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App to measure road quality

The accelerometers in phones can detect vibrations, such as when the car that the phone is in drives through a pothole. The GPS in the phone can detect the location and speed of the car. An app that connects the jolt, location and speed (and detects whether the phone is in a moving car based on its past speed and location) can automatically measure the quality of the road. The resulting data can be automatically uploaded to a database to create an almost real-time map of road quality. The same detection and reporting would work for bike paths.
Perhaps such an app has already been created, but if not, then it would complement map software nicely. Drivers and cyclists are interested in the quality of the roads as well as the route, time and distance of getting to the destination. Map software already provides congestion data and takes traffic density into account when predicting arrival time at a destination. Road quality data would help drivers select routes to minimise damage to vehicles (and the resulting maintenance cost) and to sensitive cargo. This would be useful to trucking and delivery companies, and ambulances.
A less direct use of data on road quality collected by the app is in evaluating the level of local public services provided (one aspect of the quality of local government). Municipalities with the same climate, soil and traffic density with worse roads are probably less well run. For developing countries where data on governance quality and spending is difficult to get, road quality may be a useful proxy. The public services are correlated with the wealth of a region, so road quality is also a proxy for poverty.

Spam call deterrence

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Time-wasting marketing calls to my cellphone are a bit of a problem. I have developed the habit of checking any new number against online spam call reporting websites, and if the number turns out to be a spammer, then saving it under “Spam call” in my phone. Then in the future, any call from the same number shows up on the phone as Spam call. There are probably apps for blocking numbers, but as an economist, I would prefer a tax to a ban. I would like to make callers pay me for calling me, to compensate for my time spent answering or blocking, and also to deter spam calls. In principle, charging a fee for receiving a call is possible, because there are already 1-800 numbers and others that are pricier to call than an ordinary phone.
If it was costlier to call me than most numbers, then I would refund the extra calling fee to my friends and other legitimate callers, so they would not be deterred from calling me. This is easy, because I can see the list of calls, their durations and numbers online, so can calculate how much extra each caller paid to call me. Spammers of course would not get a refund.

Unfortunately, imposing a monetary cost on spammers is infeasible for most individuals. There are now apps that block spam numbers automatically, and the user can specify additional numbers to blacklist. However, a better version of spam call deterrence is not to block the call, but to impose as large a cost on the caller as possible. A non-monetary way to punish spammers is to waste their time. If the person called pretends to be a gullible customer and keeps the spammer talking for a long time, but in the end does not buy what is being sold, then the spammer loses more than by just being blocked. Unfortunately, this also wastes time for the victim of spam, and that time is usually more valuable than the spammer’s, especially with modern robocalls and auto-dialling.

To fight fire with fire, victims of spam could have an AI on their phone respond in their stead. The time of the AI costs little, so the AI could play the part of a gullible customer, keeping the caller hopeful. The AI could say: „Tell me more,” „How much does it cost?” and other encouraging things, agree to buy what the caller is selling, provide a fake credit card number and other data. Only after a long call, entering the fake data to process the order, confirming the address, etc, would the spammer learn that no profit is forthcoming.

Similarly, AI could produce written responses to email spam to deter it. For example, provide (fake) account numbers and passwords to the self-proclaimed Nigerian prince, after asking for various confirmations and documentation.

Spam call deterrence of course is just a part of general spam deterrence, for which one way is boycotting. However, boycotts may backfire if firms spam on behalf of competitors to make them look bad, as in a false flag attack.

Sugar-free, fat-free and low-salt claims

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The three main ingredients of unhealthy food are sugar, salt and fat. The packaging of junk food often has claims of sugar-free, fat-free or low-salt in big colourful letters on the front. The trick is that the absence of one of the three ingredients is compensated by a larger amount of the other two, as can be checked from the nutrition information label.
Sometimes the claims on the front of the pack directly contradict the nutrition label, so are downright lies. I have seen packaging with the claim “sugar-free” on the front, with sugars listed in significant quantity on the nutrition label. There are some legal sanctions for falsifying the nutrition information label, but almost no restrictions on what can be claimed elsewhere on the pack, so any contradictions should almost always be resolved in favour of the nutrition label.
I have seen a sugar-free claim on a pack on which the ingredient list included brown sugar. This suggests the existence of a legal loophole (brown sugar not equalling sugar somehow) that the manufacturer wanted to use.
If the manufacturer does not want to outright lie, then a trick I have seen is to claim “no added sugar” or “no sugar or artificial sweeteners” on the pack, but add other sweeteners, e.g. sugarcane juice, molasses, high fructose corn syrup. Similarly, “no added salt” can be bypassed by adding salty ingredients, for example dried salted meat or bacon to a snack mix.
Another trick is to create the sugar in the food during the manufacturing process. For example, heating starch for a long time or adding the enzyme amylase breaks the starch into smaller-molecule sugars. So a manufacturer can claim “no added sweeteners” and yet produce sugars in the food by processing the starch in it.
A similar trick for salt is to add sodium and chloride in other ingredients and let them combine into NaCl in the food.

