by Judith Curry
So, what scientific idea do YOU think is ready for retirement?
Please take a moment and think of an answer before reading the rest of this post – I read the article before answering this question, and now I don’t know how I would have answered it.
The 2014 Edge Annual Question (EAQ) is:
Science advances by discovering new things and developing new ideas. Few truly new ideas are developed without abandoning old ones first. As theoretical physicist Max Planck (1858-1947) noted, “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” In other words, science advances by a series of funerals. Why wait that long?
WHAT SCIENTIFIC IDEA IS READY FOR RETIREMENT?
Ideas change, and the times we live in change. Perhaps the biggest change today is the rate of change. What established scientific idea is ready to be moved aside so that science can advance?
Edge received 175 responses, most of which are provocative or at least interesting, from some very interesting and even brilliant individuals. As I understand it, these individuals were invited to submit an article.
The responses can be found here [link]. I’ve selected ~20 response for brief excerpts, related to topics that have been discussed at Climate Etc.
Fiery Cushman – Big effects have big explanations
Many scientists are seduced by a two-step path to success: First identify a big effect and then find the explanation for it. Although not often discussed, there is an implicit theory behind this approach. The theory is that big effects have big explanations. This is critical because scientists are interested in the explanations, not in the effects—Newton is famous not for showing that apples fall, but for explaining why. So, if the implicit theory is wrong, then a lot of people are barking up the wrong trees.
There is, of course, an alternative and very plausible source of big effects: Many small explanations interacting. As it happens, this alternative is worse than the wrong tree—it’s a near-hopeless tree. The wrong tree would simply yield a disappointingly small explanation. But the hopeless tree has so many explanations tangled in knotted branches that extraordinary effort is required to obtain any fruit at all.
Laurie Santos – Knowing is half the battle
While there may be some domains where knowing is half the battle, there are many more where it is not. Recent work in cognitive science has demonstrated that knowing is a shockingly tiny portion of the battle for most real world decisions.
Jay Rosen – Information ‘overload’
Filter failure occurs not from too much information but from too much incoming “stuff” that neither reduces existing uncertainty nor raises questions that count for us. The likely answer is to combine the three types of filtering: smart people who do it for us, smart crowds and their choices, smart systems that learn by interacting with us as individuals. It’s at this point that someone usually shouts out: what about serendipity? It’s a fair point. We need filters that listen to our demands, but also let through what we have no way to demand because we don’t know about it yet. Filters fail when they know us too well and when they don’t know us well enough.
Paul Saffo – The illusion of scientific progress
The science establishment justifies its existence with the big idea that it offers answers and ultimately solutions. But privately, every scientist knows that what science really does is discover the profundity of our ignorance. The growing sphere of scientific knowledge is not Pope’s night-dispelling light, but a campfire glow in the gloom of vast mystery. Touting discoveries helps secure finding and gain tenure, put perhaps the time has come to retire discovery as the ultimate measure of scientific progress. Let us measure progress not by what is discovered, but rather by the growing list of mysteries that remind us of how little we really know.
Brian Christian – Scientific knowledge should be structured as ‘literature’
In my view, what’s most outmoded within science, most badly in need of retirement, is the way we structure and organize scientific knowledge itself. Academic literature, even as it moves online, is a relic of the era of typesetting, modeled on static, irrevocable, toothpaste-out-of-the-tube publication. Just as the software industry has moved from a “waterfall” process to an “agile” process—from monolithic releases shipped from warehouses of mass-produced disks to over-the-air differential updates—so must academic publishing move from its current read-only model and embrace a process as dynamic, up-to-date, and collaborative as science itself.
The scientific literature, taken as content, is stronger than it’s ever been—as, of course, it should be. As a form, the scientific literature has never been more inadequate or inept. What is in most dire need of revision is revision itself.
Kate Mills – Only ‘scientists’ can do science
If most funded and published scientific research is conducted by a sample of individuals that have been trained to be successful in academia, then we are potentially biasing scientific questions and interpretations. Individuals who might not fit into an academic mould, but nevertheless are curious to know the world through the scientific method, face many barriers. Community-supported checks and balances remain essential for scientific projects, but perhaps they too can become unbound from traditional academic settings.
