Whither experimental economics?

When I was applying to graduate school, I looked at three options: neuroscience, computer science, and economics. I had, effectively, done an economics major as an undergrad and had worked at an economic consulting firm. But the lack of controlled experimentation in economics kept me from applying and I ended up as a neuroscientist. (There is, of course, a very experimental non-human economics which goes by the name of ecology, though I did not recognize it at the time.)

I profess to being confused as to the lack of experimentation in economics, especially for a field that constantly tries to defend its status as a science. (Well, I understand: incentives, existing capabilities, and all that.)

A recent dissertation on the history of experimental economics – as opposed to behavioral economics – is enlightening:

“We were describing this mechanism and Vernon says, “You know, I can test this whether it works or not.“ I said, “What do you mean?“ And he says, “I’ll run an experiment.“ I said, “What the heck are you talking about? What do you do?“ And so he hauls seven or eight graduate students into a classroom. He ran the mechanism and it didn’t work. It didn’t converge to the equilibrium. It didn’t produce the outcomes the theory said it would produce. And I thought, okay. So back to [doing] theory. I don’t care; this doesn’t bother me.

It bothered Vernon a lot because we sat around that evening talking and he says, “Oh, I know what I did wrong.“ And he went back the next day and he hauled the students back in the room, changed the rules just a little bit in ways that the theory wouldn’t notice the difference. From our theory point of view, it wouldn’t have mattered. But he changed the rules a little bit and bang that thing zapped in and converged.“

The difference between the two experiments was the information shared with the test subjects. The first time around, the subjects wrote down their number on a piece of paper and then Smith wrote them up on the board. Then he asked the subjects to send another message and if the messages were the same twice in a row he would stop, since that stability would be interpreted as having reached equilibrium. But the messages did not stop the first time Smith had run the experiment a day earlier…

The fact that the experiment did not converge at the first attempt, but did at the second with a change of only one rule (the information structure available to the participants) not required by theory to make its prediction made a lasting impact on Ledyard.

And this is exactly why we do experiments:

[T]he theory didn’t distinguish between those two rules, but Vernon knew how to find a rule that would lead to an equilibrium. It meant he knew something that I didn’t know and he had a way of demonstrating it that was really neat.

In psychology and neuroscience, there are many laboratories doing animal experiments testing some sort of economic decision-making hypothesis, though it is debatable how much of that work has filtered into the economic profession. What the two fields could really use, though, are economic ideas about more than just basic decision-making. Much of economics is about markets and mass decisions; there is very animal experimentation of these questions.

(via Marginal Revolution)

Richard Lewontin: some perspectives on the sociology of ecology

There’s an interesting interview with Richard Lewontin over at the Evolution Institute.

First, he slags off Steven J Gould a bit:

RL: Now I should warn you about my prejudices. Steve and I taught evolution together for years and in a sense we struggled in class constantly because Steve, in my view, was preoccupied with the desire to be considered a very original and great evolutionary theorist. So he would exaggerate and even caricature certain features, which are true but not the way you want to present them. For example, punctuated equilibrium, one of his favorites. He would go to the blackboard and show a trait rising gradually and then becoming completely flat for a while with no change at all, and then rising quickly and then completely flat, etc. which is a kind of caricature of the fact that there is variability in the evolution of traits, sometimes faster and sometimes slower, but which he made into punctuated equilibrium literally. Then I would have to get up in class and say “Don’t take this caricature too seriously. It really looks like this…” and I would make some more gradual variable rates. Steve and I had that kind of struggle constantly. He would fasten on a particular interesting aspect of the evolutionary process and then make it into a kind of rigid, almost vacuous rule, because—now I have to say that this is my view—I have no demonstration of it—that Steve was really preoccupied by becoming a famous evolutionist.

And then his former advisor:

RL: Now, historically one of the most interesting—now I want to talk a little about the sociology of our science—Theodosius Dobzhansky, my professor and then greatest living evolutionary biologist…

DSW: Mr. “Nothing in biology makes sense except in the light of evolution…”

RL: Yeah, right. He was a very bad field observer. Theodosius Dobzhansky never, in his entire life, nor any of his students, me included—I would go out in the field with him, actually–ever saw a Drosophila pseudoobscura in its natural habitat…We didn’t know where they laid their eggs. We couldn’t have counted the number of eggs of different genotypes. How did we study Drosophila in the wild? We went out into the desert, into Death Valley, we moved into a little oasis, we went first to the grocery store, and bought rotten bananas. We mushed up the bananas with yeast till they fermented a bit, we dumped that into the paper containers, put it out in the field and the flies came to us…If I wanted to study evolutionary forces acting on some genetic polymorphism in Drosophila, I would go and look for some species of Drosophila where I could actually look at, perturb, and work with the actual breeding sites and egg laying sites and pick up larvae in nature and so on. And in fact there is such a group of Drosophila. They the cactophilic ones. There is a group [of scientists] from Texas and other places that studies the cactophilic Drosophila in an ecologically sensible way of going to the rot pockets and perturbing them, getting larvae out of them and so on. That group never acquired the prestige associated with the Dobzhansky school because—I don’t know why.

