Posts Tagged ‘Prediction’
I started this post in Hong Kong airport, having just finished one conference and heading to Innsbruck for another. The Hong Kong meeting was on psychometrics and the Innsbruck conference was on imprecise probabilities (believe it or not, these topics actually do overlap). Anyhow, Annemarie Zand Scholten gave a neat paper at the math psych meeting in which she pointed out that, contrary to a strong intuition that most of us have, introducing and accounting for measurement error can actually sharpen up measurement. Briefly, the key idea is that an earlier “error-free” measurement model of, say, human comparisons between pairs of objects on some dimensional characteristic (e.g., length) could only enable researchers to recover the order of object length but not any quantitative information about how much longer people were perceiving one object to be than another.
I’ll paraphrase (and amend slightly) one of Annemarie’s illustrations of her thesis, to build intuition about how her argument works. In our perception lab, we present subjects with pairs of lines and ask them to tell us which line they think is the longer. One subject, Hawkeye Harriet, perfectly picks the longer of the two lines every time—regardless of how much longer one is than the other. Myopic Myra, on the other hand, has imperfect visual discrimination and thus sometimes gets it wrong. But she’s less likely to choose the wrong line if the two lines’ lengths considerably differ from one another. In short, Myra’s success-rate is positively correlated with the difference between the two line-lengths whereas Harriet’s uniformly 100% success rate clearly is not.
Is there a way that Myra’s success- and error-rates could tell us exactly how long each object is, relative to the others? Yes. Let pij be the probability that Myra picks the ith object as longer than the jth object, and pji = 1 – pij be the probability that Myra picks the jth object as longer than the ith object. If the ith object has length Li and the jth object has length Lj, then if pij/pji = Li/Lj, Myra’s choice-rates perfectly mimic the ratio of the ith and jth objects’ lengths. This neat relationship owes its nature to the fact that a characteristic such as length has an absolute zero, so we can meaningfully compare lengths by taking ratios.
How about temperature? This is slightly trickier, because if we’re using a popular scale such as Celsius or Fahrenheit then the zero-point of the scale isn’t absolute in the sense that length has an absolute zero (i.e., you can have Celsius and Fahrenheit readings below zero, and each scale’s zero-point differs from the other). Thus, 60 degrees Fahrenheit is not twice as warm as 30 degrees Fahrenheit. However, the differences between temperatures can be compared via ratios. For instance, 40 degrees F is twice as far from 20 degrees F as 10 degrees F is.
We just need a common “reference” object against which to compare each of the others. Suppose we’re asking Myra to choose which of a pair of objects is the warmer. Assuming that Myra’s choices are transitive, there will be an object she chooses less often than any of the others in all of the paired comparisons. Let’s refer to that object as the Jth object. Now suppose the ith object has temperature Ti,the jth object has temperature Tj, and the Jth object has temperature TJ which is lower than both Ti and Tj. Then if Myra’s choice-rate ratio is
piJ/pjJ = (Ti – TJ)/( Tj – TJ),
she functions as a perfect measuring instrument for temperature comparisons between the ith and jth objects. Again, Hawkeye Harriet’s choice-rates will be piJ = 1 and pjJ = 1 no matter what Ti and Tj are, so her ratio always is 1.
If we didn’t know what the ratios of those lengths or temperature differences were, Myra would be a much better measuring instrument than Harriet even though Harriet never makes mistakes. Are there such situations? Yes, especially when it comes to measuring mental or psychological characteristics for which we have no direct access, such as subjective sensation, mood, or mental task difficulty.
Which of 10 noxious stimuli is the more aversive? Which of 12 musical rhythms makes you feel more joyous? Which of 20 types of puzzle is the more difficult? In paired comparisons between each possible pair of stimuli, rhythms or puzzles, Hawkeye Harriet will pick what for her is the correct pair every time, so all we’ll get from her is the rank-order of stimuli, rhythms and puzzles. Myopic Myra will less reliably and less accurately choose what for her is the correct pair, but her choice-rates will be correlated with how dissimilar each pair is. We’ll recover much more precise information about the underlying structure of the stimulus set from error-prone Myra.
