re a financial analyst, PowerPoint and data organization and presentation if you’re a consultant, and editing and issue spotting if you’re a lawyer). This is very appealing to most any recent graduate who may not yet feel equipped with practical skills coming right out of college. Even more than the professional skill you gain, if you spend time at a bank, consultancy, or law firm, you will become excellent at producing world-class work. Every model, report, presentation, or contract needs to be sophisticated, well done, and error free, in large part because that’s one of the core value propositions of your organization. The people above you will push you to become more rigorous and disciplined, and your work product will improve across the board as a result. You’ll get used to dressing professionally, preparing for meetings, speaking appropriately, showing up on time, writing official correspondence, and so forth. You will be able to speak the corporate language. You’ll become accustomed to working very long hours doing detail-intensive work. These attributes are transferable to and helpful in many other contexts.

Military analysis is not an exact science. To return to the wisdom of Sun Tzu, and paraphrase the great Chinese political philosopher, it is at least as close to art. But many logical methods offer insight into military problems-even if solutions to those problems ultimately require the use of judgement and of broader political and strategic considerations as well. Military affairs may not be as amenable to quantification and formal methodological treatment as economics, for example. However, even if our main goal in analysis is generally to illuminate choices, bound problems, and rule out bad options - rather than arrive unambiguously at clear policy choices-the discipline of military analysis has a great deal to offer. Moreover, simple back-of-the envelope methodologies often provide substantial insight without requiring the churning of giant computer models or access to the classified data of official Pentagon studies, allowing generalities and outsiders to play important roles in defense analytical debates. We have seen all too often (in the broad course of history as well as in modern times) what happens when we make key defense policy decisions based solely on instinct, ideology, and impression. To avoid cavalier, careless, and agenda-driven decision-making, we therefore need to study the science of war as well-even as we also remember the cautions of Clausewitz and avoid hubris in our predictions about how any war or other major military endeavor will ultimately unfold.

Changing what we think is always a sticky process, especially when it comes to religion. When new information becomes available, we cringe under an orthodox mindset, particularly when we challenge ideas and beliefs that have been “set in stone” for decades. Thomas Kuhn coined the term paradigm shift to represent this often-painful transition to a new way of thinking in science. He argued that “normal science” represented a consensus of thought among scientists when certain precepts were taken as truths during a given period. He believed that when new information emerges, old ideas clash with new ones, causing a crisis. Once the basic truths are challenged, the crisis ends in either revolution (where the information provides new understanding) or dismissal (where the information is rejected as unsound). The information age that we live in today has likely surprised all of us as members of the LDS Church at one time or another as we encounter new ideas that revise or even contradict our previous understanding of various aspects of Church history and teachings. This experience is similar to that of the Copernican Revolution, which Kuhn uses as one of his primary examples to illustrate how a paradigm shift works. Using similar instruments and comparable celestial data as those before them, Copernicus and others revolutionized the heavens by describing the earth as orbiting the sun (heliocentric) rather than the sun as orbiting the earth (geocentric). Because the geocentric model was so ingrained in the popular (and scientific!) understanding, the new, heliocentric idea was almost impossible to grasp. Paradigm shifts also occur in religion and particularly within Mormonism. One major difference between Kuhn’s theory of paradigm shift and the changes that occur within Mormonism lies in the fact that Mormonism privileges personal revelation, which is something that cannot be institutionally implemented or decreed (unlike a scientific law). Regular members have varying degrees of religious experience, knowledge, and understanding dependent upon many factors (but, importantly, not “faithfulness” or “worthiness,” or so forth). When members are faced with new information, the experience of processing that information may occur only privately. As such, different members can have distinct experiences with and reactions to the new information they receive. This short preface uses the example of seer stones to examine the idea of how new information enters into the lives of average Mormons. We have all seen or know of friends or family who experience a crisis of faith upon learning new information about the Church, its members, and our history. Perhaps there are those reading who have undergone this difficult and unsettling experience. Anyone who has felt overwhelmed at the continual emergence of new information understands the gravity of these massive paradigm shifts and the potentially significant impact they can have on our lives. By looking at just one example, this preface will provide a helpful way to think about new information and how to deal with it when it arrives.

I sought to accomplish whatever was to be accomplished for anyone in such a manner that the advantage attained for anyone would never be served at the cost of another or others.” This speaks to the integrity of Bucky’s intentions and his desire to put principle before self-gain. “I sought to cope with all humanly unfavorable conditions, customs and afflictions by searching for the family of relevant physical principles involved, and therewith through invention and technological development to solve all problems by physical data and devices that were so much more effective as to be spontaneously adopted by humans and thereby to result in producing more desirable life-styles and thus emancipate humans from the previously unfavorable circumstances. I must always ‘reduce’ my inventions to physically working models and must never talk about the inventions until physically proven— or disproven. The new favorable-to-humans environment constituted by the technological inventions and information must demonstrate that new inanimate technology could now accomplish what heretofore could not be accomplished by social reforms. I sought to reform the environment, not the humans. I determined never to try to persuade humanity to alter its customs and viewpoints.” In this declaration, we find Bucky’s thought that one way to help and change people for the better is not to try to change their thinking, but to change their environment for the better. The change will do the work of allowing others to find their own betterment of thought. He was suggesting that social reform does not always help people because their physical environment is so unimproved.

