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The Senate Is Not Fair To People

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So, I saw this tweet, by yet another person claiming that critics of U.S. Senate apportionment rules are historically illiterate:

I’m sick and tired of this argument, and I responded with what seems to me to be the obvious rejoinder:

I realize this has become a partisan issue because of the elections, but the factual claim in my tweet is incontrovertible: People in low-population states have far more voting power in the U.S. Senate than people in high-population states, because every state gets two senators, regardless of the population. Thus, the 28 million people living in Texas are represented by only two Senators, whereas the 27 million people living in the combined states of Alaska, Delaware, Hawaii, Idaho, Kansas, Maine, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Mexico, North and South Dakota, Rhode Island, Vermont, West Virginia, and Wyoming are represented by 36 senators. That’s an 18-fold advantage.

Several replies to my tweet were from people who seemed to think they were arguing with me, but who were actually agreeing with me, starting with the original tweeter I responded to:

These people are all making the same point I was making: The Senate does not represent the people fairly and equally because it privileges states. Originally, each state’s senators were elected by state legislatures. The idea was to give state legislatures a say in the federal legislative process. Thus the Senate would be more like the members of the earlier Continental Congress (or U.N. delegates today) than free-standing representatives of the people. 

The 17th amendment screwed all that up by switching the selection of senators to direct popular elections in the states. Nowadays, except for a few constitutional powers, the Senate is basically a smaller version of the House of Representatives, but with longer terms and badly disproportionate representation.

No, the 17th Amendment didn’t just make people think the Senate represents people. The 17th Amendment made the Senate represent people. Just not very well.

A few people took the tiresome path of objecting to my use of the word “democracy”:

And you should have learned in civics class that in the taxonomy of systems of government, a constitutional republic is a type of democracy. The United States is a democracy, and more specifically, a constitutional republic.

I’m not sure how to answer this one. Two conflicting responses come to mind:

  • You teach civics, and you don’t know a constitutional republic is a democracy?
  • Do you realize that in saying the U.S. is not a democracy, you’re agreeing with my assertion that we’re doing democracy wrong?

Look, the idea of fairness is a pretty basic ethical principle. It’s why the rallying cry of election reformers has so often been some variation of “one person, one vote.”  In utilitarian terms: Everybody matters, and everybody matters equally. Except when voting for U.S. Senators, where some people’s votes are a lot more influential than others.

Yes, it was designed this way on purpose. But that was a compromise (literally, it’s the Connecticut Compromise) necessary, given the concerns of the time, to form the United States into a stronger, more unified government. But just because the Senate’s structure was intentional and necessary doesn’t mean that Senate representation is fair or just.

I also realize that actually changing Senate apportionment would be extremely difficult. At minimum, we’d need a constitutional amendment, and even that wouldn’t be sufficient because Article Five of the Constitution explicitly prohibits amendments that reduce a state’s representation in the Senate without that state’s permission.

There’s nothing inherently wrong with arguing that the Senate’s structure was a good idea at one time, and there’s nothing inherently wrong with arguing that reforming the Senate is far too difficult to be workable. But you can’t argue that the existing structure is moral and right unless you can explain why you think disproportionate representation is a good thing.

Like this, for example:

That was just about the only tweet to avoid getting hung up on side issues and actually attempt to support the disproportionate representation.

My short answer to Forward Observer is that I’m against tyranny too, but tyranny of the majority is still better than tyranny of the minority. You limit tyranny by limiting the authority of government, not giving that authority to a smaller group of people.

And if you want to protect a minority, the Senate’s disproportionate representation shifts voting power to protect a vary strange minority class. It doesn’t protect racial or ethnic minorities, and it doesn’t protect religious minorities, or any of the other classes that are normally considered disadvantaged. And contrary to what Forward Observer is suggesting, it doesn’t even protect people who live in rural areas outside the “biggest cities,” because there are big cities in small states, and there are rural areas in big states. The people living in Las Vegas benefit from their influence over Nevada’s two Senators, and people living in the rural areas of Texas (or New York or California) lose by comparison.

To the extent that Senate apportionment rules protect anyone against tyranny of the majority, they only protect people who live in states with small populations, which is kind of a strangely defined group of people to be concerned about.

Model Justice 1.1 – Random Guilt

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One of the limitations of the Model Justice 1.0 model is that every defendant has the exact same probability of being found guilty at trial. That’s unrealistic, and it’s unrealistic in a way that will matter: The estimation of the probability of a conviction at trial has got to be one of the most important factors in whether a prosecutor decides to offer a plea deal and whether a defendant decides to accept one.

What I want to do in the simulation is to assign a probability p to every defendant who enters the model. This will represent the probability of conviction if the case goes to trial. I.e. if p = 0.25 then there is a 25% chance the defendant will be found guilty by the Verdict action when it runs. If p = 0, there’s no chance of a conviction, and if p = 1, there’s no chance of an acquittal. But before I can do that, I need to answer an important question: How should those probabilities be distributed? Or to put it in the language of the problem domain: How does the quality of the criminal cases vary?

The easiest approach is to assume a uniform distribution. That is, to assume that all possible values of p from 0 to 1 are equally likely, so that a 100-bin histogram of the probability of guilt for 100,000 cases would look something like this, with the values spread out more-or-less equally over all the quality levels.

That doesn’t seem realistic to me. I mean, does it seem right that they would bring just as many cases that will succeed 5% of the time as will succeed 50% of the time? I just don’t believe that the prosecution would bring exactly as many cases at each possible probability of success.

This is where it would be great to use research by professional criminologists, or get advice from people with a lot of experience in the criminal justice system, who might be able to advise me on the quality mix of criminal cases. Alternatively, it would be nice to pull these numbers from a sophisticated model of how the police and prosecutors’ offices build cases. Since I don’t have access to either of those right now, I’m just going to wing it.

I’m going to assume that building a case that will persuade a jury involves trying to achieve a whole bunch of smaller subgoals, and that each of these goals would improve the case in a way that pushes the jury toward a guilty vote — the more goals accomplished, the likely the jury is to convict. If we assume that the prosecutor and police officers have a roughly equal probability of accomplishing each of these goals, then the number of goals accomplished will conform to what is known as a binomial distribution.

A good example of a binomial distribution is the probabilities of various outcomes when we flip ten coins and count how many turn up heads. Since each coin has two possible values, and there are 10 coins, there are 210 = 1024 possible outcomes. Of those outcomes, the extreme cases of all heads or all tails can each only occur one way:

HHHHHHHHHH
TTTTTTTTTT

So the probability of either of those cases is 1/1024 ≈ 0.000977.

There are 10 possible ways of having one coin come up heads, one for each coin:

HTTTTTTTTT  THTTTTTTTT  TTHTTTTTTT  TTTHTTTTTT  TTTTHTTTTT
TTTTTHTTTT  TTTTTTHTTT  TTTTTTTHTT  TTTTTTTTHT  TTTTTTTTTH

So the probability of exactly one head in a set of flips is 10/1024 ≈ 0.00977. That’s also the probability of exactly one tail in the set of flips.

Now it gets more complicated. For two coins to come up heads, there are a bunch of ways it can happen. We already know there are 10 ways to get 1 head. So assuming we’ve got one head, there are 9 remaining coins, and any one of those can also come up heads. However, that leads to each combination appearing twice. E.g. we can start with TTTHTTTTTT and then pick anther coin to get TTTHTTTHTT. However, that gives the same result as if we had started with TTTTTTTHTT and picked another coin to get TTTHTTTHTT. In other words, the two possible ways of choosing two heads result in identical sets of flips, so we have to divide by 2. That means the number of ways of getting exactly 2 heads is (10 x 9) / 2 = 45 which occurs with a probabiliy of 45/1024 ≈ 0.0439.

