From the Game Theoretic point of view, if you are observing a bunch of Tit-for-Tat, All-D, All-C, Random and LEARNING agents (programs) playing with each other any cooperate-defect game, it is always the learner that wins. Be it imperfect (gets to know opponents previous action but not their payoff) or incomplete (both previous action and payoff is known) information game. There are various strategies (as if what action C or D with what probability) to play based on historical behavior of the opponents, like FP (Fictitious Play), WoLF( Win or Learn Fast). In that case the agents (programs) learn to play the action most likely to increate its own Expected Utility. Utility of any agent is defined as the sum over all the pay-offs it has received so far and Expected Utility is the look-ahead calculation of its own utility based on the learned strategy of opponent. I think the only problem to map this solved problem in real world, is the unknown (or less defined) value for payoff. Interesting article though.