Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-10T14:55:56.476Z Has data issue: false hasContentIssue false

Deviation bounds for the first passage time in the frog model

Published online by Cambridge University Press:  22 July 2019

Naoki Kubota*
Affiliation:
College of Science and Technology, Nihon University
*
*Postal address: College of Science and Technology, Nihon University, 24-1, Narashinodai 7-chome, Funabashi-shi, Chiba 274-8501, Japan. Email address: kubota.naoki08@nihon-u.ac.jp

Abstract

We consider the so-called frog model with random initial configurations. The dynamics of this model are described as follows. Some particles are randomly assigned to any site of the multidimensional cubic lattice. Initially, only particles at the origin are active and these independently perform simple random walks. The other particles are sleeping and do not move at first. When sleeping particles are hit by an active particle, they become active and start moving in a similar fashion. The aim of this paper is to derive large deviation and concentration bounds for the first passage time at which an active particle reaches a target site.

Type
Original Article
Copyright
© Applied Probability Trust 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahlberg, D. (2015). A Hsu–Robbins–Erdös strong law in first-passage percolation. Ann. Prob. 43, 19922025.CrossRefGoogle Scholar
Alves, O. S. M., Machado, F. P., and Popov, S. Y.. 2002. Phase transition for the frog model. Electron. J. Prob., 7, 21pp.CrossRefGoogle Scholar
Alves, O. S. M., Machado, F. P., and Popov, S. Y.. 2002. The shape theorem for the frog model. Ann. Appl. Prob., 12, 533546.Google Scholar
Alves, O. S. M., Machado, F. P., Popov, S. Y., and Ravishankar, K.. 2001. The shape theorem for the frog model with random initial configuration. Markov Process. Relat. Fields, 7, 525539.Google Scholar
Basu, R., Ganguly, S., and Hoffman, C.. 2015. Non-fixation of symmetric activated random walk on the line for small sleep rate. Preprint. Available at http://arxiv.org/abs/1508.05677v1http://arxiv.org/abs/1508.05677v1.Google Scholar
Beckman, E. et al. 2017. Asymptotic behavior of the brownian frog model. Preprint. Available at http://arxiv.org/abs/1710.05811v1http://arxiv.org/abs/1710.05811v1.Google Scholar
Boucheron, S., Lugosi, G., and Massart, P.. 2013. Concentration Inequalities: A Nonasymptotic Theory of Independence. Oxford University Press.CrossRefGoogle Scholar
Dickman, R., Rolla, L. T., and Sidoravicius, V.. 2010. Activated random walkers: facts, conjectures and challenges. J. Statist. Phys., 138, 126142.CrossRefGoogle Scholar
Döbler, C. and Pfeifroth, L.. 2014. Recurrence for the frog model with drift on. Electron. Commun. Prob., 19, 13pp.CrossRefGoogle Scholar
Döbler, C. et al. 2018. Recurrence and transience of frogs with drift on. Electron. J. Prob., 23, 23pp.CrossRefGoogle Scholar
Gantert, N. and Schmidt, P.. 2009. Recurrence for the frog model with drift on. Markov Process. Relat. Fields, 15, 5158.Google Scholar
Garet, O. and Marchand, R.. 2007. Large deviations for the chemical distance in supercritical bernoulli percolation. Ann. Prob., 35, 833866.CrossRefGoogle Scholar
Garet, O. and Marchand, R.. 2010. Moderate deviations for the chemical distance in Bernoulli percolation. ALEA Latin Amer. J. Prob. Math. Statist., 7, 171191.Google Scholar
Ghosh, A., Noren, S., and Roitershtein, A.. 2017. On the range of the transient frog model on. Adv. Appl. Prob., 49, 327343.CrossRefGoogle Scholar
