Author

Norman B. Keevil

Summary

The history of Teck Resources, Canada’s largest diversified mining company, from 1912 to 2023.

Takeaways

Commodity prices can fluctuate widely through economic cycles. It is important for a mining company to retain a strong balance sheet to survive tough times and be ready to act on opportunities when they arise.

The mining business is characterized by frequent mergers and acquisition and often complex financing arrangements involving multiple mining and investment companies. Teck’s philosophy has been to position itself to be a partner of choice for other mining companies and developers, i.e. a preferred developer through demonstrated expertise in building new mines and respectful business conduct.

Quotes

“I’ve never forgotten that. The willingness to pass ideas and information freely back and forth can often lead, synergistically, to two or more people coming up with even better ones. Hording ideas and information is the dystopian practice of the bureaucrat."

“We have said many times: ‘A mining company without ore reserves is an oxymoron,’ and ‘The three key elements of a successful resources company are its ore reserves, the right people to develop and operate them, and the financial strength to pull it off.’ It can never be said too often."

“Doing a few things very well with your nose to the grindstone always beats spinning wheels on too many things. Challenges and delays are just that, meaning one needs to put in even more effort to get past them. One should never accept easily that it is so much harder this time. It was seldom as easy as, looking back, it may now seem."

Author

Stephen J. Dubner und Steven Levitt

Summary

A collection of stories that illustrate how to think more productively, creatively, and rationally.

Takeaways

People have difficulties thinking rationally. They have biases that leads them to seek evidence that confirms what they already think, or are tempted to adopt views of friends, families, and colleagues.

“Thinking like a freak” means to get comfortable with admiting to not know the answer to a question. A “freak” re-defines the problem to answer the right questions, identifies the root cause, and thinks small to avoid intractable problems.

Quotes

“Incentives are the cornerstone of modern life. And understanding them—or, often, deciphering them—is the key to understanding a problem, and how it might be solved. Knowing what to measure, and how to measure it, can make a complicated world less so."

“A growing body of research suggests that even the smartest people tend to seek out evidence that confirms what they already think, rather than new information that would give them a more robust view of reality."

“Thinking like a Freak may sometimes sound like an exercise in using clever means to get exactly what you want, and there’s nothing wrong with that. But if there is one thing we’ve learned from a lifetime of designing and analyzing incentives, the best way to get what you want is to treat other people with decency."

Author

Vincent A. W. J. Marchau, Warren E. Walker, Pieter J. T. M. Bloemen, Steven W. Popper

Summary

A review of methods and applications for decision making under deep uncertainty.

Takeaways

Situations with deep uncertainty are characterized by a lack of knowledge about how future events will unfold. In complex systems, the predictability of potential outcomes is low.

When confronted with deep uncertainty, decision makers are advised to shift from a predict-then-act paradigm to a monitor-and-adapt strategy. Traditional planning approaches make assumptions, predict outcomes, and tailor a policy to the predictions. Decision Making Under Deep Uncertainty (DMUDU) approaches, on the other hand, propose a policy, identify vulnerabilities, and assess the best options for reducing the identified vulnerabilities.

Quotes

“The intrinsic limits to predictability, the existence of legitimate alternative interpretations of the same data, and the limits to knowability of a system have important implications for decisionmaking. Under the label of ‘decisionmaking under deep uncertainty’, these are now being explored."

“There is ample evidence that human reasoning with respect to complex uncertain systems is intrinsically insufficient. Often, mental models are event based, have an open-loop view of causality, ignore feedback, fail to account for time delays, and are insensitive to nonlinearity (Sterman 1994)."

“That is, under deep uncertainty decision support should move away from trying to define what is the right choice and instead aim at enabling deliberation and joint sense making among the various parties to a decision."

“In short, there are five categories of components: policy architecture, generation of scenarios, generation of alternatives, definition of robustness, and vulnerability analysis. Any given DMDU approach makes choices with respect to these five categories. For some, these choices are primarily or almost exclusively in one category while remaining silent on the others. For others, implicit or explicit choices are made with respect to each category."

Author

Daniel Kahneman, Olivier Sibony, Cass R. Sunstein

Summary

A summary of decision hygiene practices to reduce noise in judgments.

Takeaways

Noise is ubiquitous in situations that require judgment and leads to unwanted and costly variability and unfairness in decisions. The reasons for noise in judgments are manifold. People have cognitive biases and a natural preference for causal thinking that finds comfort in finding coherent explanations even if the reality is more complex and less predictable.

Adherence to decision hygiene principles reduces noise. The goal of the principles is to delay premature intuition and to limit the influence of cognitive biases. For example, averaging independent judgments, or relying on formulas and simple models that are noise free allows for consistent judgment in situations that are similar to each other.

