We study behavioral action sequences of players in a massive multiplayer online game. In their virtual life players use eight basic actions which allow them to interact with each other. These actions are communication, trade, establishing or breaking friendships and enmities, attack, and punishment. We measure the probabilities for these actions conditional on previous taken and received actions and find a dramatic increase of negative behavior immediately after receiving negative actions. Similarly, positive behavior is intensified by receiving positive actions. We observe a tendency towards anti-persistence in 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, indicated by large scaling exponents of the mean square displacement of the world lines. For all eight action types we find strong signs for high levels of repetitiveness, especially for negative actions. We partition behavioral sequences into segments of length (behavioral ‘words’ and ‘motifs’) and study their statistical properties. We find two approximate power laws in the word ranking distribution, one with an exponent of for the ranks up to 100, and another with a lower exponent for higher ranks. The Shannon -tuple redundancy yields large values and increases in terms of word length, further underscoring the non-trivial statistical properties of behavioral sequences. On the collective, societal level the timeseries of particular actions per day can be understood by a simple mean-reverting log-normal model.

Funding: Funding was obtained in part by the Austrian Science Fund, FWF P23378 (PhD salary for MS) and travel support from the European Cooperation in Science and Technology Action, COST MP0801. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

In this paper we do not focus on the full MPN but on the dynamics (actions) taking place on its nodes. We report on the nature of sequences of human behavioral actions in a virtual universe of a MMOG. There sequential behavioral data is available on the scale of an entire society, which is in general impossible to obtain. The unique data of the online game Pardus [19] allows to unambiguously track all actions of all players over long time periods. We focus on the stream of eight types of actions which are translated into an -letter alphabet. This code of actions of individual players is then analyzed by means of standard timeseries approaches as have been used, for example, in DNA sequence analyzes [20] – [22] .

Societies can be seen as individuals interacting through a multiplex network (MPN), i.e. a superposition of several social networks defined on the same set of nodes (individuals) [1] , [2] . Different types of networks correspond to different types of social interactions. For example the communication sub-network of the MPN is the network whose links correspond to the exchange of information by means of emails, telephone calls, or letters. Another subnetwork is the trading network, where goods or services are exchanged between individuals, in exchange for other goods, money, or –rarely– for nothing. Each of these interactions usually needs an initial action taken by one of the subjects involved in the exchange, the sender, and a target to receive it, the recipient. Actions can (but do not have to) be reciprocated, so that in general the MPN consists of a set of directed and weighted subnetworks. The MPN is a highly non-trivial dynamical object. The different social networks within the MPN are not independent but strongly influence each other through a network-network interaction. To understand systemic properties of societies it is essential to detect and quantify the organizational principles behind such mutual influences. The MPN is an example of a co-evolving structure: on one hand the actions of individuals shape and define the topological structure of the MPN. On the other hand the topology of the MPN constrains and influences the possible actions which take place on the MPN. In general the MPN of a society can not be observed due to immense requirements on synchronized data acquisition. Despite these difficulties, the analysis of small-scale MPNs has a tradition in the social sciences [1] , [3] – [5] . Concerning large-scale studies, recently there have been significant achievements in understanding a number of massive social networks on a quantitative basis, such as the cell phone communication network [6] – [8] , features of the world-trade network [9] , [10] , email networks [11] , the network of financial debt [12] and the network of financial flows [13] . The integration of various dynamical networks of an entire society has so-far been beyond the scope of any realistic data source. However with the increasing availability of vast amounts of electronic fingerprints people leave throughout their lifes, this situation is about to change. Online sources are capturing more and more aspects of life, boosting our understanding of collective human behavior [14] , [15] . One particular source where complete behavioral multiplex data is available on the society level are massive multiplayer online games (MMOGs). In MMOGs hundreds of thousands of players meet online in a ‘virtual life’ where their actions can be easily studied [16] . Players have to gain their living through economic activity and usually are integrated in several types of social networks. In such games communication networks, friendship and enmity networks have been studied, initially as separated entities [17] , [18] . In [2] trading, aggression and punishment networks have been added to the analysis and first measurements on mutual network-network influences were reported.

If we assign to any positive action C, T, F or X, and to the negative actions A, B, D and E, we can translate a sequence into a symbolic binary sequence . From the cumulative sum of this sequence a ‘world line’ or ‘random walk’ for player can be generated, , see Fig. 1 (b) . Similarly, we define a binary sequence from the combined sequence , where we assign to an executed action and to a received-action. This sequence we call its cumulative sum, is the ‘action-receive’ random-walk or world line. Finally, we denote the number of actions which occurred during a day in the game by , where indicates the day and stands for one of the eight actions.

We consider three types of sequences for any particular player. The first is the stream of consecutive actions which player performs during his ‘life’ in the game. The second sequence is the (time-ordered) stream of actions that player receives from all the other players in the game, i.e. all the actions which are directed towards player We denote by received-action sequences. Finally, the third sequence is the time-ordered combination of player 's actions and received-actions, which is a chronological sequence from the elements of and in the order of occurrence. The combined sequence we denote by ; its length is , see also Fig. 1 (a) . The th element of one of these series is denoted by , , or . We do not consider the actual time between two consecutive actions which can range from milliseconds to weeks, rather we work in ‘action-time’.

(a). Some actions of players 146 and 701 are directed toward player 199. This results in a sequence of received-actions for player 199, . The combined sequence of actions (originated from - and directed to) player 199, , is shown in the last line; red letters mark actions from others directed to player 199. (b) Schematic illustration showing the definition of a binary walk in ‘good-bad’ action space (good-bad ‘world line’). A positive action (C, T, F or X) means an upward move, a negative action (A, B, D and E) is a downward move. Good people have rising world-lines.

We consider eight different actions every player can execute at any time. These are communication (C), trade (T), setting a friendship link (F), removing an enemy link (forgiving) (X), attack (A), placing a bounty on another player (punishment) (B), removing a friendship link (D), and setting an enemy link (E). While C, T, F and X can be associated with positive (good) actions, A, B, D and E are hostile or negative (bad) actions. We classify communication as positive because only a negligible part of communication takes place between enemies [18] . Segments of action sequences of three players (146, 199 and 701) are shown in the first three lines of Fig. 1 (a) .

The game Pardus [19] is sectioned into three independent ‘universes’. Here we focus on the ‘Artemis’ universe, in which we recorded player actions over the first 1,238 consecutive days of the universe's existence. Communication between any two players can take place directly, by using a one-to-one, e-mail-like private messaging system, or indirectly, by meeting in built-in chat channels or online forums. For the player action sequences analyzed we focus on one-to-one interactions between players only, and discard indirect interactions such as e.g. participation in chats or forums [24] . Players can express their sympathy (distrust) toward other players by establishing so-called friendship (enmity) links. These links are only seen by the player marking another as a friend (enemy) and the respective recipient of that link. For more details on the game, see [18] , [19] . From all sequences of all 34,055 Artemis players who performed or received an action at least once within 1,238 days, we removed players with a life history of less than 1000 actions, leading to the set of the most active 1,758 players which are considered throughout this work. All data used in this study is fully anonymized; the authors have the written consent to publish from the legal department of the Medical University of Vienna.

The dataset contains practically all actions of all players of the MMOG Pardus since the game went online in 2004 [19] . Pardus is an open-ended online game with a worldwide player base of currently more than 370,000 people. Players live in a virtual, futuristic universe in which they interact with others in a multitude of ways to achieve their self-posed goals [23] . Most players engage in various economic activities typically with the (self-posed) goal to accumulate wealth and status. Social and economical decisions of players are often strongly influenced and driven by social factors such as friendship, cooperation, and conflict. Conflictual relations may result in aggressive acts such as attacks, fights, punishment, or even destruction of another player's means of production or transportation. The dataset contains longitudinal and relational data allowing for a complete and dynamical mapping of multiplex relations of the entire virtual society, over 1238 days. The behavioral data are free of ‘interviewer-bias’ or laboratory effects since users are not reminded of their actions being logged during playing. The longitudinal aspect of the data allows for the analysis of dynamical aspects such as the emergence and evolution of network structures. It is possible to extract multiple social relationships between a fixed set of humans [2] .

