Author

Tim Palmer

Summary

An introduction to the geometry of chaos and the important role it plays in understanding scientific phenomena.

Takeaways

The climate and the economy are examples of highly complex non-linear systems. The evolution of these systems follows a fractal geometric pattern along an attractor, i.e. a set of allowed states in a high dimensional state space. Despite the fact that an exact future state is often uncomputable, mathematical models have been successfully developed to characterize possible future behavior in the form of ensembles along the attractor. These models allow to make probabilitic forecasts and statements about the likelihood that a future state will be vastly different from the present.

Invariant set theory applies the concept of the “geometry of chaos” to quantum mechanics and concludes that the laws of physics are deterministic and that its laws describe the geometry of the attractor/invariant set of the highest-order system imaginable-the universe as a whole.

Quotes

“The whole universe is a nonlinear dynamical system evolving on some fractal attractor in cosmological state space."

“Using the geometry of chaos, Einstein’s picture of an ensemble of deterministic worlds may be right after all. If this is so, we conclude that we do live in a world in which elementary particles, and indeed the notion of reality, are certain and definite."

“In short, I am suggesting that to be conscious of an object is to be aware that the object has an existence independent of the rest of the world. I am speculating that this awareness is itself a consequence of two claims: that for reasons of energy efficiency quantum physics does play a role in cognition, and that the laws of quantum physics at their most fundamental describe the geometry of the cosmological invariant set."

Author

Sabine Hossenfelder

Summary

Scientific answers to existential questions.

Takeaways

Existential questions deal with the origin and working mechanisms of the universe, and the role of humans in it. While the fundamental theories of the standard model of particle physics and the theory of general relativity have been very successful in providing explanations, many questions have no scientifically sound answer yet. In their attempt to extend our knowledge, some scientists include assumptions in their theories that are unnecessary to explain observations, conflating scientific reasoning and belief.

Quotes

“While the situation is not entirely settled, it seems that the laws of nature preserve information entirely, so all the details that make up you and the story of your grandmother’s life are immortal."

“But in which sense are they real? Unobservable universes are by definition unnecessary to describe what we observe. Hence, assuming they are real is also unnecessary. Scientific theories should not contain unnecessary assumptions, for if we allow that, we would also have to allow the assumption that a god made the universe."

“That way, we can rephrase any discussion about free will or moral responsibility without using those terms. For example, instead of questioning someone’s free will, we can debate whether jail is really the most useful intervention."

Author

Joseph Buchdahl

Summary

An analysis of the science, psychology and philosophy of gambling.

Takeaway

Gambling is the speculation on the future that can take on different forms such as casino games, sports betting, and financial investing. Outcomes in these areas are hard to predict and it is even harder to make money from it against benchmarks that encapsulate the collective information of large crowds. Gamblers often underestimate the randomness that determine gambling outcomes and attribute positive outcomes to their own ability to predict the future.

Despite the fact that gambling is only lucrative for a negligible number of skilled gamblers, people continue to gamble. This might be related to the human desire to explain and control outcomes to instill a sense of certainy in an uncertain world.

Quotes

“According to this hypothesis, if reward uncertainty was not a source of motivation most predictive behaviours would extinguish because of the high failure rate. In other words, allowing an animal to persevere in a task is only possible if its behavior is motivated by a lack of predictability rather than the reward itself."

“If the purpose of gambling is to achieve authority over uncertainty, to feel in control of one’s destiny, surely everyone who plays sensibly and reasonably is a winner."

“Where luck is dominant, there is very little connection between the process and the outcome. If all you care about is outcomes, you’re liable to draw erroneous conclusions. On the contrary, don’t study winners to see what caused them; study the process to see whether it consistently led to success."

Author

Mike Walsh

Summary

Anecdotes about modern leaders and their approach to decision making.

Takeaways

We experience a time of accelerated change that is unlikely to slow down in the future. With new technologies emerging and more data being available to influence decision making, leaders need to adjust their way of approaching problems. For example, successful leaders in the “algorithmic age” apply first principles thinking to come up with solutions that can be carried out by computers.

Quotes

“If you are simply automating your existing processes, adding a chatbot to your website, or updating your mobile app, then in all probability you are not thinking big enough about your future opportunities. Too often, digital transformation is just digital incrementalism."

“In the future, the most effective computational thinkers will be those who can directly express their ideas and execute their strategies in domain-specific programming languages."

“Dumbing down AI platforms to the extent that we can actually understand them may undermine their effectiveness. It is often more important to know why a particular optimum or target was chosen than to be able to explain the reasoning behind an algorithmic decision."

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?'"