Quotes

“In summary, what people usually claim to strive for in verifiable judgments is a prediction that matches the outcome. What they are effectively trying to achieve, regardless of verifiability, is the internal signal of completion provided by the coherence between the facts of the case and the judgment. And what they should be trying to achieve, normatively speaking, is the judgment process that would produce the best judgment over an ensemble of similar cases."

“The illusion of validity is found wherever predictive judgments are made, because of a common failure to distinguish between two stages of the prediction task: evaluating cases on the evidence available and predicting actual outcomes. You can often be quite confident in your assessment of which of two candidates looks better, but guessing which of them will actually be better is an altogether different kettle of fish."

“Causal thinking helps us make sense of a world that is far less predictable than we think. It also explains why we view the world as far more predictable than it really is. In the valley of the normal, there are no surprises and no inconsistencies. The future seems as predictable as the past. And noise is neither heard nor seen."

“Most people are surprised to hear that the accuracy of their predictive judgments is not only low but also inferior to that of formulas. Even simple linear models built on limited data, or simple rules that can be sketched on the back of an envelope, consistently outperform human judges. The critical advantage of rules and models is that they are noise-free. As we subjectively experience it, judgment is a subtle and complex process; we have no indication that the subtlety may be mostly noise. It is difficult for us to imagine that mindless adherence to simple rules will often achieve higher accuracy than we can—but this is by now a well-established fact."

Author

Russel Jurney

Summary

Instructions for a technical setup to iteratively develop practical Data Science applications.

Takeaways

Many Data Science applications fail because of a missing feedback loop between the Data Scientists developing the solutions and the business stakeholders and users. To avoid a disconnect, Data Scientists need to share work in progress frequently. Software development methodolodies like Scrum need to be adapted to account for the larger uncertainty of data exploration.

An Agile Data Science process needs to leave room for experimentation and variable goals. Instead of providing the ship date of a predetermined artifact, an Agile Data Science team should produce working software that describes the state of exploration (“What will we ship, when?” instead of “When will we ship”).

Quotes

“A researcher who is eight persons away from customers is unlikely to solve relevant problems and more likely to solve arcane problems."

“Several changes in particular make a return to agility possible: Choosing generalists over specialists. Preferring small teams over large teams. Using high-level tools and platforms: cloud computing, distributed systems, and platforms as a service (PaaS). Continuous and iterative sharing of intermediate work, even when that work may be incomplete."

“One thing we require is that every level of the stack must be horizontally scalable. Adding another machine to a cluster is greatly preferable to upgrading expensive, proprietary hardware. If you have to rewrite your predictive model’s implementation in order to deploy it, you aren’t being very agile."

“We will only explore a simple heuristic-based approach, because it turns out that in this case that is simply good enough. Don’t allow your curiosity to distract you into employing machine learning and statistical techniques whenever you can. Get curious about results, instead."

Author

Richard H. Thaler, Cass R. Sunstein

Summary

Practical recommendations for how to design systems that support people in making the right choices.

Takeaways

The design of the environment in which choices have to be made (choice architecture) often greatly influences the results, e.g. the order of food items in a cafeteria or the order of options and default values on a website. The goal of designing choice architectures should be to nudge people to make a choice that best reflects their true intention.

Any “sludge” or obstacles that can get in the way of making the right choice should be reduced, e.g. too many or too complicated options, should be reduced.

Quotes

“Our goal, in short, is to help people make the choices that they would have made if they had paid full attention and possessed complete information, unlimited cognitive ability, and complete self-control."

“The false assumption is that almost all people, almost all the time, make choices that are in their best interest or at the very least are better than the choices that would be made by someone else. We claim that this assumption is false—indeed, obviously false. In fact, we do not think that anyone actually believes it on reflection."

“The discussion thus far suggests that people may most need a good nudge for choices that require memory or have delayed effects; those that are difficult, are infrequent, and offer poor feedback; and those for which the relationship between choice and experience is ambiguous."

“So, if you remember just one thing from this book, let it be this. If you want to encourage people to do something, Make It Easy. If you’re so inclined, hum it to the tune of the old Eagles song: ‘Take It Easy.'"

Author

Chip Heath, Dan Heath

Summary

An overview of communication techniques for effective messaging of ideas.

Takeaways

In order for ideas to stick with an audience they have to be framed as Simple Unexpected Concrete Credible Emotional Stories (SUCCES). If we follow the SUCCES framework, we can drill to the core of the message to know what to say (simple) and frame it in a way that helps people to pay attention (unexpected), understand and remember (concrete), believe and agree (credible), care (emotional), and act (stories).

Quotes

“Abstraction makes it harder to understand an idea and to remember it. It also makes it harder to coordinate our activities with others, who may interpret the abstraction in very different ways."

