To Model is to Know

Getting signal out of the noise of living systems, by network modeling.

Biology is complex. That is as true on the molecular level as it is on the organismal and ecological levels. So despite all the physics envy, even something as elegant as the structure of DNA rapidly gets wound up in innumerable complexities as it meets the real world and needs reading, winding, cutting, packaging, synapsing, recombining, repairing, etc. This is particularly true of networks of interactions- the pathways of (typically) protein interactions that regulate what a cell is and does.

An article from several years ago discussed an interesting and influential way to learn about these interactions. The advent of "big data" in biology allowed us to do things like tabulate all the interactions of individual proteins in a cell, or sample the abundance of every transcript or protein in cells of a tissue. But it turned out that these alone did not lead directly to the elucidation of how things work. Where the genome was a part ordering list, offering one catalog number and description for each part, these experiments provided the actual parts list- how many screws, how many manifolds, how many fans, etc., and occasionally, what plugs into what else. These were all big steps ahead, but hardly enough to figure out how complicated machinery works. We still lack the blueprint, one that ideally is animated to show how the machine runs. But that is never going to happen unless we build it ourselves. We need to build a model, and we need more information to do so.

These authors added one more dimension to the equation- time, via engineered perturbations to the system. Geneticists and biochemists have been doing this (aka experiments- such as mutations, reconstitutions, and titrations) forever, including in gene expression panels and other omics data collections. But employing a perturbation method in a systematic and informative way on the big data level in biology remains a significant advance. The problem is called network inference- figuring out how a complex system works, (which is to say, making a model), now that we are given some but not all important information of its composition and activities. And the problem is difficult because biological networks are frequently very large and can be modeled in an astronomical number of ways, given that we have scanty information about key internal aspects. For instance, even if many individual interactions are known from experimental data, not all are known, many key conditions (like tissue, cell type, phase of the cell cycle, local nutrient conditions etc.) are unknown, and quantitative data is very rare.

One way to get around some of these specifics is to poke the system somewhere and track what happens thereafter. It is a lot like epistasis analysis in genetics, where if you, say, mutate a set of genes acting in a linear process, the ones towards the end can not be cured by supplying chemical intermediates that are made upstream- the later genes are "epistatic" to those earlier in the process. Such logic needs to be expanded exponentially to address inference over realistic biological networks, and gets the authors into some abstruse statistics and mathematics. Their goal is to simplify the modeling and search problem enough to make it computationally tractable, while still exploring the most promising parts of their parameter space to come up with reasonably accurate models. They also seek to iterate- to bring in new perturbation information and use it to update the model. One step is to discretize the parameters, rather than exploring continuous space. Second is to use preliminary calculations to get near optimal values for their model parameters, and thereafter explore those approximated local spaces, rather than all possible space.

Speed of this article's method (teal) compared with a conventional method for the same task (green).

All this is done over again for experimental cases, where some perturbation has been introduced into their real system, generating new data for input to the model. Their improved speed of calculation is critical here, enabling as many iterations and alterations as needed, to update and refine the model. If the model is correct, it will respond accurately to the alteration, giving the output that is also observed in real life. Such a model then makes it possible to perform virtual perturbations, such as simulating the effect of a drug on the model, which then predicts entirely in silico what the effects will be on the biological network.

"It is also useful, as an exercise, to evaluate the overall performance of the BP algorithm on data sets engineered from completely known networks. With such toy datasets we achieve the following: (i) demonstrate that BP converges quickly and correctly; (ii) compare BP network models to a known data-generating network; and (iii) evaluate performance in biologically realistic conditions of noisy data from sparse perturbations."
...
"Each model is then a set of differential equations describing the behavior of the system in response to perturbations."

The upshot of all this is models that are roughly correct, and influential on later work. The figure below shows (A) the smattering of false positives and missing (false negatives) interactions, but (B) accounts for most of this error as shortcuts of various kinds- the inference of regulation that is globally correct, but may be missing a step here or there. So they suggest that the scoring is actually better than the roughly 50 - 70% correct rate that they report.

An example pair of interconnecting pathways inferred from experimental protein abundance data and perturbed abundance data, with the protein molecules as nodes and their interactions as arrows. Where would a drug have the most effect if it inhibited one of these proteins?

They offer one pathway as an example, with an inferred pattern of activity, (above), and a few predictions about what proteins would be good drug targets. For example, PLK1 in this diagram is a key node, and has dramatic effects if perturbed. This came up automatically from their analysis, but PLK1 happens to already be an anticancer drug target with two drugs under development. Any biologist in the field could have told them about this target, but they went ahead with proof-of-principle experiments to show that yes, indeed, treatment of their RAF-inhibitor drug-resistant melanoma cells, which were the subject of modeling, with an experimental anti-PLK1 drug results in dramatic cell killing at quite low concentrations. They had used other drugs as perturbation agents in the physical experiments to develop this model, but not this one, so at least in their terms, this is a novel finding, arrived at out of their modeling work.

Given that these authors were working from scratch, not starting with manually curated pathway models that incorporate a lot of known individual interactions, this is impressive work (and they note parenthetically that using such curated data would be a big help to their modeling). Having computationally tractable ways to generate and refine large molecule networks based on typical experimentation is a recipe for advancement in the field- I hope these tools become more popular.

• Ruminations on PGE. Minimally, the PUC needs to be publically elected. Maximally, the state needs to take over PGE entirely and take responsibility.
• Study on the effect of automation on labor power ... which is minor.
• What has happened to the Supreme Court?
• Will bribery help?

Meritocracy

Is meritocracy intrinsically bad, or good for some things, not so good for others?

A recent book review in the New Yorker ruminated on the progress and defects of the meritocracy, a word born in sarcasm, now become an ideology and platitude. I am not sure that the review really touched on the deeper issues involved, so am motivated to offer a followup. The term was coined by a British sociologist, which is significant, as it describes a fundamental shift from the preceding system, the class system, as a way of allocating educational opportunity, professional work, military grades, and social status in general. It would be natural for someone of the British upper class to decry such a change, though the coiner, Michael Young, was generally a socialist and egalitarian, though eventually made into a Baron for his services ... ironically.