Annemarie’s point about measurement is somewhat related to another fascinating phenomenon known as stochastic resonance. Briefly paraphrasing the Wikipedia entry for stochastic resonance (SR), SR occurs when a measurement or signal-detecting system’s signal-to-noise ratio increases when a moderate amount of noise is added to the incoming signal or to the system itself. SR usually is observed either in bistable or sub-threshold systems. Too little noise results in the system being insufficiently sensitive to the signal; too much noise overwhelms the signal. Evidence for SR has been found in several species, including humans. For example, a 1996 paper in Nature reported a demonstration that subjects asked to detect a sub-threshold impulse via mechanical stimulation of a fingertip maximized the percentage of correct detections when the signal was mixed with a moderate level of noise. One way of thinking about the optimized version of Myopic Myra as a measurement instrument is to model her as a “noisy discriminator,” with her error-rate induced by an optimal random noise-generator mixed with an otherwise error-free discriminating mechanism.
Hi, I’m back again after a few weeks’ travel (presenting papers at conferences). I’ve already posted material on this blog about the “ignorance explosion.” Numerous writings have taken up the theme that there is far too much relevant information for any of us to learn and process and the problem is worsening, despite the benefits of the internet and effective search-engines. We all have had to become more hyper-specialized and fragmented in our knowledge-bases than our forebears, and many of us find it very difficult as a result to agree with one another about the “essential” knowledge that every child should receive in their education and that every citizen should possess.
Well, here is a modest proposal for one such essential: We should all become expert about experts and expertise. That is, we should develop meta-expertise.
We can’t know everything, but knowing an expert when we see one, being able to tell the difference between an expert and an impostor, and knowing what it takes to become an expert can guide our search for assistance in all things about which we’re ignorant. A meta-expert should:
- Know the broad parameters of and requirements for attaining expertise;
- Be able to distinguish a genuine expert from a pretender or a charlatan;
- Know whether expertise is and when it is not attainable in a given domain;
- Possess effective criteria for evaluating expertise, within reasonable limits; and
- Be aware of the limitations of specialized expertise.
Let’s start with that strongly democratic source of expertise: Wikipedia’s take on experts:
“In many domains there are objective measures of performance capable of distinguishing experts from novices: expert chess players will almost always win games against recreational chess players; expert medical specialists are more likely to diagnose a disease correctly; etc.”
That said, the Wikipedia entry also raises a potentially vexing point, namely that “expertise” may come down to merely a matter of consensus, often dictated by the self-same “experts.” Examples readily spring to mind in areas where objective measures are hard to come by, such as the arts. But consider also domains where objective measures may be obtainable but not assessable by laypeople. Higher mathematics is a good example. Only a tiny group of people on the planet were capable of assessing whether Andrew Wiles really had proven Fermat’s Theorem. The rest of us have to take their word for it.
A crude but useful dichotomy splits views about expertise into two camps: Constructivist and performative. The constructivist view emphasizes the influence of communities of practice in determining what expertise is and who is deemed to have it. The performative view portrays expertise as a matter of learning through deliberative practice. Both views have their points, and many domains of expertise have elements of both. Even domains where objective indicators of expertise are available can have constructivist underpinnings. A proficient modern-day undergraduate physics student would fail late 19th-century undergraduate physics exams; and experienced medical practitioners emigrating from one country to another may find their qualifications and experience unrecognized by their adopted country.
What are the requirements for attaining deep expertise? Two popular criteria are talent and deliberative practice. Re deliberate practice, a much-discussed rule of thumb is the “10,000 hour rule.” This rule was popularized in Malcolm Gladwell’s book Outliers and some authors misattribute it to him. It actually dates back to studies of chess masters in the 1970’s (see Ericsson, K. A., R. Th. Krampe, and C. Tesch-Römer, 1993), and its generalizability to other domains still is debatable. Nevertheless, the 10K rule has some merit, and unfortunately it has been routinely ignored in many psychological studies comparing “experts” with novices, where the “experts” often are undergraduates who have been given a few hours’ practice on a relatively trivial task.
The 10K rule can be a useful guide but there’s an important caveat. It may be a necessary but it is by no means a sufficient condition for guaranteeing deep expertise. At least three other conditions have to be met: Deliberative and effective practice in a domain where deep expertise is attainable. Despite this quite simple line of reasoning, plenty of published authors have committed the error of viewing the 10K rule as both necessary and sufficient. Gladwell didn’t make this mistake, but Jane McGonigal’s recent book on video and computer games devotes considerable space to the notion that because gamers are spending upwards of 10K hours playing games they must be attaining deep “expertise” of some kind. Perhaps some may be, provided they are playing games of sufficient depth. But many will not. (BTW, McGonigal’s book is worth a read despite her over-the-top optimism about how games can save the world—and take a look at her game-design collaborator Bogost’s somewhat dissenting review of her book).