“How can we make people care about our ideas? We get them to take off their Analytical Hats. We create empathy for specific individuals. We show how our ideas are associated with things that people already care about. We appeal to their self-interest, but we also appeal to their identities—not only to the people they are right now but also to the people they would like to be”

“The story’s power, then, is twofold: It provides simulation (knowledge about how to act) and inspiration (motivation to act)."

“So, rather than guess about whether people will understand our ideas, we should ask, ‘Is it concrete?’ Rather than speculate about whether people will care, we should ask, ‘Is it emotional? Does it get out of Maslow’s basement? Does it force people to put on an Analytical Hat or allow them to feel empathy?'"

Author

Cathy O’Neil

Summary

A warning of the destructive power of black-box algorithms that govern our lives.

Takeaways

Algorithms and mathematical models are ubiquitously applied and drive decisions in every aspect of our lives. They, for example, determine college admissions, calculate insurance fees, and determine the content in our social media feeds. Many of the algorithms are intransparent, making it impossible to understand and challenge the results. If there is no alignment between the objectives of the models and the interest of the modeled subjects and if there is no feedback loop to improve the model over time, these “weapons of math destruction (WMD)” can cause significant harm.

Quotes

“The first question is: Even if the participant is aware of being modeled, or what the model is used for, is the model opaque, or even invisible?"

“That makes it extra hard to answer the second question: Does the model work against the subject’s interest? In short, is it unfair? Does it damage or destroy lives?"

“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 delimit our lives."

“So to sum up, these are the three elements of a WMD; Opacity, Scale, and Damage."

Author

Hans Rosling

Summary

A plea to overcome the human instincts that prevent us from developing a fact based worldview.

Takeaways

Knowledge of global patterns and trends is poor across demographics even though data is publicly available. Common assumptions about topics like population growth, income inequality, education and health, are not only wrong but are systematically distorted. Developments appear more negatively than they are. Reasons are a lack of statistical literacy and our instincts to generalize, blame others, and consider things without appropriate comparison frames and proportions. To develop a fact based and more accurate worldview, we need to be aware of these instincts and work actively to overcome them.

Societal change is happening steadily but slowly and often not considered newsworthy. The lack of attention makes it hard to identify emerging patterns and adapt to a changing landscape. The Western view is systematically underestimating the progress in Asia and especially Africa, and the significant role these continents will play in a future global economy.

Quotes

“The data shows that half the increase in child survival in the world happens because mothers can read and write. More children now survive because they don’t get ill in the first place. … So if you are investing money to improve health on Level 1 and 2, you should put it into primary schools, nurse education, and vaccinations. Big impressive-looking hospitals can wait."

“People in North America and Europe need to understand that most of the world population lives in Asia. In terms of economic muscles ‘we’ are becoming the 20 percent, not the 80 percent. But many of ‘us’ can’t fit these numbers into our nostalgic minds. Not only do we misjudge how big our war monuments should be in Vietnam, we also misjudge our importance in the future global marketplace. Many of us forget to behave properly with those who will control the future trade deals."

“Anyone who claims that democracy is a necessity for economic growth and health improvements will risk getting contradicted by reality. It’s better to argue for democracy as a goal in itself instead of as a superior means to other goals we like."

“In fact, resist blaming any one individual or group of individuals for anything. Because the problem is that when we identify the bad guy, we are done thinking. And it’s almost always more complicated than that."

Author

Adam Rutherford

Summary

A scientific view of the role that DNA plays in understanding human history, and what we can and cannot conclude when analyzing it.

Takeaways

Depicting our ancestry looks more like an entagled mesh than a tree. Everyone living today shares the same group of ancestors if we go back long enough in time.

DNA influences observable characteristics in a probabilitic way. There are only a few genes that have a clear physical manifestation. The scientific reality is more complex than newspaper headlines make you believe.

Quotes

“It’s important to remember that the commercial DNA ancestry tests don’t necessarily show your geographical origins in the past. They show with whom you have common ancestry today."

“The truth is that we all are a bit of everything, and we come from all over. Even if you live in the most remote parts of the Hebrides, or the edge of the Greek Aegean, we share an ancestor only a few hundred years ago. A thousand years ago, we Europeans share all of our ancestry. Triple that time and we share all our ancestry with everyone on Earth."

“No one will ever find a gene for ‘evil’, or for beauty, or for musical genius, or for scientific genius, because they don’t exist. DNA is not destiny. The presence of a particular variant of a particular gene may just have the effect of altering the odds of any particular behavior. More likely, the possession of many slight differences in many genes will have an effect on the likelihood of a particular characteristic, in consort with your environment, which includes all things that are not DNA."