Monthly Archives: August 2018

Separate Top-Down, Bottom-Up Brain Credit

Recently I decided to learn more about brain structure and organization, especially in humans. As modularity is a key concept in complex systems, a key question is: what organizing principles explain which parts are connected how strongly to which other parts? (Which in brains is closely related to which parts are physically close to which other parts.) Here are some things I’ve learned, most of which are well known, but one of which might be new.

One obvious modularity principle is functional relation: stuff related to achieving similar functions tends to be connected more to each other. For example, stuff dealing with vision tends to be near other stuff dealing with vision. But as large areas of the brain light up when we do most anything, this clearly isn’t the only organizing principle.

A second organizing principle seems clear: collect things at similar levels of abstraction. The rear parts of our brains tend to focus more on small near concrete details while the front parts of our brain tend to focus on big far abstractions. In between, the degree of abstraction tends to change gradually. This organizing principle is also important in recent deep learning methods, and it predicts the effects seen in construal level theory: when we think about one thing at a certain level of abstraction and distance, we tend to think of related things at similar levels of abstraction and distance. After all, it is easier for activity in one brain region to trigger activity in nearby regions. The trend to larger brains, culminating in humans, has been accompanied by a trend toward larger brain regions that focus on abstractions; we humans think more abstractly than do other animals.

A key fact about human brain organization is that the brain is split into two similar but weakly connected hemispheres. This is strange, as usually we’d think that, all else equal, for coordination purposes each brain module wants to be as close as possible to every other module. What organizing principle can explain this split?

There seems to be a lot of disagreement on how best to summarize how the hemispheres differ. Here are two summaries:

The left hemisphere deals with hard facts: abstractions, structure, discipline and rules, time sequences, mathematics, categorizing, logic and rationality and deductive reasoning, knowledge, details, definitions, planning and goals, words (written and spoken and heard), productivity and efficiency, science and technology, stability, extraversion, physical activity, and the right side of the body. … The right hemisphere specializes in … intuition, feelings and sensitivity, emotions, daydreaming and visualizing, creativity (including art and music), color, spatial awareness, first impressions, rhythm, spontaneity and impulsiveness, the physical senses, risk-taking, flexibility and variety, learning by experience, relationships, mysticism, play and sports, introversion, humor, motor skills, the left side of the body, and a holistic way of perception that recognizes patterns and similarities and then synthesizes those elements into new forms. (more)

The [left] is centered around action and is often the driving force behind risky behaviors. This hemisphere heavily relies upon emotional input leading it to make brash and uncalculated decisions. … The [right] … relies primarily on critical thinking and calculations to reach its decisions.[11] As such the conclusions reached by the [right] often result in avoidance of risk taking behaviors and overall inaction. … . In environments of scarcity, … taking risks is the foundational approach to survival. … However, in environments of abundance, as humans have observed, it is far more likely to die to damaging stimuli. … In areas of prosperity, … [right] domination is prevalent. … In areas of scarcity where cold and limited food are concerns [left] domination is prevalent. (more)

After reading a bit, I tentatively summarize the difference as: the right hemisphere tends to work bottom-up, while the left tends to work top-down. (In a certain sense of these terms.) Inference tends to be bottom-up, in that we aggregate many complex details into inferring fewer bigger things. For example, in a visual scene we start from a movie of pixels over time, and search for sets of possible objects and their motions that can make sense of this movie. In contrast, design tends to be top-down, in that to design a path to get us from here to there, we start with an abstract description of our goal, such as the start and end of our path, and then search for concrete details that can achieve that goal.

The right hemisphere tends to watch, mostly looking out to infer danger, while the left tends to initiate action, and thus must design actions. The right has a wide span of attention, watching the world looking out for surprises, most of which are bad, while the left has a narrow focus of attention, which supports taking purposive action, from which it expects good results. So the right hemisphere tends to do bottom-up processing, while the left does top-down processing.

