Feedback via simulation tools is likely to help people improve their decision-making against natural disasters. However, little is known on how differing strengths of experiential feedback and feedback's availability in simulation tools influence people's decisions against landslides. We tested the influence of differing strengths of experiential feedback and feedback's availability on people's decisions against landslides in Mandi, Himachal Pradesh, India. Experiential feedback (high or low) and feedback's availability (present or absent) were varied across four between-subject conditions in a tool called the Interactive Landslide Simulation (ILS): high damage with feedback present, high damage with feedback absent, low damage with feedback present, and low damage with feedback absent. In high-damage conditions, the probabilities of damages to life and property due to landslides were 10 times higher than those in the low-damage conditions. In feedback-present conditions, experiential feedback was provided in numeric, text, and graphical formats in ILS. In feedback-absent conditions, the probabilities of damages were described; however, there was no experiential feedback present. Investments were greater in conditions where experiential feedback was present and damages were high compared to conditions where experiential feedback was absent and damages were low. Furthermore, only high-damage feedback produced learning in ILS. Simulation tools like ILS seem appropriate for landslide risk communication and for performing what-if analyses.
Landslides cause massive damages to life and property worldwide (Chaturvedi et al., 2014; Chaturvedi and Dutt, 2015; Margottini et al., 2011). Imparting knowledge about landslide causes and consequences and spreading awareness about landslide disaster mitigation are likely to be effective ways of managing landslide risks. The former approach supports structural protection measures that are likely to help people take mitigation actions and reduce the probability of landslides (Becker et al., 2013; Osuret et al., 2016; Webb and Ronan, 2014). In contrast, the latter approach likely reduces people's and assets' perceived vulnerability to risk. However, it does not influence the physical processes. One needs effective landslide risk communication systems (RCSs) to educate people about cause-and-effect relationships concerning landslides (Glade et al., 2005). To be effective, these RCSs should possess five main components (Rogers and Tsirkunov, 2011): monitoring, analysing, risk communication, warning dissemination, and capacity building.
Among these components, prior research has focused on monitoring and analysing the occurrence of landslide events (Dai et al., 2002; Montrasio et al., 2011). For example, there exist various statistical and process-based models for predicting landslides (Dai et al., 2002; Montrasio et al., 2011; Reder et al., 2018; Segoni et al., 2018; Vaz et al., 2018). Several satellite-based and sensor-based landslide monitoring systems are being used in landslide RCSs (Hong et al., 2006; Quanshah et al., 2010; Rogers et al., 2011; Frodella et al., 2017; Intrieri et al., 2017). To be effective, however, landslide RCSs need not only be based upon sound scientific models, but they also need to consider human factors, i.e. the knowledge and understanding of people residing in landslide-prone areas (Meissen and Voisard, 2008). Thus, there is an urgent need to focus on the development, evaluation, and improvement of risk communication, warning dissemination, and capacity-building measures in RCSs.
Improvements in risk communication strategies are likely to help people understand the cause-and-effect processes concerning landslides and help them improve their decision-making against these natural disasters (Grasso and Singh, 2009). However, surveys conducted among communities in landslide-prone areas (including those in northern India) have shown a lack of awareness and understanding among people about landslide risks (Chaturvedi and Dutt, 2015; Oven, 2009; Wanasolo, 2012). In a survey conducted in Mandi, India, Chaturvedi and Dutt (2015) found that 60 % of people surveyed were not able to answer questions on landslide susceptibility maps, which were prepared by experts. Also, Chaturvedi and Dutt (2015) found that a sizeable population reported landslides to be “acts of God” (39 %) and attributed activities like “shifting of temple” as causing landslides (17 %). These results are surprising as the literacy rate in Mandi and surrounding areas is quite high (81.5 %) (Census, 2011), and these results show numerous misconceptions about landslides among people in landslide-prone areas. Overall, urgent measures need to be taken that improve public understanding and awareness about landslides in affected areas.
Promising recent research has shown that experiential feedback in simulation tools likely helps improve public understanding about dynamics of physical systems (Chaturvedi et al., 2017; Dutt and Gonzalez, 2010; 2011; 2012; Fischer, 2008). Dutt and Gonzalez (2012) developed a dynamic climate change simulator (DCCS) tool, which was based upon a more generic stock-and-flow task (Gonzalez and Dutt, 2011a). The authors provided frequent feedback on cause-and-effect relationships concerning Earth's climate in DCCS, and this experiential feedback helped people reduce their climate misconceptions compared to a no-DCCS intervention. Although the prior literature has investigated the role of frequency of feedback about inputs and outputs in physical systems, little is known on how differing strengths of experiential feedback (i.e. differing probabilities of damages due to landslides) influence people's decisions over time. Also, little is known on how experiential feedback's availability (presence or absence) in simulation tools influences people's decisions.