Results

The number of occurrences of the various actions of all players over the entire time period is summarized in Tab. 1 (first line). Communication is the most dominant action, followed by attacks and trading which are each about an order of magnitude less frequent. The daily number of all communications, trades and attacks, , and is shown in Fig. 2 (a), (b) and (c), respectively. These processes are reverting around a mean, . All processes of actions show an approximate Gaussian statistic of its log-increments, . The first 4 moments of the series are listed in Tab. 1. The relatively large kurtosis for and results from a few extreme outliers. The distribution of log-increments for the , and timeseries are shown in Fig. 2 (d). The lines are Gaussians for the respective mean and standard deviation from Tab. 1. As maybe the simplest mean-reverting model with approximate log-normal distributions, we propose (1)where is the mean reversion coefficient, is a realization of a zero mean Gaussian random number with standard deviation , and is the value to which the process reverts to. is given by the third line in Tab. 1. Note that this is an AR(1) process (Ornstein-Uhlenbeck process in discrete time) in logarithmic variables.

PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Figure 2. Timeseries of the daily number of (a) trades, (b) attacks, (c) communications in the first 1238 days in the game. Clearly a mean reverting tendency of three processes can be seen. (d) Simulation of a model timeseries, Eq. (1), with . We use the values from the timeseries, , and standard deviation . Compare with the actual in (c). The free parameter in the model is . Parameters are from Tab. 1. Mean reversion and log-normality motivate the model presented in Eq. (1). (e) The distributions of log-increments of the processes and the model. All follow approximate Gaussian distribution functions. https://doi.org/10.1371/journal.pone.0029796.g002

Transition probabilities With we denote the probability that an action of type follows an action of type in the behavioral sequence of a player. and stand for any of the eight actions, executed or received (received is indicated by a subscript ). In Fig. 3 (a) the transition probability matrix is shown. The axis of the matrix indicates the action (or received-action) happening at a time the probabilities for the actions (or received-actions) that immediately follow are given in the corresponding horizontal place. PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Figure 3. Transition probabilities Transition probabilitiesfor actions (and received-actions)at a time, given that a specific actionwas executed or received in the previous time-step (a). Received-actions are indicated by a subscript . Normalization is such that rows add up to one. The large values in the diagonal signal that human actions are highly clustered or repetitive. Large values for and reveal that communication is a tendentially anti-persistent activity – it is more likely to receive a message after one sent a message and vice versa, than to send or to receive two consecutive messages. (b) The ratio , shows the influence of an action at a previous time-step on a following action at a time , where and can be positive or negative actions, executed or received (received actions are indicated by the subscript ). In brackets, we report the Z-score (significance in number of standard deviations) in respect to a sample of 100 randomized versions of the dataset. The cases for which the transition probability is significantly higher (lower) than expected in uncorrelated sequences are highlighted in red (green). Receiving a positive action after performing a positive action is highly over-represented, and vice versa. Performing (receiving) a negative action after performing (receiving) another negative one is also highly over-represented. Performing a negative action has no influence on receiving a negative action next. All other combinations are strongly under-represented, for example after performing a negative action it is very unlikely to perform a positive action with respect to the uncorrelated case. https://doi.org/10.1371/journal.pone.0029796.g003 This transition matrix specifies to which extent an action or a received action of a player is influenced by the action that was done or received at the previous time-step. In fact, if the behavioral sequences of players had no correlations, i.e. the probability of an action, received or executed, is independent of the history of the player's actions, the transition probability simply is , i.e. to the probability that an action or received action occurs in the sequence is determined by its relative frequency only. Therefore, deviations of the ratio from indicate correlations in sequences. In Fig. 3 (b) we report the values of for actions and received actions (received actions are indicated with the subscript ) classified only according to their positive (+) or negative (−) connotation. In brackets we report the Z-score with respect to the uncorrelated case. We find that the probability to perform a good action is significantly higher if at the previous time-step a positive action has been received. Similarly, it is more likely that a player is the target of a positive action if at the previous time-step he executed a positive action. Conversely, it is highly unlikely that after a good action, executed or received, a player acts negatively or is the target of a negative action. Instead, in the case a player acts negatively, it is most likely that he will perform another negative action at the following time-step, while it is highly improbable that the following action, executed or received, will be positive. Finally, in the case a negative action is received, it is likely that another negative action will be received at the following time-step, while all other possible actions and received actions are under-represented. The high statistical significance of the cases and hints toward a high persistence of negative actions in the players' behavior, see below. Another finding is obtained by considering only pairs of received actions followed by performed actions. This approach allows to quantify the influence of received actions on the performed actions of players. For these pairs we measure a probability of of performing a negative action after a received positive action. This value is significantly lower compared to the probability of obtained for randomly reshuffled sequences. Similarly, we measure a probability of of performing a negative action after a received negative action. Note that this result is not in contrast with the values in Fig. 3 (b), since only pairs made up of received actions and performed actions are taken into account. Our results agree with a recent study where the emotional content of posts in online forums was analyzed similarly [24].

