Communication During Bushfires, Towards a Serious Game for a Serious Matter: Communication During Bushfires

Communication During Bushfires, Towards a Serious Game for a Serious Matter: Communication During Bushfires

Carole Adam (Univ Grenoble-Alpes, LIG, Grenoble, France), Charles Bailly (Univ Grenoble-Alpes, LIG, Grenoble, France) and Julie Dugdale (Univ Grenoble-Alps, LIG, Greenoble, France)
DOI: 10.4018/IJISCRAM.2018040104

Abstract

Australia is frequently hit by bushfires. In 2009, the ''Black Saturday'' fires killed 173 people and burnt hectares of bush. As a result, a research commission was created to investigate, and concluded that several aspects could be improved, in particular better understanding of the population actual behaviour, and better communication with them. The authors argue that agent-based modelling and simulation is a great approach to provide tools to improve mutual understanding: let managers test communication strategies, and let residents understand the managers' perspective. Concretely, they extended an existing simulator with a theoretically-grounded communication model based in social sciences; they added user interactivity with the model and investigated gamification to turn it into a serious game to involve the general public. The authors present the results of first experiments with different communication strategies, providing valuable insight for better communication with the population during such events. Finally, they discuss future extensions and generalisation of this simulator.
Article Preview
Top

1. Introduction

Nowadays, the number of crisis events is continuously increasing, be they natural disasters (fires, floods, earthquakes, tsunamis, etc.) or man-made events (industrial accidents, terrorism, refugees flow, etc.) (Guha-Sapir et al., n.d.).

In this paper, we are interested in the bushfires that strike the state of Victoria in Australia every summer, burning many hectares of forest, causing numerous deaths and injuries, and destroying property. There has been a 40% increase in the number of bushfires per week in Australia over 5 years (from 3284 per week in 2007 to 4595 events per week in 2013) (Dutta, Das, & Aryal, 2016). The current state policy is “Prepare, stay and defend, or leave early.” Thus, the population is given a choice between: evacuating early, before fire reaches their area of residence because “many people have died trying to leave at the last minute” (Country Fire Authority, 2014); or stay and defend their house, which is feasible only if a person is very well prepared both physically and mentally. In both cases, the decision must be made and a plan prepared well in advance. However, in the summer of 2009, serious bushfires devastated a part of Victoria, culminating on the Black Saturday 7th February when 173 people died despite all efforts at raising awareness. The cost of these bushfires was estimated to be 4.4 billion Australian dollars, 98932 hectares of Victorian parks were damaged, and around one million animals died with a devastating impact on agriculture due to loss of cattle and pastures, etc.

Several reports have tried to explain the reasons for this heavy death toll (Teague, McLeod, & Pascoe, 2009a; McLennan & Elliott, 2011). These reports identified three main inconsistencies: first, in behaviour in that the population did not react as expected by the decision-makers; secondly in information content since the population did not always considered the information they received to be relevant to them; and lastly in communication means, which was felt to be inefficient, especially in the case of information broadcast.

Societies can manage such crisis and emergency situations in several ways: adopt urban and territory planning policies to reduce the risks (e.g. forbid construction in exposed areas); raise awareness and prepare the population in advance; or create efficient emergency management policies to deal with crises when they happen. In this perspective, computer modelling and simulation is a powerful tool to test the effects and complex interactions of these different strategies without waiting for an actual crisis to happen. Human lives need not be put at risk and the cost of experimenting with different strategies is greatly reduced. Furthermore, it offers a great degree of control on all conditions and the possibility of reproducing exactly the same situation as many times as needed at no cost. When modelling human behaviour, mathematical, equation-based models are too limited (Parunak, Savit, & Riolo, 1998). On the contrary, agent-based models, where autonomous entities (agents) interacting with each other represent the humans involved, offer many benefits (Bonabeau, 2002). They allow capturing emergent phenomena that characterise such complex systems; they provide an intuitive and realistic description of their behaviour; they are flexible, offering different levels of abstraction by varying the complexity of agents.

As a result, many previous works have used agent-based modelling and simulation to study human behaviour in natural disasters and provide tools for emergency managers. In particular, Adam et al. (Adam & Gaudou, 2016) have designed a model of the behaviour of the Australian population in bushfires from interviews gathered after the 2009 “Black Saturday” fires by the Victorian Bushfires Research Commission (Teague, McLeod, & Pascoe, 2009b). This model aims to explain the inconsistencies observed in behaviour, in terms of a gap between objective and subjective evaluations of both risk and individual capabilities to deal with it. Their evaluation proves that the model provides good explanation for the inconsistencies in behaviour noted in the report. However, the agents in their model only represent the population, and they do not communicate with each other; as a result, this model is unable to tackle the communication problems also noted in the report.

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 11: 2 Issues (2019)
Volume 10: 4 Issues (2018)
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing