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We study behavioral action sequences of players in a massive multiplayeronline game. In their virtual life players use eight basic actions which allowthem to interact with each other. These actions are communication, trade,establishing or breaking friendships and enmities, attack, and punishment. Wemeasure the probabilities for these actions conditional on previous taken andreceived actions and find a dramatic increase of negative behavior immediatelyafter receiving negative actions. Similarly, positive behavior is intensifiedby receiving positive actions. We observe a tendency towards anti-persistencein communication sequences. Classifying actions as positive (good) and negative(bad) allows us to define binary 'world lines' of lives of individuals.Positive and negative actions are persistent and occur in clusters, indicatedby large scaling exponents alpha~0.87 of the mean square displacement of theworld lines. For all eight action types we find strong signs for high levels ofrepetitiveness, especially for negative actions. We partition behavioralsequences into segments of length n (behavioral `words' and 'motifs') and studytheir statistical properties. We find two approximate power laws in the wordranking distribution, one with an exponent of kappa-1 for the ranks up to 100,and another with a lower exponent for higher ranks. The Shannon n-tupleredundancy yields large values and increases in terms of word length, furtherunderscoring the non-trivial statistical properties of behavioral sequences. Onthe collective, societal level the timeseries of particular actions per day canbe understood by a simple mean-reverting log-normal model.

Emergence of good conduct, scaling and Zipf laws in human behavioral sequences in an online world