There are 10 x 9 x 8 ways to get to 3 heads, divided by 3 x 2 ways to arrange 3 heads identically, leaving 120 possible ways to get exactly 3 heads or 3 tails, with a probability of 0.117. By similar logic, 4 heads or 4 tails come up 210 ways with a probability of 0.205, and 5 of each comes up 252 ways with a probability of 0.246

If we plot the possibility of each possible number of heads, the probabilities look like this:

For Model Justice 1.1, I’m going to be assuming that the quality of cases charged against defendants assume a similar profile. I don’t know how many factors go into building a criminal case, but I assume it’s a lot more than 10. And since I’m just picking a distribution rather than modeling each of the factors, I don’t really have to care. That means that instead of using a binomial distribution, which depends on a fixed number of factors (you can’t flip 8.7 coins), I’m going to use a continuous approximation to the binomial distribution called the normal distribution, which looks something like this:

This is, I must admit, something of a cop-out. The normal distribution is probably the most common distribution ever, at least when discussing real-world phenomena. (Uniform distributions are common in games and gambling.) In fact, scientists often assume data points are normally distributed even when they really aren’t. It’s one of the sins of statistics, usually committed because there are so many more statistical tools available if you can just assume normality.

However, when dealing with data taken from the real world, there are solid statistical reasons for believing that the normal distribution may be close enough. The real world is often messy, with lots of different effects contributing to the thing you are trying to measure. And when a measurement is influenced by lots of small random variables, each of which can nudge it slightly higher or lower, it turns out that the result often does look a lot like a normal distribution.

This can be illustrated with something called a Galton board. That’s a slanted board with pins sticking up out of it in a regular pattern of offset rows. You drop balls into it at the top, and they trickle down. Each time they hit a pin, they can roll off to the left or right, at which point they will hit a pin from the row below, again rolling off to the left or right, and so on. At the bottom of the board, right below the last row of pins, there’s a set of vertical slots to catch and stack the balls. The height of the stack will tell you how many balls ended up there, and if you dump enough balls into the board, the stacks will follow (more or less) a normal curve.

Here’s a decent video with some explanation (skipping the Galton bio at the beginning):

(You can buy the Galton board used in the video here.)

So that’s my justification for using a normal distribution. I can imagine of a number of reasons why the quality of actual criminal cases wouldn’t follow a normal curve but, at least for now, it’s good enough.

I do need to make some adjustments to fit the model. For one thing, the allowable values for a defendants probability of guilt are from 0 to 1, so I might want to drop the normal curve right in the middle of it, something like this:

There are a couple of problems with that idea.

First of all, the normal distribution is a mathematical construct, so despite the nice appearance here, the left and right tails actually extend out to infinity. So what does it mean if we pick a random value out of the normal curve and the probability of guilt is 3.5? My thinking is that the normal curve doesn’t really represent the probability of guilt. Rather, it’s a crude representation of the quality of the case. So if we get values that are greater than 1, it means the case is more than good enough to guarantee (with p = 1) a conviction. Similarly, a case quality less than 0 means there is no way (p = 0) it could lead to a conviction. So if I get a value outside the range 0 to 1, I’ll snap it to 0 or 1.

The second problem is that there’s no reason to believe the average probability of guilt is exactly 0.5. As I said, the normal distribution actually represents the quality of the case, and it would be quite a coincidence if the average case quality just happened to be exactly good enough to produce a perfect split between guilty and not guilty verdicts. It’s likely that strong cases are more work to put together than week cases, so there will be a tendency to produce a lot of weak cases:

Then again, it’s possible that prosecutors avoid charging extremely weak cases that they don’t think they can win, leaving only the strong cases:

The third problem is that cases may deviate from normality in important ways. For example, the practice of discarding bad cases may lead to a different shape of the left-hand side of the distribution curve. Or maybe there’s always a small chance of a not-guilty verdict, from jury excursions or random black swan events, so that even the best cases can sometimes lose. I have no idea, so I’m going to ignore these possibilities for now. (I may add something in later.)

Incorporating this into the model is pretty straightforward, and you can follow along on the new Model Justice 1.1 model.