The means for collecting and analyzing data are becoming more accessible to the public each day. New ethical issues will need to be discussed and infrastructures built to accommodate those conducting research outside of traditional settings. With this, we will see an increase in the number of scientific discoveries made by informally trained “citizen scientists” of all ages and backgrounds. These previously unheard voices will add valuable contributions to our knowledge of the world.
Tom Griffiths – Bias is always bad
But bias isn’t always bad. In fact, for certain kinds of questions, the only way to produce better answers is to be biased.
Many of the most challenging problems that humans solve are known as inductive problems—problems where the right answer cannot be definitively identified based on the available evidence.
The only way to solve inductive problems well is to be biased. Because the available evidence isn’t enough to determine the right answer, you need to have predispositions that are independent of that evidence. And how well you solve the problem—how often your guesses are correct—depends on having biases that reflect how likely different answers are.
Mary Catherine Bateson – The illusion of certainty
Scientists sometimes resist new ideas and hang on to old ones longer than they should, but the real problem is the failure of the public to understand that the possibility of correction or disproof is a strength and not a weakness. We live in an era when it is increasingly important that the voting public be able to evaluate scientific claims and be able to make analogies between different kinds of phenomena, but this can be a major source of error. The process by which scientific knowledge is refined is largely invisible to the public. The truth-value of scientific knowledge is dependent upon its openness to correction, yet we all carry around ideas that science has long since revised—and are disconcerted when asked to abandon them. Surprise: You will not necessarily drown if you go swimming after lunch.
Most people are not comfortable with the notion that knowledge can be authoritative, can call for decision and action, and yet be subject to constant revision, because they tend to think of knowledge as additive, not recognizing the necessity of reconfiguring in response to new information. It is precisely this characteristic of scientific knowledge that encourages the denial of climate change and makes it so difficult to respond to what we do know in a context where much is still unknown.
Laurence Smith – Stationarity
But a growing body of research shows that stationarity is often the exception, not the norm. As new satellite technologies scan the earth, more geological records are drilled, and the instrument records lengthen, they commonly reveal patterns and structures quite inconsistent with a fixed envelope of random noise. Instead, there are transitions to different quasi-stable states, each characterized by a different set of physical conditions and associated statistical properties. In climate science, for example, we have discovered multi-decadal patterns like the Pacific Decadal Oscillation (PDO), an El Niño-like phenomenon in the north Pacific that triggers far-reaching changes in climate averages that persist for decades (for example during the 20th century the PDO experienced a “warm” phase from 1922-1946 and 1977-1998, and a “cool” phase from 1947-1976) with far-reaching impacts on water resources and fisheries. And anthropogenic climate change, induced by our steady ramping up of greenhouse gas concentrations in the atmosphere, is by its very definition the opposite of a fixed, stationary process. This imperils the basis of many societal risk calculations because as the statistical probabilities of the past break down, we enter a world that operates outside of expected and understood norms.
Robert Provine – Common sense
We fancy ourselves intelligent, conscious and alert, and thinking our way through life. This is an illusion. We are deluded by our brain’s generation of a sketchy, rational narrative of subconscious, sometimes irrational or fictitious events that we accept as reality. These narratives are so compelling that they become common sense and we use them to guide our lives. In cases of brain damage, neurologists use the term confabulation to describe a patient’s game but flawed attempt to produce an accurate narrative of life events. I suggest we be equally wary of everyday, non-pathological confabulation and retire the common sense hypothesis that we are rational beings in full conscious control of our lives. Indeed, we may be passengers in our body, just going along for the ride, and privy only to second-hand knowledge of our status, course and destination.
Gert Gigerenzer – Scientific inference via statistical rituals
Enter the “Handprint,” the sum total of all the ways we lower our footprint. To calculate a handprint, take the footprint as the baseline, and then go a step further: assess the amount ameliorated by the good things we do: recycle, reuse, bike not drive. Convince other people to do likewise. Or invent a replacement for a high-footprint technology, like the sytrofoam subsititute made from rice hulls and mycelium rather than petroleum.
The handprint calculation applies the same methodology as for footprints, but reframes the total as a positive value: Keep growing your handprint and you are steadily reducing your negative impacts on the planet. Make your handprint bigger than your footprint and you are sustaining the planet, not damaging it.
And such a positive spin, motivational research tells us, will be more likely to keep people moving toward the target.
