H. Chase Stevens Logo

Most Common Tags

  1. programming
  2. python
  3. code
  4. philosophy
  5. evolution
  6. game design
  7. probability
  8. video games
  9. genetic algorithms
  10. government

Archives

Loading

The Anonymized Prisoner's Dilemma Challenge – Exploiting Limited Knowledge

posted on: Tuesday, August 20, 2013 (7:28 am) by

The competition

Recently, a couple of friends and I entered the Brilliant.org Hunger Games competition, in which competitors must create programs which are pitted against others in an arena of sorts. In any given round of the competition, the programs must choose for each other program whether to cooperate with them or take advantage of them. In the Hunger Games competition, these are framed as "hunting" and "slacking".

Tit-for-tat

At face value, the competition is a very standard iterated prisoner’s dilemma. For the general case, the optimal strategy for this game has already been discovered: tit-for-tat. This essence of this strategy is to reciprocate everything done to you in the previous round, and cooperate with everyone in the first round in a show of "good faith". However, the Hunger Games competition had a slight twist: although your program would be able to see the overall reputation of each opponent (id est how many times they hunted versus slacked), no other identifying information would be supplied. This limited knowledge makes true tit-for-tat impossible, since your opponents are effectively anonymized. Although you may know that a player you decided to cooperate with in the previous round defected against you, there’s no way to retaliate against the same player in this round with 100% confidence.

Our strategy

My team’s strategy, nicknamed "The Underminer", both compensated for this lack of knowledge and managed to exploit it. We started with the assumption that tit-for-tat is possible, to a degree. As the number of rounds increases, the reputations of individual opponents becomes solidified, thus making this a means of identification. Although a player with a reputation of 1.0 in round one could drop to a reputation of 0.5 in round two, a player with a reputation of 1.0 in round 20 can only drop to 0.95. Based on this, one can say that the ranking of players by reputation remains mostly static: the worst-ranked opponent will have a very difficult time becoming the highest-ranked. While this is very untrue in the first rounds, at a certain point changing ranks becomes almost impossible. This phenomenon can be imagined like a zipper: before any zipping has occurred, there’s a very large degree of freedom of motion available. However, as you begin to zip, the freedom of motion becomes more and more constrained, until none remains.

Undermining

While our program implements tit-for-tat as described above in most circumstances, there’s a very specific scenario in which it deviates from this otherwise optimal strategy. As mentioned, the "tit-for-tat" allowed by the Hunger Games competition is not foolproof, since player identities can only be approximately tracked at the beginning of the game. Assuming other players are also tracking opponents by reputation, we can exploit this limitation in knowledge by occasionally attempting to "undermine" another player, assuming their identity. This technique is probably best illustrated by example. Suppose in some round we’re ranked 2nd by reputation at 0.7, while another player is ranked 3rd at 0.6 reputation. Assuming both we and the 3rd ranked player slacked completely this round, there would be no way for us to displace our opponent as 3rd ranked player, since they already have the "bad reputation" head-start. However, the likelihood of our opponent slacking completely this round is very low. In fact, the decisions of our opponent can be estimated given their current reputation, the number of rounds elapsed so far, and a rating of how certain we want our estimation to be by using the lower bound of the Wilson score interval. While this formula is most often used to rank items based on user reviews (and is employed perhaps most famously by Reddit’s "hot" algorithm), in this case we can use it to infer the "true" cooperation rate of opponents and, based on this, their (probable) reputation at the end of the round. Supposing in this circumstance that we predict with a large amount of certainty that our opponent’s reputation at the end of this round will be at worst 0.55, and we can manage to lower our reputation below that, then we choose to slack completely this round. Assuming that the other player remained at 0.6 reputation, while we dove down to 0.5, from a third player’s perspective, this is what happened: the 2nd ranked player went from 0.7 reputation to 0.6 reputation, and the 3rd ranked player went from 0.6 reputation to 0.5 reputation. For the third player to make the assumption that the 2nd ranked player went under the 3rd ranked player - going from 0.7 reputation to 0.5 - would be a strange and unintuitive leap of logic. So, in this way, we can choose to take the advantageous route of fully slacking while passing off some of the negative repercussions of this to a worse-ranked player.

Graph of player reputation over time

In the above, we can see The Underminer repeatedly performing this tactic against a standard tit-for-tat bot. After each undermine, The Underminer reverts to a basic tit-for-tat strategy, which in this simulation caused its reputation to increase over time. As soon as it’s again in a position where it can undermine the tit-for-tat player, it does, repeatedly reaping the rewards of doing so.

Unfortunately, I can’t yet reveal whether The Underminer was successful or not – mostly because I haven’t found out myself. Hopefully, however, in two weeks’ time the results of the Hunger Games competition will be announced, at which point I’ll write another analysis on how our program fared overall. In the meantime, you can go check out the source code for our program as well as the testing/simulation program we used at this github repo.

Tags: anonymity, game theory, probability, programming, python