Back to the caveats. First, no deliberation makes practice useless. Having spent approximately 8 hours every day sleeping for the past 61 years (178,120 hours) hasn’t made me an expert on sleep. Likewise, deliberative but ineffective practice methods deny us top-level expertise. Early studies of Morse Code experts demonstrated that mere deliberative practice did not guarantee best performance results; specific training regimes were required instead. Autodidacts with insight and aspirations to attain the highest performative levels in their domains eventually realise how important getting the “right” coaching or teaching is.
Finally, there is the problem of determining whether effective, deliberative practice yields deep expertise in any domain. The domain may simply not be “deep” enough. In games of strategy, tic-tac-toe is a clear example of insufficient depth, checkers is a less obvious but still clear example, whereas chess and go clearly have sufficient depth.
Tic-tac-toe aside, are there domains that possess depth where deep expertise nevertheless is unattainable? There are, at least, some domains that are deeply complex where “experts” perform no better then less trained individuals or simple algorithms. Psychotherapy is one such domain. There is a plethora of studies demonstrating that clinical psychologists’ predictions of patient outcomes are worse than simple linear regression models (cf. Dawes’ searing indictment in his 1994 book) and that sometimes experts’ decisions are no more accurate than beginners’ decisions and simple decision aids. Similar results have been reported for financial planners and political experts. In Philip Tetlock’s 2005 book on so-called “expert” predictions, he finds that many so-called experts perform no better than chance in predicting political events, financial trends, and so on.
What can explain the absence of deep expertise in these instances? Tetlock attributes experts’ poor performance to two factors, among others: Hyperspecialization and overconfidence. “We reach the point of diminishing marginal predictive returns for knowledge disconcertingly quickly,” he reports. “In this age of academic hyperspecialization, there is no reason for supposing that contributors to top journals—distinguished political scientists, area study specialists, economists, and so on—are any better than journalists or attentive readers of the New York Times in ‘reading’ emerging situations.” And the more famous the forecaster the more overblown the forecasts. “Experts in demand,” Tetlock says, “were more overconfident than their colleagues who eked out existences far from the limelight.” Tetlock also claims that cognitive style counts: “Foxes” tend to outperform “hedgehogs.” These terms are taken from Isaiah Berlin’s popular essay: Foxes know a little about lots of things, whereas hedgehogs know one big thing.
Another contributing factor may be a lack of meta-cognitive insight on the part of the experts. A hallmark of expertise is ignoring (not ignorance). This proposition may sound less counter-intuitive if it’s rephrased to say that experts know what to ignore. In an earlier post I mentioned Mary Omodei and her colleagues’ chapter in a 2005 book on professionals’ decision making in connection with this claim. Their chapter opens with the observation of a widespread assumption that domain experts also know how to optimally allocate their cognitive resources when making judgments or decisions in their domain. Their research with expert fire-fighting commanders cast doubt on this assumption.
The key manipulations in the Omodei simulated fire-fighting experiments determined the extent to which commanders had unrestricted access to “complete” information about the fires, weather conditions, and other environmental matters. They found that commanders performed more poorly when information access was unrestricted than when they had to request information from subordinates. They also found that commanders performed more poorly when they believed all available information was reliable than when they believed that some of it was unreliable. The disquieting implication of these findings is that domain expertise doesn’t include meta-cognitive expertise.
Cognitive biases and styles aside, another contributing set of factors may be the characteristics of the complex, deep domains themselves that render deep expertise very difficult to attain. Here is a list of tests you can apply to such domains by way of evaluating their potential for the development of genuine expertise:
- Stationarity? Is the domain stable enough for generalizable methods to be derived? In chaotic systems long-range prediction is impossible because of initial-condition sensitivity. In human history, politics and culture, the underlying processes may not be stationary at all.
- Rarity? When it comes to prediction, rare phenomena simply are difficult to predict (see my post on making the wrong decisions most of the time for the right reasons).
- Observability? Can the outcomes of predictions or decisions be directly or immediately observed? For example in psychology, direct observation of mental states is nearly impossible, and in climatology the consequences of human interventions will take a very long time to unfold.
- Objective or even impartial criteria? For instance, what is “good,” “beautiful,” or even “acceptable” in domains such as music, dance or the visual arts? Are such domains irreducibly subjective and culture-bound?
