Abstract
When a large disaster occurs, many volunteers can be garnered. After the Tohoku Earthquake in Japan in 2011, about 1.4 million volunteers rushed to the disaster area. Survivors-turned-volunteers from the 2004 Chuetsu Earthquake helped people in Noda Village, which was affected by the 2011 Tohoku Earthquake and Tsunami, while in turn people affected by the Chuetsu Earthquake were assisted by survivors of the 1995 Kobe Earthquake from Nishinomiya City. Such a chain of support is called a “Pay-It-Forward” Network (PFN) and can serve as a valuable reserve of volunteer support in the next disaster. This paper studies PFNs using mathematical simulation and cellular automata. First, we compare two variables: whether volunteerism tended to be affected by neighborhoods (N) or by a disaster itself (R) following two large disasters in Japan: the 2011 Tohoku Earthquake and the 1995 Kobe Earthquake. Second, we simulated volunteerism in the 1995 Kobe Earthquake and the 2011 Tohoku Earthquake using cellular automata, including changing the variables (α) reflected by sympathy based on similar disaster experience. We found that the neighborhood (N) factor prevented the PFN from triggering inter-survivor support especially in the Tohoku case; however, the simulation revealed that, under other conditions, the PFN can spread volunteerism dramatically. On the whole, this study suggests the importance of two factors, N and R, and shows that PFNs can effectively activate the chain of support in some cases.
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Notes
The data on the number of volunteers who passed through official volunteer centers include only some of the volunteers after the Tohoku Earthquake, and the total number is unknown; however, it is likely that it was still less than after the Kobe Earthquake.
This flood killed 299 people and caused more than 300 billion yen (about 2.8 billion US dollar) in damage.
In this simulation, nine cells representing N_sum are chosen, including the Moore Neighborhood (e.g., Wolfram 1986) and the target cell, because the Von Neumann Neighborhood (just four cells) is too narrow, whereas the r range Von Neumann Neighborhood (all cells) is too broad. However, the neighborhood in this simulation is composed of not only the cells in the Moore neighborhood but also the target cell itself. One reason is that we regard the atmosphere to increases in volunteerism in both neighborhood areas and one’s own area (cell).
d is referred to as Yamori’s (1996) data, and u is decided by the fitness of the simulation results.
Both k and e are also decided by fitness.
a is referred to by the Statistics Bureau (2012) regarding the percentage of disaster volunteering at each prefecture and the distance between one’s own prefecture and the disaster-affected prefectures (chosen closer from the Iwate or Miyagi Prefectures). We use this initial condition, but there is no huge difference between this condition and the other initial condition.
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Acknowledgements
The first author is deeply grateful to James D. Goltz for his generous guidance on the paper. The authors have been inspired by the ideas and experiences of survivors in Nishinomiya, Shiodani, Kariwa, and Noda, with whom we interacted through our fieldwork. The authors owe a special debt of thanks to them all.
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Daimon, H., Atsumi, T. Simulating disaster volunteerism in Japan: “Pay It Forward” as a strategy for extending the post-disaster altruistic community. Nat Hazards 93, 699–713 (2018). https://doi.org/10.1007/s11069-018-3309-9
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DOI: https://doi.org/10.1007/s11069-018-3309-9