Most of my economics colleagues know little about engineering. Yet much of what they actually want to do in the world, i.e., get people to adopt new better institutions, is better seen as engineering than as science. To help educate For them, I quote from Eric Drexler in his new book, explaining the difference between science and engineering:

The essence of science is inquiry; the essence of engineering is design. Scientific inquiry expands the scope of human perception and understanding; engineering design expands the scope of human plans and results. …

• Scientists seek unique, correct theories, and if several theories seem plausible, all but one must be wrong, while engineers seek options for working designs, and if several options will work, success is assured.
• Scientists seek theories that apply across the widest possible range (the Standard Model applies to everything), while engineers seek concepts well-suited to particular domains (liquid-cooled nozzles for engines in liquid-fueled rockets).
• Scientists seek theories that make precise, hence brittle predictions (like Newton’s), while engineers seek designs that provide a robust margin of safety.
• In science a single failed prediction can disprove a theory, no matter how many previous tests it has passed, while in engineering one successful design can validate a concept, no matter how many previous versions have failed. ..

Simple systems can behave in ways beyond the reach of predictive calculation. This is true even in classical physics. …. Engineers, however, can constrain and master this sort of unpredictability. A pipe carrying turbulent water is unpredictable inside (despite being like a shielded box), yet can deliver water reliably through a faucet downstream. The details of this turbulent flow are beyond prediction, yet everything about the flow is bounded in magnitude, and in a robust engineering design the unpredictable details won’t matter. Likewise, in APM systems, thermal fluctuations are unpredictable in detail, but bounded in magnitude, and a discipline of robust design once again can give satisfactory results. …

The reason that aircraft seldom fall from the sky with a broken wing isn’t that anyone has perfect knowledge of dislocation dynamics and high-cycle fatigue in dispersion-hardened aluminum, nor because of perfect design calculations, nor because of perfection of any other kind. Instead, the reason that wings remain intact is that engineers apply conservative design, specifying structures that will survive even unlikely events, taking account of expected flaws in high-quality components, crack growth in aluminum under high-cycle fatigue, and known inaccuracies in the design calculations themselves. This design discipline provides safety margins, and safety margins explain why disasters are rare. …

The key to designing and managing complexity is to work with design components of a particular kind— components that are complex, yet can be understood and described in a simple way from the outside. … Exotic effects that are hard to discover or measure will almost certainly be easy to avoid or ignore. … There’s another, contrasting principle, however: Exotic effects that can be discovered and measured can sometimes be exploited for practical purposes. …

When faced with imprecise knowledge, a scientist will be inclined to improve it, yet an engineer will routinely accept it. Might predictions be wrong by as much as 10 percent, and for poorly understood reasons? The reasons may pose a difficult scientific puzzle, yet an engineer might see no problem at all. Add a 50 percent margin of safety, and move on.

I’ve been vacationing with family this week, and (re-) noticed a few things. When we played miniature golf, the winners were treated as if shown to be better skilled than previously thought, even though score differences were statistically insignificant. Same for arcade shooter contests. We also picked which Mexican place to eat at based on one person saying they had eaten there once and it was ok, even though given how random is who likes what when, that was unlikely to be a statistically significant difference for estimating what the rest of us would like.

The general point is that we quite often collect and act on rather weak info clues. This could make good info sense. We might be slowly collecting small clues that eventually add up to big clues. Or if we know well which parameters matter the most, it can make sense to act on weak clues; over a lifetime this can add up to net good decisions. When this is what is going on, then people will tend to know many things they cannot explicitly justify. They might have seen a long history of related situations, and have slowly accumulated enough relevant clues to form useful judgments, but not be able to explicitly point to most of those weak clues which were the basis of that judgement.

Another thing I noticed on vacation is that a large fraction of my relatives age 50 or older think that they know that their lives were personally saved by medicine. They can tell of one or more specific episodes where a good doctor did the right thing, and they’d otherwise be dead. But people just can’t on average have this much evidence, since we usually find it hard to see effects of medicine on health even when we have datasets with thousands of people. (I didn’t point this out to them – their beliefs seemed among the ones they held most deeply and passionately.) So clearly this intuitive collection of weak clues stuff goes very wrong sometimes, even on topics where people suffer large personal consequences. It is not just that random errors can show up; there are topics on which our minds are systematically structured, probably on purpose, to greatly mislead us in big ways.

One of the biggest questions we face is thus: when are judgements trustworthy? When can we guess that the intuitive slow accumulation of weak clues by others or ourselves embodies sufficient evidence to be a useful guide to action? At one extreme, one could try to never act on anything less than explicitly well-founded reasoning, but this would usually go very badly; we mostly have no choice but to rely heavily on such intuition. At the other extreme, many people go their whole lives relying almost entirely on their intuitions, and they seem to mostly do okay.

