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Alex Jones And the Fake “Gotcha”

In early August, when Alex Jones’ infamous defamation case was the top legal news in the nation, this clip went viral:

Alex Jones’ brand sank from “right-wing conspiracy jerk who bullies dead kids,” to all that plus a perjury-committing moron. Never mind that no one can point out exactly where the perjury is–if the Reddit echo chamber repeats it enough, it becomes true. But did you know that everything you’ve read about this moment is based on a false premise?

That is, the plaintiff’s attorneys committed a clear ethics violation by recklessly using protected information on cross-examination.

Here is what you have been led to believe:

1. Defense counsel accidentally disclosed protected documents to the plaintiff’s attorneys.
2. The plaintiff’s lead attorney noticed the disclosed documents contained protected information, then notified the defense counsel immediately.
3. Upon notification, Texas Rules of Civil Procedure dictate that an attorney has 10 days to respond to the opposing counsel in order to maintain privilege over accidentally disclosed documents.
4. The defense counsel ignored the polite notification, thus failing to assert privilege.
5. Therefore, by the defense counsel’s own incompetence, the accidentally disclosed documents became free and clear for the plaintiff’s attorney to use upon Jones’s cross-examination. Thus leading to the viral moment you see in the clip above.

Bullshit. I’m here to tell you the media deliberately ignored the full context of the situation. Here are the emails you never read about:

The defense counsel was notified about the accidental disclosure on Friday at 11:24 PM. 6 hours and 42 minutes later they responded, “Please disregard the link…”

These emails were revealed in an emergency motion filed immediately after Jones’ cross-examination. In the motion it is revealed that the plaintiff’s attorneys completely ignored the defense counsel’s request to disregard the information that was sent.

Why haven’t you read a single story about the defense counsel’s response? Is it because the media was more interested in pissing on Alex Jones’ face, rather than covering events in a comprehensive manner?

It doesn’t take a quantum physicist to figure out why the media ignored the response, but why did the court let this behavior slide? I’ve only been able to find two decent excuses:

1. The defense counsel’s response wasn’t specific enough.
2. The defense counsel’s response didn’t cover the items used to trap Jones during his cross-examination.

These are both post hoc rationalizations for the ethical violations committed by the plaintiff’s attorneys. Read what the plaintiff’s attorney says during cross. He clearly believed the information was protected, and it only became free for him to use after the 10-day grace period had passed.

Your attorneys messed up and sent me an entire digital copy of your entire cell phone with every text message you’ve sent for the past two years and when informed did not take any steps to identify it as privileged or protected in any way and as of two days ago it fell free and clear into my possession and that is how I know you lied to me when you said you didn’t have text messages about Sandy Hook.

Even if you buy the rationalizations for the use of Jones’ text messages, do you think the above statement is an accurate representation of his correspondence with the defense? In what world is responding at 6 in the morning on a Saturday “not taking any steps to identify privileged or protected information?”

In light of the information above, do you think the plaintiff’s attorney is projecting his own unprofessional tendency to ignore correspondence onto the defense, or is he simply a liar?

Introducing The SolCalc Legal Blog

I just started my 1L semester of law school. Now is as good a time as any to start writing on legal topics, right?

As of now, I don’t have much of a plan for topics to be covered. For now I’ll mostly write about hot topics in the news (from a hard conservative perspective) and whatever noteworthy topics I come across in law school.

Eventually I may start to focus more on gaming law issues. We’ll see

Choosing a Game Theme

Long ago I listened to Scott Adams talk about the failure of the Dilbert animated series. Fans of the comic strip wouldn’t watch the cartoon. One reason is they hated the sound of Dilbert’s voice.

Now contrast the success of the Dilbert comic strip with the lack of details within the strip itself. We don’t know Dilbert’s last name, where he lives, his job title, or even what his company does. His boss’ name is just The Boss.

Now consider the most successful casino games on the market and look at their themes:

• Generic Chinese theme
• Generic Egyptian theme
• Generic Greek theme
• Jewels
• Strong animal
• etc…

These themes may sound high level, but there’s actually not much more to them than what’s written on each bullet point.

The more details you add when it comes to casino games, the more chances you have to turn off the player.

