On Social Networks

John Kasich was elected governor in Ohio in 2010 as a strong Tea Party advocate. One of his first legislative campaigns in 2011 (Senate Bill 5) was to restrict collective bargaining for public employees: police, firefighters, and teachers. After it passed the hue and cry was huge: before the year was out a referendum put its repeal on the ballot, where it was soundly rejected – and since then Kasich has been a more moderate governor.

From 2002 to 2012 I spent a lot of time in Columbus, Ohio, and played handball at an athletic club there, with mostly Republican members. One of the regulars there was a retired state policeman who was on Kasich’s security detail. I remember him saying to us, “We told him, don’t go after the police and fire, just the teachers,” because he assumed that it would be an easy win to focus on a mostly female profession.

This is no longer the case. The strikes in West Virginia, Oklahoma, and Kentucky, coupled with the Parkland students’ activism, make me think about how social media has changed the way people organize – and that unions may be strengthened (or even superseded) by social networking, Facebook, and tweets. When a union calls a strike, it’s often a top-down decision. True, the leadership polls its membership to make that decision, but then it issues a proclamation. With social media involved in strikes it’s based on networking, which to my mind is a much more powerful way to rally support.

An additional note: it seems to be going worldwide. Today’s NY Times has articles about the Dalit (formerly “untouchables”) in India and physicians in Togo using social media to push for change. While we may deplore its use by Cambridge Analytica to promote lies and influence elections, it can also be used to foster positive change.

McArdle on Denmark

My cousin alerted me to a post Megan McArdle wrote about Denmark and Danes, about trust and what we could learn from them. I have a slightly different take, although it’s from over fifty years ago. But I think that my perspective still has some validity.

In September 1963, with my newly minted PhD, I accepted a one-year postdoc position at the Technical University of Denmark. It was a time of ferment in the US, especially after the Kennedy assassination that November, and Johnson’s pushing for action on civil rights. A lot of racist bile cropped up in the media and was published in Denmark as well. My colleagues Gunnar and Erling were constantly on me about how terrible we treated Negroes in our country, implying that such a thing would never happen in Denmark.

I, of course, tried to explain that, yes, it was terrible but that we were working on it — as the 1964 Civil Rights Act subsequently showed. But before that passed, I brought up to them something that I noticed locally, that I hope they could explain: why were all of the menial workers, street sweepers and the like, apparently Greenlanders (recognizable due to their Inuit descent rather than northern European descent)? After I brought that up I never again heard about the mote in our eye. Yes, I’m sure we can learn a lot from Danes about mutual trust, but let’s be a bit moderate with our praise.

The NY Times Posted a Lott of Crap

So John Lott is promoting guns again, this time in an op-ed piece in yesterday’s New York Times. But this time he’s taking a different tack. Some years ago Lott maintained that a survey he conducted on defensive gun use showed its benefits. However, no one could check it; he said that he lost the data in a hard drive crash – but he couldn’t even provide evidence that he hired and paid interviewers to perform the survey.

He also used published crime statistics to promote his idea that relaxed gun laws prevented homicide. He subsequently was found to have misused the statistics in, shall we say, “innovative” ways, to “prove” that more guns leads to less crime.

Abandoning data and surveys to promote guns, this time he uses a couple of anecdotes relating to individuals. That is, one person who was improperly denied a concealed carry license is more salient to him than the deaths of dozens of schoolchildren across the country.

I first encountered Lott when he began to use crime data improperly and wrote to him explaining the issues. When he did nothing about it, I wrote an article criticizing his research. To counteract my criticism, a woman named Mary Rosh started appearing on the web, who vilified me and who praised Lott as one of the best teachers she ever had. Then it turned out that Mary Rosh was a fiction, a persona created by Lott to debunk his critics. [He even implicated his four children: he admitted that the name “Mary Rosh” was cobbled together using the first two letters of his kids’ names.] In other words, he hid behind the skirts of a woman he created out of whole cloth, just to promote himself and his pro-gun ideology. Here is my take on his actions in 2003.

At the time Lott was a resident scholar at the American Enterprise Institute, an organization with which he is no longer affiliated – which makes me look more kindly on AEI. Now he hangs his at the Crime Prevention Research Center, where he is president. I have no idea who funds this center, but I can guess. Those who want to learn more about the organization and Lott should read this article.