Joining together detached houses saves energy

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Suburbs in many countries consist of detached houses that very close to each other – I have seen neighbours’ walls half a metre apart. Both houses could save energy by joining their adjacent walls together, which reduces heat loss in cold weather and heat entry (thus the need for air conditioning) in hot temperatures. Ideally, the joining should happen at the construction stage, but it is not difficult to do after the houses are built. Just enclose the space between the sides of two houses by extending the front and back wall and the roof of each house. It is not a load-bearing construction, it just has to keep the wind out from the space between the houses and provide some insulation to the space.
An added bonus is the creation of a covered storage area (a door to the space between houses should be created if the houses don’t already have a door on that side). A possible downside is that to get from the front of the house to the back, now one has to pass through the house or the storage area. But given the narrowness of the typical walkway between suburban detached houses, passing through the house may be the best route anyway. Also, when enclosing the walkway, a door can be made in each end to keep it open for passage.
Another downside is that windows on the side of the house now look into a covered storage area, not outside. But if the houses are so close together, then the only view from the window is the wall or window of the neighbour. After enclosing the side, this view becomes darker, but that does not seem a great loss. If it is, then energy-efficient lights can be installed in the enclosed area and kept on during waking hours, so people can admire their neighbour’s wall or window. Really, windows with such views can be replaced by a poster-size print-out of a photo of the view, because if the window looks into the neighbour’s window, then the neighbour probably keeps the curtains closed to prevent spying. And a wall through a window looks pretty similar to a photo of the wall stuck over the window.
The real reason to not join the houses is probably marketing and the desire to show off that it targets. People want to boast of owning a detached house, even if it is less than two metres from the neighbour’s. Knowing this, property developers construct such dwellings and market them as detached (“own your own house”, really owned by the mortgage issuer for 25 years). This is similar to the reason why McMansions are built, only the income of the buyers differs. Also similar are the pride and marketing that make people buy large SUVs, pickups and all-terrain vehicles for driving solely on paved roads.

Signs with strong language are not enforced

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Various prohibiting signs are posted in many places, saying for example “No smoking”, “No trespassing”, “No skateboarding”, etc. The language of a sign with the same message can be stronger or weaker, e.g. “Smoking prohibited” vs “No smoking. Strictly enforced” or “Trespassing forbidden” vs “Strictly no trespassing. Violators will be prosecuted to the fullest extent of the law.” It seems that there is a negative correlation between the strength of the language and the strength of enforcement. The rules stated on signs threatening strict enforcement or prosecution seem not enforced at all. Non-enforcement is certainly the case for the “Smoke-free campus. Strictly no smoking” signs around the Australian National University and for similar signs around other universities that I have visited.
Why might stronger language of the sign indicate non-enforcement? Stronger language is also longer, requiring a larger sign and more paint, which makes signs with stronger language slightly more expensive to put up. So why pay more for signs that suggest non-enforcement?
Local people learn the rules and their actual level of enforcement over time. For them, signs are not really necessary. Therefore, signs are put up only for first-time visitors or for legal reasons. Legalities usually just require some legible sign, not a long and strongly worded one, so to satisfy the law, the optimal choice is a shorter and simpler text that fits on a smaller, cheaper sign.
First-time visitors are either uncertain about the enforcement level or know it. If they know, then they are effectively locals – for them, there is no need for a long sign. If the visitors are uncertain, then they might infer the enforcement level from the strength of the language. This can go two ways. If the visitors are rational and the strength of the language is negatively correlated with the level of enforcement, then a shorter sign signals stronger enforcement and deters rule-breaking better. Then all signs would optimally be short. If the visitors are irrational and interpret the signs literally, then more strongly worded signs deter rule-breaking more. The negative correlation between strong language and enforcement suggests that people are irrational and take threats literally. Or that those putting up signs are irrational and for some reason choose the less effective strongly worded signs.
An alternative explanation is countersignalling (Feltovich, Harbaugh and To 2002). In this model, there are three types of sign-posters. The first type does not care much about whether the text of the sign is obeyed (may post the sign only for legal reasons). The second type cares a little bit, but not enough to pay for much enforcement. Still, the second type slightly enforces, so there is a small positive probability of some kind of punishment when breaking the rules. The third type really wants the rules to be followed and invests in enforcement correspondingly. The potential rule-breakers quickly learn from punishments whether they are dealing with the third type. So the third type does not need any particular kind of signs to distinguish himself from the first two.
The first two types are harder to tell apart. The first type is not interested in distinguishing himself from the others – in fact, being confused with the others is beneficial, because it is more likely to make people obey the rules. The second type would like to distinguish himself from the first type and be confused with the third type, but this desire is not strong enough to pay the same enforcement cost as the third. So the second type will settle for distinguishing himself from the first type. This is done by paying for slightly larger signs with stronger, longer language on them. In this case, in the absence of experienced enforcement, the potential rule-breakers respond more to strongly worded signs than to short ones.
For an external observer, it is difficult to distinguish a small amount of enforcement from none, so the perception arises that enforcement goes together with short signs, but non-enforcement sometimes with long, strongly worded signs and sometimes with short signs. So there is a negative correlation between the strength of enforcement and the strength of the language. This correlation is stronger if the fraction of the first type in the population of sign-posters is small, for example because some first types do not post signs at all. People posting signs are a selected sample from the population of those who want some rule obeyed. The selection oversamples those who care more.