Lewontin is not normally my cup of tea, but this view is very interesting.

The unappreciated animals of science

Would you believe it – I actually forgot that I had a blog for a few of weeks. I guess I was busy?

If you don’t work on a particular organism, you tend to forget that each has its own history outside of the laboratory. Catherine Dulac has a great video wild-caught mice: whereas laboratory strains are sedentary, moseying about their cage without a care in the world, wild-caught mice are little ninjas, running around and jumping off the sides. These ain’t the same creatures.

eLife has a good series on the natural history of model organisms. Right now they have C. elegans, zebrafish, and E. coli, though I expect there will be more.

On nasty E. coli:

In 1884, the German microbiologist and pediatrician Theodor Escherich began a study of infant gut microbes and their role in digestion and disease. During this study, he discovered a fast-growing bacterium that he calledBacterium coli commune, but which is now known as the biological rock star that is Escherichia coliE. coli‘s relationship with a host literally begins at birth. Newborns are typically inoculated with maternal E. coli through exposure to her fecal matter during birth and from subsequent handling. Although perhaps disconcerting to ponder, this inoculation seems to be quite important. Indeed, E. coli becomes more abundant in the mother’s microbiome during pregnancy, increasing the chances of her newborn’s inoculation…

The external world was long thought to be so harsh as to preclude E. coli‘s growth outside of its host. While a tiny minority might eventually reach a new host, most cells were expected to eventually die. This is the basal assumption behind using the presence of E. coli as an indicator of fecal contamination. However, recent studies have shown that E. coli can, in fact, establish itself as a member of microbial soil, water, and plant-associated communities

On fishies:

Field observations of zebrafish behavior are few and anecdotal, and so much of what zebrafish do in nature has to be inferred from their behavior in the lab…Interestingly, wild-caught and lab fish (both previously imprinted on the ‘wild type’) have similar preferences for prospective shoaling partners…Lab strains of zebrafish spawn all year round, but breeding in the wild occurs primarily during the summer monsoons, when ephemeral pools appear; these presumably offer plenty to eat and some shelter from currents and predators.

Analyses of wild zebrafish suggest a reason for the discrepancies: these fish have a major sex determinant (WZ/ZZ) on chromosome 4—which has features similar to sex chromosomes in other species—yet this determinant has been lost from lab strains (Wilson et al., 2014). This suggests that founder effects, or domestication itself, led to seemingly ad hoc systems employing multiple sex determinants, probably of small original effect in the wild.

On wormies:

This species was originally isolated in rich soil or compost, where it is mostly found in a non-feeding stage called the dauer. More recently, feeding and reproducing stages of C. elegans have been found in decomposing plant material, such as fruits and thick herbaceous stems. These rotting substrates in their late stages of decomposition provide abundant bacterial food for the nematode…Population demographic surveys at the local scale in orchards and woods indicate that C. elegans has a boom-and-bust lifestyle. C. elegans metapopulations evolve in a fluctuating environment where optimal habitats are randomly distributed in space and time… Over the year, in surveys performed in France and Germany, C. eleganspopulations in rotting fruits typically peak in the fall, with proliferation possible in spring through to early winter…

If not with E. coli, it is noteworthy that C. elegans shares its rotting fruit habitat with two other top model organisms, Drosophila melanogaster and Saccharomyces cerevisiae…A specific association is actually found between another Caenorhabditis species and another Drosophila species: this nematode species, C. drosophilae, feeds on rotting cactus in desert areas and its dauer juveniles use a local Drosophila species as a vector to move between cacti.