I realize that the New York Times is trying to do its best to look at both sides of controversial policies, but this really takes the cake. To publish a person who admitted to lying about his professional life, and who is writing about the policies he lied about, is offensive to me and should be to all those who look upon the Times as a credible source of information.

Bleg on Guns

Some years ago (can it really be 14 years?!) I guest-posted on this website a screed on John Lott, whose integrity, shall we say, leaves a lot of room for improvement. In that same time period I recall seeing a cartoon (it might have been in the NY Times) which had everyone in the street, including babies in their carriages, packing guns. Do any of you remember it, have the URL for it, or have a scan/copy of it? It unfortunately seems to be appropriate once again, with kids shooting kids at an unimaginable pace. If you have copied it but can’t post it here, please send it to me at my gmail address, maltzmd.

Some Frustrations of Daily Life

  1. Truck passing truck on a road with one passing lane, with about a 1 mph speed difference.
  2. Pulling a tissue out of a tissue box, and having to dive in to get the next one.
  3. Emptying the second rack of a dishwasher (Caution: always empty the bottom one first!) and having to empty the water from the bottoms of teacups and dry them.


Ah, Ken Rhodes’ comment reminded me of the reason I often don’t answer my land line, since it’s very often a call from “Rachel from Card Services calling about your credit card account.”


Our current President likes to give individuals he dislikes nicknames: “Rocket Man,” “Liddle,” “Pocahantas,” etc. I’d like to suggest one for him, one that considers where his militaristic motivation may come from. Anyone who, like Senator McCain, truly experienced war would not have the same aggressive stance as Donald Trump. So I think that Mr. Trump should be known as “President Bone Spurs,” in recognition of how he personally dealt with his possible involvement with the military. Other suggestions?

Type K Error

What statisticians call Type 1 errors (incorrectly rejecting the null hypothesis) and Type 2 errors (incorrectly accepting the null hypothesis) initially arose from signal detection theory: is that blip on the radar screen a signal or just noise? The two errors were known to us engineers (my former life) as either a false alarm or a missed detection.

But these are not the only statistical errors that can occur. Andrew Gelman proposed two additional statistical errors, Type S (confidently stating that a value is positive when it is negative, or vice versa) and Type M (confidently stating that a value is small in magnitude when it is large, or vice versa). They have less to do with the actual statistics than with interpretation of those statistics.

In furtherance of Gelman’s extension of statistical errors, I’d like to propose a new one, the Type K error. This is in recognition of the attempt by Kris Kobach (Kansas Secretary of State and vice chair of a federal voter fraud commission) to deny the vote to (at least)  tens of thousands of US citizens in order to prevent the two or three improper votes (out of millions cast) from occurring. [My numbers may be off, but you get my meaning.]

There have been other manifestations of this “error” in recent days. A report detailing the economic consequences of admitting refugees did not include the overwhelming financial benefits they provide over the long haul. In other words, the Type K error might be defined as “the deliberate and wrongful act associated with a statistical evaluation of the effect of only one side of a policy.”

On Using Consistent Units

Although the media have been getting better in dealing with numbers, the statistician in me sometimes recoils at such reports. An example is in today’s NY Times op-ed piece on Vietnam, in which the author writes that now is “an appropriate time to honor the suffering and the sacrifice of all those who served, including the 58,000 American service members, the estimated 1.3 million North and South Vietnamese fighters and the two million civilians who were killed during the conflict.”

I’m sure that it follows strict Times guidelines as to how to report numbers, but it gives one a visual impression that is akin to the quote apocryphally attributed to Stalin: “the death of one person is a tragedy; the death of one million is a statistic.” I would think that a better way to report this would be to write of “the 58,000 American service members, the estimated 1,300,000 North and South Vietnamese fighters and the 2,000,000 civilians who were killed during the conflict.” It provides a more accurate indication of the extent of the tragic consequences of the Vietnam war.

Crime and Big Data: Autopilot vs. Power Steering

There has been a host of recent articles and books decrying the use of “big data” to make decisions about individual behaviors. This is true in commerce (Amazon, Facebook, etc.), but also true in criminal justice, my field of research. Moreover, some of the algorithms that forecast dangerousness are proprietary, making it all but impossible to determine the basis for challenging a sentence based on the algorithm’s outcome. Recent books, such as Weapons of Math Destruction and The Rise of Big Data Policing, underscore the dangers of such activity. This is the essence of an autopilot approach to forecasting behavior – hands off the wheel, leave the driving to us.

