Eugenics rebooted – what could go wrong?

Does DNA IQ testing create a meritocracy, or merely reinforce existing biases?

Technology Review covers new efforts to use associations between DNA and IQ.

… Intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait. Recent genome-wide association studies have successfully identified inherited genome sequence differences that account for 20% of the 50% heritability of intelligence. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of genetic variants.

The new genetics of intelligence

Robert Plomin and Sophie von Stumm

I have no doubt that there’s much to be learned here. However, research is not all they’re proposing:

IQ GPSs will be used to predict individuals’ genetic propensity to learn, reason and solve problems, not only in research but also in society, as direct-to-consumer genomic services provide GPS information that goes beyond single-gene and ancestry information. We predict that IQ GPSs will become routinely available from direct-to-consumer companies along with hundreds of other medical and psychological GPSs that can be extracted from genome-wide genotyping on SNP chips. The use of GPSs to predict individuals’ genetic propensities requires clear warnings about the probabilistic nature of these predictions and the limitations of their effect sizes (BOX 7).

Although simple curiosity will drive consumers’ interests, GPSs for intelligence are more than idle fortune telling. Because intelligence is one of the best predictors of educational and occupational outcomes, IQ GPSs will be used for prediction from early in life before intelligence or educational achievement can be assessed. In the school years, IQ GPSs could be used to assess discrepancies between GPSs and educational achievement (that is, GPS-based overachievement and underachievement). The reliability, stability and lack of bias of GPSs make them ideal for prediction, which is essential for the prevention of problems before they occur. A ‘precision education’ based on GPSs could be used to customize education, analogous to ‘precision medicine’

There are two ways “precision education” might be implemented. An egalitarian model would use information from DNA IQ measurements to customize resource allocations, so that all students could perform up to some common standard:

An efficiency model, by contrast, would use IQ measurements to set achievement expectations for each student, and customize resources to ensure that students who are underperforming relative to their DNA get a boost:

This latter approach is essentially a form of tracking, in which DNA is used to get an early read on who’s destined to flip bonds, and who’s destined to flip burgers.

One problem with this scheme is noise (as the authors note, seemingly contradicting their own abstract’s claim of reliability and stability). Consider the effect of a student receiving a spuriously low DNA IQ score. Under the egalitarian scheme, they receive more educational resources (enabling them to overperform), while under the efficiency scheme, resources would be lowered, leading self-fulfillment of the predicted low performance. The authors seem to regard this as benign and self-correcting:

By contrast, GPSs are ‘less dangerous’ because they are intrinsically probabilistic, not hardwired and deterministic like single-gene disorders. It is important to recall here that although all complex traits are heritable, none is 100% heritable. A similar logic can be applied to IQ scores: although they have great predic­tive validity for key life outcomes, IQ is not determin­istic but probabilistic. In short, an individual is always more than the sum of their genes or their IQ scores.

I think this might be true when you consider the local effects on the negative loops governing resource allocation. But I don’t think that remains true when you put it in context. Education is a nest of positive feedbacks. This creates path dependence that amplifies errors in resource allocation, whether they come from subjective teacher impressions or DNA measurements.

In a perfect world, DNA-IQ provides an independent measurement that’s free of those positive feedbacks. In that sense, it’s perfectly meritocratic:

But how do you decide what to measure? Are the measurements good, or just another way to institutionalize bias? This is hotly contested. Let’s suppose that problems of gender and race/ethnicity bias have been, or can be solved. There are still questions about what measurements correlate with better individual or societal outcomes. At some point, implicit or explicit choices have to be made, and these are not value-free. They create reinforcing feedbacks:

I think it’s inevitable that, like any other instrument, DNA IQ scores are going to reflect the interests of dominant groups in society. (At a minimum, I’d be willing to bet that IQ tests don’t measure things that would result in low scores for IQ test designers.) If that means more Einsteins, Bachs and Ghandis, maybe it’s OK. But I don’t think that’s guaranteed to lead to a good outcome. First, there’s no guarantee that a society composed of apparently high-performing individuals is in itself high-performing. Second, the dominant group may be dominant, not by virtue of faster CPUs in their heads, but something less appetizing.

I think there’s no guarantee that DNA IQ will not reflect attributes that are dysfunctional for society. We would hate to inadvertently produce more Stalins and Mengeles by virtue of inadvertent correlations with high achievement of less virtuous origin. And certainly, like any instrument used for high-stakes decisions, the pressure to distort and manipulate results will increase with use.