Alex Holcombe – Science is self-correcting
The bias against corrections is especially harmful in areas where the results are cheap, but the underlying measurements are noisy. In those scientific realms, the literature may quickly become polluted with statistical flukes. Unfortunately, these two features of cheap results and noisy measurement are characteristic of most sub-areas of psychology, my own discipline. Some other fields, such as contemporary epidemiology, may have it even worse, particularly with regards to a third exacerbating factor: the small size of the true effects investigated. As John Ioannidis has pointed out, the smaller the true effects in an area, the more likely it is that a given claimed effect is instead a statistical fluke (a false positive).
Daniel Hillis – Cause and effect
The notion of cause-and-effect breaks down when the parts that we would like to think of as outputs affect the parts that we would prefer to think of as inputs. The paradoxes of quantum mechanics are a perfect example of this, where our mere observation of a particle can “cause” a distant particle to be in a different state. Of course there is no real paradox here, there is just a problem with trying to apply our storytelling framework to a situation where it does not match.
Unfortunately, the cause-and-effect paradigm does not just fail at the quantum scale. It also falls apart when we try to use causation to explain complex dynamical systems like the biochemical pathways of a living organism, the transactions of an economy, or the operation of the human mind. These systems all have patterns of information flow that defy our tools of storytelling. A gene does not “cause” the trait like height, or a disease like cancer. The stock market did not go up “because” the bond market went down. These are just our feeble attempts to force a storytelling framework onto systems that do not work like stories. For such complex systems, science will need more powerful explanatory tools, and we will learn to accept the limits of our old methods of storytelling. We will come to appreciate that causes and effects do not exist in nature, that they are just convenient creations of our own minds.
Luca De Biase – The tragedy of the commons
There is no tragedy: there are conflicts though. And they can be better understood by embracing a vision that is open to Ostrom’s notion of polycentric governance of complex economic systems. The danger of a closed vision that only understands conflicts between state regulation and market freedom seem to be even more catastrophic when thinking at climate change and other environmental issues. When we think about the environment, the commons idea seems to be a much more generative notion than many other solutions. It is not a guarantee for a solution, but it is better point to start. The theory of “the tragedy of the commons” has now clearly become a comedy. But it can be a really sad comedy if we don’t finish with it and move on.
Aubrey De Grey – Science progresses most effectively by allocating funds via peer review
I claim that this would be largely solved by a system based on peer recognition rather than peer review. When a scientist first applies for public research funds, his or her career would be divided into five-year periods, starting with the past five years (period 0), the coming five (period 1), etc. Period 1 is funded at a low, entry-level rate on the basis of simple qualifications (possession of a doctorate, number of years of postdoctoral study, etc), and without the researcher having provided any description of what specific research is to be undertaken. Period 2’s funding level is determined, as a percentage of total funds available for the scientist’s discipline of choice, again without any description of what work is planned to be performed, but instead on the basis of how well cited was his or her work performed in period 0.
Melanie Swan – The scientific method
The scientific idea that is most ready for retirement is the scientific method itself. More precisely it is the idea that there would be only one scientific method, one exclusive way of obtaining scientific results. The problem is that the traditional scientific method as an exclusive approach is not adequate to the new situations of contemporary science like big data, crowdsourcing, and synthetic biology. Hypothesis-testing through observation, measurement, and experimentation made sense in the past when obtaining information was scarce and costly, but this is no longer the case. In recent decades, we have already been adapting to a new era of information abundance that has facilitated experimental design and iteration. One result is that there is now a field of computational science alongside nearly every discipline, for example computational biology and digital manuscript archiving. Information abundance and computational advance has promulgated the evolution of a scientific model that is distinct from the traditional scientific method, and three emerging areas are advancing it even more.
Samuel Barondes – Science advances by funerals
So Plank got it wrong. The development of new scientific truths does not depend on the passing of stubborn conservative opponents. It is, instead, mainly dependent on the continuous enrollment of talented newcomers who are eager to make their mark by changing the existing order. In Planck’s case it was, in fact, the arrival of the young Albert Einstein, rather than the demise of his senior opponents, that propelled quantum theory forward. As Douglas Stone showed, in Einstein and the Quantum, it was the 25-year-old patent clerk, a fledgling outsider with nothing to lose, who became the driving force in the development of this theory. As for his elders, Einstein couldn’t care less.