- Testability? Are there clear criteria for when an expert has succeeded or failed? Or is there too much “wiggle-room” to be able to tell?
Finally, here are a few tests that can be used to evaluate the “experts” in your life:
- Credentials: Does the expert possess credentials that have involved testable criteria for demonstrating proficiency?
- Walking the walk: Is the expert an active practitioner in their domain (versus being a critic or a commentator)?
- Overconfidence: Ask your expert to make yes-no predictions in their domain of expertise, and before any of these predictions can be tested ask them to estimate the percentage of time they’re going to be correct. Compare that estimate with the resulting percentage correct. If their estimate was too high then your expert may suffer from over-confidence.
- Confirmation bias: We’re all prone to this, but some more so than others. Is your expert reasonably open to evidence or viewpoints contrary to their own views?
- Hedgehog-Fox test: Tetlock found that Foxes were better-calibrated and more able to entertain self-disconfirming counterfactuals than hedgehogs, but allowed that hedgehogs can occasionally be “stunningly right” in a way that foxes cannot. Is your expert a fox or a hedgehog?
- Willingness to own up to error: Bad luck is a far more popular explanation for being wrong than good luck is for being right. Is your expert balanced, i.e., equally critical, when assessing their own successes and failures?
The title of this post is, of course, a famous quotation from Edmund Burke. This is a personal account of an attempt to find an appropriate substitute for such a plan. My siblings and I persuaded our parents that the best option for financing their long-term in-home care is via a reverse-mortgage. At first glance, the problem seems fairly well-structured: Choose the best reverse mortgage setup for my elderly parents. After all, this is the kind of problem for which economists and actuaries claim to have appropriate methods.
There are two viable strategies for utilizing the loan from a reverse mortgage: Take out a line of credit from which my parents can draw as they wish, or a tenured (fixed) schedule of monthly payments to their nominated savings account. The line of credit (LOC) option’s main attraction is its flexibility. However, the LOC runs out when the equity in my parents’ property is exhausted, whereas the tenured payments (TP) continue as long as they live in their home. So if either of them is sufficiently long-lived then the TP could be the safer option. On the other hand, the LOC may be more robust against unexpected expenses (e.g., medical emergencies or house repairs). Of course, one can opt for a mixture of TP and LOC.
So, this sounds like a standard optimization problem: What’s the optimal mix of TP and LOC? Here we run into the first hurdle: “Optimal” by what criteria? One criterion is to maximize the expected remaining equity in the property. This criterion might be appealing to their offspring, but it doesn’t do my parents much good. Another criterion that should appeal to my parents is maximizing the expected funds available to them. Fortunately, my siblings and I are more concerned for our parents’ welfare than what we’d get from the equity, so we’re happy to go with the second criterion. Nevertheless, it’s worth noting that this issue poses a deeper problem in general—How would a family with interests in both criteria come up with an appropriate weighting for each of them, especially if family members disagreed on the importance of these criteria?
Meanwhile, having settled on an optimization criterion, the next step would seem to be computing the expected payout to my parents for various mixtures of TP and LOC. But wait a minute. Surely we also should be worried about the possibility that some financial exigency could exhaust their funds altogether. So, we could arguably consider a third criterion: Minimizing the probability of their running out of funds. So now we encounter a second hurdle: How do we weigh up maximizing expected payout to our parents against the likelihood that their funds could run out? It might seem as if maximizing payout would also minimize that probability, but this is not necessarily so. A strategy that maximized expected payout could also increase the variability of the available funds over time so that the probability of ruin is increased.
Then there are the unknowns: How long our parents might live, what expenses they might incur (e.g., medical or in-home care), inflation, the behaviour of the LIBOR index that determines the interest rate on what is drawn down from the mortgage, and appreciation or deprecation of the property value. It is possible to come up with plausible-looking models for each of these by using standard statistical tools, and that’s exactly what I did.
I pulled down life-expectancy tables for American men and women born when my parents were born, more than two decades of monthly data on inflation in the USA, a similar amount of monthly data on the LIBOR, and likewise for real-estate values in the area where my parents live. I fitted a several “lifetime” distributions to the relevant parts of the life-expectancy tables to model the probability of my parents living 1, 2, 3, … years longer given that they have survived to their mid-80’s and arrived at a model that fitted the data very well. I modeled the inflation, LIBOR and real-estate data with standard time-series (ARIMA) models whose squared correlations with the data were .91, .98, and .91 respectively—All very good fits.