In between, people often act like they rely on intuition except when good solid evidence is presented to the contrary, but they usually rely on their intuition to judge when to look for explicit evidence, and when that is solid enough. So when those intuitions fail the whole process fails.

Prediction markets seem a robust way to cut through this fog; just believe the market prices when available. But people are usually resistant to creating such market prices, probably exactly because doing so might force them to drop treasured intuitive judgements.

On this blog I often present weak clues, relevant to important topics, but by themselves not sufficient to draw strong conclusions. Usually commenters are eager to indignantly point out this fact. Each and every time. But on many topics we have little other choice; until many weak clues are systematically collected into strong clues, weak clues are what we have. And the topics of where our intuitive conclusions are most likely to be systematically biased tend to be those sort of topics. So I’ll continue to struggle to collect whatever clues I can there.

Why do we have fewer kids today, even though we are rich? In ancient societies, richer folks usually had more kids than poorer folks. Important clues should be found in the first place where fertility fell lots, France from 1750 to 1850. The fall in fertility seems unrelated to contraception and the fall in infant mortality. England at the time was richer, less agrarian and more urban, yet its fertility didn’t decline until a century later. The French were mostly rural, their farming was primitive, and they had high food prices.

A new history paper offers new clues about this early rural French decline. Within that region, the villages where fertility fell first tended to have less wealth inequality, less correlation of wealth across generations, and wealth more in the form of property relative to cash. Fertility fell first among the rich, and only in those villages; in other villages richer folks still had more kids. The French revolution aided this process by reducing wealth inequality and increasing social mobility.

It seems that in some poor rural French villages, increasing social mobility went with a revolution-aided cultural change in the status game, encouraging families to focus their social ambitions on raising a fewer higher quality kids. High status folks focused their resources on fewer kids, and your kids had a big chance to grow up high status too if only you would also focus your energies on a few of them.

It seems to me this roughly fits with the fertility hypothesis I put forward. See also my many posts on fertility. Here are many quotes from that history paper:

This analysis links fertility life histories to wealth at death data for four rural villages in France, 1750–1850. … Where fertility is declining, wealth is a powerful predictor of smaller family size. … [and] economic inequality is lower than where fertility is high. … The major difference in the wealth–fertility relationship at the individual level. Where fertility is high and non-declining, this relationship is positive. Where fertility is declining, this relationship is negative. It is the richest terciles who reduce their fertility first. … Social mobility, as proxied by the level of inequality in the villages and the perseverance of wealth within families, is associated with fertility decline. …

The exceptional fertility decline of France is a … spectacular break from the past has never been satisfactorily explained. … The decline of marital fertility during the late nineteenth century was almost completely unrelated to infant mortality decline. … Time was the best indicator for the onset of sustained fertility decline: excluding France, 59 per cent of the provinces of Europe began their fertility transition during the decades of 1890–1920. …

Any socio-economic explanation for early French fertility decline must consider that England, with a higher level of GDP per capita, a smaller agrarian sector, and a larger urbanization rate, lagged behind French fertility trends by over 100 years. … almost 80 per cent of the French population were rural, and nearly 70 per cent lived off farming at the time of the decline. … ‘farming remained primitive’ and that there were numerous indicators of overpopulation (such as increases in wheat prices from the 1760s to the 1820s). … It is widely accepted that many localities began their fertility transition long before [the Revolution of] 1789. …

Weir … states ‘evidence on fertility by social class is scarce, but tends to support the idea that fertility control was adopted by an ascendant “bourgeois” class of (often small) landowners’. … Children became ‘superfluous as labourers and costly as consumers’. The decline of fertility in France in the early-to-mid-nineteenth century was primarily due [he said] to the decline of the demand for children by this new class. … The results of this analysis support Weir’s hypothesis. … Compared to cash wealth alone, property wealth is a better predictor of the total negative wealth effect in the decline villages. …

The old social stratifications under the Ancien Régime, where hereditary rights had determined social status, were weakened by the Revolution. All of this served to facilitate individuals’ social ambition, and the limitation of family size was a tool in achieving upward social mobility. … For the villages where fertility is declining, the Gini coefficient is significantly lower than where it is not. …

Where the environment for social mobility is more open, father’s wealth should have less importance in the determination of son’s wealth than would be the case where social mobility is limited. … Where fertility is high and not declining, father’s wealth is a highly significant predictor of son’s wealth.This relationship appears to be far weaker where fertility is declining. …

Wrigley’s proposition of a neo-Malthusian response cannot be valid as it was the richest terciles who reduced their fertility, and Weir’s explanation, again, does not uniquely identify France. What was unique to France was the pattern of landholding and relatively low level of economic inequality.