The shape of a normal distribution curve is described with two values:

  • Mean: The average value. Or the value right in the middle of the curve at the highest point. (One of the handy properties of normal distributions is that the mean, median, and mode are all the same.)
  • Standard Deviation: This is basically a measurement of how wide the curve is. The greater the standard deviation, the more the curve stretches out to the left and right.

I’ve replaced the Probability of Guilt slider, and the underlying variable, with Guilt Mean and Guilt Standard Deviation sliders that control the shape of the normal distribution. The values shown will cause this model to produce the same ProbGuilty values as version 1.0:

I also modified the code in the Arrest action to generate a random value from the normal distribution. If the value is outside the range [0,1], it will be snapped to the nearest bound. I.e. Values greater than 1 will be snapped to 1 and negative values will be snapped to 0 so they are valid probabilities. Unfortunately, Insight Maker doesn’t give me any way to show the shape of the generated curve. If you want to get some idea what the curves look like, you can use an online normal curve generator, like this one.

(I’ve also added an ID generator that slaps an ID number on each defendant agent, and I’ve added a global Stats object that collects some internal data that I use for debugging.)

Sadly, after all this discussion, the results from the new model are not noticeably different from the results from the old model. It was generating random guilt probabilities around a specified mean, now the new model is generating random guilt probabilities around a mean that is itself randomly generated around a mean. That tends to look like the same thing.

Nevertheless, these changes are a step toward modeling plea bargaining, because they will keep all defendants from making the same decisions.

A Chicago-esque Voting Moment in the ‘Burbs

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Yesterday morning, for the second time, I voted from my new address in the suburbs, after many years of voting in Chicago. It’s pretty much the same experience out here as it was in the city, except out here they have good parking.

I only had to stand in line a minute or two before giving my name to one of the election judges, who looked me up on the computer and printed out a label for my application to vote. I signed the application, and then both judges checked it against the copy of my signature on the label. Satisfied, they initialed it and sent me down to receive my ballot.

This was the first election where I’ve seen them use the computerized ePollbook system to check my registration. In the past, they always used a giant printed book. Normally, I’m skeptical of computerized election procedures, but since the giant printed book was computer-generated anyway, this seemed like an improvement without an increased risk. I thought it worked well.

The system broke down a bit, however, when I got to the ballot judge. He checked my application, grabbed the appropriate ballot form, initialed it, and handed it to me. As I stepped away, I noticed something odd. He had given me two ballot forms.

Oh my God, this was my big chance! After decades of voting in Chicago, I was finally getting to live the Chicago election motto: Vote early, vote often!

My mind was off in a flash: Nobody was paying attention, so I could just walk to the booth. Voting is private by law and design, so nobody would be able to see what I was doing. I could fill out both ballots and vote twice. Problem: The judge had only initialed one ballot. But would anyone notice? Maybe I could just feed the ballot to the AccuVote tabulator and let it be counted. Probably no one would notice it was missing the judge’s initials unless they had to do a manual recount. Alternatively, I could forge a copy of the judge’s initials on the other ballot. Problem: Can I get away with feeding two ballots to the tabulator? Maybe, if no one was watching. Or I could leave one of the ballots behind in the voting booth, where another voter would discover it and report it to a judge, who might solve the problem by feeding it to the tabulator. Better yet, I could try depositing both ballots, and then if someone spotted me, I could claim that I had found the second ballot in the booth, and I was just trying to be helpful because I didn’t want someone to lose their vote…

With a heavy heart, I turned back to the judge’s table and handed back the extra ballot, and then I went to vote. Just once. Like all the other losers.

But for that one shining moment…

Model Justice — Part 1

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A few of days ago, defense lawyer Mark Bennett tweeted an interesting thread about how loss aversion bias could affect a criminal defendant’s decision to take a plea deal:

(If you’re interested, read the whole thread.)

