👓 Why Facts Don’t Change Our Minds | The New Yorker

Why Facts Don’t Change Our Minds by Elizabeth Kolbert (The New Yorker)
New discoveries about the human mind show the limitations of reason.
The vaunted human capacity for reason may have more to do with winning arguments than with thinking straight.
The vaunted human capacity for reason may have more to do with winning arguments than with thinking straight. Credit Illustration by Gérard DuBois

In 1975, researchers at Stanford invited a group of undergraduates to take part in a study about suicide. They were presented with pairs of suicide notes. In each pair, one note had been composed by a random individual, the other by a person who had subsequently taken his own life. The students were then asked to distinguish between the genuine notes and the fake ones.

Some students discovered that they had a genius for the task. Out of twenty-five pairs of notes, they correctly identified the real one twenty-four times. Others discovered that they were hopeless. They identified the real note in only ten instances.

As is often the case with psychological studies, the whole setup was a put-on. Though half the notes were indeed genuine—they’d been obtained from the Los Angeles County coroner’s office—the scores were fictitious. The students who’d been told they were almost always right were, on average, no more discerning than those who had been told they were mostly wrong.

In the second phase of the study, the deception was revealed. The students were told that the real point of the experiment was to gauge their responses to thinking they were right or wrong. (This, it turned out, was also a deception.) Finally, the students were asked to estimate how many suicide notes they had actually categorized correctly, and how many they thought an average student would get right. At this point, something curious happened. The students in the high-score group said that they thought they had, in fact, done quite well—significantly better than the average student—even though, as they’d just been told, they had zero grounds for believing this. Conversely, those who’d been assigned to the low-score group said that they thought they had done significantly worse than the average student—a conclusion that was equally unfounded.

“Once formed,” the researchers observed dryly, “impressions are remarkably perseverant.”

A few years later, a new set of Stanford students was recruited for a related study. The students were handed packets of information about a pair of firefighters, Frank K. and George H. Frank’s bio noted that, among other things, he had a baby daughter and he liked to scuba dive. George had a small son and played golf. The packets also included the men’s responses on what the researchers called the Risky-Conservative Choice Test. According to one version of the packet, Frank was a successful firefighter who, on the test, almost always went with the safest option. In the other version, Frank also chose the safest option, but he was a lousy firefighter who’d been put “on report” by his supervisors several times. Once again, midway through the study, the students were informed that they’d been misled, and that the information they’d received was entirely fictitious. The students were then asked to describe their own beliefs. What sort of attitude toward risk did they think a successful firefighter would have? The students who’d received the first packet thought that he would avoid it. The students in the second group thought he’d embrace it.

Even after the evidence “for their beliefs has been totally refuted, people fail to make appropriate revisions in those beliefs,” the researchers noted. In this case, the failure was “particularly impressive,” since two data points would never have been enough information to generalize from.

The Stanford studies became famous. Coming from a group of academics in the nineteen-seventies, the contention that people can’t think straight was shocking. It isn’t any longer. Thousands of subsequent experiments have confirmed (and elaborated on) this finding. As everyone who’s followed the research—or even occasionally picked up a copy of Psychology Today—knows, any graduate student with a clipboard can demonstrate that reasonable-seeming people are often totally irrational. Rarely has this insight seemed more relevant than it does right now. Still, an essential puzzle remains: How did we come to be this way?

In a new book, “The Enigma of Reason” (Harvard), the cognitive scientists Hugo Mercier and Dan Sperber take a stab at answering this question. Mercier, who works at a French research institute in Lyon, and Sperber, now based at the Central European University, in Budapest, point out that reason is an evolved trait, like bipedalism or three-color vision. It emerged on the savannas of Africa, and has to be understood in that context.

Stripped of a lot of what might be called cognitive-science-ese, Mercier and Sperber’s argument runs, more or less, as follows: Humans’ biggest advantage over other species is our ability to coöperate. Coöperation is difficult to establish and almost as difficult to sustain. For any individual, freeloading is always the best course of action. Reason developed not to enable us to solve abstract, logical problems or even to help us draw conclusions from unfamiliar data; rather, it developed to resolve the problems posed by living in collaborative groups.

