Errors are common in strategic situations. We use a genetic algorithm to simulate the evolution of error-prone finite automata in the repeated Prisoner's Dilemma game. In particular, the automata are subjected to implementation and perception errors. The computational experiments assess whether and how the distribution of outcomes and structures in the population changes with different levels of errors. We find that the complexity of the automata is decreasing in the probability of errors. Furthermore, the prevailing structures tend to exhibit low reciprocal cooperation and low tolerance to defections as the probability of errors increases. In addition, by varying the error-level, the study identifies a threshold error-level. At and above the threshold error-level, the prevailing structures converge to the open-loop (history-independent) automaton Always-Defect. On the other hand, below the threshold, the prevailing structures are closed-loop (history-dependent) and diverse, which impedes any inferential projections on the superiority of a particular machine. <br><br> Keywords; automata, repeated games, prisoner's dilemma, genetic algorithms, local polynomial regression