For as long as I’ve been following criminal defense lawyers on the web, they’ve been discussing the way the criminal justice system encourages defendants to accept plea bargains rather than insisting on trials to force the state to prove its case in court. Often it’s some pissed off lawyer who wants to eliminate plea bargaining or end pretrial detention for routine offenses.

I think these kinds of reform proposals raise some interesting questions, and I’d like to explore them. I have some of my own ideas about how plea bargaining works, and why the system is the way it is, and I’ve been thinking of writing a blog post or two about it. But then I got a crazy idea…

For a while now, I’ve been trying to teach myself how to do system dynamics modeling, and these kinds of questions seem like they would make an interesting subject. I’m going to try to build models for some of the strategic issues affecting trials and punishment.

Models of systems are necessarily much simpler than actual systems because the real world is too complex and messy to recreate in a hand-built computer model with perfect detail, and yet we build models because we want them to tell us useful things about the real world. Thus the art and science of model design involves choosing which details the model must capture and which details it can safely ignore.

Before you can do that, however, you have to know how the model will be used. If you are designing a new type of commercial airliner, and you want to know the predicted fuel efficiency under a variety of flight regimes, you may need to build a detailed simulation of the engine and flight control surfaces and connect them to an aerodynamics model parameterized by a weather model of the troposphere and stratosphere. On the other hand, if you work for an airline that is operating the new airplane, you may be able to plan flights around the country using little more than a few tables that relate speed, altitude, and fuel consumption.

A related concern is the accuracy of the model in making predictions. A company planning to build a package sorting facility may do extensive modeling to test the layout and operation of machinery and workstations to achieve optimum efficiency. This has to be good enough to generate quantitative results that will help project managers spot subtle problems, detect conflicting operations, capture transient events, and calculate costs.

At the other end of the accuracy spectrum are relatively simple models that capture broad relationships within a system without attempting to produce mathematically precise predictions. A lot of social science and economic models fall into this category. These are tools of exploration and learning, and we use them to test our intuition and refine our understanding.

That’s what I’m aiming for here. The model I’m building is going to start out extremely simple — it’s not even going to have plea bargaining at first. I hope to improve the model in future posts so it can exhibit more complex behavior including, eventually, plea bargaining. But before I can model plea bargaining, I have to model the circumstances under which plea bargaining arises, and that’s going to take a while.

I’m building this model using a simulation tool called Insight Maker. I chose it because it’s totally free, and because the simulation engine and development environment are written in JavaScript, which means it runs in a web browser. In addition, once I’ve got a model I like, I can share it on the Insight Maker website, and other people can run it directly in their browsers without needing to download and install specialized software.

I’ve decided to use an agent-based model. That means each defendant will be represented in the model by a separate simulation entity which goes through a series of state changes as the model runs. For this initial model, each defendant will progress through three states: Awaiting Trial, On Trial, and (if found guilty) Prison.

I’ve decided to use a simulated time interval of one month between each iteration of the model. That strikes me as a reasonable unit of time for describing the period spent awaiting trial and the period spent in prison. For the sake of some technical convenience, the period spent on trial will be modeled as lasting exactly one month. That’s unrealistic, but I don’t think it’s unrealistic in a way that matters.

I could switch to a shorter unit of simulated time, such as a single day, to make the model more flexible, but I decided to keep it at a month for three reasons: First, I don’t think a finer time resolution will produce a better model. Second, using a small time unit suggests that the model is more accurate than it ever could be. Third, iterating daily instead of monthly would increase the number of iterations by a factor of 30, greatly extending the simulation’s run time.

The complete model, or at least the first version of it, looks like this:

(The actual model is here on the Insight Maker website if you want to follow along or play with it yourself.)

The model has four variable parameters which can be controlled by sliders appearing on the page.

