Tag Archives: Science

Yuval Noah Harari’s “Sapiens—A Brief History of Humankind”

Homo appeared roughly 2 million years ago in Africa and Homo sapiens roughly 200’000 years ago in East Africa. Harari divides his account of the last 70’000 years into four parts: The cognitive revolution (language), the agricultural revolution (about 10’000 years ago in today’s Turkey, Iran, Levant), the unification of humankind (through money, empire, and religion), and the scientific revolution. According to Harari, Sapiens developed more efficient strategies for cooperation than other species and in particular, Neanderthals (which sapiens eradicated around 30’000 years ago). The rest is history, i.e., evolutionary biology and cultural history.

On his website, Harari summarizes:

Homo sapiens rules the world because it is the only animal that can believe in things that exist purely in its own imagination, such as gods, states, money and human rights.

Starting from this provocative idea, Sapiens goes on to retell the history of our species from a completely fresh perspective. It explains that money is the most pluralistic system of mutual trust ever devised; that capitalism is the most successful religion ever invented; that the treatment of animals in modern agriculture is probably the worst crime in history; and that even though we are far more powerful than our ancient ancestors, we aren’t much happier.

According to Harari, the agricultural revolution fostered population growth but made life harsher for most humans (due to less varied diet, harder work, infectious diseases)—and for the animals that Sapiens domesticated; religion, empires, money and trade fostered globalization and unification; the scientific revolution arose from Europeans’ admission of ignorance, and it was intertwined with imperialism and capitalism; whether humankind has become happier over time is unknown but doubtful; and we may soon confront a singularity:

Physicists define the Big Bang as a singularity. It is a point at which all the known laws of nature did not exist. Time too did not exist. It is thus meaningless to say that anything existed `before’ the Big Bang. We may be fast approaching a new singularity, when all the concepts that give meaning to our world—me, you, men, women, love and hate—will become irrelevant. Anything happening beyond that point is meaningless to us (p. 461 in the Vintage 2015 edition).

Other tidbits:

  • Settlement of Australia (“The Flood”), America, New Zealand: 45’000, 16’000, 800 years ago. Each settlement was associated with mass extinction of species.
  • “[F]iction has enabled us not merely to imagine things, but to do so collectively” (p. 27). “Ever since the Cognitive Revolution Homo sapiens has been able to revise its behaviour rapidly in accordance with changing needs. This opened a fast lane of cultural evolution, bypassing the traffic jams of genetic evolution.” (p. 36).
  • “The Agricultural Revolution was history’s greatest fraud. … These plants domesticated Homo sapiens, rather than vice versa” (p. 90). The revolution bred worries about the future. Food surpluses brought rulers and elites, palaces and temples, politics, wars, art and philosophy (p. 114). One `imagined order’ with three classes and two genders—the Code of Hammurabi—dates from 1’776 B.C. (p. 117). Writing, archiving, cataloguing (invented by Sumerians around 3’500 B.C.) preserves information about imagined social order; this is critical because the information is not preserved in DNA. Script undermined holistic thought. Hindus invented `Arab’ numerals around 800 AD (pp. 137–146).
  • Cognitive dissonance, contradictory beliefs are necessary to maintain any human culture (p. 184). Over the last 10’000 years, thousands of `human worlds’ have collapsed to a single one (p. 186). Three universal (imagined) orders: Money, empire, religion (p. 191). “Money is the most universal and most efficient system of mutual trust ever devised” (p. 201). Empires are stable, inclusive, not that bad (p. 219). Religious norms are founded on a belief in a superhuman order (p. 234). “Much of ancient mythology is in fact a legal contract in which humans promise everlasting devotion to the gods in exchange for mastery over plants and animals” (p. 236). Polytheist and animist religions recognize a supreme power in the background, devoid of biases and interests (p. 238). Humanist religions worship Homo sapiens. Liberal humanism believes in the humanity of the individual. Socialist humanism believes in the humanity of the collective. (Both build on Christian tradition). Evolutionary humanism (e.g., Nazism) believes that humankind can evolve or degenerate  (pp. 256–263).
  • Science started from the admission of ignorance; observation and math; and the acquisition of new powers (p. 279). Social stability requires that certain `scientific results’ are a dogma or that basic truths are non-scientific (p. 282). With the capitalist system and the industrial revolution, science, industry and military technology intertwined (p. 294). “[S]cientific research can flourish only in alliance with some religion or ideology. The ideology justifies the costs of the research” (p. 305). Science and empire supported each other (ch. 15, 16). The scientific revolution and the idea of progress fostered credit; this reinforced each other (p. 346). The industrial revolution has been a revolution in energy conversion (p. 379) and it was a second agricultural revolution (p. 382). Animal suffering, consumerism (ch. 17). The national time (p. 396). State and market replace family and local community (p. 398). “The state and the market are the mother and the father of the individual” (p. 402). “The nation is the imagined community of the state” (p. 406). The world is safer than ever, and war does not pay any more. Have humans become happier? Answer 1: “Lasting happiness comes only from serotonin, dopamine and oxytocin” (p. 436). Answer 2: Meaning. But “[p]erhaps happiness is synchronising one’s personal delusions of meaning with the prevailing collective delusions” (p. 438). Answer 3: Feelings are not to be trusted; of key import is whether people know the truth about themselves (p. 443). Intelligent design and extreme inequality (ch. 20).