Note that if education is really egalitarian, the link between Measured IQ and Educational Resources Allocated reverses polarity, becoming negative. Then the positive loops become negative loops, and a lot of these problems go away. But that’s not often a choice societies make, presumably because egalitarian education is in itself contrary to the interests of dominant groups.

I understand researchers’ optimism for this technology in the long run. But for now, I remain wary, due to the decided lack of systems thinking about the possible side effects. In similar circumstances, society has made poor choices about teacher value added modeling, easily negating any benefits it might have had. I’m expecting a similar outcome here.

Vi Hart on positive feedback driving polarization

Vi Hart’s interesting comments on the dynamics of political polarization, following the release of an innocuous video:

I wonder what made those commenters think we have opposite views; surely it couldn’t just be that I suggest people consider the consequences of their words and actions. My working theory is that other markers have placed me on the opposite side of a cultural divide that they feel exists, and they are in the habit of demonizing the people they’ve put on this side of their imaginary divide with whatever moral outrage sounds irreproachable to them. It’s a rather common tool in the rhetorical toolset, because it’s easy to make the perceived good outweigh the perceived harm if you add fear to the equation.

Many groups have grown their numbers through this feedback loop: have a charismatic leader convince people there’s a big risk that group x will do y, therefore it seems worth the cost of being divisive with those who think that risk is not worth acting on, and that divisiveness cuts out those who think that risk is lower, which then increases the perceived risk, which lowers the cost of being increasingly divisive, and so on.

The above feedback loop works great when the divide cuts off a trust of the institutions of science, or glorifies a distrust of data. It breaks the feedback loop if you act on science’s best knowledge of the risk, which trends towards staying constant, rather than perceived risk, which can easily grow exponentially, especially when someone is stoking your fear and distrust.

If a group believes that there’s too much risk in trusting outsiders about where the real risk and harm are, then, well, of course I’ll get distrustful people afraid that my mathematical views on risk/benefit are in danger of creating a fascist state. The risk/benefit calculation demands it be so.

AI is killing us now

I’ve been watching the debate over AI with some amusement, as if it were some other planet at risk. The Musk-Zuckerberg kerfuffle is the latest installment. Ars Technica thinks they’re both wrong:

At this point, these debates are largely semantic.

I don’t see how anyone could live through the last few years and fail to notice that networking and automation have enabled an explosion of fake news, filter bubbles and other information pathologies. These are absolutely policy relevant, and smarter AI is poised to deliver more of what we need least. The problem is here now, not from some impending future singularity.

Ars gets one point sort of right:

Plus, computer scientists have demonstrated repeatedly that AI is no better than its datasets, and the datasets that humans produce are full of errors and biases. Whatever AI we produce will be as flawed and confused as humans are.

I don’t think the data is really the problem; it’s the assumptions the data’s treated with and the context in which that occurs that’s really problematic. In any case, automating flawed aspects of ourselves is not benign!

Here’s what I think is going on:

AI, and more generally computing and networks are doing some good things. More data and computing power accelerate the discovery of truth. But truth is still elusive and expensive. On the other hand, AI is making bullsh!t really cheap (pardon the technical jargon). There are many mechanisms by which this occurs:

These amplifiers of disinformation serve increasingly concentrated wealth and power elites that are isolated from their negative consequences, and benefit from fueling the process. We wind up wallowing in a sea of information pollution (the deadliest among the sins of managing complex systems).

As BS becomes more prevalent, various reinforcing mechanisms start kicking in. Accepted falsehoods erode critical thinking abilities, and promote the rejection of ideas like empiricism that were the foundation of the Enlightenment. The proliferation of BS requires more debunking, taking time away from discovery. A general erosion of trust makes it harder to solve problems, opening the door for opportunistic rent-seeking non-solutions.

I think it’s a matter of survival for us to do better at critical thinking, so we can shift the balance between truth and BS. That might be one area where AI could safely assist. We have other assets as well, like the explosion of online learning opportunities. But I think we also need some cultural solutions, like better management of trust and anonymity, brakes on concentration, sanctions for lying, rewards for prediction, and more time for reflection.

The survival value of wrong beliefs

… reasons for the survival of antiscientific views. It’s basically a matter of evolution. When crazy ideas negatively affect survival, they die out. But evolutionary forces are vastly diminished under some conditions, or even point the wrong way …

NPR has an alarming piece on school science.