The book review focused mostly on the educational establishment, where the greatest sea change has occurred. Where elite schools used to lazily accept their students from elite prep academies, from certain rich families and class backgrounds, now they make a science of student selection, searching far and wide, high and low, for the most meritorious candidates. Are SAT scores useful? Not very, the new consensus has it, especially as such tests unconsciously reproduce various cultural biases, instead of rendering the true grail- a score of merit, whatever that really might be. But anyhow the slicing is done, higher education is now an intense, mostly meritocratic sorting process, granting opportunities and education on the basis of qualifications, intent on funneling the most capable people into the higher rungs of the ladder of professional activities and status.

One question is whether all this laborious sorting of students has been a good thing, overall. Do we get better staffed hospitals, better filled jobs throughout the economic system by virtue of this exquisitely and remorselessly selective weeding system? Yes we do, perhaps at the cost of some social serendipity, of finding CEO material in the mailroom, and the like.

But the deeper question is whether all this selection has been good for our society at large. There is answer has to be more guarded. If economic efficiency is the only goal, then sure. But it isn't, and some of our social atomization, and creeping class-ism and despair in the lower rungs of society comes from the intensification of meritocratic selection, which spills over to many other areas of society, directly through income and wealth, and indirectly through many other mechanisms of status, particularly politics. Much of Trump's support comes from people sick of the "elites"- those selected by SAT scores, course grades, and the like to rule over the working class. It is not clear that grubbing for grades and mastering standardized exams have done such a good job at selecting a ruling political class. That class has not done a very good job, and that poor performance has sapped our social solidarity. The crisis is most glaring in the stark cost of losing out- homelessness and destitution- the appalling conditions that are the mirror of billionaires also produced by this Darwinian system.

The problem is that we need areas of our lives that are not plugged into the rat race, for both psychological and sociological reasons. Such areas are increasingly scarce as this new gilded age gobbles up all our social relations under the rubric of the market, paticularly with its newly internet-extended capabilities. Religion has traditionally been a social locus where every one is worth the same- many classes come together to share some profound feelings, and occasionally explicit anti-establishment messages, (though also often a message of exalted status vs some other sect, faith, or unbelievers). But religion is dying, for good reason.

A town meeting

Civic associations and volunteer life have in the US been a frequent antidote to class-ism, with people of all classes coming together to make each others' lives better. But modern transportation has enabled the definitive sorting of classes by socioeconomic level, rendering civic activity, even when it occurs, poor at social mixing. No longer does a geographic community have to include those of all professions and walks of life to be viable. We can have lilly-white suburbs and gated communities, and have any tradespeople and retail employees commute in from far away. That is a problem, one caused ultimately by fossil fuels and the freedom that they bring. The civic sector has also been invaded by an army of vanity foundations sponsored by the rich- a patronizing and typically futile approach to social betterment. Volunteerism has also been sapped by lack of time and money, as employees throughout the economic system are lashed ever more tightly to their jobs, stores kept open at all hours, and wages for most stagnate. Unions are another form of civic association that have withered.

All this has frayed the local civic and social connections, which are the ultimate safety net and source of civic solidarity. While Republicans bray about how terrible government is at replacing these services with top-down programs, (with some justification), they have at the same time carried out a decades-long battle to weaken both government and civic life, leaving a smoldering ruin in the name of a new feudal overlordship of the "job-creators"- the business class. That is the ultimate problem with meritocracy, and while appreciating its role in spreading social justice in the distribution of educational and professional opportunity, (a promise that is far from fully realized), we need to realize its cost in other areas of our national culture, and work to restore community diversity, community institutions, and community solidarity.

• Are corporations amoral and evil?
• Is market manipulation part of the story?
• Don't go to the hospital.
• Japan vs MMT.
• Amazing Chopin.
• Quote of the week:

Where love rules, there is no will to power; where power predominates, there love is lacking. The one is the shadow of the other. – Carl Jung

The Participation Mystique

How we relate to others, things, environments.

We are all wrapped up in the impeachment drama now, wondering what could be going on with a White House full of people who have lost their moral compasses, their minds. Such drama is an exquisite example of participation mystique, on our part as we look on in horror as the not very bright officials change their stories by the day, rats start to leave the sinking ship, and the president twists in the wind. We might not sympathize, but we recognize, and voyeuristically participate in, the emotions running and the gears turning.

Carl Jung took the term, participation mystique, from the anthropologist Lucien Levy Bruhl. The original conception was a rather derogotory concept about the animism common among primitive people, that they project anthropomorphic and social characters to objects in the landscape, thus setting up mystical connections with rocks, mountains, streams, etc. Are such involvements characteristic of children and primitive people, but not of us moderns? Hardly. Modern people have distancing and deadening mechanisms to manage our mental involvement with projected symbologies, foremost among which is the scientific mindset. But our most important and moving experiences partake of identification with another- thing or person, joining our mental projection with their charisma, whatever that might be.

Participation mystique remains difficult to define and use as a concept, despite books being written about it. But I would take it as any empathetic or identification feelings we have toward things and people, by which the boundaries in between become blurred. We have a tremendous mental power to enter into other's feelings, and we naturally extend such participation (or anthropomorphism) far beyond its proper remit, to clouds, weather events, ritual objects, etc. This is as true today with new age religions and the branding relationships that every company seeks to benefit from, as it is in the more natural setting of imputing healing powers to special pools of water, or standing in awe of a magnificent tree. Such feelings in relation to animals has had an interesting history, swinging from intense identification on the part of typical hunters and cave painters, to an absurd dismissal of any soul or feeling by scientistic philosophers like Descartes, and back to a rather enthusiastic nature worship, nature film-making, and a growing scientific and philosophical appreciation of the feelings and moral status of animals in the present day.

Participation mystique is most directly manipulated and experienced in the theater, where a drama is specifically constructed to draw our sympathetic feeings into its world, which may have nothing to do with our reality, or with any reality, but is drenched in the elements of social drama- tension, conflict, heroic motivations, obstacles. If you don't feel for and with Jane Eyre as she grows from abused child, to struggling adult, to lover, to lost soul, and finally to triumphant partner, your heart is made of stone. We lend our ears, but putting it psychologically, we lend a great deal more, with mirror neurons hard at work.