Grimmett, G.. 1999. Percolation, 2nd edn. Springer, Berlin.CrossRefGoogle Scholar
Grimmett, G. and Kesten, H.. 1984. First-passage percolation, network flows and electrical resistances. Z. Wahrscheinlichkeitsth. 66, 335366.CrossRefGoogle Scholar
Höfelsauer, T. and Weidner, F.. 2016. The speed of frogs with drift on. Markov Process. Relat. Fields, 22, 379392.Google Scholar
Hoffman, C., Johnson, T., and Junge, M.. 2016. From transience to recurrence with Poisson tree frogs. Ann. Appl. Prob., 26, 16201635.CrossRefGoogle Scholar
Hoffman, C., Johnson, T., and Junge, M.. 2017. Infection spread for the frog model on trees. Preprint. Available at http://arxiv.org/abs/1710.05884v1.Google Scholar
Hoffman, C., Johnson, T., Junge, M. 2017. Recurrence and transience for the frog model on trees. Ann. Prob., 45, 28262854.CrossRefGoogle Scholar
Hughes, B. D.. 1995. Random Walks and Random Environments. Vol. 1. Oxford University Press.Google Scholar
Johnson, T. and Junge, M.. 2018. Stochastic orders and the frog model. Ann. Inst. H. Poincaré Prob. Statist. 54, 10131030.CrossRefGoogle Scholar
Kesten, H.. 1986. Aspects of first passage percolation. In École d’été de Probabilités de Saint-Flour, XIV (Lecture Notes Math. 1180), Springer, Berlin, pp. 125264.CrossRefGoogle Scholar
Kesten, H. and Sidoravicius, V.. 2005. The spread of a rumor or infection in a moving population. Ann. Prob., 33, 24022462.CrossRefGoogle Scholar
Kesten, H. and Sidoravicius, V.. 2008. A shape theorem for the spread of an infection. Ann. Math. (2), 167, 701766.CrossRefGoogle Scholar
Kosygina, E. and Zerner, M. P. W.. 2017. A zero-one law for recurrence and transience of frog processes. Prob. Theory Relat. Fields, 168, 317346.CrossRefGoogle Scholar
Kurkova, I., Popov, S., and Vachkovskaia, M.. 2004. On infection spreading and competition between independent random walks. Electron. J. Prob., 9, 293315.CrossRefGoogle Scholar
Lawler, G. F.. 1991. Intersections of Random Walks. Birkhäuser, Boston, MA Google Scholar
Liggett, T. M.. 1999. Stochastic Interacting Systems: Contact, Voter and Exclusion Processes (Fundamental Principles Math. Sci. 324). Springer, Berlin.CrossRefGoogle Scholar
Mourrat, J.-C.. 2012. Lyapunov exponents, shape theorems and large deviations for the random walk in random potential. ALEA Latin Amer. J. Prob. Math. Statist., 9, 165211.Google Scholar
Popov, S. Y.. 2001. Frogs in random environment. J. Statist. Phys., 102, 191201.CrossRefGoogle Scholar
Popov, S. Y.. 2003. Frogs and some other interacting random walks models. Discrete Math. Theoret. Comput. Sci. AC, 277288.Google Scholar
Ramrez, A. F. and Sidoravicius, V.. 2004. Asymptotic behavior of a stochastic combustion growth process. J. Europ. Math. Soc., 6, 293334.CrossRefGoogle Scholar
Rolla, L. T., Tournier, L.. 2018. Non-fixation for biased activated random walks. Ann. Inst. H. Poincaré Prob. Statist., 54, 938951.CrossRefGoogle Scholar
Rolla, L. T. and Sidoravicius, V.. 2012. Absorbing-state phase transition for driven-dissipative stochastic dynamics on. Invent. Math., 188, 127150.CrossRefGoogle Scholar
Sidoravicius, V. and Teixeira, A.. 2017. Absorbing-state transition for stochastic sandpiles and activated random walks. Electron. J. Prob., 22, 35pp.CrossRefGoogle Scholar
Telcs, A. and Wormald, N. C.. 1999. Branching and tree indexed random walks on fractals. J. Appl. Prob., 36, 9991011.CrossRefGoogle Scholar