She tells her students — like Nick Gurol, whose middle-schoolers believe the Earth is flat — that, as hard as they try, science teachers aren’t likely to change a student’s misconceptions just by correcting them. Gurol says his students got the idea of a flat planet from basketball star Kyrie Irving, who said as much on a podcast.

“And immediately I start to panic. How have I failed these kids so badly they think the Earth is flat just because a basketball player says it?” He says he tried reasoning with the students and showed them a video. Nothing worked.

“They think that I’m part of this larger conspiracy of being a round-Earther. That’s definitely hard for me because it feels like science isn’t real to them.”

For cases like this, Yoon suggests teachers give students the tools to think like a scientist. Teach them to gather evidence, check sources, deduce, hypothesize and synthesize results. Hopefully, then, they will come to the truth on their own.

This called to mind a post from way back, in which I considered reasons for the survival of antiscientific views.

It’s basically a matter of evolution. When crazy ideas negatively affect survival, they die out. But evolutionary forces are vastly diminished under some conditions, or even point the wrong way:

  1. Non-experimental science (reliance on observations of natural experiments; no controls or randomized assignment)
  2. Infrequent replication (few examples within the experience of an individual or community)
  3. High noise (more specifically, low signal-to-noise ratio)
  4. Complexity (nonlinearity, integrations or long delays between cause and effect, multiple agents, emergent phenomena)
  5. “Unsalience” (you can’t touch, taste, see, hear, or smell the variables in question)
  6. Cost (there’s some social or economic penalty  imposed by the policy implications of the theory)
  7. Commons (the risk of being wrong accrues to society more than the individual)

These are, incidentally, some of the same circumstances that make medical trials difficult, such that most papers are false.

Consider the flat earth idea. What cost accrues to students who hold this belief? None whatsoever, I think. A flat earth model will make terrible predictions of all kinds of things, but students are not making or relying on such predictions. The roundness of the earth is obviously not salient. So really, the only survival value that matters to students is the benefit of tribal allegiance.

If there are intertemporal dynamics, the situation is even worse. For any resource or capability investment problem, there’s worse before better behavior. Recovering depleted fish stocks requires diminished effort, and less to eat, in the near term. If a correct belief implies good long run stock management, adherents of the incorrect belief will have an advantage in the short run. You can’t count on selection weeding out the “dumb tribes” for planetary-scale problems, because we’re all in one.

This seems like a pretty intractable problem. If there’s a way out, it has to be cultural. If there were a bit more recognition of the value on making correct predictions, the halo of that would spill over to diminish the attractiveness of silly theories. That’s a case that ought to be compelling for basketball fans. Who wants to play on a team that can’t predict what the opponents will do, or how the ball will bounce?

System 3 thinking

There was lots of talk of dual process theory at the 2017 System Dynamics Conference. Nelson Repenning discussed it in his plenary presentation. The Donella Meadows Award paper investigated the effects on stock-flow task performance of priming subjects to think in System 2:

The dual-process theory and understanding of stocks and flows

Arash Baghaei Lakeh and Navid Ghaffarzadegan

Recent evidence suggests that using the analytic mode of thinking (System 2) can improve people’s performance in stock–flow (SF) tasks. In this paper, we further investigate the effects by implementing several different interventions in two studies. First, we replicate a previous finding that answering analytical questions before the SF task approximately doubles the likelihood of answering the stock questions correctly. We also investigate effects of three other interventions that can potentially prime participants to use their System 2. Specifically, the first group is asked to justify their response to the SF task; the second group is warned about the difficulty of the SF task; and the third group is offered information about cognitive biases and the role of the analytic mode of thinking. We find that the second group showed a statistically significant improvement in their performance. We claim that there are simple interventions that can modestly improve people’s response in SF tasks.

Dual process refers to the idea that there are two systems of thinking at work in our minds. System 1 is fast, automatic intuition. System 2 is slow, rational reasoning.

I’ve lost track of the conversation, but some wag at the conference (not me; possibly Arash)  coined the term “System 3” for model-assisted thinking.

In a sense, any reasoning is “model-assisted,” but I think there’s an important distinction between purely mental reasoning and reasoning with a formal (usually computerized) modeling method like a dynamic simulation or even a spreadsheet.