“Reason is an adaptation to the hypersocial niche humans have evolved for themselves,” Mercier and Sperber write. Habits of mind that seem weird or goofy or just plain dumb from an “intellectualist” point of view prove shrewd when seen from a social “interactionist” perspective.

Consider what’s become known as “confirmation bias,” the tendency people have to embrace information that supports their beliefs and reject information that contradicts them. Of the many forms of faulty thinking that have been identified, confirmation bias is among the best catalogued; it’s the subject of entire textbooks’ worth of experiments. One of the most famous of these was conducted, again, at Stanford. For this experiment, researchers rounded up a group of students who had opposing opinions about capital punishment. Half the students were in favor of it and thought that it deterred crime; the other half were against it and thought that it had no effect on crime.

The students were asked to respond to two studies. One provided data in support of the deterrence argument, and the other provided data that called it into question. Both studies—you guessed it—were made up, and had been designed to present what were, objectively speaking, equally compelling statistics. The students who had originally supported capital punishment rated the pro-deterrence data highly credible and the anti-deterrence data unconvincing; the students who’d originally opposed capital punishment did the reverse. At the end of the experiment, the students were asked once again about their views. Those who’d started out pro-capital punishment were now even more in favor of it; those who’d opposed it were even more hostile.

If reason is designed to generate sound judgments, then it’s hard to conceive of a more serious design flaw than confirmation bias. Imagine, Mercier and Sperber suggest, a mouse that thinks the way we do. Such a mouse, “bent on confirming its belief that there are no cats around,” would soon be dinner. To the extent that confirmation bias leads people to dismiss evidence of new or underappreciated threats—the human equivalent of the cat around the corner—it’s a trait that should have been selected against. The fact that both we and it survive, Mercier and Sperber argue, proves that it must have some adaptive function, and that function, they maintain, is related to our “hypersociability.”

Mercier and Sperber prefer the term “myside bias.” Humans, they point out, aren’t randomly credulous. Presented with someone else’s argument, we’re quite adept at spotting the weaknesses. Almost invariably, the positions we’re blind about are our own.

A recent experiment performed by Mercier and some European colleagues neatly demonstrates this asymmetry. Participants were asked to answer a series of simple reasoning problems. They were then asked to explain their responses, and were given a chance to modify them if they identified mistakes. The majority were satisfied with their original choices; fewer than fifteen per cent changed their minds in step two.

In step three, participants were shown one of the same problems, along with their answer and the answer of another participant, who’d come to a different conclusion. Once again, they were given the chance to change their responses. But a trick had been played: the answers presented to them as someone else’s were actually their own, and vice versa. About half the participants realized what was going on. Among the other half, suddenly people became a lot more critical. Nearly sixty per cent now rejected the responses that they’d earlier been satisfied with.

Cartoon“Thanks again for coming—I usually find these office parties rather awkward.”

This lopsidedness, according to Mercier and Sperber, reflects the task that reason evolved to perform, which is to prevent us from getting screwed by the other members of our group. Living in small bands of hunter-gatherers, our ancestors were primarily concerned with their social standing, and with making sure that they weren’t the ones risking their lives on the hunt while others loafed around in the cave. There was little advantage in reasoning clearly, while much was to be gained from winning arguments.

Among the many, many issues our forebears didn’t worry about were the deterrent effects of capital punishment and the ideal attributes of a firefighter. Nor did they have to contend with fabricated studies, or fake news, or Twitter. It’s no wonder, then, that today reason often seems to fail us. As Mercier and Sperber write, “This is one of many cases in which the environment changed too quickly for natural selection to catch up.”

Steven Sloman, a professor at Brown, and Philip Fernbach, a professor at the University of Colorado, are also cognitive scientists. They, too, believe sociability is the key to how the human mind functions or, perhaps more pertinently, malfunctions. They begin their book, “The Knowledge Illusion: Why We Never Think Alone” (Riverhead), with a look at toilets.

Virtually everyone in the United States, and indeed throughout the developed world, is familiar with toilets. A typical flush toilet has a ceramic bowl filled with water. When the handle is depressed, or the button pushed, the water—and everything that’s been deposited in it—gets sucked into a pipe and from there into the sewage system. But how does this actually happen?