Here’s what they control:

  • Arrest Rate: This is the number of defendants arrested each month. Every iteration of the simulation will fire the Arrest action, which will create this many defendant agent entities and add them to the Defendants pool, starting in the Awaiting Trial state.
  • Time to Trial: This is the amount of time (in months) that defendants will spend waiting for their trial. In this version of the model, nothing interesting happens during this period, and I could have omitted it. However, I think it will be useful in future versions of the model.
  • Probability of Guilt: This is the chance that a defendant will be found guilty at the end of the trial. In this version of the model, every defendant has the same probability of conviction. Future models will allow different probabilities of conviction, as a crude measure of the quality of the case. In preparation for that, each defendant agent in this model has an independent ProbGuilty property, but they are currently all set to the same value when the defendant agent is created, and that value is used in determining the trial outcome.
  • MaxSentence: Specifies the maximum sentence for the defendant if convicted. As with probability of guilt, this is currently the same for every defendant, but the agents have independent NominalSentence properties that can be used in the future to adjust sentences on a per-defendant basis.

In addition, each agent has other properties associated with it to contain more detailed state information. I’ve added data items to represent the actual sentence (which is always the maximum sentence in this version of the model) and to help count the sentence down as it is served.

The meat of the model is the Defendant agent and its lifecycle, pictured above. Defendant agents begin their life in the model when the Arrest action fires, at which point they receive their probability of being convicted and their expected sentence. They sit in the Awaiting Trial state for however long Time to Trial specifies, and then they transition to the On Trial state and go through a one-month trial. In the Verdict action, they are found guilty or not guilty with a random draw, based on the probability of guilt assigned to their agent when it was created.

If found not guilty, they are removed from the defendant pool and from the model. If found guilty, they move to the Prison state, and the Serve Time action counts down their sentence with each iteration until their sentence is done, at which point they are removed from the model.

Running the model with the above parameters yields a graph displaying the number of agents in each model state over a simulated period of 10 years:

During the first four months, defendants are added to the model in the Awaiting Trial state (green line) at a rate of 30 per month, for a total population of 120. After that, as each new group of 30 defendants is added to the model, the group of 30 from four months ago stop waiting and enters the Trial state (red line). A month later, those 30 are remove from the Trial state, and a new batch of 30 begins trial. For the rest of the simulation run, these values remain in a steady equilibrium state, with 120 defendants awaiting trial, and 30 defendants on trial.

Starting at the 5th month, 30 defendants per month conclude their trials. Half of them, chosen randomly, are found not guilty and removed from trial. The other half — about 15 per month, on average — go to the Prison state. The prison population continues to build up at that rate until the 41st month, at which point the first batch of defendants is done serving their sentence. Since 15 prisoners per month serve for 36 months, the prison population stabilizes at around (30 x 0.5 x 36 =) 540 prisoners from there on out. Unlike the other populations in the model, the Prison population is influenced by the random events of trial, therefore the Prison population exhibits random rises and falls 

The advantage of using a model is that we can play around with it. If we “get tough on crime” by arresting an extra 10 defendants per month and sentencing the guilty ones to an extra year in prison, the steady state prison population booms to (40 x 0.5 x 48 =) 960 prisoners.

These results are not surprising, nor should they be, with such a simple model, and we didn’t really need an agent-based model to figure out these numbers, as demonstrated by the simple math in the preceding paragraphs. We could easily have modeled these same results with a few spreadsheet formulas. I think, however, that an agent-based model will be more suitable when we want to model more complex situations.

A few limitations or simplifications of this model are worth noting:

  • The model assumes there are no resource limits. No matter how many defendants are arrested, there will always be enough prosecutors, defense lawyers, and criminal court rooms to try them all without delay. There’s also plenty of prison space for everyone found guilty.
  • Everyone commits the same crime. If you wanted to model multiple crimes, or multiple degrees of severity, you’d have to re-run the model multiple times with different parameter settings and combine the results. Note that this implicitly assumes that cases of one type do not interfere with cases of another type, for which it is necessary, but not sufficient, to assume that there are no resource limits.
  • As mentioned, all defendants have the same chance of being found guilty, which implicitly assumes that the cases against all defendants are of equal quality.
  • If found guilty, all defendants are hit with the same sentence. There is no uncertainty about the sentence if found guilty.

As I develop future models, I’ll be relaxing some of those constraints.