All this is involuntary and unconscious. Not that it does not affect our conscious experience, but the participation mystique arises as an automatic response from brain levels that we doubtless share with many other animals. Seeing squirrels chase each other around a tree gives an impression of mutual involvement and drama that is inescapable. Being a social animal requires this kind of participation in each other's feelings. So what of the psychopath? He seems to get these participatory insights, indeed quite sensitively, but seems unaffected- his own feelings don't mirror, but rather remain self-centered. He uses his capabilities not to sympathise with, but to manipulate, others around him or her. His version of participation mystique is a truncated half-experience, ultimately lonely and alienating.

And what of science, philosophy and other ways we systematically try to escape the psychology of subjective identification and participation? As mentioned above in the case of animal studies, a rigid attitude in this regard has significantly retarded scientific progress. Trying to re-establish objectively what is so obvious subjectively is terribly slow, painstaking work. Jane Goodall's work with chimpanzees stands as a landmark here, showing the productive balance of using both approaches at once. But then when it comes to physics and the wide variety of other exotic phenomena that can not be plausibly anthropomorphized or participated in via our subjective identification, the policy of rigorously discarding all projections and identifications pays off handsomely, and it is logic alone that can tell us what reality is.

• The Democratic candidates on worker rights.
• Was it trade or automation? Now that everything is made in China, the answer should be pretty clear.
• On science.
• Turns out that Google is evil, after all.
• Back when some Republicans had some principles.
• If all else fails, how about a some nice culture war?
• What is the IMF for?
• #DeleteFacebook
• Graphic: who is going to tax the rich? Who is pushing a fairer tax system overall? Compare Biden with Warren carefully.

Thinking Ahead in Waves

A computational model of brain activity following simple and realistic Bayesian methods of internal model development yields alpha waves.

Figuring how the brain works remains an enormous and complicated project. It does not seem susceptible to grand simplifying theories, but has in contrast been a mountain climbed by thousands, in millions of steps. A great deal of interest revolves around brain waves, which are so tantalizingly accessible and reflective of brain activity, yet still not well understood. They are definitely not carrying information in the way radio stations send information, whether in the AM or FM. But they do seem to represent synchronization between brain regions that are exchanging detailed information through their anatomical, i.e. neuronal, connections. A recent paper and review discuss a theory-based approach to modeling brain computation, one that has the pleasant side effect of generating alpha waves- one of the strongest and most common of the brain wave types, around 10 Hz, or 10 cycles per second- automatically, and in ways that explain some heretofore curious features.

The model follows the modern view of sensory computation, as a Bayesian modeling system. Higher levels make models of what reality is expected to look/hear/feel like, and the lower level sensory systems, after processing their inputs in massively parallel fashion, send only error signals about what differs from the model (or expectation). This is highly efficient, such that boring scenery is hardly processed or noticed at all, while surprises form the grist of higher level attention. The model is then updated, and rebroadcast back down to the lower level, in a constant looping flow of data. This expectation/error cycle can happen at many levels, creating a cascade or network of recurrent data flows. So when such a scheme is modeled with realistic neuronal communication speeds, what happens?

A super-simple model of what this paper implements. The input node sends data, in the form of error reports, (x(t)), to the next, higher level node. In return, it gets some kind of data (y(t)), indicating what that next level is expecting, as its model of how things are at time t.

The key parameters are the communication speeds in both directions, (set at 12 milliseconds), the processing time at each level, (set at 17 milliseconds), and a decay or damping factor accounting for how long neurons would take to return to their resting level in the absence of input, (set at 200 milliseconds). This last parameter seems most suspect, since the other parameters assume instantaneous activation and de-activation of the respective neurons involved. A damping outcome/behavior is surely realistic from general principles, but one wonders why a special term would be needed if one models the neurons in a realistic way, which is to say, mostly excitatory and responsive to inputs. Such a system would naturally fall to inactivity in the absence of input. On the other hand, a recurrent network is at risk of feedback amplification, which may necessitate a slight dampening bias.

The authors generate and run numerical models for a white noise visual field being processed by a network with such parameters, and generate two-dimensional fields of waves for two possibilities. First is the normal case of change in the visual field, generating forward inputs, from lower to higher levels. Second is a lack of new visual input, generating stronger backward waves of model information. Both waves happen at about 8 times the communication delay, or about 100 milliseconds, right in the alpha range. Not only did such waves happen in 2-layer models with just one pair of interacting units, but when multiple modules were modeled in a 2-dimensional field, traveling alpha waves appeared.

When modeled with multiple levels and two dimensions, the outcome, aside from data processing, is a traveling alpha wave that travels in one direction when inputs predominate (forward) and in the opposite direction when inputs are low and backward signals predominate.

Turning to actual humans, the researchers looked more closely at actual alpha waves, and found the same thing happening. Alpha waves have been considered odd in that, while generally the strongest of all brain waves, and the first to be characterized, they are strongest while resting, (awake, not sleeping), and become less powerful when the brain is actively attending/watching, etc. Now it turns out that what had been considered the "idle" brain state is not idle at all, but a sign of existing models / expectations being propagated in the absence of input- the so-called default mode network. Tracking the direction of alpha waves, they were found to travel up the visual hierarchy when subjects viewed screens of white noise. But when their eyes were closed, the alpha waves traveled in the opposite direction. The authors argue that normal measurements of alpha waves fail to properly account for the power of forward waves, which may be hidden in the more general abundance of backward, high-to-low level alpha waves.

Example of EEG from human subjects, showing the directionality of alpha wave travel, in this case forward from input (visual cortex in the back of the brain) to higher brain levels.

"Therefore, we conjecture that it may simply not be possible for a biological brain, in which communication delays are non-negligible, to implement predictive coding without also producing alpha-band reverberations. Moreover, a major characteristic of alpha-band oscillations—i.e., their propagation through cortex as a traveling wave—could also be explained by a hierarchical multilevel version of our predictive coding model. The waves predominantly traveled forward during stimulus processing and backward in the absence of inputs. ...  Importantly though, in our model none of the units is an oscillator or pacemaker per se, but oscillations arise from bidirectional communication with temporal delays."