Sam Harris – Our narrow definition of ‘science’
Search your mind, or pay attention to the conversations you have with other people, and you will discover that there are no real boundaries between science and philosophy—or between those disciplines and any other that attempts to make valid claims about the world on the basis of evidence and logic. When such claims and their methods of verification admit of experiment and/or mathematical description, we tend to say that our concerns are “scientific”; when they relate to matters more abstract, or to the consistency of our thinking itself, we often say that we are being “philosophical”; when we merely want to know how people behaved in the past, we dub our interests “historical” or “journalistic”; and when a person’s commitment to evidence and logic grows dangerously thin or simply snaps under the burden of fear, wishful thinking, tribalism, or ecstasy, we recognize that he is being “religious.”
The remedy for all this confusion is simple: We must abandon the idea that science is distinct from the rest of human rationality. When you are adhering to the highest standards of logic and evidence, you are thinking scientifically. And when you’re not, you’re not.
Emanuel Derman – The power of statistics
Science is a battle to find causes and explanations amidst the confusion of data. Let us not get too enamored of data science, whose great triumphs so far are mainly in advertising and persuasion. Data alone has no voice. There is no “raw” data, as Kepler’s saga shows. Choosing what data to collect and how to think about it takes insight into the invisible; making good sense of the data collected requires the classic conservative methods: intuition, modeling, theorizing, and then, finally, statistics.
Victoria Stodden – Reproducibility
A problem with any one of these three types of reproducibility, empirical, computational, and statistical, can be enough to derail the process of establishing scientific facts. Each type calls for different remedies, from improving existing communication standards and reporting (empirical reproducibility) to making computational environments available for replication purposes (computational reproducibility) to the statistical assessment of repeated results for validation purposes (statistical reproducibility), each with different implementations. Of course these are broad suggestions, and each type of reproducibility can demand different actions depending on the details of the scientific research context, but confusing these very different aspects of the scientific method will slow our resolution of Boyle’s old discussion that started with the vacuum chamber.
Hugo Mercier – Planck’s cynical view of scientific change
If people who disagree with us are never going to change their mind, then why even talk to them? If we do not engage people who disagree with us in discussion, we will never learn of the—often perfectly good—reasons why they disagree with us. If we cannot address these reasons, then our arguments are likely to prove unconvincing. Our failures to convince will only reinforce the belief that we face pigheadedness rather than rational disagreement. A belief in the inefficiency of argumentation can be a destructive self-fulfilling prophecy. We should give scientists, and argumentation more generally, more credit: it is well deserved. Let’s retire Planck’s cynical view of scientific change.
Sean Carroll – Falsifiability
The falsifiability criterion gestures toward something true and important about science, but it is a blunt instrument in a situation that calls for subtlety and precision. It is better to emphasize two more central features of good scientific theories: they are definite, and they are empirical. By “definite” we simply mean that they say something clear and unambiguous about how reality functions. String theory says that, in certain regions of parameter space, ordinary particles behave as loops or segments of one-dimensional strings. The relevant parameter space might be inaccessible to us, but it is part of the theory that cannot be avoided. In the cosmological multiverse, regions unlike our own are unambiguously there, even if we can’t reach them. This is what distinguishes these theories from the approaches Popper was trying to classify as non-scientific. (Popper himself understood that theories should be falsifiable “in principle,” but that modifier is often forgotten in contemporary discussions.)
Science is not merely armchair theorizing; it’s about explaining the world we see, developing models that fit the data. But fitting models to data is a complex and multifaceted process, involving a give-and-take between theory and experiment, as well as the gradual development of theoretical understanding in its own right. In complicated situations, fortune-cookie-sized mottos like “theories should be falsifiable” are no substitute for careful thinking about how science works. Fortunately, science marches on, largely heedless of amateur philosophizing. If string theory and multiverse theories help us understand the world, they will grow in acceptance. If they prove ultimately too nebulous, or better theories come along, they will be discarded. The process might be messy, but nature is the ultimate guide.
I have only given a small flavor of some of the responses, I encourage you to visit the site and explore. You are guaranteed to find things that interest you and that drive you crazy (of course we would never agree on which is which). In any event, I think at least the ones that I cited have some relevance to climate science, I look forward to your comments.