Wikipedia points to critical scholarly reception.

Objective Reality? Refuted

MIT Technology Review reports about the results of an experiment (arxiv.org/abs/1902.05080: Experimental Rejection of Observer-Independence in the Quantum World) suggesting that objective reality … does not exist.

The experiment produces an unambiguous result. It turns out that both realities can coexist even though they produce irreconcilable outcomes, just as Wigner predicted.

That raises some fascinating questions that are forcing physicists to reconsider the nature of reality.

The idea that observers can ultimately reconcile their measurements of some kind of fundamental reality is based on several assumptions. The first is that universal facts actually exist and that observers can agree on them.

But there are other assumptions too. One is that observers have the freedom to make whatever observations they want. And another is that the choices one observer makes do not influence the choices other observers make—an assumption that physicists call locality.

If there is an objective reality that everyone can agree on, then these assumptions all hold.

But Proietti and co’s result suggests that objective reality does not exist. In other words, the experiment suggests that one or more of the assumptions—the idea that there is a reality we can agree on, the idea that we have freedom of choice, or the idea of locality—must be wrong.

Of course, there is another way out for those hanging on to the conventional view of reality. This is that there is some other loophole that the experimenters have overlooked. Indeed, physicists have tried to close loopholes in similar experiments for years, although they concede that it may never be possible to close them all.

Nevertheless, the work has important implications for the work of scientists. “The scientific method relies on facts, established through repeated measurements and agreed upon universally, independently of who observed them,” say Proietti and co. And yet in the same paper, they undermine this idea, perhaps fatally.

Truth, Triviality, and Contradiction

Nils Bohr chose

Contraria Sunt Complementa

as motto for his coat of arms. According to his son and others, Bohr distinguished between the logical properties of trivialities on the one hand and profound truths on the other:

The opposite of a correct statement is a false statement. But the opposite of a profound truth may well be another profound truth. [Unsourced]

There are two sorts of truth: Profound truths recognized by the fact that the opposite is also a profound truth, in contrast to trivialities where opposites are obviously absurd. [Quoted by Hans Bohr]

It is the hallmark of any deep truth that its negation is also a deep truth. [Quoted by Max Delbrück]

Models Make Economics A Science

In the Journal of Economic Literature, Ariel Rubinstein discusses Dani Rodrik’s “superb” book “Economics Rules.” The article nicely articulates what economics and specifically, economic modeling is about. Some quotes (emphasis my own) …

… on the nature of economics:

[A] quote … by John Maynard Keynes to Roy Harrod in 1938: “It seems to me that economics is a branch of logic, a way of thinking”; “Economics is a science of thinking in terms of models joined to the art of choosing models which are relevant to the contemporary world.”

[Rodrik] … declares: “Models make economics a science” … He rejects … the … common justification given by economists for calling economics a science: “It’s a science because we work with the scientific method: we build hypotheses and then test them. When a theory fails the test, we discard it and either replace it or come up with an improved version.” Dani’s response: “This is a nice story, but it bears little relationship to what economists do in practice …”

… on models, forecasts, and tests:

A good model is, for me, a good story about an interaction between human beings …

A story is not a tool for making predictions. At best, it can help us realize that a particular outcome is possible or that some element might be critical in obtaining a particular result. … Personally, I don’t have any urge to predict anything. I dread the moment (which will hopefully never arrive) when academics, and therefore also governments and corporations, will be able to predict human behavior with any accuracy.

A story is not meant to be “useful” in the sense that most people use the word. I view economics as useful in the sense that Chekhov’s stories are useful—it inspires new ideas and clarifies situations and concepts. … [Rodrik] is aware … “Mischief occurs when economists begin to treat a model as the model. Then the narrative takes on a life of its own and becomes dislodged from the setting that produced it. It turns into an all-purpose explanation that obscures alternative, and potentially more useful, story lines”.