World lines The world lines of good-bad action sequences are shown in Fig. 4 (a), the action-reaction world lines in Fig. 4 (b). As a simple measure to characterize these world lines we define the slope of the line connecting the origin of the world line to its end point (last action of the player). A slope of in the good-bad world lines indicates that the player performed only positive (negative) actions. The slope is an approximate measure of ‘altruism’ for player . The histogram of the slopes for all players is shown in Fig. 4 (b), separated into good (blue) and bad (red) players, i.e. players who have performed more good than bad actions and vice versa. The mean and standard deviation of slopes of good, bad, and all players are , , and , respectively. Simulated random walks with the same probability of performing a positive action yield a much lower variation, , pointing at an inherent heterogeneity of human behavior. For the combined action–received-action world line the slope is a measure of how well a person is integrated in her social environment. If the person only acts and receives no input, she is ‘isolated’ but dominant. If the slope is the person is driven by the actions of others and does never act nor react. The histogram of slopes for all players is shown in Fig. 4 (e). Most players are well within the degree cone. Mean and standard deviation of slopes of good, bad, and all players are and respectively. Bad players are tendentially dominant, i.e. they perform significantly more actions than they receive. Simulated random walks with equal probabilities for up and down moves for a sample of the same sequence lengths, we find again a much narrower distribution with slope . PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Figure 4. World lines of good-bad action random walks of the 1,758 most active players (a), distribution of their slopes World lines of good-bad action random walks of the 1,758 most active players (a), distribution of their slopes(b), and of their scaling exponents(c). By definition, players who perform more good (bad) than bad (good) actions have the endpoints of their world lines above (below) 0 in (a) and only fall into the ( ) category in (b). (d) World lines of action-received random walks, (e) distribution of their slopes and (f) of their scaling exponents . The inset in (d) shows only the world lines of bad players. These players are typically dominant, i.e. they perform significantly more actions than they receive. In total the players perform many more good than bad actions and are strongly persistent with good as well as with bad behavior, see (c), i.e. actions of the same type are likely to be repeated. https://doi.org/10.1371/journal.pone.0029796.g004 As a second measure we use the mean square displacement of world lines to quantify the persistence of action sequences, (2)where and is the average over all . The asymptotic behavior of yields information about the ‘persistence’ of a world line. is the pure diffusion case, with scaling exponent indicates persistence for , and anti-persistence for . Persistence means that the probability of making an up(down) move at time is larger(less) than , if the move at time was an up move. For calculating the exponents we use a fit range of between 5 and 100. We checked from the mean square displacement of single world lines that this fit range is indeed reasonable. The histogram of exponents for the good-bad random walk, separated into good (blue) and bad (red) players, is shown in Fig. 4 (c), for the action–received-action world line in (f). In the first case strongly persistent behavior is obvious, in the second there is a slight tendency towards persistence. Mean and standard deviation for the good-bad world lines are , for the action-received actions . Simulated sequences of random walks have – as expected by definition – an exponent of , again with a very small standard deviation of about . Figure 4 (a) also indicates that the lifetime of players who use negative actions frequently is short. The average lifetime for players with a slope is actions, compared to players with a slope with actions. The average lifetime of the whole sample of players is actions.