When we reason in our heads, we have to simultaneously (A) describe the structure of the problem, (B) predict the behavior implied by the structure, and (C) test the structure against available information. Mentally, we’re pretty good at A, but pretty bad at B and C. No one can reliably simulate even a low-order dynamic system in their head, and there are too many model checks against data and thought experiments (like extreme conditions) to “run” without help.

System 3’s great weakness is that it takes still more time than using System 2. But it makes up for that in three ways. First, reliable predictions and tests of behavior reveal misconceptions about the problem/system structure that are otherwise inaccessible, so the result is higher quality. Second, the model is shareable, so it’s easier to convey insights to other stakeholders who need to be involved in a solution. Third, formal models can be reused, which lowers the effective cost of an application.

But how do you manage that “still more time” problem? Consider this advice:

I discovered a simple solution to making challenging choices more efficiently at an offsite last week with the CEO and senior leadership team of a high tech company. They were facing a number of unique, one-off decisions, the outcomes of which couldn’t be accurately predicted.

These are precisely the kinds of decisions which can linger for weeks, months, or even years, stalling the progress of entire organizations. …

But what if we could use the fact that there is no clear answer to make a faster decision?

“It’s 3:15pm,” He [the CEO] said. “We need to make a decision in the next 15 minutes.”

“Hold on,” the CFO responded, “this is a complex decision. Maybe we should continue the conversation at dinner, or at the next offsite.”

“No,” The CEO was resolute, “We will make a decision within the next 15 minutes.”

And you know what? We did.

Which is how I came to my third decision-making method: use a timer.

I’m in favor of using a timer to put a stop to dithering. Certainly a body with scarce time should move on when it perceives that it can’t add value. But this strikes me as a potentially costly reversion to System 1.

If a problem is strategic enough to make it to the board, but the board sees a landscape that prevents a clear decision, it ought to be straightforward to articulate why. Are there tradeoffs that make the payoff surface flat? The timer is a sensible response to that, because the decision doesn’t require precision. Are there competing feedback loops that suggest different leverage points, for which no one can agree about the gain? In that case, the consequence of an error could be severe, so the default answer should include a strategy for detection and correction. One ought to have a way to discriminate between these two situations, and a simple application of System 3 might be just the tool.

 

The intuitive mind is a gag gift

I saw Einstein quoted yesterday, “The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift.”

I wondered what he meant, because I think of the intuitive mind as a treacherous friend. We can’t do without it, because we have too many decisions to make. You’d never get out of bed if everything had to be evaluated rationally. But at the same time, whatever heuristics are going on in there are the same ones that,

… and indulge in dozens of other biases. I think they’re also why Lightroom’s face recognition mixes me up with my dog.

How could Einstein revere intuition above reason? Perhaps he relished the intuitive guess at an equation, or some kind of Occam’s Razor argument about simplicity and beauty?

Well, it appears that the answer is simple, but not too simple. He didn’t say it.

Why so many incompetent leaders, period?

The HBR has a nice article asking, Why Do So Many Incompetent Men Become Leaders? Gender may amplify the problem, but I think its roots lie much deeper. We have a general surplus of incompetence across all walks of life.

So, how does this unhappy situation persist? One would hope that evolution would take care of this – that companies or nations that were systematically fooled by confidence over substance would be naturally selected out of the population. But that doesn’t seem to happen.

I think the explanation lies in the weaknesses of our mental models (and failure to refine them with formal models), and therefore our inability to attribute success and failure to decisions, in hindsight or prospects.

The HBR has a nice article asking, Why Do So Many Incompetent Men Become Leaders? Some excerpts:

In my view, the main reason for the uneven management sex ratio is our inability to discern between confidence and competence. That is, because we (people in general) commonly misinterpret displays of confidence as a sign of competence, we are fooled into believing that men are better leaders than women. In other words, when it comes to leadership, the only advantage that men have over women … is the fact that manifestations of hubris — often masked as charisma or charm — are commonly mistaken for leadership potential, and that these occur much more frequently in men than in women.

The truth of the matter is that pretty much anywhere in the world men tend to think that they that are much smarter than women. Yet arrogance and overconfidence are inversely related to leadership talent — the ability to build and maintain high-performing teams, and to inspire followers to set aside their selfish agendas in order to work for the common interest of the group.

The paradoxical implication is that the same psychological characteristics that enable male managers to rise to the top of the corporate or political ladder are actually responsible for their downfall. In other words, what it takes to get the job is not just different from, but also the reverse of, what it takes to do the job well. …

In fact, most leaders — whether in politics or business — fail. That has always been the case: the majority of nations, companies, societies and organizations are poorly managed, as indicated by their longevity, revenues, and approval ratings, or by the effects they have on their citizens, employees, subordinates or members. Good leadership has always been the exception, not the norm.