A story is not testable. But when we read a story, we ask ourselves whether it has any connection to reality. In doing so, we are essentially trying to assess whether the basic scenario of the story is a reasonable one, rather than whether the end of the story rings true. … Similarly, … testing an economic model should be focused on its assumptions, rather than its predictions. On this point, I am in agreement with Economics Rules: “. . . what matters to the empirical relevance of a model is the realism of its critical assumptions”.

… on facts:

The big “problem” with interpreting data collected from experiments, whether in the field or in the lab, is that the researchers themselves are subject to the profession’s incentive system. The standard statistical tests capture some aspects of randomness in the results, but not the uncertainty regarding such things as the purity of the experiment, the procedure used to collect the data, the reliability of the researchers, and the differences in how the experiment was perceived between the researcher and the subjects. These problems, whether they are the result of intentional sleight of hand or the natural tendency of researchers to ignore inconvenient data, make me somewhat skeptical about “economic facts.”

Nobel Laureates? École Normale Supérieure

In Nature, Tom Clynes reports about research indicating that École Normale Supérieure has the highest proportion of undergraduates that eventually win a Nobel prize. The California Institute of Technology comes second ahead of Harvard, Swarthmore, Cambridge, École Polytechnique, MIT, Columbia, Amherst, and Chicago.

Doubts about Empirical Research

The Economist reports about research by Paul Smaldino and Richard McElreath indicating that studies in psychology, neuroscience and medicine have low statistical power (the probability to correctly reject a null hypothesis). If, nevertheless, almost all published studies contain significant results (i.e., rejections of null hypotheses), then this is suspicious.

Furthermore, Smaldino and McElreath’s research suggests that

the process of replication, by which published results are tested anew, is incapable of correcting the situation no matter how rigorously it is pursued.

With the help of a model of competing research institutes, Smaldino and McElreath simulate how empirical scientific research  progresses. Labs that find more new results also tend to produce more false positives. More careful labs try to rule out false positives but publish less. More “successful” labs are allowed to replicate. As a consequence, less careful labs spread out. Replication—repetition of randomly selected findings—does not stop this process.

poor methods still won—albeit more slowly. This was true in even the most punitive version of the model, in which labs received a penalty 100 times the value of the original “pay-off” for a result that failed to replicate, and replication rates were high (half of all results were subject to replication efforts).

Smaldino and McElreath conclude that “top-performing laboratories will always be those who are able to cut corners”—even in a world with frequent replication. The Economist concludes that

[u]ltimately, therefore, the way to end the proliferation of bad science is not to nag people to behave better, or even to encourage replication, but for universities and funding agencies to stop rewarding researchers who publish copiously over those who publish fewer, but perhaps higher-quality papers.

Science and the Senate

The Economist’s Graphic Detail reports about research documenting that

While the Senate’s interest in science is generally quite low, Senate Democrats are three times more likely than Republicans to follow science-related Twitter accounts like NASA or the National Oceanic and Atmospheric Administration. Interest in science, the authors conclude, “may now primarily be a ‘Democrat’ value”.

20160618_woc656_0

Self-Correcting Research?

The Economist doubts that science is self-correcting as “many more dodgy results are published than are subsequently corrected or withdrawn.”

Referees do a bad job. Publishing pressure leads researchers to publish their (correct and incorrect) results multiple times. Replication studies are hard and thankless. And everyone seems to be getting the statistics wrong.

A researcher suffers from a type I error when she incorrectly rejects an hypothesis although it is true (false positive); and from a type II error when she incorrectly accepts an hypothesis although it is wrong (false negative). A good testing procedure minimises the type II error given a specified type I error that is, it maximises the power of the test. While employing a test with a power of 80% is considered good practice actual hypothesis testing often suffers from much lower power. As a consequence, many or even a majority of apparent “results” identified by a test might be wrong while most of the “non-results” are correctly identified. Quoting from the article:

… consider 1,000 hypotheses being tested of which just 100 are true (see chart). Studies with a power of 0.8 will find 80 of them, missing 20 because of false negatives. Of the 900 hypotheses that are wrong, 5%—that is, 45 of them—will look right because of type I errors. Add the false positives to the 80 true positives and you have 125 positive results, fully a third of which are specious. If you dropped the statistical power from 0.8 to 0.4, which would seem realistic for many fields, you would still have 45 false positives but only 40 true positives. More than half your positive results would be wrong.