Ready-to-hand Dean Eckles on people, technology & inference

In the film The Descendents, George Clooney’s character Matt King wrestles — sometimes comically — with new and old choices involving his family and Hawaii. In one case, King decides he wants to meet a rival, both just to meet him and to give him some news; that is, he (at least explicitly) has generally good reason to meet him. Perhaps he even ought to meet him. When he actually does meet him, he cannot just do these things, he also argues with his rival, etc. King’s unplanned behaviors end up causing his rival considerable trouble.¹

This struck me as related to some challenges in formulating what one should do — that is, in the “practical reasoning” side of ethics.

One way of getting practical advice out of virtue ethics is to say that one should do what the virtuous person would do in this situation. On its face, this seems right. But there are also some apparent counterexamples. Consider a short-tempered tennis player who has just lost a match.² In this situation, the virtuous person would walk over to his opponent, shake his hand, and say something like “Good match.” But if this player does that, he is likely to become enraged and even assault his victorious opponent. So it seems better for him to walk off the court without attempting any of this — even though this is clearly rude.

The simple advice to do what the virtuous person would do in the present situation is, then, either not right or not so simple. It might be right, but not so simple to implement, if part of “the present situation” is one’s own psychological weaknesses. Aspects of the agent’s psychology — including character flaws — seem to license bad behavior and to remove reasons for taking the “best” actions.

King and other characters in The Descendents face this problem, both in the example above and at some other points in the movie. He begins a course of action (at least in part) because this is what the virtuous person would do. But then he is unable to really follow through because he lacks the necessary virtues.³ We might take this as a reminder of the ethical value to being humble — to account for our faults — when reasoning about what we ought to do.⁴ It is also a reminder of how frustrating this can be, especially when one can imagine (and might actually be able to) following through on doing what the virtuous person would do.

One way to cope with these weaknesses is to leverage other aspects of one’s situation. We can make public commitments to do the virtuous thing. We can change our environment, sometimes by binding our future selves, like Ulysses, from acting on our vices once we’ve begun our (hopefully) virtuous course of action. Perhaps new mobile technologies will be a substantial help here — helping us intervene in our own lives in this way.

1. Perhaps deserved trouble. But this certainly didn’t play a stated role in the reasoning justifying King’s decision to meet him. [↩]
2. This example is first used by Gary Watson (“Free Agency”, 1975) and put to this use by Michael Smith in his “Internalism” (1995). Smith introduces it as a clear problem for the “example” model of how what a virtuous person would do matters for what we should each do. [↩]
3. Another reading of some of these events in The Descendents is that these characters actually want to do the “bad behaviors”, and they (perhaps unconciously) use their good intentions to justify the course of action that leads to the bad behavior. [↩]
4. Of course, the other side of such humility is being short on self-efficacy. [↩]

The Atlantic reports on the data deluge and its value for innovation.¹ I particularly liked how Erik Brynjolfsson and Andrew McAfee, who wrote the Atlantic piece, highlight the value of experimentation for addressing causal questions — and that many of the questions we care about are causal.²

In writing about experimentation, they report that Hal Varian, Google’s Chief Economist, estimates that Google runs “100-200 experiments on any given day”. This struck me as incredibly low! I would have guessed more like 10,000 or maybe more like 100,000.

The trick of course is how one individuates experiments. Say Google has an automatic procedure whereby each ad has a (small) random set of users who are prevented from seeing it and are shown the next best ad instead. Is this one giant experiment? Or one experiment for each ad?

This is a bit of a silly question.³

But when most people — even statisticians and scientists — think of an experiment in this context, they think of something like Google or Amazon making a particular button bigger. (Maybe somebody thought making that button bigger would improve a particular metric.) They likely don’t think of automatically generating an experiment for every button, such that a random sample see that particular button slightly bigger. It’s these latter kinds of procedures that lead to thinking about tens of thousands of experiments.

That’s the real deluge of experiments.

1. I don’t know that I would call much of it ‘innovation’. There is some outright innovation, but a lot of that is in the general strategies for using the data. There is much more gained in minor tweaking and optimization of products and services. [↩]
2. Perhaps they even overstate the power of simple experiments. For example, they do not mention the fact that many times the results these kinds of experiments often change over time, so that what you learned 2 months ago is no longer true. [↩]
3. Note that two single-factor experiments over the same population with independent random assignment can be regarded as a single experiment with two factors. [↩]

Are the conditions required to assert something conventions? Can they be formalized? Donald Davidson on whether convention is foundational to communication:

But Frege was surely right when he said, “There is no word or sign in language whose function is simply to assert something.” Frege, as we know, set out to rectify matters by inventing such a sign, the turnstile ⊢’ [sometimes called Frege’s ‘judgment stroke’ or ‘assertion sign’]. And here Frege was operating on the basis of a sound principle: if there is a conventional feature of language, it can be made manifest in the symbolism. However, before Frege invented the assertion sign he ought to have asked himself why no such sign existed before. Imagine this: the actor is acting a scene in which there is supposed to be a fire. (Albee’s Tiny Alice, for example.) It is his role to imitate as persuasively as he can a man who is trying to warn others of a fire. “Fire!” he screams. And perhaps he adds, at the behest of the author, “I mean it! Look at the smoke!” etc. And now a real fire breaks out, and the actor tries vainly to warn the real audience. “Fire!” he screams, “I mean it! Look at the smoke!” etc. If only he had Frege’s assertion sign.

It should be obvious that the assertion sign would do no good, for the actor would have used it in the first place, when he was only acting. Similar reasoning should convince us that it is no help to say that the stage, or the proscenium arch, creates a conventional setting that negates the convention of assertion. For if that were so, the acting convention could be put into symbols also; and of course no actor or director would use it. The plight of the actor is always with us. There is no known, agreed upon, publically recognizable convention for making assertions. Or, for that matter, giving orders, asking questions, or making promises. These are all things we do, often successfully, and our success depends in part on our having made public our intention to do them. But it was not thanks to a convention that we succeeded.¹

1. Davidson, Donald. (1984). Communication and convention. Synthese 59 (1), 3-17. [↩]

Sometimes an approximate method is in some important sense better than the “exact” one — and not just because it is easier or faster.

In statistical inference, a standard example here is the Agresti-Coull confidence interval for a binomial proportion: the “exact” interval from inverting the binomial test is conservative — giving overly wide intervals with more than the advertised coverage — but the standard (approximate) Wald interval is too narrow.¹ The Agresti-Coull confidence interval, which is a modification of the Wald interval that can be justified on Bayesian grounds, has “better” performance than either.²

Even knowing this, like many people I suspect, I am a sucker for the “exact” over the approximate. The rest of this post gives another example of “approximate is better than exact” that Art Owen and I recently encountered in our work on bootstrapping big data with multiple dependencies.

The bootstrap is a computational method for estimating the sampling uncertainty of a statistic.³ When using bootstrap resampling or bagging, one normally draws observations without replacement from the sample to form a bootstrap replicate. Each replicate then consists of zero or more copies of each observation. If one wants to bootstrap online — that is, one observation at a time — or generally without synchronization costs in a distributed processing setting, machine learning folks have used the Poisson approximation to the binomial. This approximate bootstrap works as follows: for each observation in the sample, take a Poisson(1) draw and include that many of that observation in this replicate.

Since this is a “lossy” approximation, investigators have sometimes considered and advocated “lossless” alternatives (Lee & Clyde, 2004). The Bayesian bootstrap, in which each replicate is a n-dimensional draw from the Dirichlet, can be done exactly online: for each observation, take a Exp(1) draw and use it as the weight for that observation for this replicate.⁴ Being a sucker for methods labeled “lossless” or “exact”, I implemented this method in Hive at Facebook, and used it instead of the already available Poisson method. I even chortled to others, “Now we have an exact version implemented to use instead!”

But is this the best of all possible distributions for bootstrap reweighting? Might there be some other, better distribution (nonnegative, with mean 1 and variance 1)? In particular, what distribution minimizes our uncertainty about the variance of the mean, given the same number of bootstrap replicates?

We examined this question (Owen and Eckles, 2011, section 3.3) and found that the Poisson(1) weights give a sharper estimate of the variance than the Exp(1) weights: the lossy approximation to the standard resampling bootstrap is better than the exact Bayesian reweighting bootstrap. Interestingly, both of these are beat by using “double-or-nothing” U{0, 2} weights — that is, something close to half-sampling.⁵ Furthermore, the Poisson(1) and U{0, 2} versions are more general, since they don’t require using weighting (observations can duplicated) and, when using them as weights, they don’t require using floating point numbers.⁶

1. The Wald interval also gives zero-width intervals when observations are all either y=1 or y=0. [↩]
2. That is, it contains the true value closer to 100 * (1 – alpha)% of the time than the others. This example is a favorite of Art Owen’s. [↩]
3. Hesterberg et al. (PDF) is a good introduction to the bootstrap. Efron (1979) is the first paper on the bootstrap. [↩]
4. In what sense is this “exact” or “lossless”? This online method is exactly the same as the offline Bayesian bootstrap in which one takes a n-dimensional draw from the Dirichlet. On the other hand, the Poisson(1) method is often seen as an online approximation to the offline bootstrap. [↩]
5. Why is this? See the paper, but the summary is that U{0, 2} has the lowest kurtosis, and Poisson(1) has lower kurtosis than Exp(1). [↩]
6. This is especially useful if one is doing factorial weighting, as we do in the paper, where multiplication of weights for different grouping factors is required. [↩]

A less widely known reason for using within-subjects experimental designs in psychological science. In a within-subjects experiment, each participant experiences multiple conditions (say, multiple persuasive messages), while in a between-subjects experiment, each participant experiences only one condition.