Thus brain waves are increasingly understood as a side effect of functional synchronization and, in this case, intrinsically associated with the normal back-and-forth of data processing, which looks nothing like the stream of data from a video camera, but something far more efficient, using a painstakingly-built internal database of reality to keep tabs on, and detect deviations from, new sensations arriving. It remains to ask what exactly this model function is, which the authors term y(t) - the data sent from higher levels to lower levels. Are higher levels of the brain modeling their world in some explicit way and sending back a stream of imagery which the lower levels compare with their new data? No- the language must far more localized. The rendition of full scenery would be reserved for conscious consideration. The interactions considered in this paper are small-scale and progressive, as data is built up over many small steps. Thus each step would contain a pattern of what is assumed to be the result of the prior step. Yet what exactly gets sent back, and what gets sent onwards, is not intuitively clear.

• Don't forget the beta waves.
• Status of the GOP.
• To each his own.. delusions.
• China gets some pushback.
• Full-on corruption.

High Intelligence is Highly Overrated by Highly Intelligent People

AI, the singularity, and watching way too much science fiction: Review of Superintelligence by Nic Bostrom.

How far away is the singularity? That is the point when machine intelligence exceeds human intelligence, after which it is thought that this world will no longer be ours to rule. Rick Bostrom, a philosopher at Oxford, doesn't know when this will be, but is fearful of its consequences, since, if we get it wrong, humanity's fate may not be a happy one.

The book starts strongly, with some well argued and written chapters about the role of intelligence in humanity's evolution, and the competitive landscape of technology today that is setting the stage for this momentous transition. But thereafter, the armchair philosopher takes over, with tedious chapters of hairsplitting and speculation about how fast or slow the transition might be, how collaborative among research groups, and especially, how we could pre-out-think these creations of ours, to make sure they will be well-disposed to us, aka "the control problem".

Despite the glowing blurbs from Bill Gates and others on the jacket, I think there are fundamental flaws with this whole approach and analysis. One flaw is a failure to distinguish between intelligence and power. Our president is a moron. That should tell us something about this relationship. It is not terribly close- the people generally acknowledged as the smartest in history have rarely been the most powerful. This reflects a deeper flaw, which is, as usual, a failure to take evolution and human nature seriously. The "singularity" is supposed to furnish something out of science fiction- a general intelligence superior to human intelligence. But Bostrom and others seem to think that this means a fully formed human-like agent, and those are two utterly different things. Human intelligence takes many forms, and human nature is composed of many more things than intelligence. Evolution has strained for billions of years to form our motivations in profitable ways, so that we follow others when necessary, lead them when possible, define our groups in conventional ways that lead to warfare against outsiders, etc., etc. Our motivational and social systems are not the same as our intelligence system, and to think that anyone making an AI with general intelligence capabilities will, will want to, or even can, just reproduce the characteristics of human motivation to tack on and serve as its control system, is deeply mistaken.

The fact is that we have AI right now that far exceeds human capabilities. Any database is far better at recall than humans are, to the point that our memories are atrophying as we compulsively look up every question we have on Wikipedia or Google. And any computer is far better at calculations, even complex geometric and algebraic calculations, than we are in our heads. That has all been low-hanging fruit, but it indicates that this singularity is likely to be something of a Y2K snoozer. The capabilities of AI will expand and generalize, and transform our lives, but unless weaponized with explicit malignant intent, it has no motivation at all, let alone the motivation to put humanity into pods for its energy source, or whatever.

People-pods, from the Matrix.

The real problem, as usual, is us. The problem is the power that accrues to those who control this new technology. Take Mark Zuckerberg for example. He stands at the head of multinational megacorporation that has inserted its tentacles into the lives of billions of people, all thanks to modestly intelligent computer systems designed around a certain kind of knowledge of social (and anti-social) motivations. All in the interests of making another dollar. The motivations for all this do not come from the computers. They come from the people involved, and the social institutions (of capitalism) that they operate in. That is the real operating system that we have yet to master.

• Facebook - the problem is empowering the wrong people, not the wrong machines.
• Barriers to health care.
• What it is like to be a psychopathic moron.

Investing in the Future

People's Capitalism- the economics of James Albus.

A curious thing happened on the way to a recent post about the cerebellum. One of its primary theorists was not a neurobiologist, but an engineer, roboticist, and control system designer. It turned out that James Albus, mild-mannered government employee all of his career, had several side projects, another one of which was an odd blend of libertarian and communist economics, which he called peoples' capitalism. It incorporates some unconventional monetary theory, and throws in a proposal for oceanic algae harvesting as a bonus. All in all, Albus is clearly a fellow crank.

This book "Path to a Better World" is not easy to find, probably for good reason. Putting aside its lengthy self-encomiums and visions for a peaceful and problem-free future, the basic proposition is that the government should issue credit to everyone for the purpose of setting up a personal investment fund, which over time would then generate on everyone's behalf a steady and growing stream of income that will replace that lost from the automation revolution to come (and pay back the original loan). He estimates that if the annual increment is $5,000, the portfolio would be worth $1.5 million after 50 years, generating $55,000 of income. This would all be invested in government-approved vehicles like mutual funds, thereby increasing total capital investment. And lastly, to offset inflation, he proposes a payroll deduction-style system whereby some proportion of each person's income could be forcibly diverted to savings when inflation threatens.

One of the core justifications of these schemes is gaining a higher rate of overall capital investment. Albus recounts some of the interesting literature in economics that shows that productivity growth, overall growth, and an increased living standard all come mostly from capital investment. It is capital (as opposed to straight consumption of short-lived items like food and services) that funds the machinery, education, and training that continues to give back, year after year, productive services like roads, new inventions, manufacturing plants, and housing. We all know that the US has had a low rate of capital investment, which Albus contrasts with China's extraordinarily high rate, and thus high growth which is overtaking us.