Silly sunglasses

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Most sunglasses do not cover the eye fully. For example, any design where the lenses are close to flat (aviator, retro) or small does not protect the eye from rays coming from above or the side. Sunlight commonly comes from above, so these sunglass designs do not block a significant part of it. If the lenses are tinted (not clear), then they worsen the outcome for the eyes, because the dark glass in the centre of the visual field makes the pupils expand. When that happens, the pupils let in more light from any direction, including sunlight from the unprotected top and side directions.
In comparison, the polycarbonate safety glasses that currently cost 2 AUD in a construction shop have a wrap-around design and large lenses that leave only a small gap between the forehead and the glasses. Light from the side and nearly all other directions has to pass through the glasses before reaching the eye. The material for the safety glasses is polycarbonate, which block 99.9% of UV light. In order for sunglasses to provide better UV protection than the safety glasses, they have to block a larger percentage of UV light or cover more ray paths into the eye.
Suppose that sunglasses were made from a material that blocks 100% of UV. Then to improve on the safety glasses, the sunglasses would have to cover at least 99.9% of the ray paths into the eye that safety glasses cover. In other words, the sunglasses would have to have the same wrap-around design and as large or larger lenses.
An improved design for both safety glasses and sunglasses would take the wrap-around design one step further: cover the eyes from top to bottom as well as from side to side. One way to achieve such cover is a half-dome over each eye that touches the forehead above the eye and the cheek below, as well as the bridge of the nose and the temple.
A brimmed hat that blocks light coming from above compensates to some extent for flat-lensed or small sunglasses. The hat does not protect the eyes from light coming from the side and below the brim, so the classic sunglass designs are still inferior to wrap-arounds even combined with a hat.

Adding anticipation to automatic transmission

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Some of the downsides of automatic transmission in cars are that it does not anticipate hills or overtaking, and does not respond to slippery conditions appropriately. The technology that could enable the transmission to anticipate hills or overtaking is already available and incorporated in some cars, namely GPS, maps and sensors that look ahead of the car. If the map data includes altitude, then the location and movement direction of the car on the map predicts the slope that the car will be on in the near future. This information could be sent to the automatic transmission to enable it to shift gears in anticipation of a hill. A forward-looking sensor that has a range of a few hundred metres can also see a hill if the road does not curve too much. The sensor data could also be sent to the transmission. Similarly, a sensor could detect the nearing of the car in front and shift to a lower gear to prepare to accelerate for overtaking.
Slippery conditions can be predicted using the car’s thermometer, perhaps with the addition of a humidity sensor, or detected using a wheel slip sensor. This information could also be sent to the computer controlling the automatic transmission, to prevent it from spinning the wheels too fast when there is little grip. The GPS or forward-looking sensor could also tell whether the car is moving relative to the landscape. Comparing the movement data with the wheel spinning speed reveals whether the wheels are slipping.

Speculative etymology of the word “partner”

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I was confused when I first encountered the word “partner” in the context of referring to a spouse or the person one is in a romantic relationship with. I thought: “What partner? Business partner? Tennis partner? Oh, sex partner – that’s where the reference to partner comes from.”
A similar speculative etymological derivation can be applied to the US slang phrase “hook up”, which means to start a romantic relationship. The phrase arose to describe a situation in which a “hook” goes up something, specifically someone’s bottom.