In a study conducted at Yale, graduate students were asked to rate their understanding of everyday devices, including toilets, zippers, and cylinder locks. They were then asked to write detailed, step-by-step explanations of how the devices work, and to rate their understanding again. Apparently, the effort revealed to the students their own ignorance, because their self-assessments dropped. (Toilets, it turns out, are more complicated than they appear.)

Sloman and Fernbach see this effect, which they call the “illusion of explanatory depth,” just about everywhere. People believe that they know way more than they actually do. What allows us to persist in this belief is other people. In the case of my toilet, someone else designed it so that I can operate it easily. This is something humans are very good at. We’ve been relying on one another’s expertise ever since we figured out how to hunt together, which was probably a key development in our evolutionary history. So well do we collaborate, Sloman and Fernbach argue, that we can hardly tell where our own understanding ends and others’ begins.

“One implication of the naturalness with which we divide cognitive labor,” they write, is that there’s “no sharp boundary between one person’s ideas and knowledge” and “those of other members” of the group.

This borderlessness, or, if you prefer, confusion, is also crucial to what we consider progress. As people invented new tools for new ways of living, they simultaneously created new realms of ignorance; if everyone had insisted on, say, mastering the principles of metalworking before picking up a knife, the Bronze Age wouldn’t have amounted to much. When it comes to new technologies, incomplete understanding is empowering.

Where it gets us into trouble, according to Sloman and Fernbach, is in the political domain. It’s one thing for me to flush a toilet without knowing how it operates, and another for me to favor (or oppose) an immigration ban without knowing what I’m talking about. Sloman and Fernbach cite a survey conducted in 2014, not long after Russia annexed the Ukrainian territory of Crimea. Respondents were asked how they thought the U.S. should react, and also whether they could identify Ukraine on a map. The farther off base they were about the geography, the more likely they were to favor military intervention. (Respondents were so unsure of Ukraine’s location that the median guess was wrong by eighteen hundred miles, roughly the distance from Kiev to Madrid.)

Surveys on many other issues have yielded similarly dismaying results. “As a rule, strong feelings about issues do not emerge from deep understanding,” Sloman and Fernbach write. And here our dependence on other minds reinforces the problem. If your position on, say, the Affordable Care Act is baseless and I rely on it, then my opinion is also baseless. When I talk to Tom and he decides he agrees with me, his opinion is also baseless, but now that the three of us concur we feel that much more smug about our views. If we all now dismiss as unconvincing any information that contradicts our opinion, you get, well, the Trump Administration.

“This is how a community of knowledge can become dangerous,” Sloman and Fernbach observe. The two have performed their own version of the toilet experiment, substituting public policy for household gadgets. In a study conducted in 2012, they asked people for their stance on questions like: Should there be a single-payer health-care system? Or merit-based pay for teachers? Participants were asked to rate their positions depending on how strongly they agreed or disagreed with the proposals. Next, they were instructed to explain, in as much detail as they could, the impacts of implementing each one. Most people at this point ran into trouble. Asked once again to rate their views, they ratcheted down the intensity, so that they either agreed or disagreed less vehemently.

Sloman and Fernbach see in this result a little candle for a dark world. If we—or our friends or the pundits on CNN—spent less time pontificating and more trying to work through the implications of policy proposals, we’d realize how clueless we are and moderate our views. This, they write, “may be the only form of thinking that will shatter the illusion of explanatory depth and change people’s attitudes.”

One way to look at science is as a system that corrects for people’s natural inclinations. In a well-run laboratory, there’s no room for myside bias; the results have to be reproducible in other laboratories, by researchers who have no motive to confirm them. And this, it could be argued, is why the system has proved so successful. At any given moment, a field may be dominated by squabbles, but, in the end, the methodology prevails. Science moves forward, even as we remain stuck in place.