In bottom-up processing, to explain one set of details one must consider many possible sets of abstractions, while in top-down processing, one set of goals gives rise to many possible specific details to achieve those goals. As a result, we should expect bottom-up work to need more resources at high abstraction levels, while top-down work needs more resources at detailed levels. And it fact, this is what we see in brain structure: the right hemisphere has a larger front abstract end, while the left hemisphere has a larger back concrete end. Our brains are “twisted” in this predicted way.

Why would it make sense to separate bottom-up from top-down thinking? A key problem in the design of intelligent systems is that of how to distribute reward or credit. And a common solution to this problem is to create a standard of good in one part of the system, today often called a “cost function” in AI circles, and then reward or credit other parts of the system for getting closer to achieving that standard. In inference, the standard is typically some form of statistical fit: how well a model of the world predicts the data that one sees. In design, the standard is more naturally centered on goals: how well does a plan achieve its goals?

Top-down and bottom-up styles of processing seem to me to use incompatible systems of credit assignment. That is, it seems hard to design a system that simultaneously credits abstract world scenarios for predicting details seen, while also rewarding details chosen for achieving abstract goals. Credit assignment systems work better when they have a single common direction in which credit flows. One can allow multiple design goals at a similar high level of abstraction, as then the design process can give credit for synergy, and search for details that satisfy all the goals. And one can allow multiple sources of detail, like sight and sound, and combine their statistical credit to infer which objects are moving how. But it seems hard to combine the two systems of credit.

And so that is my proposal for a third organizing principle of brains: separate bottom-up from top-down systems of credit assignment. I haven’t heard anyone else say this, though I wouldn’t be surprised if someone has said it before.

Added 1Sep: The main risk of mixing credit directions is creating self-supporting credit cycles not well connected to real needs. This may be why the connections between the two hemispheres are mostly inhibitory, reducing activity.

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My Market Board Game

From roughly 1989 to 1992, I explored the concept of prediction markets (which I then called “idea futures”) in part via building and testing a board game. I thought I’d posted details on my game before, but searching I couldn’t find anything. So here is my board game.

The basic idea is simple: people bet on “who done it” while watching a murder mystery. So my game is an add-on to a murder mystery movie or play, or a game like How to Host a Murder. While watching the murder mystery, people stand around a board where they can reach in with their hands to directly and easily make bets on who done it. Players start with the same amount of money, and in the end whoever has the most money wins (or maybe wins in proportion to their winnings).

Together with Ron Fischer (now deceased) I tested this game a half-dozen times with groups of about a dozen. People understood it quickly and easily, and had fun playing. I looked into marketing the game, but was told that game firms do not listen to proposals by strangers, as they fear being sued later if they came out with a similar game. So I set the game aside.

All I really need to explain here is how mechanically to let people bet on who done it. First, you give all players 200 in cash, and from then on they have access to a “bank” where they can always make “change”:

Poker chips of various colors can represent various amounts, like 1, 5, 10, 25, or 100. In addition, you make similar-sized cards that read things like “Pays 100 if Andy is guilty.” There are different cards for different suspects in the murder mystery, each suspect with a different color card. The “bank” allows exchanges like trading two 5 chips for one 10 chip, or trading 100 in chips for a set of all the cards, one for each suspect.

Second, you make a “market board”, which is an array of slots, each of which can hold either chips or a card. If there were six suspects, an initial market board could look like this:

For this board, each column is about one of the six suspects, and each row is about one of these ten prices: 5,10,15,20,25,30,40,50,60,80. Here is a blow-up of one slot in the array:

Every slot holds either the kind of card for that column, or it holds the amount of chips for that row. The one rule of trading is: for any slot, anyone can swap the right card for the right amount of chips, or can make the opposite swap, depending on what is in the slot at the moment. The swap must be immediate; you can’t put your hand over a slot to reserve it while you get your act together.

This could be the market board near the end of the game:

Here the players have settled on Pam as most likely to have done it, and Fred as least likely. At the end, players compute their final score by combining their cash in chips with 100 for each winning card; losing cards are worth nothing. And that’s the game!