As a concrete example, take a look at a game like Crash. You watch a rocket ship and a number go up, then you decide to cash out. That’s it. Do you think the game would be as successful if it was themed around hang gliding? What if they told you who was inside the rocket ship? Where the rocket’s trying to go? Why it’s trying to get there?

Measuring Casino Game Volatility

Standard Deviation

When we think about the volatility of data, the first concept that comes to mind is the almighty standard deviation metric. This is a good metric for 99% of casino games on the market, but over the last 5 years or so we’ve started to see a new class of game emerge: persistent state games (think games like Scarab or Ocean Magic). What happens in these games is bets are not independent. Instead some characteristic of the game carries over from one bet to the next, usually resulting in large wins at somewhat regular intervals.

The violation of bet independence makes standard deviation a lackluster metric for assessing the volatility of these kinds of games. The regularity of these large wins results in a deceptively large impression of the game’s volatility. Can you say a game is volatile if your typical player gets a lot of time on device?

A Better Metric

I propose a much more effective metric for volatility that works for this emerging class of games, as well as more traditional casino games: Median Spins (or Median Bets). The idea is to capture how long it takes the typical player to exhaust their bankroll. The more bets a typical player is able to make on a game, the less volatile it is. You can visualize how volatility affects median spins by comparing the histograms below.

How to calculate median spins

Unfortunately for most games there’s no simple formula for determining median spins, but it’s easy enough to determine it via simulation.

1. Start by running a full game simulation where a player starts with a bankroll of 50 times the cost to cover (for a 40 cent game this would be \$20).
2. Determine how many bets it takes for the players to exhaust their bankroll. For calculating median spins you can usually cap this metric at 1000 spins to save simulation time.
3. Repeat this process X times, keeping a log of how many spins it took each player to exhaust their bankroll.
4. Finally, calculate the median value from the data to determine median spins.

In my experience, if we start with a bankroll of 50 times the cost to cover, median spins of less than 100 could be considered a high volatility game, and median spins higher than 150 would be considered low volatility.

Average Spins?

In case you’re curious, average spins is an absolutely useless metric for measuring anything, except for maybe reverse engineering the RTP of a game. This is because the average number of spins is completely determined by the RTP of a game. Imagine the player has enough to cover a single bet on a game. On average how many spins will this single bet give you?

This is a geometric series with a known solution. From here we can just plug all the known values into the following formula.

If a player starts with a bankroll 50 times the cost to cover, and the game has a 90% payback, then on average they’ll get 500 spins out of the game regardless of the game’s volatility.

Assessing Fake News: An Information Theoretic Approach

In 1948 Claude Shannon published a landmark paper that gave rise to a new field of science: Information theory. A few years ago I also published my not-so groundbreaking post on how we can make inferences from biased sources of information. I’m going to follow up that post by assessing the quality of a news source by using one of the key insights of Shannon’s research.

Let’s say you have a random source of information. The probability that it outputs a given message $x$ is given by $p(x)$. Furthermore, let’s say we wish to construction a function, $s(x)$, that indicates how surprising a given message is. How might you want to construct such a function? An intuitive approach might be to give a few constraints on things we want from $s(x)$.

1. $s(x)$ decreases as $p(x)$ increases. The more likely an event, the less surprsing it is.
2. If $p(x)=1$, then $s(x)=0$. A event that is certain should yield no surprise.
3. As $p(x)\rightarrow0$, then $s(x)\rightarrow\infty$. The surprise of an message knows no bounds.

One such function that satisfies these conditions is

$s(x) = \log \frac{1}{p(x)}$.

From here we can go a step further and measure the average surprise of a source (aka the Shannon Entropy) given by

$H(X) = E[s(X)] = \sum p(x) \log \frac{1}{p(x)}$

If we take this formula in its most literal sense it seems to reinforce our own intuitions about the quality of a news source. If a news source is always pro or anti one side or the other then it’s Shannon entropy is 0. i.e. There is no information to be gleaned from the signal. But if it occasionally surprises us then $H(X)>0$. In fact, $H(X)$ reach its maximum when all messages are equally likely.

Do you buy this literal interpretation of Shannon’s equations? If not, do you think it can be adjusted somehow?