In “Denying to the Grave: Why We Ignore the Facts That Will Save Us” (Oxford), Jack Gorman, a psychiatrist, and his daughter, Sara Gorman, a public-health specialist, probe the gap between what science tells us and what we tell ourselves. Their concern is with those persistent beliefs which are not just demonstrably false but also potentially deadly, like the conviction that vaccines are hazardous. Of course, what’s hazardous is not being vaccinated; that’s why vaccines were created in the first place. “Immunization is one of the triumphs of modern medicine,” the Gormans note. But no matter how many scientific studies conclude that vaccines are safe, and that there’s no link between immunizations and autism, anti-vaxxers remain unmoved. (They can now count on their side—sort of—Donald Trump, who has said that, although he and his wife had their son, Barron, vaccinated, they refused to do so on the timetable recommended by pediatricians.)

The Gormans, too, argue that ways of thinking that now seem self-destructive must at some point have been adaptive. And they, too, dedicate many pages to confirmation bias, which, they claim, has a physiological component. They cite research suggesting that people experience genuine pleasure—a rush of dopamine—when processing information that supports their beliefs. “It feels good to ‘stick to our guns’ even if we are wrong,” they observe.

The Gormans don’t just want to catalogue the ways we go wrong; they want to correct for them. There must be some way, they maintain, to convince people that vaccines are good for kids, and handguns are dangerous. (Another widespread but statistically insupportable belief they’d like to discredit is that owning a gun makes you safer.) But here they encounter the very problems they have enumerated. Providing people with accurate information doesn’t seem to help; they simply discount it. Appealing to their emotions may work better, but doing so is obviously antithetical to the goal of promoting sound science. “The challenge that remains,” they write toward the end of their book, “is to figure out how to address the tendencies that lead to false scientific belief.”

“The Enigma of Reason,” “The Knowledge Illusion,” and “Denying to the Grave” were all written before the November election. And yet they anticipate Kellyanne Conway and the rise of “alternative facts.” These days, it can feel as if the entire country has been given over to a vast psychological experiment being run either by no one or by Steve Bannon. Rational agents would be able to think their way to a solution. But, on this matter, the literature is not reassuring. 

Source: Why Facts Don’t Change Our Minds | The New Yorker

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🔖 Want to read: From Bacteria to Bach and Back: The Evolution of Minds by Daniel C. Dennett

From Bacteria to Bach and Back: The Evolution of Minds by Daniel C. Dennett (W. W. Norton & Company; 1 edition, 496 pages (February 7, 2017))
One of America’s foremost philosophers offers a major new account of the origins of the conscious mind.

How did we come to have minds?

For centuries, this question has intrigued psychologists, physicists, poets, and philosophers, who have wondered how the human mind developed its unrivaled ability to create, imagine, and explain. Disciples of Darwin have long aspired to explain how consciousness, language, and culture could have appeared through natural selection, blazing promising trails that tend, however, to end in confusion and controversy. Even though our understanding of the inner workings of proteins, neurons, and DNA is deeper than ever before, the matter of how our minds came to be has largely remained a mystery.

That is now changing, says Daniel C. Dennett. In From Bacteria to Bach and Back, his most comprehensive exploration of evolutionary thinking yet, he builds on ideas from computer science and biology to show how a comprehending mind could in fact have arisen from a mindless process of natural selection. Part philosophical whodunit, part bold scientific conjecture, this landmark work enlarges themes that have sustained Dennett’s legendary career at the forefront of philosophical thought.

In his inimitable style―laced with wit and arresting thought experiments―Dennett explains that a crucial shift occurred when humans developed the ability to share memes, or ways of doing things not based in genetic instinct. Language, itself composed of memes, turbocharged this interplay. Competition among memes―a form of natural selection―produced thinking tools so well-designed that they gave us the power to design our own memes. The result, a mind that not only perceives and controls but can create and comprehend, was thus largely shaped by the process of cultural evolution.

An agenda-setting book for a new generation of philosophers, scientists, and thinkers, From Bacteria to Bach and Back will delight and entertain anyone eager to make sense of how the mind works and how it came about.