Finally, you may be wondering why I’m bothering building models. After all, there are people who study this kind of thing for a living. They probably have much better models than I could ever build in my spare time. Why don’t I just use their models? Or better yet, why don’t I just read their criminology papers to find out what they’ve already learned?

The short answer is: Where’s the fun in that? I like exploring new ideas and learning new things.

And if you’re a criminal lawyer, prosecution or defense, who finds this project at all interesting, I could use some help. I want to build more elaborate versions of this model, and I need help figuring out what aspects of the criminal justice system belong in this model, and what aspects can be safely ignored. I need to figure out how to model plea bargain decision making on both sides, and I’m not sure where to begin. I’m a professional software developer, but my interest in the criminal justice system is strictly amateur. Nevertheless, I’d hate to put in so much work into the models — and these posts — only to discover that I’ve been making my readers stupider.

You don’t want that on your conscience, do you? Besides, it might be fun.

It Sometimes Begins with Virginia Postrel

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Reason magazine is celebrating its 50th anniversary this year, and a bunch of the staff are sharing their libertarian origin stories. I think I’ll share mine.

The first time I remember hearing the word “libertarian” was when I was in high school in 1980 and Ed Clark was running for President on the Libertarian ticket. I remember that he wanted to legalize all drugs, and I thought it might make sense in theory but that really doing it would be nuts.

When I got to college, I got interested in some common political issues, and I decided to make an effort to learn more about them. This was years before the first web page traversed the internet, so my approach was to scour the magazine racks for publications that covered political issues. The magazines ranged from liberal to conservative, and they were often interesting reading, but all of them took editorial stances that struck me as inconsistent and misguided. 

Except for one magazine, where I found myself nodding along with almost everything they wrote. It wasn’t so much that they agreed with my views, but that the authors thought about the issues in a way that made a lot of sense to me. I’d start reading a piece about the war on drugs or transportation policy, and at some point there’d be this mental ping and I’d realize they had found exactly the right way to think about the issue.

Unfortunately, I had read so many different magazines that next time I went looking for reading material, I couldn’t remember what it was called. I had to just continue buying a variety magazines, hoping to pick it up again. I think it was a few months before I opened up a magazine, started reading, and got that ping sensation again. This time I made a point of noting the magazine’s name.

Of course it was Reason.

This was during the years when Virginia Postrel was Editor in Chief, and I remember her editorials had a way of drawing libertarian ideas out of all kinds of topics, from contact lenses to breast implants. Every issue of Reason had articles that helped me clarify my thinking about the issues of the day. It was during this time that I really began to think about the world in libertarian terms and of myself as a libertarian. I will always be grateful to Postrel and the rest of the Reason staff for the clarity they gave me.

In the following years, four books also heavily colored and solidified my libertarian thinking:

  • Ain’t Nobody’s Business If You Do: The Absurdity of Consensual Crimes in a Free Society. This is Peter McWilliams’ magnum opus. His passionate argument convinced me forever that the government has no business telling us what to do if we aren’t hurting anyone.
  • The Armchair Economist This is Steven Landsburg’s easy-to-read and thought-provoking introduction to some very serious economic concepts. It helped me organize my thinking about policy analysis, and kicked off my now decades-long amateur interest in economics. (Link is to the latest edition.)
  • Hard Heads, Soft Hearts: Tough-minded Economics For A Just Society. This book by liberal economist Alan Blinder argues that some public policies are so bad, and so economically ill-informed, that we can find solutions that improve on hard-headed conservative concerns about limiting spending and regulation, while also doing a better job of satisfying soft-hearted liberal goals like reducing pollution and improving the lives of the poor.
  • Lost Rights: The Destruction of American Liberty. In this book, James Bovard presents hundreds of examples of how laws and regulations can get out of control when enforced by the petty and power-mad. It’s a searing and heartbreaking account of ruined lives. (Bovard turns out a ton of books like this. This is just the first one I read.)

Sorry. No Ayn Rand.

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