Orchid mantis: more interesting than cryptic mimicry


I know, I know, you read the title and exclaim: what can be more exciting than cryptic mimicry?! Well, listen to this:

On the face of it, this is a classic evolutionary story, and a cut-and-dried case: the mantis has evolved to mimic the flower as a form of crypsis – enabling it to hide among its petals, feeding upon insects that are attracted by the flower…

O’Hanlon and colleagues set about systematically testing the ideas contained within the traditional view of the orchid mantis’ modus operandi. First, they tested whether mantises actually camouflage amongst flowers, or, alternatively, attract insects on their own…

However, when paired alongside the most common flower in their habitat, insects approached mantises more often than flowers, showing that mantises are attractive to insects by themselves, rather than simply camouflaging among the flowers…Surprisingly mantises did not choose to hide among the flowers. They chose leaves just as often. Sitting near flowers did bring benefits, though, because insects were attracted to the general vicinity – the “magnet effect”.

But wait: there’s more!

As an aside, I’ve heard that Preying Mantis’ make great pets. They are social creatures that will creepily watch you everywhere you go, but also kind of ignore you. They’re like insect-cats.

(Photo from)

Science blogs: still kinda there, I guess

I have bemoaned the lack of a neuroscience blogosphere before. Neuroscience blogs exist as independent fiefdoms, rarely responding to one another. And if we were to cut out the cognitive and psychological sides of neuroscience, the field of blogs would be more like a field of half-grown trees cut down and abandoned, with only a rare leaf or two peaking out of the desiccation.

So in the interests of navel-gazing, it is interesting to think about a post from DynamicEcology (Blogs are dying; long live science blogs):

The classic blog is “the unedited voice of an author”, who thinks out loud over an extended period of time and carries on an open-ended conversation with readers who like that author enough to read a significant fraction of his or her posts. That turns out to be a poor way to make money compared to the alternatives, which is a big reason blogs as a whole are dying. Another reason blogs as a whole are dying is that some of things they used to be for are better done via other means (e.g., Twitter for sharing links, various apps for sharing photos and videos). A third reason is that not that many people actually want to blog…

Fortunately, most of the reasons why blogs as a whole are dying don’t apply to science blogs written by academics. Academic scientists have day jobs that often pay pretty well, and tenured ones have as much job security as anyone ever does. Academics don’t need to make money from blogs, they can do it for real but intangible rewards…

So how come there’s no ecology blogosphere? And how come many ecology blogs either have died or post much less often than they used to (e.g., Just Simple Enough*, Jabberwocky Ecology)? And how come new ecology blogs are so scarce, and mostly peter out after only a few posts without ever building much of an audience? Not that you’d expect most ecologists to blog, but so few puzzles me a little. And it’s not just a puzzle for ecology, since there’s no blogosphere worthy of the name for any scholarly field except economics

But Paige Brown Jarreau actually studies this and is writing a dissertation on this. Here is what she has to say:

Many science bloggers I interviewed and surveyed talked about their blogs today as a place for extended thoughts from Twitter and other “faster” social media streams. According to my dissertation data, academics and science writers alike continue to use their blogs…

– as a home for their writing

– as a portfolio

– as a place to be able to write without strict editorial oversight

– as a place to stick extras that don’t fit elsewhere, either in the academic publishing world or in the larger science content ecosystem

– as a place for opinion, interpretation, analysis and curation

– as a place to cover in depth the stories and scientific papers not being covered by the media (what I call Ecosystem Blogging, or covering what’s missing from the existing content ecosystem)

– as a place to add context missing from news and social media

And here is her fantastic network diagram of how blogs are linked (I have a small little dot in between the neuroscience blogs and the ecology blogs, ironically):

BlogsRead_ModularityClass3_InDegreeSize (1)

I only started blogging something like a year or two ago so I certainly couldn’t tell you if blogs are dying or growing or changing or what. Things seem pretty much the same to me. There are a lot of blogs about science and science culture; there are a lot of blogs explaining science to a lay audience; there are a few blogs that discusses the science at a professional level. But I know that there is demand for it; every conference I go to, I meet people who read my blog.

But we can’t pretend that the community isn’t fragmenting in strange ways. Last week, I posted one of my intermittent Monday Open Questions. It got 0 comments on my blog. However! It go comments on Google+ and tons on Twitter. There was a lot of discussion – it just routed around my blog. Blogs aren’t hubs for discussion and interaction they are the start of the conversation.

I always find it a bit of a shame because it is hard to make everything accessible to a large audience. I know there are people who read this blog through my RSS feed, and who read it through G+, and who read it through Twitter, and who just come to it every so often. And they are going to have very different experiences with it.

(As an addendum: it would be quite nice if there was a way to automatically grab responses to specific blog posts on twitter/G+ and embed them in the comments section.)