4 color, 18 black-and-white illustrations

🔖 Want to read: From Bacteria to Bach and Back: The Evolution of Minds by Daniel C. Dennett

 

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IPAM Workshop on Regulatory and Epigenetic Stochasticity in Development and Disease, March 1-3

IPAM Workshop on Regulatory and Epigenetic Stochasticity in Development and Disease (Institute for Pure and Applied Mathematics, UCLA | March 1-3, 2017)
Epigenetics refers to information transmitted during cell division other than the DNA sequence per se, and it is the language that distinguishes stem cells from somatic cells, one organ from another, and even identical twins from each other. In contrast to the DNA sequence, the epigenome is relatively susceptible to modification by the environment as well as stochastic perturbations over time, adding to phenotypic diversity in the population. Despite its strong ties to the environment, epigenetics has never been well reconciled to evolutionary thinking, and in fact there is now strong evidence against the transmission of so-called “epi-alleles,” i.e. epigenetic modifications that pass through the germline.

However, genetic variants that regulate stochastic fluctuation of gene expression and phenotypes in the offspring appear to be transmitted as an epigenetic or even Lamarckian trait. Furthermore, even the normal process of cellular differentiation from a single cell to a complex organism is not understood well from a mathematical point of view. There is increasingly strong evidence that stem cells are highly heterogeneous and in fact stochasticity is necessary for pluripotency. This process appears to be tightly regulated through the epigenome in development. Moreover, in these biological contexts, “stochasticity” is hardly synonymous with “noise”, which often refers to variation which obscures a “true signal” (e.g., measurement error) or which is structural, as in physics (e.g., quantum noise). In contrast, “stochastic regulation” refers to purposeful, programmed variation; the fluctuations are random but there is no true signal to mask.

This workshop will serve as a forum for scientists and engineers with an interest in computational biology to explore the role of stochasticity in regulation, development and evolution, and its epigenetic basis. Just as thinking about stochasticity was transformative in physics and in some areas of biology, it promises to fundamentally transform modern genetics and help to explain phase transitions such as differentiation and cancer.

This workshop will include a poster session; a request for poster titles will be sent to registered participants in advance of the workshop.

Speaker List:
Adam Arkin (Lawrence Berkeley Laboratory)
Gábor Balázsi (SUNY Stony Brook)
Domitilla Del Vecchio (Massachusetts Institute of Technology)
Michael Elowitz (California Institute of Technology)
Andrew Feinberg (Johns Hopkins University)
Don Geman (Johns Hopkins University)
Anita Göndör (Karolinska Institutet)
John Goutsias (Johns Hopkins University)
Garrett Jenkinson (Johns Hopkins University)
Andre Levchenko (Yale University)
Olgica Milenkovic (University of Illinois)
Johan Paulsson (Harvard University)
Leor Weinberger (University of California, San Francisco (UCSF))

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🔖 Statistical Physics of Adaptation

Statistical Physics of Adaptation by Nikolay Perunov, Robert A. Marsland, and Jeremy L. England (journals.aps.org Phys. Rev. X 6, 021036 (2016))
Whether by virtue of being prepared in a slowly relaxing, high-free energy initial condition, or because they are constantly dissipating energy absorbed from a strong external drive, many systems subject to thermal fluctuations are not expected to behave in the way they would at thermal equilibrium. Rather, the probability of finding such a system in a given microscopic arrangement may deviate strongly from the Boltzmann distribution, raising the question of whether thermodynamics still has anything to tell us about which arrangements are the most likely to be observed. In this work, we build on past results governing nonequilibrium thermodynamics and define a generalized Helmholtz free energy that exactly delineates the various factors that quantitatively contribute to the relative probabilities of different outcomes in far-from-equilibrium stochastic dynamics. By applying this expression to the analysis of two examples—namely, a particle hopping in an oscillating energy landscape and a population composed of two types of exponentially growing self-replicators—we illustrate a simple relationship between outcome-likelihood and dissipative history. In closing, we discuss the possible relevance of such a thermodynamic principle for our understanding of self-organization in complex systems, paying particular attention to a possible analogy to the way evolutionary adaptations emerge in living things.
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🔖 Meaning = Information + Evolution by Carlo Rovelli

Meaning = Information + Evolution by Carlo Rovelli (arxiv.org)
Notions like meaning, signal, intentionality, are difficult to relate to a physical word. I study a purely physical definition of "meaningful information", from which these notions can be derived. It is inspired by a model recently illustrated by Kolchinsky and Wolpert, and improves on Dretske classic work on the relation between knowledge and information. I discuss what makes a physical process into a "signal".
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🔖 How Life (and Death) Spring From Disorder | Quanta Magazine

How Life (and Death) Spring From Disorder by Philip Ball (Quanta Magazine)
Life was long thought to obey its own set of rules. But as simple systems show signs of lifelike behavior, scientists are arguing about whether this apparent complexity is all a consequence of thermodynamics.