For the initial board, fill a row with chips when the number of suspects times the price for that row is less than 100, and fill that row with cards otherwise. Any number of suspects can work for the columns, and any ordered set of prices between 0 and 100 can work for the rows. I made my boards by taping together clear-color M512 boxes from Tap Plastics, and taping printed white paper on tops around the edge.

Added 30Aug: Here are a few observations about game play. 1) Many, perhaps most, players were so engaged by “day trading” in this market that they neglected to watch and think enough about the murder mystery. 2) You can allow players to trade directly with each other, but players show little interest in doing this. 3) Players found it more natural to buy than to sell. As a result, prices drifted upward, and often the sum of the buy prices for all the suspects was over 100. An electronic market maker could ensure that such arbitrage opportunities never arise, but in this mechanical version some players specialized in noticing and correcting this error.

Added 31Aug: A twitter poll picked a name for this game: Murder, She Bet.

Added 9Sep: Expert gamer Zvi Mowshowitz gives a detailed analysis of this game. He correctly notes that incentives for accuracy are lower in the endgame, though I didn’t notice substantial problems with endgame accuracy in the trials I ran.

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Spaceship Earth Explores Culture Space

Space: the final frontier. These are the voyages of the starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before. (more)

Many love science fiction stories of brave crews risking their lives to explore strange new spaces, stories much like the older adventure stories about European explorers risking their lives centuries ago to explore new places on Earth. (Yes, often to conquer and enslave the locals.) Many lament that we don’t have as many real such explorer stories today, and they say that we should support more human space exploration now in order to create such real heroic exploration stories. Even though human space exploration is crazy expensive now, and offers few scientific, economic, or humanity-survival gains anytime soon. They say the good stories will be worth all that cost.

Since Henry George first invoked it in 1879, many have used the metaphor of Spaceship Earth to call attention to our common vulnerability and limited resources:

Spaceship Earth … is a world view encouraging everyone on Earth to act as a harmonious crew working toward the greater good. … “we must all cooperate and see to it that everyone does his fair share of the work and gets his fair share of the provisions” … “We travel together, passengers on a little space ship, dependent on its vulnerable reserves of air and soil.” (more)

In this post, I want to suggest that Spaceship Earth is in fact a story of a brave crew risking much to explore a strange new territory. But the space we explore is more cultural than physical.

During the industrial era, the world economy has doubled roughly every fifteen years. Each such doubling of output has moved us into new uncharted cultural territory. This growth has put new pressures on our environment, and has resulted in large and rapid changes to our culture and social organization.

This growth results mostly from innovation, and most innovations are small and well tested against local conditions, giving us little reason to doubt their local value. But all these small changes add up to big overall moves that are often entangled with externalities, coordination failures, and other reasons to doubt their net value.

So humanity continues to venture out into new untried and risky cultural spaces, via changes to cultural conditions with which we don’t have much experience, and which thus risk disaster and destruction. The good crew of Spaceship Earth should carefully weigh these risks when considering where and how fast to venture.

Consider seven examples:

  1. While humans seem to be adapting reasonably well to global warming, we risk big lumpy disruptive changes to Atlantic currents and Antarctic ice. Ecosystems also seem to be adapting okay, but we are risking big collapses to them as well.
  2. While ancient societies gave plenty of status and rewards to fertility, today high fertility behaviors are mostly seen as low status. This change is entwined with complex changes in gender norms and roles, but one result is that human fertility is falling toward below replacement in much of the world, and may fall much further. Over centuries this might produce a drastic decrease in world population, and productivity-threatening decreases in the scale of world production.
  3. While the world has become much more peaceful over the last century, this has been accompanied by big declines in cultural support for military action and tolerance for military losses. Is the world now more vulnerable to conquest by a new military power with more local cultural support and tolerance for losses?
  4. Farmer era self-control and self-discipline has weakened over time, in part via weaker religion. This has weakened cultural support for work and cultural suspicion of self-indulgence in sex, drugs, and media. So we now see less work and more drug addiction. How far will we slide?
  5. Via new media, we are exploring brave new worlds of how to make friends, form identities, achieve status, and learn about the world. As many have noted, these new ways risk many harms to happiness and social capital.
  6. Innovation was once greatly aided by tinkering, i.e., the ability to take apart and change familiar devices. Such tinkering is much less feasible in modern devices. Increasing regulation and risk aversion is also interfering with innovation. Are we as a result risking cultural support for innovation?
  7. Competition between firms has powered rapid growth, but winning bets on intangible capital is allowing leading firms to increasingly dominate industries. Does this undermine the competition that we’ve relied on so far to power growth?