Finally, my brothers and sisters-in-law obtained the necessary information from my mother regarding our parents’ expenses in the recent past, their income from pensions and so on, and we made some reasonable forecasts of additional expenses that we can foresee in the near term. The transition in this post from “I” to “we” is crucial. This was very much a joint effort. In particular, my youngest brother’s sister-in-law made most of the running on determining the ins and outs of reverse mortgages. She has a terrifically analytical intelligence, and we were able to cross-check one another’s perceptions, intuitions, and calculations.
Armed with all of this information and well-fitted models, it would seem that all we should need to do is run a large enough batch of simulations of the future for each reverse-mortgage scenario under consideration to get reliable estimates of expected payout, expected equity, the probability of ruin, and so on. The inflation model would simulate fluctuations in expenses, the LIBOR model would do so for the interest-rates, the real-estate model for the property value, and the life-expectancy model for how long our parents would live.
But there are at least two flaws in my approach. First, it assumes that my parents’ life-spans can best be estimated by considering them as if they are randomly chosen from the population of American men and women born when they were born who have survived to their mid-80’s. Should I take additional characteristics about them into account and base my estimates on only those who share those characteristics as well as their nation and birth-year? What about diet, or body-mass index, or various aspects of their medical histories? This issue is known as the reference-class problem, and it bedevils every school of statistical inference.
What did I do about this? I fudged my life-expectancy model to be “conservative,” i.e., so that it assumes my parents have a somewhat longer life-span than the original model suggests. In short, I tweaked my model as a risk-averse agent would—The longer my parents live, the greater the risk that they will run short of funds.
The second flaw in my approach is more fundamental. It assumes that the future is going to be just like the past. And before anyone says anything, yes, I’ve read Taleb’s The Black Swan (and was aware of most of the material he covered before reading his book), and yes, I’m aware of most criticisms that have been raised against the kind of models I’ve constructed. The most problematic assumption in my models is what is called stationarity, i.e., that the process driving the ups and downs of, say, the LIBOR index has stable characteristics. There were clear indications that the real-estate market fluctuations in the area where my parents live do not resemble a stationary process, and therefore I should not trust my ARIMA model very much despite its high correlation with the data.
Let me also point out the difference between my approach and the materials provided to us by potential lenders and the HUD counsellor. Their scenarios and forecasts are one-shot spreadsheets that don’t simulate my parents’ expenses, the impact of inflation, or fluctuations in real-estate markets. Indeed, the standard assumption about the latter in their spreadsheets is a constant appreciation in property value of 4% per year.
My simulations are literally equivalent to 10,000 spreadsheets for each scenario, each spreadsheet an appropriate random sample from an uncertain future, and capable of being tweaked to include possibilities such as substantial real-estate downturns. I also incorporated random “shock” expenditures on the order of $5-$75K to see how vulnerable each scenario was to unexpected expenses.
The upshot of all this was that the mix of LOC and TP had a substantial effect on the probability of running out of money, but not a large impact on expected balance or equity (the other factors had large impacts on those). So at least we could home in on a robust mix of LOC and TP, one that would have a lower risk of running out of money than others. This criterion became the primary driver in our choice. We also can monitor how our parents’ situation evolves and revise the mix if necessary.
What about maximizing expected utility? Or optimizing in any sense of the term? No, and no. The deep unknowns inherent even in this relatively well-structured problem make those unattainable goals. What can we do instead? Taleb’s advice is to pay attention to consequences instead of probabilities. This is known as “dominance reasoning.” If option A yields better outcomes than option B no matter what the probabilities of those outcomes are, choose option A. But life often isn’t that simple. We can’t do that here because the comparative outcomes of alternative mixtures of LOC and TP depend on probabilities.
Instead, we have ended up closer to the “bounded rationality” that Herbert Simon wrote about. We can’t claim to have optimized, but we do have robustness and corrigibility on our side, two important criteria for good decision making under ignorance (described in my recent post on that topic). Perhaps most importantly, the simulations gave us insights none of our intuitions could, into how variable the future can be and the consequences of that variability. Sir Edmund was right. We can’t plan the future by the past. But sometimes we can chart a steerable course into that future armed with a few clues from the past to give us an honest check on our intuitions, and a generous measure of scepticism about relying too much on those clues.
