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*Guest:David Banks*

David Banks is a statistician at Duke University. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics, and a former editor of the * Journal of the American Statistical Association * and * Statistics and Public Policy *. His major areas of research include risk analysis, syndromic surveillance, agent-based models, dynamic text networks, and computational advertising.

**John Bailer**
: I'd like to welcome you to today's Stats and Short Stories episode. Stats
and Short Stories is a partnership between Miami University and the
American Statistical Association. Today's guest is David Banks. He's a
Professor of Statistics at Duke University. I am John Bailer. I'm Chair of
the department of Statistics at Miami University and I'm joined by my
colleague Richard Campbell, Chair of the Department of Media, Journalism
and Film. We're delighted to have David speaking with us today on the short
episode about Adversarial Risk Assessment. David that is quite a mouthful
and it's an intriguing topic. Can you just give us a quick sense of what
Adversarial Risk Assessment might be?

**David Banks**
: Absolutely. It's a decision theoretic alternative to Classical Game
Theory.

**Bailer**
: I'm going to have to stop you David. I looked over and Richard turned a
little green.

**Banks**
: Well I hope it will get easier. Game Theory is studied a whole lot in
adversarial conflicts such as the prisoner's dilemma or mutually assured
destruction. And in these contexts it quickly became apparent that human
beings don't make decisions the way Game Theory says they should. Very
often they will cooperate in a prisoner's dilemma experiment, where college
students are put in that situation. And Game Theory says that should never
ever happen. So there's a lot of evidence that Game Theory is not really a
good guide for that behavior, an alternative to that is to use Statistical
Risk Analysis. And Risk Analysis is an attempt to try to understand what
types of threats an opponent might provide, and Classical Risk Analysis
assumes that your opponent is non-strategic. So it's like a hurricane. A
hurricane doesn't decide to aim for New Orleans and then do a quick faint
towards Biloxi because you're off guard. So that type of thing is not
useful in strategic games. So Adversarial Risk Analysis proposes that one
build a model for the decision making of one's opponent and then make the
best choices against that model. And it's actually something that human
beings do all the time. Richard, if you were playing chess with Boris
Spassky you would think really hard. You'd spend months studying chess
openings. You know, in advance, and you'd be looking for the move behind
the move behind the move at every single step of the game. On the other
hand if you're playing chess with your 8 year old niece you'd probably have
a very different mental model for how she approaches the game of chess. And
you would adapt your responses accordingly. If you're playing your niece
you might look three moves ahead. You're probably not going to look ten
moves ahead. And you certainly wouldn't spend months studying chess
openings before you play her.

**Richard Campbell**
: And if I was really nice I'd let her win.

**Banks**
: And there is that too, yes. I'm not assuming you're that nice. So my
point is, in lots of adversarial situations such as arise in federal
regulation when you have the Federal Government trying to regulate the
amount of pollution that a company releases into the environment, and
there's a third stake-holder, which is sort of the community. There you
have three decision-makers all with different interests and they're going
to try and come up with a compromise or a solution that will work well for
everybody. And in order to do that each has to build a model for the
objectives of their opponents. Similarly if I'm Coke and you're Pepsi,
well, there's certain things I can invest in. I can invest in trying to
open up a Chinese market. I can invest in new products. You can try and
invest in opening up India. You might try and invest in buying better
shelf-space in the grocery stores. We're going to make our decisions based
upon our best guess of what our opponent is going to do. And that involves
building a model for what the goals of the opponent are, what the resources
of the opponent are, and how the opponent is trying to make decision. Is
the opponent working on a five year business plan? Or is the opponent
working on a quarterly business plan? And those are all pieces of
information that we won't know but we can make shrewd guesses about. And
that is what drives an adversarial analyst.

**Bailer**
: Wow. So one of the things that comes to mind is how good is your model?
And what do you do in terms of predicting this? How do you check and
calibrate such a model for your opponent's behavior?

**Banks**
: This is extremely difficult, and I don't think one should try and
minimize the difficulty of that in lots of situations. But remember the
short answer is if you have a bad model about your opponent's behavior,
then basically you're going to lose. It's no surprise that if you don't
know what your opponent is trying to do or you don't know what resources
your opponent has, or you don't know how your opponent is weighing the
situation, you're at a real disadvantage. And you could try and say, "well,
then that's why we should do Game Theory", but Game Theory is going to be a
real disadvantage too. Game Theory makes the assumption that all of the
opponents have common knowledge and know that that knowledge is held in
common. Which is completely unrealistic from most applications. So if
you're in a situation where, Richard is a terrorist and I'm a counter
terrorist I need to understand what Richard's goals are. I need to
understand, does he have a nuclear device? Does he have weaponized
smallpox? And I need to understand whether or not he thinks he's got a
really good chance of smuggling a bomb on a plane or whether he thinks he's
got a really poor chance of smuggling a bomb on a plane. If I don't know
these things I'm behind the 8-ball and I have to expect to lose.

**Campbell**
: You talked about guessing at what your opponent might do. Guessing is not
something I associate with John and the other statisticians that I know. So
you're talking about making best guesses, or are you using "guess" in a
particular way here?

**Banks**
: I'm using "guess" in a very particular way, and I apologize, I was trying
to be clear. I'm a Bayesian statistician. And a Bayesian is allowed to put
their own subjective probabilities on any set of events as long as those
probabilities are consistent with each other and as long the Bayesian
learns according to Bayes' rule. For example, I can't say that the
probability of heads on a coin is 0.7 and the probability of tails on the
same coin is 0.8 because those are inconsistent beliefs. But I can say that
probability of heads is about 0.8 and the probability of tails therefore is
about 0.2 because that is consistent. Then if I observe a series of coin
flips in 100 tosses I get 51 heads and 49 tails then I'm going to have to
change my mind according to Bayes' rule, so that instead of having an 80/20
coin I now believe that that coin is a lot closer to a 50/50 coin. And
there's a formal mathematical procedure for doing that but that's how a
Bayesian makes guesses.

**Bailer**
: This is great. Always a pleasure to chat with you David. It's been our
pleasure to have David Banks join us on Stats and Short Stories. Stats and
Stories is a partnership between Miami University's department of
statistics and media journalism and film and the American Statistical
Association. Stay tuned and keep following us on Twitter or Apple podcasts,
if you'd like to share your thoughts on our program send your email to
statsandstories@miamioh.edu
and be sure to listen for future episodes where we discuss the statistics
behind the stories, and the stories behind the statistics.

Click to close the script.