# Evidence under Adversarial Conditions

I recently noticed similarities between how I decide what stock market
evidence to look at, and how the legal system decides what lawyers are
allowed to tell juries.

This post will elaborate on Eliezer\'s [Scientific Evidence, Legal
Evidence, Rational
Evidence](https://www.lesswrong.com/s/zpCiuR4T343j9WkcK/p/fhojYBGGiYAFcryHZ).
In particular, I\'ll try to generalize about why there\'s a large class
of information that I actively avoid treating as Bayesian evidence.

Here\'s [an
example](https://www.fastcompany.com/42575/rule-3-leadership-confusing-hell)
of a briefly popular meme that helped teach me to ignore certain types
of information:

> Companies need to be forgetting organizations. Enron Corp., which has
> repeatedly been tagged as the nation's most innovative corporation, is
> exhibit A as a world-class forgetting organization. It's not wedded to
> what it did yesterday. Enron chiefs Kenneth Lay and Jeff Skilling have
> figured out how to operate like a band of pirates. Got an idea? Don't
> dally. Go for it while it's an original!

Everything here seems at least technically true ([The Media Very Rarely
Lies](https://astralcodexten.substack.com/p/the-media-very-rarely-lies)).
I suspect it was mostly intended to be honest and helpful. But as a
reader, I found it discouraged me from focusing on some key questions,
such as: who was tagging Enron as innovative? What were the goals of
those taggers? What was their track record at helping readers identify
good investments? How much of a connection is there between innovation
and good investments? What are some examples of Enron\'s innovation, and
how do they resemble the kind of innovations that have caused other
companies to succeed?

Enron seems to have done a few things which qualify as fraud. But most
of the Enron stock price bubble was generated through misdirecting
investor\'s attention (and maybe the attention of Lay and Skilling) away
from the risks that Enron was taking.

I was in fact tempted to invest in Enron when I listened to that kind of
meme. Whenever I felt that temptation, I looked at its financial
statements and charts of its stock price, and decided I wouldn\'t miss
much if I waited for better evidence.

The standard Bayesian worldview asks me to update on all relevant
information. Why do both I as an investor and the legal system exclude
large classes of information from that rule?

Both contexts involve some zero sum or negative sum conflicts. That
makes it likely that someone is packaging information in order to
mislead.

If I, or a jury, only notice a datum because someone thought it would
mislead me or the jury, odds are that updating on that information would
reduce the accuracy of my (or the jury\'s) beliefs. We reject that
information in order to ensure that its expected value will be zero,
because the alternative tends to be that its expected value is negative.

In both contexts, rules have been developed to admit evidence only if
it\'s hard to manipulate or if it comes from a source that\'s unlikely
to manipulate it.

Accounting rules strongly limit how many ways key information can be
presented. They also ensure that someone can in principle be punished if
the information misleads.

Similar rules ensure that company press releases are fairly safe for
investors to read. The company will often be sued if the press release
contains questionable claims that subsequently become linked to investor
losses. That deters a wide variety of harmful propaganda (and presumably
a fair amount of helpful evidence). It still leaves some gray areas for
vaguely misleading memes, such as talk of Enron\'s innovation. Companies
have mostly shifted to getting other [respected
sources](https://www.princeton.edu/~pkrugman/enron.html) to promote
those ideas for them, rather than putting them in their own press
releases.

Still, I consider it an important skill to read those press releases in
somewhat the same way that courts admit evidence: carefully
distinguishing between \"facts\" and everything else.

Science has similar problems with adversarial sources of evidence.

A century or two ago, there may have been small enough communities of
scientists and science journals, and a good enough reward structure,
that everyone involved could trust each other to be pursuing the common
good.

Today, wide areas of science have grown big enough that it\'s hard to
have a high-trust relationship with everyone who\'s publishing in the
field.

I expect that would still have been achieved if there had been
sufficiently good incentives. But few scientists want to think about
incentives. Economists being the obvious exception - did they do better?
The evidence seems unclear.

Increased science funding over the past century has contributed to a
large increase in competition for tenured positions. Publications have
become an overly important part of how people get tenure. That
combination means increased incentives for science communications to be
adversarial rather than truth-oriented.

I expect drug-related science to have the biggest problem with
adversarial evidence, since billions of dollars can depend on whether
the science convinces the FDA that a drug falls on the right side of a
threshold.

This model suggests that medical science likely has the most
constraining rules of evidence. I\'d guess that the least constraining
rules would be found in physics, computer science, chemistry, and
geology. Those seem to be the fields that are least likely to use rules
such as p \> 0.05 to reject evidence.

Scientists, like lawyers and experienced investors, focus on
communications that rigorously minimize ambiguity and subtext, in spite
of the cost in readability and entertainment value.

Science, law, and investing all use different heuristics in deciding
what qualifies as evidence, adapted to particular problems they\'ve
faced and to what types of information they need to allow. The
adversarial perspective illustrates a unifying theme behind those
heuristics.