This is a nice little general interest article by Philip Ball that does a relatively good job of covering several of my favorite topics (information theory, biology, complexity) for the layperson. While it stays relatively basic, it links to a handful of really great references, many of which I’ve already read, though several appear to be new to me. [1][2][3][4][5][6][7][8][9][10]

While Ball has a broad area of interests and coverage in his work, he’s certainly one of the best journalists working in this subarea of interests today. I highly recommend his work to those who find this area interesting.

References

[1]
E. Mayr, What Makes Biology Unique? Cambridge University Press, 2004.
[2]
A. Wissner-Gross and C. Freer, “Causal entropic forces.,” Phys Rev Lett, vol. 110, no. 16, p. 168702, Apr. 2013. [PubMed]
[3]
A. Barato and U. Seifert, “Thermodynamic uncertainty relation for biomolecular processes.,” Phys Rev Lett, vol. 114, no. 15, p. 158101, Apr. 2015. [PubMed]
[4]
J. Shay and W. Wright, “Hayflick, his limit, and cellular ageing.,” Nat Rev Mol Cell Biol, vol. 1, no. 1, pp. 72–6, Oct. 2000. [PubMed]
[5]
X. Dong, B. Milholland, and J. Vijg, “Evidence for a limit to human lifespan,” Nature, vol. 538, no. 7624. Springer Nature, pp. 257–259, 05-Oct-2016 [Online]. Available: http://dx.doi.org/10.1038/nature19793
[6]
H. Morowitz and E. Smith, “Energy Flow and the Organization of Life,” Santa Fe Institute, 07-Aug-2006. [Online]. Available: http://samoa.santafe.edu/media/workingpapers/06-08-029.pdf. [Accessed: 03-Feb-2017]
[7]
R. Landauer, “Irreversibility and Heat Generation in the Computing Process,” IBM Journal of Research and Development, vol. 5, no. 3. IBM, pp. 183–191, Jul-1961 [Online]. Available: http://dx.doi.org/10.1147/rd.53.0183
[8]
C. Rovelli, “Meaning = Information + Evolution,” arXiv, Nov. 2006 [Online]. Available: https://arxiv.org/abs/1611.02420
[9]
N. Perunov, R. A. Marsland, and J. L. England, “Statistical Physics of Adaptation,” Physical Review X, vol. 6, no. 2. American Physical Society (APS), 16-Jun-2016 [Online]. Available: http://dx.doi.org/10.1103/PhysRevX.6.021036 [Source]
[10]
S. Still, D. A. Sivak, A. J. Bell, and G. E. Crooks, “Thermodynamics of Prediction,” Physical Review Letters, vol. 109, no. 12. American Physical Society (APS), 19-Sep-2012 [Online]. Available: http://dx.doi.org/10.1103/PhysRevLett.109.120604 [Source]
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🎧 Fearless | Invisibilia (NPR)

Fearless by Lulu Miller and Alix Spiegel (Invisibilia | NPR.org)
In "Fearless," co-hosts Alix Spiegel and Lulu Miller explore what would happen if you could disappear fear. A group of scientists believe that people no longer need fear — at least not the kind we live with — to navigate the modern world. We'll hear about the striking (and rare) case of a woman with no fear. The second half of the show explores how the rest of us might "turn off" fear.

Our evolution certainly hasn’t been keeping up with our level of fear in the modern world. Even simple things like kids playing around their own neighborhood like I did as a kid in the 70’s and 80’s has changed drastically. How can we keep ourselves from being held back unnecessarily?

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Disconnected, Fragmented, or United? A Trans-disciplinary Review of Network Science

Abstract

Applied Network Science

During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.

