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On the Inability of Markov Models to Capture Criticality in Human Mobility

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11729)

Abstract

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish an upper bound on the predictability of human mobility, based on the temporal entropy. Since its inception, this bound has been widely used for validating the performance of mobility prediction models. We show that the variants of recurrent neural network architectures can achieve significantly higher prediction accuracy surpassing this upper bound. The central objective of our work is to show that human-mobility dynamics exhibit criticality characteristics which contributes to this discrepancy. In order to explain this anomaly, we shed light on the underlying assumption that human mobility characteristics follow an exponential decay that has resulted in this bias. By evaluating the predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, bearing resemblance to power-law decay, contrasting with the initial Markovian assumption. We experimentally validate that this assumption inflates the estimated mobility entropy, consequently lowering the upper bound on predictability. We demonstrate that the existing approach of entropy computation tends to overlook the presence of long-distance dependencies and structural correlations in human mobility. We justify why recurrent-neural network architectures that are designed to handle long-distance dependencies surpass the previously computed upper bound on mobility predictability.

Keywords

  • Predictability limits
  • Human-mobility
  • Criticality

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Acknowledgements

This research work was partially supported by the Swiss National Science Foundation grant 157160. This research was also supported by the ADAPT Research Centre, funded under the SFI Research Centres Programme (Grant 13/ RC/2106) and is co-funded under the European Regional Development Funds. The research was also supported by an IBM Shared University Research Award.

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Correspondence to Vaibhav Kulkarni .

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Kulkarni, V., Mahalunkar, A., Garbinato, B., Kelleher, J.D. (2019). On the Inability of Markov Models to Capture Criticality in Human Mobility. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_39

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  • DOI: https://doi.org/10.1007/978-3-030-30508-6_39

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