Gender equity either on paper or by forcing people to change research fields

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Suppose that a university employs roughly equal numbers of men and women overall, but the proportions of the genders differ across research fields, or across research and administration, and the university wants gender equality within each subfield. Using the narrowest definition of a research field, each field has either one or zero people, so in the smallest fields, gender equality is impossible. From now on, the focus is on fields or administrative units that have at least two people.
Gender equity can be achieved on paper by redrawing the administrative boundaries or redefining research fields. A simple algorithm (not the only possible algorithm) is to form pairs of one male and one female employee and call each such pair an administrative unit. Larger units can be formed by adding many male-female pairs together. If the numbers of men and women are not exactly equal, then some single-gender pairs will be left over, but most administrative units will have perfect gender equality.
The same idea can be used less radically by reassigning people who do interdisciplinary work and could plausibly belong to multiple research fields. Each person who is „between” fields gets assigned to the field that has a smaller fraction of that person’s gender. This increases gender equality in both the field that the person joins and the field that the person left. The field with a bigger surplus of that person’s gender loses one of that gender, and the field with a smaller surplus gains one.
Other than reassigning people or redrawing administrative boundaries, gender equity requires inducing people to change their research fields. To some of my colleagues, directing researchers to change their area is ideologically unacceptable. However, if equity is the goal and people’s field change is necessary to achieve it, then several methods can be used. The inducements can be softer or harder. A hard inducement is a hiring policy (and job ads) that restrict hiring to only one gender. The same can be done with promotion and retention. If this policy was explicitly stated to everyone, then it would become more effective, and also more acceptable to people than when they are surprised with it upon applying for promotion.
Soft inducements consist of hints that one gender is preferred, which are usually stated in political doublespeak like „we are committed to equity” or „we are an equal opportunity employer”. If many more candidates of one gender apply, then giving all candidates an equal opportunity of getting hired does not result in equal proportions of men and women employed. I am in favour of clear guidelines and transparency, for example of explicitly stating in job ads and promotion policies that the underrepresented gender is preferred, and which gender is currently underrepresented. Clearly telling people that switching fields is good for their career is likely to have a bigger effect than the currently used hints.
It may be easier to shift the research areas of people who are earlier in their careers. Encouraging more young people of a given gender to go to an area where their gender is underrepresented is one way of inducing them to change their field (relative to their preference).
Current equity policies are focussing almost exclusively on the inflow of employees. Gender balance can also be improved by managing the outflow, for example by offering early retirement schemes to one gender, or expanding these to a wider age range for one gender. If there are gender differences in the propensity of accepting certain inducements to leave, then the same inducements can be offered to both genders (seemingly gender-neutrally), with the desired result that one gender exits more.

Laplace’s principle of indifference makes history useless

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Model the universe in discrete time with only one variable, which can take values 0 and 1. The history of the universe up to time t is a vector of length t consisting of zeroes and ones. A deterministic universe is a fixed sequence. A random universe is like drawing the next value (0 or 1) according to some probability distribution every period, where the probabilities can be arbitrary and depend in arbitrary ways on the past history.
The prior distribution over deterministic universes is a distribution over sequences of zeroes and ones. The prior determines which sets are generic. I will assume the prior with the maximum entropy, which is uniform (all paths of the universe are equally likely). This follows from Laplace’s principle of indifference, because there is no information about the distribution over universes that would make one universe more likely than another. The set of infinite sequences of zeroes and ones is bijective with the interval [0,1], so a uniform distribution on it makes sense.
After observing the history up to time t, one can reject all paths of the universe that would have led to a different history. For a uniform prior, any history is equally likely to be followed by 0 or 1. The prediction of the next value of the variable is the same after every history, so knowing the history is useless for decision-making.
Many other priors besides uniform on all sequences yield the same result. For example, uniform restricted to the support consisting of sequences that are eventually constant. There is a countable set of such sequences, so the prior is improper uniform. A uniform distribution restricted to sequences that are eventually periodic, or that in the limit have equal frequency of 1 and 0 also works.
Having more variables, more values of these variables or making time continuous does not change the result. A random universe can be modelled as deterministic with extra variables. These extras can for example be the probability of drawing 1 next period after a given history.
Predicting the probability distribution of the next value of the variable is easy, because the probability of 1 is always one-half. Knowing the history is no help for this either.