The most common framing today for such issues is one of cultural war. You ask yourself which side feels right to you, commiserate with your moral allies, then puff yourself up with righteous indignation against those who see things differently, and go to war with them. But we might do better to frame these as reasonable debates on how much to risk as we explore culture space.

In a common scene from exploration stories, a crew must decide if to take a big risk. Or choose among several risks. Some in the crew see a risk as worth the potential reward, while others want to search longer for better options, or retreat to try again another day. They may disagree on the tradeoff, but they all agree that both the risks and the rewards are real. It is just a matter of tradeoff details.

We might similarly frame key “value” debates as reasonable differing judgements on what chances to take as spaceship Earth explores culture space. Those who love new changes could admit that we are taking some chances in adopting them so quickly, with so little data to go on, while those who are suspicious of recent changes could admit that many seem to like their early effects. Rather than focus on directly evaluating changes, we might focus more on setting up tracking systems to watch for potential problems, and arranging for repositories of old culture practices that might help us to reverse changes if things go badly. And we might all see ourselves as part of a grand heroic adventure story, wherein a mostly harmonious crew explores a great strange cosmos of possible cultures.

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If The Future Is Big

One way to predict the future is to find patterns in the past, and extend them into the future. And across the very long term history of everything, the one most robust pattern I see is: growth. Biology, and then humanity, has consistently grown in ability, capacity, and influence. Yes, there have been rare periods of widespread decline, but overall in the long run there has been far more growth than decline. 

We have good reasons to expect growth. Most growth is due to innovation, and once learned many innovations are hard to unlearn. Yes there have been some big widespread declines in history, such as the medieval Black Death and the decline of the Roman and Chinese empires at about the same time. But the historians who study the biggest such declines see them as surprisingly large, not surprisingly small. Knowing the details of those events, they would have been quite surprised to see such declines be ten times larger than as seen. Yes it is possible in principle that we’ve been lucky and most planets or species that start out like ours went totally extinct. But if smaller declines are more common than bigger ones, the lack of big but not total declines in our history suggests that the chances of extinction level declines was low. 

Yes, we should worry about the possibility of a big future decline soon. Perhaps due to global warming, resource exhaustion, falling fertility, or institutional rot. But this is mainly because the consequences would be so dire, not because such declines are likely. Even declines comparable in magnitude to the largest seen in history do not seem to me remotely sufficient to prevent the revival of long term growth afterward, as they do not prevent continued innovation. Thus while long-term growth is far from inevitable, it seems the most likely scenario to consider.

If growth is our most robust expectation for the future, what does that growth suggest or imply? The rest of this post summarizes many such plausible implications. There far more of them than many realize. 

Before I list the implications, consider an analogy. Imagine that you lived in a small mountain village, but that a huge city lie down in the valley below. While it might be hard to see or travel to that city, the existence of that city might still change your mountain village life in many important  ways. A big future can be like that big city to the village that is our current world. Now for those implications:   Continue reading "If The Future Is Big" »

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My Kind of Atheist

I think I’ve mentioned somewhere in public that I’m now an atheist, even though I grew up in a very Christian family, and I even joined a “cult” at a young age (against disapproving parents). The proximate cause of my atheism was learning physics in college. But I don’t think I’ve ever clarified in public what kind of an “atheist” or “agnostic” I am. So here goes.

The universe is vast and most of it is very far away in space and time, making our knowledge of those distant parts very thin. So it isn’t at all crazy to think that very powerful beings exist somewhere far away out there, or far before us or after us in time. In fact, many of us hope that we now can give rise to such powerful beings in the distant future. If those powerful beings count as “gods”, then I’m certainly open to the idea that such gods exist somewhere in space-time.