Albus shows fanciful graphs going far into the future of the US maintaining a 9% economic growth rate, which would enable us to stay ahead of the Chinese indefinitely. The problem is that not all investment is productive. We learned from Japan that the dizzying rates of capital formation and investment in a developing economy that is committed to catching up with the first world do not last forever. As long as one is behind the technological frontier, productive investments are easy to find- just steal them from more advanced cultures. But once one reaches the technological frontier, the search is far more difficult. Much more investment is wasted in exploratory research, and it is less attractive to rip out current sunk investments to keep up with every tiny increment on the slowly advancing frontier. This explains why China's growth will inevitably slow, as did Japan's and ours.

This is not to say that we should not raise our capital investment rate, but that we need to be more judicious than simply shovelling more money into mutual funds. Since the value of the stock market is based on a relatively coherent estimation of future income flows to corporations, pouring in more money on behalf of passive small investors will mostly just nudge out other, more liquid, investors, keeping the overall level of investment stable (with the caveat that price/earnings ratios have indeed risen (perhaps doubled) over the last few decades as a larger pool of investors has flooded the market). This would be a good thing from an economic justice standpoint. One of the points of Albus's plans is to distribute capital ownership more widely, in preparation for the time when none have jobs, but all need income. But it is unlikely to raise net capital investment much or raise economic growth rates.

The ironic thing (given Albus's government career in the highest levels of its research enterprise) is that he is so focused, perhaps due to libertarian leanings, on pumping money into the private capital markets, that he neglects the real capital shortfall- that of public investment. It is now a common mantra that our infrastructure is crumbling, and that education is too expensive. Both are areas where government investment is the most productive way we have to build for future economic and social returns.

Otherwise, there are some positive aspects to these ideas. What goes unmentioned is that the personal investment scheme will have to be heavily controlled by the government, since most people getting that kind of money are going to spend it. That is why so many poor people exist, after all, and so few capitalists. And the inflation control scheme is also rather heavy-handed, if effective, though one has to ask where this savings would go so as to not be inflationary. Putting it into mutual funds would put it into the markets again, and thus be ultimately inflationary. It would probably have to go into newly issued government bonds, which is to say, into a money black hole.

But the idea of spreading around capital and its income stream is very interesting. It is a far better idea than a simple UBI, which is structured as a sort of pittance handed out to keep the jobless from gathering into mobs with pitchforks. As we enter an economic era where capital is ever more dominant, through its comprehensive ability to generate economic value with ever fewer workers, the whole economic system needs to be rethought, with an eye to the middle class, not just the homeless and jobless. We already have vast pension funds and mutual funds, which have spread around the income flows from capital, if not taken effective control of the system from capitalists of the traditional variety. We already tax income and capital gains and inheritances to divert some of those gains to the common good. More of that kind of redistribution, of both capital and its proceeds, needs to happen in order to achieve the economic justice and stable future that Albus seeks.

• A criminologist takes a look at our president ... the dark triad.
• Who are the oligarchs?
• MAGA movie.
• Steps to fascism.
• Oligarchy in business isn't so good either.
• What does a financial risk manager say about climate change? $120 per ton of carbon.
• Economic democracy.

Cells Put Their Best Face Outward

Structure and function of the flippase enzyme.

This dates me a little, but when I was in grad school, the fluid bilayer hypothesis of membrane lipids was still new and exciting. Now canonical, it proposed that cellular membranes have no more structure than a soap bubble, being flat fluids of phospholipids that self-organize into a bilayer with two leaflets, each leaflet keeping its polar or charged head groups out towards aqueous solution, and their lipid tails on the inside, facing the complementary leaflet. At our scale, it seems shockingly fragile and structure-less. But at the micro scale, it is a pretty tough affair. Typical membranes are about 5 nm thick, which seems negligible, but it takes a protein at least 7 alpha helical turns, or 25 amino acids, to span it. Given that the fatty tail length is freely adjustable, as is the chemical nature and charge of the head groups, evolution has evidently optimized the thickness of membranes to provide an optimal tradeoff of structure and lightness. They are tougher than they look.

In this microscopic technique, cells are frozen and cleaved sideways, causing some of the membranes to split along their inner leaflet boundaries. This highlights the proteins and other material embedded within them. Note at the top that a small portion of the plasma membrane of this cell has a quasi-crystalline raft of proteins- a sign of active signalling taking place.

Membranes are also chemically tough, impervious to charged molecules due to their fatty interior. These features made membranes incredibly successful- one of the key foundations of life. Eukaryotes developed a whole second frontier of membranes, as internal organelles like the nucleus, endoplasmic reticulum, golgi, lysozome, and mitochondria. Mitochondria particularly use the imperviousness of membranes to set up complex charge and chemical asymmetries, to serve as batteries, storing up electromotive force from respiration of food and using it to synthesize ATP.

But it turns out that there are some forms of structure amid all the fluidity of the fluid bilayer. There are the proteins, of course, which can organize into crystalline rafts, or hook onto cell walls (in plants and bacteria) or cytoskeletal supports to enforce overall cell shape. There are features of composition that can make membranes more stiff, such as using more rigid, more saturated lipid tails, or having more cholesterol, which serves as a plate-like stiffener. And it also turns out that the two sides of membranes can have markedly different compositions, another indication of just how stable and tough these tiny structures are.

A recent paper revealed the structure of an enzyme (flippase) that helps to enforce the asymmetry of composition between the inner and outer leaflets of eukaryotic plasma membranes. Why would such asymmetry exist? The reasons are not all clear, really. One aspect is the charge imbalance, whereby the inner (cytoplasmic) leaflet has more heavily charged phospholipids. There could also be defense issues, particularly among bacterial, which might want to present certain lipid head groups externally, and use other ones internally. Another is signaling, where certain phospholipids are chemically modified to serve as protein attachments and other forms of signaling, and thus need to be on the correct side of the wall. One prominent example is phosphotidylserine, which is usually kept on the inner leaflet. During cell suicide, (apoptosis), however, the (flippase) enzymes that keep it there are cleaved and disabled, while other enzymes (scramblases) that degrade the membrane composition asymmetry are activated, causing phosphatidylserine to be shown on the cell's outside, which is in turn a signal to traveling macrophages to attack and eat that cell.