To claim that mathematics is purely a human invention and is successful in explaining nature only because of evolution and natural selection ignores some important facts in the nature of mathematics and in the history of theoretical models of the universe. First, while the mathematical rules (e.g., the axioms of geometry or of set theory) are indeed creations of the human mind, once those rules are specified, we lose our freedom. The definition of the Golden Ratio emerged originally from the axioms of Euclidean geometry; the definition of the Fibonacci sequence from the axioms of the theory of numbers. Yet the fact that the ratio of successive Fibonacci numbers converges to the Golden Ratio was imposed on us-humans had not choice in the matter. Therefore, mathematical objects, albeit imaginary, do have real properties. Second, the explanation of the unreasonable power of mathematics cannot be based entirely on evolution in the restricted sense. For example, when Newton proposed his theory of gravitation, the data that he was trying to explain were at best accurate to three significant figures. Yet his mathematical model for the force between any two masses in the universe achieved the incredible precision of better than one part in a million. Hence, that particular model was not forced on Newton by existing measurements of the motions of planets, nor did Newton force a natural phenomenon into a preexisting mathematical pattern. Furthermore, natural selection in the common interpretation of that concept does not quite apply either, because it was not the case that five competing theories were proposed, of which one eventually won. Rather, Newton's was the only game in town!

Modern statistics is built on the idea of models — probability models in particular. [...] The standard approach to any new problem is to identify the sources of variation, to describe those sources by probability distributions and then to use the model thus created to estimate, predict or test hypotheses about the undetermined parts of that model. […] A statistical model involves the identification of those elements of our problem which are subject to uncontrolled variation and a specification of that variation in terms of probability distributions. Therein lies the strength of the statistical approach and the source of many misunderstandings. Paradoxically, misunderstandings arise both from the lack of an adequate model and from over reliance on a model. […] At one level is the failure to recognise that there are many aspects of a model which cannot be tested empirically. At a higher level is the failure is to recognise that any model is, necessarily, an assumption in itself. The model is not the real world itself but a representation of that world as perceived by ourselves. This point is emphasised when, as may easily happen, two or more models make exactly the same predictions about the data. Even worse, two models may make predictions which are so close that no data we are ever likely to have can ever distinguish between them. […] All model-dependant inference is necessarily conditional on the model. This stricture needs, especially, to be borne in mind when using Bayesian methods. Such methods are totally model-dependent and thus all are vulnerable to this criticism. The problem can apparently be circumvented, of course, by embedding the model in a larger model in which any uncertainties are, themselves, expressed in probability distributions. However, in doing this we are embarking on a potentially infinite regress which quickly gets lost in a fog of uncertainty.

All addictions — whether to drugs or to nondrug behaviours — share the same brain circuits and brain chemicals. On the biochemical level the purpose of all addictions is to create an altered physiological state in the brain. This can be achieved in many ways, drug taking being the most direct. So an addiction is never purely “psychological” all addictions have a biological dimension. And here a word about dimensions. As we delve into the scientific research, we need to avoid the trap of believing that addiction can be reduced to the actions of brain chemicals or nerve circuits or any other kind of neurobiological, psychological or sociological data. A multilevel exploration is necessary because it’s impossible to understand addiction fully from any one perspective, no matter how accurate. Addiction is a complex condition, a complex interaction between human beings and their environment. We need to view it simultaneously from many different angles — or, at least, while examining it from one angle, we need to keep the others in mind. Addiction has biological, chemical, neurological, psychological, medical, emotional, social, political, economic and spiritual underpinnings — and perhaps others I haven’t thought about. To get anywhere near a complete picture we must keep shaking the kaleidoscope to see what other patterns emerge. Because the addiction process is too multifaceted to be understood within any limited framework, my definition of addiction made no mention of “disease.” Viewing addiction as an illness, either acquired or inherited, narrows it down to a medical issue. It does have some of the features of illness, and these are most pronounced in hardcore drug addicts like the ones I work with in the Downtown Eastside. But not for a moment do I wish to promote the belief that the disease model by itself explains addiction or even that it’s the key to understanding what addiction is all about. Addiction is “all about” many things. Note, too, that neither the textbook definitions of drug addiction nor the broader view we’re taking here includes the concepts of physical dependence or tolerance as criteria for addiction. Tolerance is an instance of “give an inch, take a mile.” That is, the addict needs to use more and more of the same substance or engage in more and more of the same behaviour, to get the same rewarding effects. Although tolerance is a common effect of many addictions, a person does not need to have developed a tolerance to be addicted.