If you ask a random social psychologist, “Why would you run a within-subjects experiment instead of a between-subjects experiments?”, the most likely answer is “power” — within-subjects experiments provide more power. That is, with the same number of participants, within-subjects experiments allow investigators to more easily tell that observed differences between conditions are not due to chance.¹

Why do within-subjects experiments increase power? Because responses by the same individual are generally dependent; more specifically, they are often positively correlated. Say an experiment involves evaluating products, people, or policy proposals under different conditions, such as the presence of different persuasive cues or following different primes. It is often the case that participants who rate an item high on a scale under one condition will rate other items high on that scale under other condition. Or participants with short response times for one task will have relatively short response times for another task. Et cetera. This positive association might be due to stable characteristics of people or transient differences such as mood. Thus, the increase in power is due to heterogeneity in how individuals respond to the stimuli.

However, this advantage of within-subjects designs is frequently overridden in social psychology by the appeal of between-subjects designs. The latter are widely regarded as “cleaner” as they avoid carryover effects — in which one condition may effect responses to subsequent conditions experienced by the same participant. They can also be difficult to design when studies involve deception — even just deception about the purpose of the study — and one-shot encounters. Because of this, between-subjects designs are much more common in social psychology than within-subjects designs: investigators don’t regard the complexity of conducting within-subjects designs as worth it for the gain in power, which they regard as the primary advantage of within-subjects designs.

I want to point out another — but related — reason for using within-subjects designs: between-subjects experiments often do not allow consistent estimation of the parameters of interest. Now, between-subjects designs are great for estimating average treatment effects (ATEs), and ATEs can certainly be of great interest. For example, if one is interested how a design change to a web site will effect sales, an ATE estimated from an A-B test with the very same population will be useful. But this isn’t enough for psychological science for two reasons. First, social psychology experiments are usually very different from the circumstances of potential application: the participants are undergraduate students in psychology and the manipulations and situations are not realistic. So the ATE from a psychology experiment might not say much about the ATE for a real intervention. Second, social psychologists regard themselves as building and testing theories about psychological processes. By their nature, psychological processes occur within individuals. So an ATE won’t do — in fact, it can be a substantially biased estimate of the psychological parameter of interest.

To illustrate this problem, consider an example where the outcome of an experiment is whether the participant says that a job candidate should be hired. For simplicity, let’s say this is a binary outcome: either they say to hire them or not. Their judgements might depend on some discrete scalar X. Different participants may have different thresholds for hiring the applicant, but otherwise be effected by X in the same way. In a logistic model, that is, each participant has their own intercept but all the slopes are the same. This is depicted with the grey curves below.²

Marginal (blue) and conditional (grey) expectation functions

These grey curves can be estimated if one has multiple observations per participant at different values of X. However, in a between-subjects experiment, this is not the case. As an estimate of a parameter of the psychological process common to all the participants, the estimated slope from a between-subjects experiment will be biased. This is clear in the figure above: the blue curve (the marginal expectation function) is shallower than any of the individual curves.

More generally, between-subjects experiments are good for estimating ATEs and making striking demonstrations. But they are often insufficient for investigating psychological processes since any heterogeneity — even only in intercepts — produces biased estimates of the parameters of psychological processes, including parameters that are universal in the population.

I see this as a strong motivation for doing more within-subjects experiments in social psychology. Unlike the power motivation for within-subjects designs, this isn’t solved by getting a larger sample of individuals. Instead, investigators need to think carefully about whether their experiments estimate any quantity of interest when there is substantial heterogeneity — as there generally is.³

1. And to more precisely estimate these differences. Though social psychologist often don’t care about estimation, since many social psychological theories are only directional. [↩]
2. This example is very directly inspired by Alan Agresti’s Categorical Data Analysis, p. 500. [↩]
3. The situation is made a bit “better” by the fact that social psychologists are often only concerned with determining the direction of effects, so maybe aren’t worried that their estimates of parameters are biased. Of course, this is a problem in itself if the direction of the effect varies by individual. Here I have only treated the simpler case of universal function subject to a random shift. [↩]