So flippases spend their lives scavanging phophotidylserine from the outer membrane leaflet and transferring it to the interior leaflet, constituting one sign to the outside that yes, I am still alive. The process violates the concentration gradient of phosphatidylserine, so needs energy, which comes in as ATP. We end up with a rather complex two protein system that itself has to be consistently oriented the right way in the plasma membrane, cleaves ATP, phosphorylates itself briefly, grabs phosphatidylserine specifically from the outer leaflet of the membrane, and then transports it across to the inner membrane.

This schematic illustrates the enzymatic cycle. The phosphatidylserine to be transported is at bottom, in green, on the external face of the membrane. A complex ATP=>ADP cycle dramatically alters the shape of the top of the enzyme on the cytoplasmic face, which at the E2P step is propagated down to a gap which opens between the two proteins- the portions colored purple and beige, which are situated in the membrane. This lets a phosphatidylserine to slip into a pocket that binds it selectively, after which the phosphate leaves the upper part, the enzyme recloses, and the phosphatidylserine is released to the other face of the membrane.

This structure was arrived at with the new techniques of electron microscopy that have allowed protein structures to be determined without crystallization, a development that has been particularly beneficial for membrane proteins that tend to be very hard to crystallize. The project also used a series of ATP and phosphatidylserine analogs that helped freeze the proteins in certain conformations through the reaction cycle, providing the data that informs the model above.

A closeup of the phosphatidylserine binding site, the lipid tails pointing upward. Ther are numerous amino acid side chains from the protein (such as asparagine (N) 353, serine (S) 358, etc. that coordinate the phosphatidylserine specifically, making this a transporter almost exclusively for this phospholipid alone. Other hydrophobic side chains such as phenylalanine (F) 107 and 368 form congenial interactions with the lipid tails.

Binding of phosphatidylserine is specific, but it can not be very strong, since the point of the reaction cycle is to release it again rapidly. Once binding has established specificity, it induces dephosphorylation, which then induces further conformation changes that lock the outward access of the phospholipid and destabilize its binding to the protein.

A cross-section of the full structure (right), and schematic showing (left) the series of structural elements of the two proteins of the transporter (CDC50A, now called TMEM30A in red, and ATPA1, the ATPase, in all the other colors.) The full structure (with no phospholipid or ATP present) has the ATPase on a large domain sticking out into the cytoplasm, and the key phosphatidylserine binding cleft (between the purple and beige sections, buried in the membrane.

It is wonderful to live in an age when such secrets of life, once utterly unsuspected, and then veiled in unreachable technical obscurity, are revealed in mechanistic detail.

• A voyage through the cytoplasm. Warning- extreme colors.
• What an anti-corruption platform looks like.
• Whither Afghanistan?
• The pending impeachment, cont.
• The birds are not happy.
• What is earth's carrying capacity?

Goal: One Billion

The Earth can't take 10 billion people. 

We have environmental and cultural problems at all scales, from the local to the global. From water shortages, drought, plastic pollution, overfishing, and species extinction, to global warming, authoritarianism, social fraying, anti-immigrant fervor, and gridlocked traffic and real estate markets. There is a common thread, which is that there are way too many people. We have (at least in some places) remediated some of the worst practices we used to take for granted, like killing whales for oil, using explosives for fishing, or dumping chemical wastes into rivers and soils. But there are are few practical ways to remediate our carbon emissions, water scarcity, or need for vast farmlands. We need to take a long look in the mirror and realize that the Earth can't take it, and we are the problem- the shear number of us.

Consider the range of problems like housing costs gone wild, traffic choked to a standstill, rising education costs and competition, and political gridlock. Are these related to overpopulation as well? I think very much so. Real estate is self-explanatory. As the old saying goes, they aren't making more land. Even while plenty of land is worthless, the need for people to live near other people means that we need to live together in what have become increasingly choked megalopolises. While rich metropolises like San Francisco and London struggle with traffic congestion and decaying public services, poorer ones like Lagos, Sao Paulo, and Mumbai had few services to start with and attract ever widening circles of destitute slums.

Lagos

A deeper issue is why our political systems are breaking down as well. Public services are decaying for a reason, which is that solidarity has weakened. Half of the US electorate has checked out of communal projects of good governance, rational and positive foreign policy, and caring for others. After two centuries of extraordinary growth, first sponsored especially in the US by a marvelously depopulated New World, and then again by bounding over technological frontiers such as fossil fuels, electricity, and the green revolution, we seem to have reached a general growth plateau, (barring development of robots who will do everything for us, but burn ever more fuel in doing so), and the expansive mood has ground to a halt. One consequence is that the elites of the culture, principally the rich, no longer subscribe to an egalitarian ethic. Growth can not be relied on to lift all boats, rather it is now every class for itself. Which class wins, when money runs politics and the media, and has been turned into "free speech" by the supreme court, is obvious.

It used to be, in the "population bomb" 1970's, that we thought that famine would be the limit on population. But it turns out that, given enough fossil fuel inputs for fertilizer production, machinery, and clearing new arable land, plus a green revolution in crop breeding, food is not the limiting factor. It is a thousand other things that we are doing to the biosphere and to our societies. The tide against immigrants is clearly borne of fear, that the number of the poor who want to flee their wretched conditions is essentially limitless, and thus that prosperous countries, i.e. Europe and the US, can not offer the relatively free immigration conditions they have heretofore. The US gained vast goodwill throughout the world over the last couple of centuries by admitting countless immigrants and playing a central role in many of the technological improvements that have allowed populations to grow everywhere.

But that process seems to have reached an end point. We have picked much of the low-hanging fruit, and have come up against insurmountable barriers. Fusion power has not happened. Space colonization is completely impractical. Even electricity storage is presenting tremendous difficulties, making a large scale switch to renewable electricity virtually impossible. And the biosphere is being degraded every day. We have come up against Malthusian limits that are more subtle than famine, but need to be heeded, lest we relentlessly immiserate ourselves.