Among the sacred cows of Western culture is the notion that the more information and knowledge we have, the better decisions we’ll make. I’m a subscriber to this notion too; after all, I’m in the education and research business! Most of us have been rewarded throughout our lives for knowing things, for being informed. Possessing more information understandably makes us more confident about our decisions and predictions. There also is good experimental evidence that we dislike having to make decisions in situations where we lack information, and we dislike it even more if we’re up against an opponent who knows more.
Nevertheless, our intuitions about the disadvantages of ignorance can lead us astray in important ways. Not all information is worth having, and there are situations where “more is worse.” I’m not going to bother with the obvious cases, such as information that paralyses us emotionally, disinformation, or sheer information overload. Instead, I’d like to stay with situations where there isn’t excessive information, we’re quite capable of processing it and acting on it, and its content is valid. Those are the conditions that can trip us up in sneaky ways.
An intriguing 2007 paper by Crystal Hall, Lynn Ariss and Alexander Todorov presented an experiment in which half their participants were given statistical information (halftime score and win-lose record) about opposing basketball teams in American NBA games and asked to predict the game outcomes. The other half were given the same statistical information plus the team names. Basketball fans’ confidence in their predictions was higher when they had this additional knowledge. However, knowing the team names also caused them to under-value the statistical information, resulting in less accurate predictions. In short, the additional information distracted them away from the useful information.
Many of us believe experts make better decisions than novices in their domain of expertise because their expertise enables them to take more information into account. However, closer examination of this intuition via studies comparing expert and novice decision making reveals a counterintuitive tendency for experts to actually use less information than novices, especially under time pressure or when there is a large amount of information to sift through. Mary Omodei and her colleagues’ chapter in a 2005 book on professionals’ decision making presented evidence on this issues. They concluded that experts know which information is important and which can be ignored, whereas novices try to take it all on board and get delayed or even swamped as a consequence.
Stephan Lewandowsky and his colleagues and students found evidence that even expert knowledge isn’t always self-consistent. Again, seemingly relevant information is the culprit. Lewandowsky and Kirsner (2000) asked experienced wildfire commanders to predict the spread of simulated wildfires. There are two primary relevant variables: Wind velocity and the slope of the terrain. In general, fires tend to spread uphill and with the wind. Given a downhill wind, a sufficiently strong wind pushes the fire downhill with it, otherwise the fire spreads uphill against the wind.
But it turned out that the experts’ predictions under these circumstances depended on an additional piece of information. If it was a wildfire to be brought under control, experts expected it to spread downhill with the wind. If an identical fire was presented as a back burn (i.e., lit by the fire-fighters themselves) experts predicted the reverse, that the fire would spread uphill against the wind. Of course, this is ridiculous: The fire doesn’t know who lit it. Lewandowsky’s group reproduced this phenomenon in the lab and named it knowledge partitioning, whereby people learn two opposing conclusions from the same data, each triggered by an irrelevant contextual cue that they mistake for additional knowledge.
Still, knowing more increases the chances you’ll make the right choices, right? About 15 years ago Peter Ayton, an English professor visiting a Turkish university, had the distinctly odd idea of getting Turkish students to predict 32 English FA cup third-round match winners. After all, the Turkish students knew very little about English soccer. To his surprise, not only did the Turkish students do better than chance (63%), they did about as well as a much better-informed sample of English students (66%).
How did the Turkish students manage it? They were using what has come to be called the recognition heuristic: If they recognized one team name or its city of origin but not the other, in 95% of the cases they predicted the recognized team would win. If they recognized both teams, some of them applied what they knew about the teams to decide between them. Otherwise, they guessed.
So, how could the recognition heuristic do better than chance? The teams or team cities that the Turkish students recognized were more likely than the other teams to appear in sporting news because they were the more successful teams. So the more successful the team, the more likely it would be one of those recognized by the Turkish students. In other words, the recognition cue was strongly correlated with the FA match outcome.
Many of the more knowledgeable English students, on the other hand, recognized all of the teams. They couldn’t use a recognition cue but instead had to rely on knowledge cues, things they knew about the teams. How could the recognition cue do as well as the knowledge-based cues? An obvious possible explanation is that the recognition cue was more strongly correlated with the FA match outcomes than the knowledge cues were. This was the favored explanation for some time, and I’ll return to it shortly.
In two classic papers (1999 and 2002) Dan Goldstein and Gerd Gigerenzer presented several empirical demonstrations like Ayton’s. For instance, a sample of American undergraduates did about as well (71.4% average accuracy) at picking which of two German cities has the larger population as they did at choosing between pairs of American cities (71.1% average accuracy), despite knowing much more about the latter.