It also isn’t crazy to imagine powerful beings that are “closer” in space and time, but far away in causal connection. They could be in parallel “planes”, in other dimensions, or in “dark” matter that doesn’t interact much with our matter. Or they might perhaps have little interest in influencing or interacting with our sort of things. Or they might just “like to watch.”

But to most religious people, a key emotional appeal of religion is the idea that gods often “answer” prayer by intervening in their world. Sometimes intervening in their head to make them feel different, but also sometimes responding to prayers about their test tomorrow, their friend’s marriage, or their aunt’s hemorrhoids. It is these sort of prayer-answering “gods” in which I just can’t believe. Not that I’m absolutely sure they don’t exist, but I’m sure enough that the term “atheist” fits much better than the term “agnostic.”

These sort of gods supposedly intervene in our world millions of times daily to respond positively to particular prayers, and yet they do not noticeably intervene in world affairs. Not only can we find no physical trace of any machinery or system by which such gods exert their influence, even though we understand the physics of our local world very well, but the history of life and civilization shows no obvious traces of their influence. They know of terrible things that go wrong in our world, but instead of doing much about those things, these gods instead prioritize not leaving any clear evidence of their existence or influence. And yet for some reason they don’t mind people believing in them enough to pray to them, as they often reward such prayers with favorable interventions.

Yes, the space of possible minds is vast, as is the space of possible motivations. So yes somewhere in that space is a subspace of minds who would behave in exactly this manner, if they were powerful enough to count as “gods”. But the relative size of that subspace seems to me rather small, relative to that total space. And so the prior probability that all or most nearby gods have this sort of strange motivation also seems to me quite small. It seems a crazy implausible hypothesis.

Yes, the fact that people claim to feel that gods answer their prayers is, all else equal, evidence for that hypothesis. But the other obvious hypothesis to consider here is that people claim this because it comforts them to believe so, not because they’ve carefully studied their evidence. Long ago people had much less evidence on physics and the universe, and for them it was both plausible and socially functional to believe in powerful gods who sometimes responded to humans, including their prayers. This belief became deeply embedded in cultures, cultures which just do not respond very quickly or strongly to recent changes in our best evidence on physics and the universe. (Though they respond quickly enough to make up excuses like “God wants you to believe in him for special reasons.”) And so many still believe that gods answer prayers.

In conclusion, it isn’t crazy to think there are powerful gods far away in space or time, and perhaps close but far in causal connection. But it does seem to me crazy to believe in gods nearby who favorably answer prayers, but who also hide and don’t intervene much in world affairs. That hypothesis seems vastly less likely than the obvious alternative, of slowly updating cultures.

I expect my position to be pretty widely held among thoughtful intellectuals; can we find a good name for it? Prayer-atheists perhaps?

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Sanctimonious Econ Critics

The New Yorker review of Elephant in the Brain raved about Cents and Sensibility, by Gary Morson and Morton Shapiro, a book said to confirm that “intellectual overextension is often found in economics.” Others have similarly raved. But I don’t care much for this book, so let me explain why. (Be warned: this post is LONG.)

In its first sentence, the book declares its aim:

This book creates a dialogue between two fields that rarely have anything to say to each other: economics and the humanities. We mean to show how that dialogue could be conducted and why it has a great deal to contribute. (p.1)

Morson and Shapiro seem to want the sort of “dialogue” where one side talks and the other just listens. All but one chapter elaborates how economists should listen to the humanities, and the one remaining chapter is on how some parts of the humanities should listen to another part, not to economists. There’s only a two page section near the end on “What Humanists Can Learn From Economists,” which even then can’t resist talking more about what economists can learn:

Economists could learn from humanists the complexity of ethical issues, the need for stories, the importance of empathy, and the value of unformalizable good judgement. But humanists could also learn from economists how to think about scarce resources, about the nature of efficiency, and the importance of rational decision making. (p.261)

So what exactly can we economists learn? Continue reading "Sanctimonious Econ Critics" »

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