There are two general political responses to all this. The Left response is to cooperate as best we can and tighten our belts to fit in a few billion more. Open borders, save the children, conserve water and reduce electricity usage, so that all can have at least a share of whatever resources are left. The Right response is to deny that there are significant ecological limits, cast whatever limits there are in economic terms and compete to take what we can while we can, and devil take the hindmost. Neither response is very forward-looking. One can make the argument that development is the only proven way to reduce demographic growth. Therefore, we should promote development, and bring everyone up to first world standards of resource consumption, which will in turn bring birth rates down to what in Europe and Japanare less than replacement rates. But the Earth can't take that policy either. Global heating is already having dire effects. The biosphere is already decimated and impoverished.

Thus we need an even more impractical, impolitic, and direct strategy, which is to aim to dramatically reduce the human population. A rigorously enforced one-child policy over three generations would get us from the current 7+ billion people to 1 billion, which, I think, given the current technological state, is reasonably sustainable. China did an amazing thing with its one-child policy, nipping in the bud its most significant problem- that of vastly too many people for its capabilities and resources. China is now reaping the rewards of that policy, though it hardly went far enough, and China remains heavily overpopulated and rapacious as it ascends the ladder of development.

If combating climate change is a problem from hell, structurally diffuse and resistant to responsible policy, then population control is far more so. National power is to a great extent dependent on economic and population size. We have for centuries had a mania for growth, embedded in every fiber of our economic policy and national outlook. We are Malthusian to the core, and our major religions are even worse offenders, propagating the most Darwinian of reproduction policies, even while they so ironically decry Darwin's intellectual bequests. No, it is not an easy problem. But at very least, we should not fear declining birth rates as some existential catastrophe and sign of general decline. No, they should be welcomed as the least we can do, and a small part of our path to a sustainable future, for ourselves and for the biosphere that is our home.

• Jupiter flyby.
• Accounting for Iraq.
• What the Kochs and their ilk have wraught.
• Are the Taliban more trustworthy than Donald Trump?
• Have richer people have become more handsome?
• Bonus quote of the week, from "If We Can Keep It", by Michael Tomask.

We are in trouble. Our political culture is broken, but it is not broken for the reasons you often read that it's broken- because Washington is 'dysfunctional' or because politicians have no 'will'. No. It's broken because some people broke it. It was broken by the people who pushed the economic theory on the rest of us that has driven trillions of dollars that were once in middle-class people's pockets to the comparative few at the very top. Who refused to invest in the country anymore. Who will not even negotiate real investment. Who have been telling us for years that the market will take care of all our needs, while the market has in fact left thousands of towns and communities strafed and full of people addicted to drugs- the drugs, by the way, tht the same free market is pumping out in vastly greater quantities, and for vastly greater profits, than it did twenty years ago. And who have built up a parallel media universe in which any of these commonsense assertions are dismissed as socialist, and in which anyone who doesn't endorse the thesis of Donald Trump's greatness is denounced as un-American. 

They broke it. They broke it to gain power and to remake society in a way that was less communitarian, explicitly less equal, than the society we were building from 1945 to 1980. And- let me not forget this part- less democratic. I wrote earlier of Donald Trump's contempt for our institutions, our processes, put another way, for the democratic allocation of power. Many observers (me included, sometimes) have wondered why this didn't make Republicans recoil. The typical explanation has to do with fear of his base, but I've come to believe that the simplest explanation is the best: They didn't recoil because they're not especially bothered. They find him embarrassing at times, and they disagree with him here and there, but his demagogic approach doesn't really trouble them on the whole. They- not all of them, but certainly a critical mass of elected officials, operatives, and billionaires- no longer want to compete with and merely defeat liberalism on a level democratic playing field. They want to destroy it. This is why they do things like aggressive gerrymandering, the voter suppression laws, the attemt to change the way we elect senators, the blocking of Merrick Garland- all of which preceded Trump. They want to change the rules so they they never lose. And if destroying liberalism requires breaking the system- as it surely does- then so be it as far as they're concerned.

Altruism Through Execution

Does our good behavior arise from artificial selection against norm-violators?

This is a companion piece to the prior "Altruism Through Genocide", which presented a group selection theory for our human moral nature. In that piece, group cohesiveness was the driving force that benefitted those cooperative people who could effectively conduct warfare to exterminate their enemies, who were, on balance, less effective in their in-group altruism/cooperation.

Now we are considering a new book, "The Goodness Paradox", by Richard Wrangham, which presents an alternative, only slightly less grisly, theory. The book generally argues that humans show many signs of selective domestication- a syndrome common in animals that we have domesticated- of arrest in many aspects of development, towards more juvenile characteristics, such as docility, lower aggression, floppy ears, white fur patches, and skeletal and especially facial juvenilization. That much is clear. Despite our love of warfare, we are on balance, and compared to our chimpanzee relatives and most other wild creatures, far less violent, less reactive, and far more effectively cooperative. This is not just a cognitive development, but an emotional change and a deep change to our moral natures. So who or what did the domestication?

Remember in Western movies how good it feels when the bad guy gets killled? It is an archetype of deep power, and we hardly think about its moral and genetic implications. Chimpanzees don't have this moral sense, as far as we know. Wrangham cites various experiments and natural observations to show that no matter how terrible some chimpanzees are, the others of their group will not or can not cooperate effectively to ostracize or disable them. It just isn't done. In the modern world, we have grown squeemish about capital punishment, but primitive cultures had no prisons, thus pervasively practiced ostracism or death as the only practical punishments for serious crimes and unredeemable people. It turns out to have been common for communities (typically the men of the group) to gang up on a member who got egregiously out of line and kill that person. Wrangham places this development at roughly the emergence of modern Homo sapiens, two to three hundred thousand years ago. Thus there is quite a bit of speculation about the relative backwardness of Neanderthals, who had much more limited cooperative capacities, though being roughly as intelligent as moderns, and having many advanced characteristics such as complex stone technology and control of fire.

For a Few Dollars More ... Clint Eastwood hunts down the bad men.