It gets worse. An earlier study by Hoffrage in his 1995 PhD dissertation had found that a sample of German students actually did better on this task with American than German cities. Goldstein and Gigerenzer also reported that about two thirds of an American sample responded correctly when asked which of two cities, San Diego or San Antonio, is the largest whereas 100% of a German sample got it right. Only about a third of the Germans recognized San Antonio. So not only is it possible for less knowledgeable people to do about as well as their more knowledgeable counterparts in decisions such as these, they may even do better. The phenomenon of more ignorant people outperforming more knowledgeable ones on decisions such as which of two cities is the more populous became known as the “less-is-more” effect.
And it can get even worse than that. A 2007 paper by Tim Pleskac produced simulation studies showing that it is possible for imperfect recognition to produce a less-is-more effect as well. So an ignoramus with fallible recognition memory could outperform a know-it-all with perfect memory.
For those of us who believe that more information is required for better decisions, the less-is-more effect is downright perturbing. Understandably, it has generated a cottage-industry of research and debate, mainly devoted to two questions: To what extent does it occur and under what conditions could it occur?
I became interested in the second question when I first read the Goldstein-Gigerenzer paper. One of their chief claims, backed by a mathematical proof by Goldstein, was that if the recognition cue is more strongly correlated than the knowledge cues with the outcome variable (e.g., population of a city) then the less-is-more effect will occur. This claim and the proof were prefaced with an assumption that the recognition cue correlation remains constant no matter how many cities are recognized.
What if this assumption is relaxed? My curiosity was piqued because I’d found that the assumption often was false (other researchers have confirmed this). When it was false, I could find examples of a less-is-more effect even when the recognition cue correlation was less than that of the knowledge cue. How could the recognition cue be outperforming the knowledge cue when it’s a worse cue?
In August 2009 I was visiting Oxford to work with two colleagues there, and through their generosity I was housed at Corpus Christi College. During the quiet nights in my room I tunnelled my way toward understanding how the less-is-more effect works. In a nutshell, here’s the main part of what I found (those who want the whole story in all its gory technicalities can find it here).
We’ll compare an ignoramus (someone who recognizes only some of the cities) with a know-it-all who recognizes all of them. Let’s assume both are using the same knowledge cues about the cities they recognize in common. There are three kinds of comparison pairs: Both cities are recognized by the ignoramus, only one is recognized, and neither is recognized.
In the first kind the ignoramus and know-it-all perform equally well because they’re using the same knowledge cues. In the second kind the ignoramus uses the recognition cue whereas the know-it-all uses the knowledge cues. In the third kind the ignoramus flips a coin whereas the know-it-all uses the knowledge cues. Assuming that the knowledge-cue accuracy for these pairs is higher than coin-flipping, the know-it-all will outperform the ignoramus in comparisons between unrecognized cities. Therefore, the only kind of comparison where the ignoramus can outperform the know-it-all is recognized vs unrecognized cities. This is where the recognition cue has to beat the knowledge cues, and it has to do so by a margin sufficient to make up for the coin-flip-vs-knowledge cue deficit.
It turns out that, in principle, the recognition cue can be so much better than the knowledge cues in comparisons between recognized and unrecognized cities that we get a less-is-more effect even though, overall, the recognition cue is worse than the knowledge cues. But could this happen in real life? Or is it so rare that you’d be unlikely to ever encounter it? Well, my simulation studies suggest that it may not be rare, and at least one researcher has informally communicated empirical evidence of its occurrence.
Taking into account all of the evidence thus far (which is much more than I’ve covered here), the less-is-more effect can occur even when the recognition cue is not, on average, as good as knowledge cues. Mind you, the requisite conditions don’t arise so often as to justify mass insurrection among students or abject surrender by their teachers. Knowing more still is the best bet. Nevertheless, we have here some sobering lessons for those of us who think that more information or knowledge is an unalloyed good. It ain’t necessarily so.
To close off, here’s a teaser for you math freaks out there. One of the results in my paper is that the order in which we learn to recognize the elements in a finite set (be it soccer teams, cities,…) influences how well the recognition cue will work. For every such set there is at least one ordering of the items that will maximize the average performance of this cue as we learn the items one by one. There may be an algorithm for finding this order, but so far I haven’t figured it out. Any takers?