Okay, lets go with a D- then.
Here is something I came across almost 2 decades ago that has some relevance.
Carnival Booth: An Algorithm for Defeating the Computer-Assisted Passenger Screening System
To address these vexing security problems, the FAA has been trying in recent years to employ information technology to boost the overall efficiency of security screening. The kernel idea behind their approach is to be moe intelligent about which passengers are selected for rigorous inspections. Intuitively, the FAA argues, if you only have the ability to scrutinize a small percentage of passengers, it seems best to spend the bulk of time carefully searching those who are likely to be terrorists and not waste much time searching those who have a small chance of posing harm. Why frisk Eleanor, the 80-year-old grandmother from Texas when you can stop Omar, the 22-year-old student fresh from Libya? If you can develop a profile describing who is likely to be a terrorist, then those are the people upon whom you should concentrate your security efforts.
Drawing from these intuitive underpinnings, the crown jewel of the FAA’s information technology efforts is a system called the Computer Assisted Passenger Screening system (CAPS). The FAA contends that since CAPS uses profiles to pinpoint potential terrorists for closer inspection, it will not only result in the apprehension of more criminals, but will also make security screening more expedient for well-meaning citizens. Though in place since 1999, CAPS has gained much more attention as a promising counter-terrorism tool in the wake of September 11. The FAA already augmented the system in January, and plans for further expansion are underway.[3]
In our paper, we show that although these intuitive foundations might be compelling, their implementation in CAPS is flawed. That is to say that any CAPS-like airport security system that uses profiles to select passengers for increased scrutiny is bound to be less secure than systems that randomly select passengers for thorough inspection. Using mathematical models and computer simulation, we show how a terrorist cell can increase their chances of mounting a successful attack under the CAPS system as opposed to a security system that uses only random searches. Instinct may suggest that CAPS strengthens security, but it in fact introduces a gaping security hole easily exploitable by terrorist cells. It should be noted that CAPS has also received immense criticism from privacy advocates and civil libertarians[4], but in this paper we restrict our discussion to a purely technical perspective and the legal and policy implications of such an analysis.
Replace the FAA with CPP and terrorists with bots. Mechanistics approaches will always be defeated just as easily as the NPCs in Damsel in Distress are defeated.
This transparency is the Achilles’ Heel of CAPS; the fact that individuals know their CAPS status enables the system to be reverse engineered. You, like Simonyi, know if you’re carryons have been manually inspected. You know if you’ve been questioned. You know if you’re asked to stand in a special line. You know if you’ve been frisked. All of this open scrutiny makes it possible to learn an anti-profile to defeat CAPS, even if the profile itself is always kept secret. We call this the “Carnival Booth Effect” since, like a carnie, it entices terrorists to “Step Right Up! See if you’re a winner!” In this case, the terrorist can step right up and see if he’s been flagged.
Sure, set up a program to look for “Dumb bots” that are ratting 23.5/7, but here is what will happen, those programs will eventually catch very little. They’ll catch the guy who can code who writes his own botting program and is dumb about it. But even that guy will realize that botting 23.5/7 will get him caught, so he’ll modify his program to bot 2-4 hours with a random log off timer after say an hour. He might even have his bot dock up for 45 minutes just like a real person who h as to deal with some RL crap or wife aggro.
Here is another article that could shed some light.
Asset Pricing Under Endogenous Expectations in an Artificial Stock Market
In this paper we propose a theory of asset pricing that assumes fully heterogeneous agents whose expectations continually adapt to the market these expectations aggregatively create. We argue that under heterogeneity, expectations have a recursive character: agents have to form their expectations from their anticipations of other agents’ expectations, and this self-reference precludes expectations being formed by deductive means. So, in the absence of being able to deduce expectations, agents—no matter how rational—are forced to hypothesize them. Agents therefore continually form individual, hypothetical, expectational models or “theories of the market,” test these, and trade on the ones that predict best. From time to time they drop hypotheses that perform badly, and introduce new ones to test. Prices are driven endogenously by these induced expectations. Individuals’ expectations therefore evolve and “compete” in a market formed by others’ expectations. In other words, agents’ expectations co-evolve in a world they co-create.
This article looks at stock market traders and the strategies they use to try and increase their returns. Given different traders (the assumption of heterogeneity) and the trial-and-error for new strategies the result is that while strategies work for awhile, they inevitable fail, and as a result you’ll see bubbles and crashes. Thinking similarly about botting we’d likely see strategies emerge that work…and as CCP adapts, those strategies will disappear possibly quite quickly.
This does not mean “Do nothing.” But the idea here is that there is a complex game being played. And we are sitting largely on the outside of game and trying to look in. It is like watching a poker game where we only know one of the players and we don’t know anything about their hand or their strategies. We can’t even see their bets. And this discussion is similar to thinking we are watching a chess game where we can see everything. We are all ignorant doofii…where some of us think they aren’t one of the ignorant doofii.
I’m going to stop at this point.