The development of advanced hunting and killing technologies made each person, and especially each man, in primitive human bands quite powerful. But even more important was language and great scope it offered to organize, to collude with and against others, This created enormous incentives to maintain a good reputation. Primitive societies are characterized by an almost pathological fear of rising above one's peers- there is a notable lack of ambition, for the very good reason that the group is all-powerful, and signs that one wants to rule others, abuse them, or collude against them, are all treated very harshly. The idea, then, is that the unique human ability and motivation to detect and eliminate threats inside the group led to a process of natural selection that quickly domesticated the species in superficial metrics of reactive aggression, while advancing our organizational, deceptive, and language capabilities, which have made us by far the most deadly species when it comes to organized hunting and warfare.

The explains rather easily the intense motivation that teens have to conform to their groups, to party, to bond and seek power, and to be forever uncertain about their status. It explains conventionality. But does it explain the nature of the morality that human groups generally express? The posses that hunt down criminals, and the modern state apparatus that does the same on a more legalistic basis, the value we put on altruism and kindness? Not quite. For example, the morality could have become one of extermination, where leaders would use all their guile to eliminate, one by one, each of the other males of the group, thus gaining all the females for themselves. This harem structure is common among other animals, and has occurred occasionally in humans in historical times. But it has obvious defects. If such an endpoint is common knowledge, then coalitions would be difficult to build, though perhaps not necessary since even crude technologies allow relatively easy killing, even one-on-one, given a small amount of planning. More importantly, however, such an endpoint would leave the group very weak relative to other groups.

So both overall hypotheses are relevant, I think, the group selection hypothesis and the execution hypothesis, to explain the complexity and explosiveness of our group relations, and the generally pro-social and cooperative instincts that form our group values most of the time. There is a complex calculation to be made, in light of the status of the whole group, with regard to the value of each person, each one of whom would on the face of it benefit the group in any outward encounter, but who might also be so disruptive and destructive of group cohesion as to instead be a net negative asset. Wrangham unfortunately finesses this problem, of the actual content of our moral group ethics, and suggests instead that pure relativism prevails- that our groupishness / conformity / docility is genetic, but our morals are not, and become whatever the leading (male) coalition says they should be. One can grant that human groups have adopted very unusual moral codes, like sacrificing their own children into volcanoes, or conducting constant ritual slaughter as the Aztecs did, or making a fetish of celibacy, as the Buddhist and Catholic theocracies do. Nevertheless, there is a core of cooperativity and deep-seated conceptions of right and wrong (including the rightness of killing when the target is damaging the group, or is an enemy outside the group) that demand a better evolutionary explanation, one that focuses on the value of the group as a unit.

Wrangham also finesses another issue- that of eugenics. His theory is essentially eugenic. We have been our own selective agents, however unintentionally. In an afterword, he gives a brief case against capital punishment. Though it has had such positive effects by his theory, capital punishment is now unnecessary, since we have prisons and other mechanisms of social control. Yet the deeper issue is whether genetic selection is still needed to bias reproduction towards the well-behaved and away from the aggressive, psychopathic, misogynistic, and congenitally sleazy. Not a word on this, since it is a far more explosive and difficult issue, not to mention politically tinged at the moment.

• Mind control in North Korea.
• Insurers are promoting, rather than fighting, ransomware. Make sure to keep backups!
• "This administration is ultimately driven by sociopathic disregard for our future"
• North Carolina finally puts a nail in gerrymandering.
• Figuring out common threads in the diverse genetics of autism.
• Notes by Veblen.
• Just because ....

Good Fences Make Good Neighbors

Ephrins are cell surface receptor-ligand pairs that create boundaries throughout the body.

Ever wonder how organs form and stay distinct? All our DNA is the same, yet the cells it gives rise to differentiate, migrate all over the place, through each other's neighborhoods, and then form various distinct tissues, including 700 unique muscles. Cells don't have eyes or brains, so the mechanism is more like ants following pheromone trails rather than an architect following a masterplan of the body. It is a far more complicated story than anyone understands at the moment, but some of the actors are known.

There are a lot of cell adhesion proteins, such as integrins, cadherins, NCAMs. and selectins. But adhesion can't be the whole story, lest every cell adhere to every other. That is where Ephrins come in, which are a family of cell surface molecules which typically have repulsive effects, when they find and bind to their receptors (called Eph). They are widely used in development and mature tissues to keep proper boundaries and help guide that way for migrating cells and cell processes.

It has long been known that if you dissociate embryonic tissue and allow the cells to float about, they will re-associate in an organized (though far from perfect) way, like sticking to like, with boundaries forming between those from different tissues. This indicates the power of selective adhesion and repulsion to help cells position themselves, which operate in conjunction with other systems that keep their identity straight- what organ or tissue they are supposed to be. Each such cell type expresses its own complement of adhesion and repulsion surface molecules, forming part of the code that helps it to find and keep its place, as well as deciding whether to continue dividing and moving, or to stop when the local structure has reached its expected proportions.

On the left, one tissue expressing an Eph receptor keeps separate from another tissue expressing the  Ephrin ligand which it recognizes, thanks to repulsive effects that counter-act several other adhesive interactions. On the right are a few of the details of the mechanism. Each side of the Ephrin-Eph interaction can tell its cell that an encounter has happened.

These mechanisms take another quantum leap in the nervous system, which involves a particularly high level of cell migration during development, and pathfinding of dendrites and axons throughout life. Axons travel huge distances, both in the central nervous system and in the peripheral nervous system, using adhesion and repulsion cues all along the way. Ephrins are used dynamically to guide growth cones. For example in the serotonin network, serotonin neurons traveling from the dorsal raphae (B7) to the forebrain olfactory bulb pass by the amygdala. Do they stop there to extend some input fibers? Normally they do. But not if the amygdala has been genetically altered (in these mice) to express the EphrinA ligand, which pairs with the EphrinA5 receptor that is normally expressed on these neurons.

EphrinA is typically expressed in the hypothalamus, keeping serotonergic projections from the dorsal raphae (on the left, B7) from innervating. But if it is expressed (in mice) in the amygdala, it prevents that normal innervation as well, as these neurons travel during development into the forebrain, in this case the olfactory bulb (OB).

• Review of muscle development.
• Review of cell migration.
• Rising Asia.
• Sweden is more entrepreneurial than the US- because incumbents have less power and people have more power.