Gender may amplify the problem, but I think its roots lie much deeper. We have a general surplus of incompetence across all walks of life.

So, how does this unhappy situation persist? One would hope that evolution would take care of this – that companies or nations that were systematically fooled by confidence over substance would be naturally selected out of the population. But that doesn’t seem to happen.

I think the explanation lies in the weaknesses of our mental models (and failure to refine them with formal models), and therefore our inability to attribute success and failure to decisions, in hindsight or prospects.  Here’s the purest expression of this line of thinking I’ve seen:

Why I Switched My Endorsement from Clinton to Trump

1. Things I Don’t Know: There are many things I don’t know. For example, I don’t know the best way to defeat ISIS. Neither do you. I don’t know the best way to negotiate trade policies. Neither do you. I don’t know the best tax policy to lift all boats. Neither do you. …. So on most political topics, I don’t know enough to make a decision. Neither do you, but you probably think you do.

3. Party or Wake: It seems to me that Trump supporters are planning for the world’s biggest party on election night whereas Clinton supporters seem to be preparing for a funeral. I want to be invited to the event that doesn’t involve crying and moving to Canada. (This issue isn’t my biggest reason.)

Scott Adams, Dilbert creator

If you can’t predict which leader’s proposals or methods will work, why not go with the ones that sound the best? Or, if you can’t figure out how to grow the pie for everyone, why not at least choose the tribal affiliation that gives you the best chance at a slice of patrimony?

Still, at the end of the day, the honeymoon is over, and the effects of decisions should come home to roost, right? Not necessarily. Even after the fact, attribution of causality in dynamic systems is difficult, because causes and effects are separated in space in time. So, you can’t learn to do better by simple pattern matching; you have to understand the structure that’s producing behavior. Firm policies and election rules worsen the problem by rotating people around, so that they can launch initiatives and be gone before the consequences are observed, defeating evolution.

John Sterman and Nelson Repenning explain in another context:

The Capability Trap
The capability trap arises from the interactions between judg-
mental biases and the physical structure of work processes.
For example, machine operators or design engineers facing a
shortfall may initially work harder …, do more rework
…, or focus on throughput …, all of which
reduce the time available for improvement. These responses
are tempting because they yield immediate gains, while their
costs are distant in time and space, uncertain, and hard to
detect. But, while throughput improves in the short run, the
reduction in time dedicated to learning causes process capa-
bility to decline. Eventually, workers find themselves again
falling short of their throughput target, forcing a further shift
toward working and away from improving. Instead of making
up for the improvement activity they skipped earlier, their
own past actions, by causing the reinvestment loops … to work as vicious cycles, trap them in a downward
spiral of eroding process capability, increasing work hours,
and less and less time for improvement.

Misperceptions of Feedback
While the literature and field data support the links in the
model, our account of the capability trap raises several
questions. First, wouldn’t people recognize the existence of
the reinforcing feedbacks that create the trap and take
actions to avoid it? Second, if they find themselves stuck in
the trap, wouldn’t people learn to escape it by making appro-
priate short-term sacrifices? Studies of decision making in
dynamic environments suggest that such learning is far from
automatic.
Consider the outcome feedback received from a decision to
spend more time working and less on improvement. Perfor-
mance quickly increases, producing a clear, salient, unam-
biguous outcome. In contrast, the negative consequences of
this action—the decline in process capability—take time, are
hard to observe, and may have ambiguous interpretations. In
experiments ranging from running a simulated production and
distribution system (Sterman, 1989) to fighting a simulated
forest fire (Brehmer, 1992) or managing a simulated fishery
(Moxnes, 1999), subjects have been shown to grossly over-
weight the short-run positive benefits of their decisions while
ignoring the long-run, negative consequences. Participants in
these experiments produce wildly oscillating production
rates, allow their fire-fighting headquarters to burn down, and
find their fleets idled after overexploiting their fisheries.
….

Once caught in the capability trap, people are also unlikely to
learn to escape it. A new improvement program, by reducing
the time available for throughput, causes an immediate and
salient drop in performance, while its benefits are uncertain,
delayed, difficult to assess, and may be insufficient to switch
the reinforcing feedbacks to virtuous cycles. People are likely
to conclude that the improvement program they attempted
does not work and should be abandoned.