There is some research that supports this type of algorithmic decision-making. In particular, Paul Meehl, in Clinical versus Statistical Prediction, showed that, overall, clinicians were not as good as statistical methods in forecasting failure on parole, as well as the efficacy of various mental health treatments. True, this book was written over fifty years ago, but it seems to have stood the test of time.

It is dangerous, however, to relegate to the algorithm the last word, which all too many decision-makers are wont to do (and against which Meehl cautioned). All too often the algorithms, often based on so-so (i.e., same-old, same-old) variables – age, race, sex, income, prior record – are used to “predict” future conduct, ignoring other variables that may be more meaningful on the individual level. And the algorithms may not be sufficiently sensitive to real differences: two people may have the same score even though one person may have started out doing violent crime and then moved on to petty theft, while the other may have started out with petty crime and graduated to violent crime.

That is, the fact that a person has a high recidivist score based on the so-so variables should be seen as a threshold issue, a potential barrier to stopping criminal activity. It should be followed by a more nuanced look at the individual’s additional life experiences (which do not fit into simple categories, and therefore cannot be included as “variables” in the algorithms). That is, everyone has an age and a race, etc., but not everyone was abused as a child, was born in another country, or spent their teen years shuffling through foster homes. Therefore, these factors (and as important, the timing and sequence of these factors) are not part of the algorithm but may be as determinative of future behavior as the aforementioned variables. This is the essence of a power steering approach to forecasting behavior – you crunch the data, but I decide how to use it and where to go.

Regarding power steering, I’m sure that many of you would rather look at an animated map of weather heading your way than to base your decisions (umbrella or not?) on a static (autopilot) weather forecast (BTW, does a 30 percent chance of rain refer to the likelihood of my getting wet in a given time period or to the fact that 30% of the area will be rainy and may skip me entirely?). The same issues are there in crime analysis. A few years ago I coauthored a book on crime mapping, which introduced the term that heads this post. In that book we described the benefit of giving the crime analyst the steering wheel, to guide the analysis based on his/her knowledge of the unique time and space characteristics of the areas in which the crime patterns developed.

In summary, there’s nothing wrong with using big data to assist with decision-making. The mistake comes in when using such data to forecast individual behavior, to the exclusion of information that is not amenable to data-crunching because it is highly individualistic – and may be as important in assessing behavior than the aforementioned variables.

What Does It Take to Found a Religion?

Now that the Senate-blessed Neil Gorsuch has donned the Robes of the Righteous, we have to reconsider the way we live our lives. Not very religious myself, I am now thinking that I should be more active in this area.

So here’s how one might fight fire with fire (and a touch of brimstone). As far as I know, there are no hard-and-fast criteria for defining a religion. For example, Scientology was started by the science fiction writer L. Ron Hubbard (who knows, maybe it started out as a joke?), and has been able to gull some of Hollywood’s dimmest stars into joining it.

So what might it take to become a religion recognized by the Supreme Court? My brilliant idea: turn tennis into a religion. Let’s call it Tênis, using the Portuguese spelling to make it more exotic.

Here are some of my p-baked thoughts (hopefully, p > 0.5):

  • Shrines: we have Forest Hills, Wimbledon, Roland-Garros, and Melbourne Park, to which we can make our pilgrimages.
  • Matriarchs and patriarchs: the Grand Slam tournament winners would surely qualify.
  • Club fees: since you are contributing to a religious endeavor, you should be able to include your fees as a charitable deduction on your income taxes.
  • Government grants: as per the Trinity Lutheran decision, if the court surfaces need to be redone, a government grant is not out of the question.
  • Rackets (an obvious double entendre) and balls: they could be purchased tax-free.

I don’t mean to imply that all religions are as shallow as the one I’m suggesting; it’s just that if we are going to remove the barrier between church and state, as Gorsuch, Alito, and Thomas seem to want to do, we should consider how to leverage it to our advantage, or at least to point out the inconsistencies in their arguments.

PS: I originally entitled this post “What Does It Take to Start a Religion?“ but felt that “Found” would be more …. profound.

PPS: I considered focusing on golf instead of tennis, but thought that it might give someone in high office an idea.

PPPS: In adding comments to this post, be thoughtful. After all, this could be the founding Testament for a new religion. I don’t want it to include a shopping list, as in “A Canticle for Leibowitz.”