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Weekly Recap: Interesting Articles 7/24-7/31 2016

Some of the interesting things I saw and read this week

Went on vacation or fell asleep at the internet wheel this week? Here’s some of the interesting stuff you missed.

Science & Math

Publishing

Indieweb, Internet, Identity, Blogging, Social Media

General

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Penguin Revives Decades-Old Software for 30th Anniversary Edition of “The Blind Watchmaker” | The Digital Reader

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A New Thermodynamics Theory of the Origin of Life | Quanta Magazine

A New Physics Theory of Life by Natalie Wolchover (quantamagazine.org)

References:

Hypothesis annotations

[ hypothesis user = 'chrisaldrich' tags = 'EnglandQM']

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Nick Lane and Philip Ball Discuss Mitochondria, Sex, and How to Live Longer

Nick Lane and Philip Ball Discuss Mitochondria, Sex, and How to Live Longer by Philip Ball (Nautil.us)
In his 2010 book, Life Ascending: The Ten Great Inventions of Evolution, Nick Lane, a biochemist at University College London, explores with eloquence and clarity the big questions of life: how it began, why we age and die, and why we have sex. Lane been steadily constructing an alternative view of evolution to the one in which genes explain it all. He argues that some of the major events during evolutionary history, including the origin of life itself, are best understood by considering where the energy comes from and how it is used. Lane describes these ideas in his 2015 book, The Vital Question: Why Is Life the Way It Is?. Recently Bill Gates called it “an amazing inquiry into the origins of life,” adding, Lane “is one of those original thinkers who make you say: More people should know about this guy’s work.” Nautilus caught up with Lane in his laboratory in London and asked him about his ideas on aging, sex, and death.

Biochemist Nick Lane explains the elements of life, sex, and aging in an engaging popular science interview.

Read more

Books by Nick Lane

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Devourer of Encyclopedias: Stanislaw Lem’s “Summa Technologiae”

A review of Summa Technologiae by Stanislaw Lem by David Auerbach from the Los Angeles Review of Books.

Summa Technologiae

AT LAST WE have it in English. Summa Technologiae, originally published in Polish in 1964, is the cornerstone of Stanislaw Lem’s oeuvre, his consummate work of speculative nonfiction. Trained in medicine and biology, Lem synthesizes the current science of the day in ways far ahead of most science fiction of the time.

His subjects, among others, include:

  • Virtual reality
  • Artificial intelligence
  • Nanotechnology and biotechnology
  • Evolutionary biology and evolutionary psychology
  • Artificial life
  • Information theory
  • Entropy and thermodynamics
  • Complexity theory, probability, and chaos
  • Population and ecological catastrophe
  • The “singularity” and “transhumanism”

Source: Devourer of Encyclopedias: Stanislaw Lem’s “Summa Technologiae” – The Los Angeles Review of Books

I came across this book review quite serendipitously today via an Auerbach article in Slate, which I’ve bookmarked. I found a copy of the book and have added it to the top of my reading pile. As I’m currently reading an advance reader edition of Sean Carroll’s The Big Picture, I can only imagine how well the two may go together despite being written nearly 60 years apart.

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The Hidden Algorithms Underlying Life | Quanta Magazine

I did expect something more entertaining from Google when I searched for “what will happen if I squeeze a paper cup full of hot coffee?”

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Can computers help us read the mind of nature? by Paul Davies | The Guardian

Paul Davies waxes poetic about the application of physics, chemistry, and information theory to biology, genetics, and the origin of life.

For too long, scientists focused on what we can see. Now they are at last starting to decode life’s software.

“A soup of chemicals may spontaneously form a reaction network, but what does it take for such a molecular muddle to begin coherently organising information flow and storage? Rather than looking to biology or chemistry, we can perhaps dream that advances in the mathematics of information theory hold the key.”

Paul Davies, physicist, writer, and broadcaster
in Can computers help us read the mind of nature? in The Guardian

 

 ‘When we look at a plant or an animal we see the physical forms, not the swirling patterns of instructions inside them.’ Photograph: Abir Sultan/EPA
‘When we look at a plant or an animal we see the physical forms, not the swirling patterns of instructions inside them.’ Photograph: Abir Sultan/EPA
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