#Cosyne2015, by the numbers



Another year, another Cosyne. Sadly, I will be there only in spirit (and not, you know, reality.) But I did manage to get my hands all over the Cosyne abstract authors data…I can now tell you everyone who has had a poster or talk presented there and who it was with. Did you know Steven Pinker was a coauthor on a paper in 2004?!

This year, the winner of the ‘most posters’ award (aka, the Hierarch of Cosyne)  goes to Carlos Brody. Carlos has been developing high-throughput technology to really bang away at the hard problem of decision-making in rodents, and now all that work is coming out at once. Full disclosure notice, his lab sits above me and they are all doing really awesome work.

Here are the Hierarchs, historically:

  • 2004: L. Abbott/M. Meister
  • 2005: A. Zador
  • 2006: P. Dayan
  • 2007: L. Paninski
  • 2008: L. Paninski
  • 2009: J. Victor
  • 2010: A. Zador
  • 2011: L. Paninski
  • 2012: E. Simoncelli
  • 2013: J. Pillow/L. Abbott/L. Paninski
  • 2014: W. Gerstner
  • 2015: C. Brody


Above is the total number of posters/abstracts by author. There are prolific authors, and there is Liam Paninski. Congratulations Liam, you maintain your iron grip as the Pope of Cosyne.

As a technical note, I took ‘unique’ names by associating first letter of the name with last name. I’m pretty sure X. Wang is at least two or three different people and some names (especially those with an umlaut or, for some reason, Paul Schrater) are especially likely to change spelling from year to year. I tried correcting a bit, but fair warning.

Power law 2004-2015


As I mentioned last year, the distribution of posters follows a power law.

But now we have the network data and it is pretty awesome to behold. I was surprised that if we just look at this year’s posters, there is tons of structure (click here for a high-res, low-size PDF version):

When you include both 2014 and 2015, things get even more connected (again, PDF version):


Beyond this it starts becoming a mess. The community is way too interconnected and lines fly about every which way. If anyone has an idea of a good way to visualize all the data (2004-2015), I am all ears. And as I said, I have the full connectivity diagram so if anyone wants to play around with the data, just shoot me an email at adam.calhoun at gmail.

Any suggestions for further analyses?


The not-so frivolous function of play?

We play. Cats play. Dogs play. Horses play. Do fish play? Do cockroaches play? What is the function of play?!

[P]lay is actually at the center of a spectrum of three behavior types: [exploration, play, and stereotypies]. Both exploration and stereotypic behaviors can be easily mistaken for play. Exploration refers to an animal’s reaction to a novel environment or stimuli. For example, if you give a child a new toy, they will generally eagerly take it and examine and manipulate it. However, after thoroughly investigating the new toy, the child may toss it aside and play with their favorite beat-up GI Joe doll…

This doesn’t mean that every species plays, mind you; certainly not every mammal species. Even closely related groups can be vastly different- rats play mountains more than mice do, for example, and some species like aardvarks don’t appear to play at all. Still, almost every major group of mammals has some representatives that show play behavior…

Despite the popular conception that play is practice for later life skills, there is almost zero evidence to back it up. Cats who pounced and batted at objects as kittens were no better at hunting than cats with limited object play;  the same went for coyotes and grasshopper mice. Rats, meerkats, wolves, and many primate species are no better at winning fights based on how often they play fight as youngsters.

Did you know that there is a ‘preeminent play scientist’ and he has five criteria to define play? They are:

  1. The performance of the behavior is not fully functional in the form or context in which it is expressed; that is, it includes elements, or is directed towards stimuli, that do not contribute to current survival.

  2. The behavior is spontaneous, voluntary, intentional, pleasurable, rewarding, reinforcing, or autotelic (done for its own sake).

  3. It differs from the “serious” performance of ethotypic behavior structurally ortemporally in at least one respect: it is incomplete (generally through inhibited or dropped final element), exaggerated, awkward, or precocious; or it involves behavior patterns with modified form, sequencing, or targeting.

  4. The behavior is performed repeatedly in a similar, but not rigidly stereotyped, form during at least a portion of the animal’s ontogeny.

  5. The behavior is initiated when the animal is adequately fed, healthy, relaxed, and free from stress (e. g. predator threat, harsh microclimate, social instability) or intense competing systems (e. g. feeding, mating, predator avoidance).

You have to go read the full article, if for nothing other than all the adorable videos of animals playing.

This is much, much better than that really dumb David Graeber article that science needs to be about play and fun.

Monday Open Question: The unsolved problems of neuroscience?