Attribution Errors in Judging the Cause of Low Throughput
When choosing to emphasize first- or second-order improve-
ment, managers must make a judgment about the causes of
low process throughput. If they believe the cause of low per-
formance lies in the physical structure of the process, they
are likely to focus their efforts on process improvement. If,
however, low throughput is thought to result from lack of
worker effort or discipline, then managers are better off

focusing on increasing the quantity of work. The cues people

use to make causal attributions include temporal order,
covariation, and contiguity in time and space (Einhorn and
Hogarth, 1986). Attributing low throughput to inadequate
worker effort is consistent with all these cues: …. Managers are thus likely to attribute a throughput shortfall to inadequate worker effort, even when the true causes are systemic process
problems.
Managers’ tendency to attribute performance shortfalls to
problems with the workforce rather than the production sys-
tem is reinforced by the so-called fundamental attribution
error, or dispositional bias. …. Existing research thus suggests that managers facing throughput gaps are likely to conclude that workers, not the process, are the cause of low throughput, reinforcing the bias against fundamental improvement.
As Sterman & Repenning go on to explain, these attribution errors are likely to become self-confirming, and to be institutionalized in organizational routines, leading to self-reinforcing organizational pathologies.
Blaming workers for productivity shortfalls that ultimately arise from the firm leadership’s failure to focus on process improvement is a lot like blaming poverty on the shortcomings of poor people, rather than their social environment, which subjects them to poor education, predatory monopolies and disproportionate criminal and environmental burdens. Programs that focus exclusively on motivating (or punishing) the impoverished are at least as naive as those that seek to alleviate poverty through transfers of money without creating skills or opportunities.
Back to Scott Adams, one argument in favor of unfounded overconfidence remains:

6. Persuasion: Economies are driven by psychology. If you expect things to go well tomorrow, you invest today, which causes things to go well tomorrow, as long as others are doing the same. The best kind of president for managing the psychology of citizens – and therefore the economy – is a trained persuader. You can call that persuader a con man, a snake oil salesman, a carnival barker, or full of shit. It’s all persuasion. And Trump simply does it better than I have ever seen anyone do it.

Most of the job of president is persuasion. Presidents don’t need to understand policy minutia. They need to listen to experts and then help sell the best expert solutions to the public. Trump sells better than anyone you have ever seen, even if you haven’t personally bought into him yet. You can’t deny his persuasion talents that have gotten him this far.

Psychology is in steady state over any reasonably long time horizon, so it’s not really psychology that drives economic growth. Psychology is necessary, in that people have to feel that conditions are right for risk-taking, but it’s not sufficient. The real long run driver is innovation, embodied in people, technology and organizations. That means it’s also necessary that innovations work, so investments produce, GDP makes people happy, schools teach, infrastructure serves, and wars defeat more enemies than they create. Mere bluster does not get you those things.
So here’s the problem: the overconfidence that lends itself to persuasion has side effects:
  • It’s hostile to “listening to experts” (or to anyone).
  • It favors naive, simple causal attributions over inquiry into system structure.
  • It opposes learning from feedback, whether from constituents or objective measurements.
  • Confronted by adversity, it retreats into confirmation bias and threat rigidity.

So, overconfidence doesn’t make the economy grow faster. It just makes things go faster, whether paradise or a cliff lies ahead.

I don’t think firms or planets want to speed off a cliff, so we need to do better. It’s a tall order, but I think awareness of the problem is a good start. After that, it’s a hard road, but we all need to become better system scientists, and spend more time participating in governance and attempting to understand how systems work.

There’s reason for hope – not all firms fall into capability traps. Emulating those that succeed, we might start by investing some time in process improvement. The flawed processes by which we now pick leaders look like low-hanging fruit to me.

Another field ponders rationality

The reasoning criminal vs. Homer Simpson: conceptual challenges for crime science

A recent disciplinary offshoot of criminology, crime science (CS) defines itself as “the application of science to the control of crime.” One of its stated ambitions is to act as a cross-disciplinary linchpin in the domain of crime reduction. Despite many practical successes, notably in the area of situational crime prevention (SCP), CS has yet to achieve a commensurate level of academic visibility. The case is made that the growth of CS is stifled by its reliance on a model of decision-making, the Rational Choice Perspective (RCP), which is inimical to the integration of knowledge and insights from the behavioral, cognitive and neurosciences (CBNs).