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## Understanding mobility in a social petri dish. (2012)

Venue: | Sci. Rep. |

Citations: | 13 - 3 self |

### Citations

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(Show Context)
Citation Context ...c properties are fundamentally relevant for issues of epidemics spreading or traffic management. This ‘‘Time Order Memory’’ (TOM) model incorporates a powerlaw distribution of first return times, together with a power-law distribution of waiting times and an exponential distribution of jump distances, as those observed empirically in Fig. 2. We show below that these ingredients are sufficient to reproduce the subdiffusive behaviour reported in Fig. 5 (a). The model works as follows: an individual stands still in a given sector for a number of days drawn from the waiting time distribution, Eq. (2). Then, the individual jumps. There are two possibilities: (i) with a probability v she returns to an already visited sector, (ii) with the probability 1 – v she jumps to a so far unexplored sector. In case (i), one of the previously visited sectors is chosen according to Eq. (4). In the exploration case (ii), the individual draws a distance d from the distance distribution, Eq. (1), and jumps to a randomly selected, unexplored sector at that distance. The model has four parameters. The parameters l, b and a of equations (1), (2) and (4) respectively, are fixed by the data. Further, averaging ... |

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(Show Context)
Citation Context ...sufficient to reproduce the subdiffusive behaviour reported in Fig. 5 (a). The model works as follows: an individual stands still in a given sector for a number of days drawn from the waiting time distribution, Eq. (2). Then, the individual jumps. There are two possibilities: (i) with a probability v she returns to an already visited sector, (ii) with the probability 1 – v she jumps to a so far unexplored sector. In case (i), one of the previously visited sectors is chosen according to Eq. (4). In the exploration case (ii), the individual draws a distance d from the distance distribution, Eq. (1), and jumps to a randomly selected, unexplored sector at that distance. The model has four parameters. The parameters l, b and a of equations (1), (2) and (4) respectively, are fixed by the data. Further, averaging over all jumps and players, the probability of returning to an already visited location is v < 0.83. Similarly to the measured data, the MSD of the TOM model, black squares in Fig. 5 (b), exhibits no saturation effects and displays an exponent uTOM 5 0.23 6 0.02 (the error is calculated over an ensemble of realizations) in agreement with the exponent observed for the players. Discus... |

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(Show Context)
Citation Context ...ion of jump distances, as those observed empirically in Fig. 2. We show below that these ingredients are sufficient to reproduce the subdiffusive behaviour reported in Fig. 5 (a). The model works as follows: an individual stands still in a given sector for a number of days drawn from the waiting time distribution, Eq. (2). Then, the individual jumps. There are two possibilities: (i) with a probability v she returns to an already visited sector, (ii) with the probability 1 – v she jumps to a so far unexplored sector. In case (i), one of the previously visited sectors is chosen according to Eq. (4). In the exploration case (ii), the individual draws a distance d from the distance distribution, Eq. (1), and jumps to a randomly selected, unexplored sector at that distance. The model has four parameters. The parameters l, b and a of equations (1), (2) and (4) respectively, are fixed by the data. Further, averaging over all jumps and players, the probability of returning to an already visited location is v < 0.83. Similarly to the measured data, the MSD of the TOM model, black squares in Fig. 5 (b), exhibits no saturation effects and displays an exponent uTOM 5 0.23 6 0.02 (the error is cal... |

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