Over at NeuroSkeptic, there was a post asking “what are the unsolved problems of neuroscience”? For those interested in this type of questions, there are more such questions here and here. This, obviously, is catnip to me.

Modeled on Hilbert’s famous 23 problems in mathematics, the list comes from Ralph Adolphs and has questions such as “how do circuits of neurons compute?” and “how could we cure psychiatric and neurological diseases?” For me, I found the meta-questions most interesting:

Meta-question 1: What counts as understanding the brain?

Meta-question 2: How can a brain be built?

Meta-question 3: What are the different ways of understanding the brain?

But the difference between the lists from Hilbert and Adolphs is very important: Hilbert asked precise questions. The Adolphs questions often verge on extreme ambiguity.

Mathematics has an advantage over biology in its precision. We (often) know what we don’t know. Is neuroscience even at that point? Or would it be more fruitful to propose a systematic research plan?

Me, I would aim my specific questions at something more basic and precise than most of those on the list. For the sake of argument, here are a couple possible questions:

  • Does the brain compute Bayesian probabilities, and if so how? (Pouget says yes, Marcus says no?)
  • How many equations are needed to model any given process in the nervous system?
  • How many distinct forms of long-term potentiation/depression exist?

So open question time:

What (specific) open question do you think is most important?

or What are some particularly fruitful research programs? I am thinking in relation to the Langlands program here.

How Deep Mind learns to win

About a year ago, DeepMind was bought for half a billion dollars by Google for creating software that could learn to beat video games. Over the past year, DeepMind has detailed how they did it.


Let us say that you were an artificial intelligence that had access to a computer screen, a way to play the game (an imaginary video game controller, say), and its current score. How should it learn to beat the game? Well, it has access to three things: the state of the screen (its input), a selection of actions, and a reward (the score). What the AI would want to do is find the best action to go along with every state.

A well-established way to do this without any explicit modeling of the environment is through Q-learning (a form of reinforcement learning). In Q-learning, every time you encounter a certain state and take an action, you have some guess of what reward you will get. But the world is a complicated, noisy place, so you won’t necessarily always get the same reward back in seemingly-identical situations. So you can just take the difference between the reward you find and what you expected, and nudge your guess a little closer.

This is all fine and dandy, though when you’re looking at a big screen you’ve got a large number of pixels – and a huge number of possible states. Some of them you may never even get to see! Every twist and contortion of two pixels is, theoretically, a completely different state. This would make it implausible to check each state, choose the action and play it again and again to get a good estimate of reward.

What we could do, if we were clever about it, is to use a neural network to learn features about the screen. Maybe sometimes this part of the screen is important as a whole and maybe other times those two parts of the screen are a real danger.

But that is difficult for the Q-learning algorithm. The DeepMind authors list three reasons: (1) correlations in sequence of observations, (2) small updates to Q significantly change the policy and the data distribution, and (3) correlations between action values and target values. It is how they tackle these problems that is the main contribution to the literature.

The strategy is to implement a Deep Convolutional Neural Network to find ‘filters’ that can more easily represent the state space. The network takes in the states – the images on the screen – processes them, and then outputs a value. In order to get around problems (1) and (3) above (the correlations in observations), they take a ‘replay’ approach. Actions that have been taken are stored into memory; when it is time to update the neural network, they grab some of the old state-action pairs out of their bag of memories and learn from that. They liken this to consolidation during sleep, where the brain replays things that had happened during the day.

Further, even though they train the network with their memories after every action, this is not the network that is playing the game. The network that is playing the game stays in stasis and only ‘updates itself’ with what it has learned after a certain stretch of time – again, like it is going to “sleep” to better learn what it had done during the day.

Here is an explanation of the algorithm in a hopefully useful form:


Throughout the article, the authors claim that this may point to new directions for neuroscience research. This being published in Nature, any claims to utility should be taken with a grain of salt. That being said! I am always excited to see what lessons arise when theories are forced to confront reality!

What this shows is that reinforcement learning is a good way to train a neural network in a model-free way. Given that all learning is temporal difference learning (or: TD learning is semi-Hebbian?), this is a nice result though I am not sure how original it is. It also shows that the replay way of doing it – which I believe is quite novel – is a good one. But is this something that  sleep/learning/memory researchers can learn from? Perhaps it is a stab in the direction of why it is useful (to deal with correlations).


Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, & Hassabis D (2015). Human-level control through deep reinforcement learning. Nature, 518 (7540), 529-533 PMID: 25719670

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, & Martin Riedmiller (2013). Playing Atari with Deep Reinforcement Learning arXiv arXiv: 1312.5602v1