The primary goal of this research is to evaluate how differing strengths of experiential feedback and feedback's availability influence people's mitigation decisions against landslides. A study of how the strength of experiential feedback influences people's decisions against landslides is important because people's experience of landslide consequences due to differing probabilities of landslide damages could range from no damages at all to large damages involving several injuries, infrastructure damages, and deaths. Thus, due to differing probabilities of landslide damages, some people may experience severe landslide damages and consider landslides to be a serious problem requiring immediate actions, whereas other people may experience no damages and consider landslides to be a trivial problem requiring very little attention.
In addition, the availability of feedback in simulation tools is also likely to influence people's decisions against landslides. When feedback is absent, people are only likely to acquire descriptive knowledge about the cause-and-effect relationships governing the landslide dynamics (Dutt and Gonzalez, 2010). However, when feedback is present, people get to repeatedly experience the positive or negative consequences of their decisions against landslide risks (Dutt and Gonzalez, 2010, 2011). This repeated experience will likely help people understand the cause-and-effect relationships governing the landslide dynamics.
Chaturvedi et al. (2017) proposed a computer-simulation tool, called the Interactive Landslide Simulator (ILS). The ILS tool is based upon a landslide model that considers the influence of both human factors and physical factors on landslide dynamics. Thus, in ILS, both physical factors (e.g. spatial geology and rainfall) and human factors (e.g. monetary contributions to mitigate landslides) influence the probability of catastrophic landslides. In a preliminary investigation involving the ILS tool, Chaturvedi et al. (2017) varied the probability of damages due to landslides at two levels: low probability and high probability. The high probability was set about 10 times higher than the low probability. People were asked to make monetary investment decisions, where people's monetary payments would be used for mitigating landslides (e.g. by building a retaining wall, planned road construction, provision of proper drainage or by planting crops with long roots in landslide-prone areas; please see Patra and Devi (2015) for a review of such mitigation measures). People's investments were significantly greater when the damage probability was high than when this probability was low. However, Chaturvedi et al. (2017) did not fully evaluate the effectiveness of experiential feedback of damages in the ILS tool against control conditions where this experiential feedback was not present. Also, Chaturvedi et al. (2017) did not investigate people's investment decisions over time and certain strategies in ILS, where these decisions and strategies would be indicative of learning of landslide dynamics in the tool.
The prior literature on learning from experiential feedback (Baumeister et al., 2007; Dutt and Gonzalez, 2012; Finucane et al., 2000; Knutti, 2005; Reis and Judd, 2013; Wagner, 2007) suggests that increasing the strength of damage feedback by increasing the probabilities of landslide damages in simulation tools would likely increase people's mitigation decisions. That is because a high probability of landslide damages will make people suffer monetary losses, and people would tend to minimize these losses by increasing their mitigation actions over time. It is also expected that the presence of experiential feedback about damages in simulation tools is likely to increase people's landslide mitigation actions over time (Dutt and Gonzalez, 2010, 2011, 2012). That is because the experiential feedback about damages will likely enable people to make decisions and see the consequences of their decisions; however, the absence of this feedback will not allow people to observe the consequences of their decisions once these decisions have been made (Dutt and Gonzalez, 2012). At first glance, these explanations may seem to assume people to be economically rational individuals while facing landslide disasters (Bossaerts and Murawski, 2015; von Neumann and Morgenstern, 1947), where one disregards people's bounded rationality, risk perceptions, attitudes, and behaviours (De Martino et al., 2006; Gigerenzer and Selten, 2002; Kahneman and Tversky, 1979; Simon, 1959; Slovic et al., 2005; Thaler and Sunstein, 2008; Tversky and Kahneman, 1992). However, in this paper, we consider people to be bounded rational agents (Gigerenzer and Selten, 2002; Simon, 1959), who tend to minimize their losses against landslides slowly over time via a trial-and-error learning process driven by personal experience in an uncertain environment (Dutt and Gonzalez, 2010; Slovic et al., 2005).
In this paper, we evaluate the influence of differing strengths of experiential feedback about landslide-related damages and the experiential feedback's availability in the ILS tool. More specifically, we test whether people increase their mitigation actions in the presence of experiential damage feedback compared to in the absence of this feedback. In addition, we evaluate how different probabilities of damages influence people's mitigation actions in the ILS tool. Furthermore, we also analyse people's mitigation actions over time across different conditions.
In what follows, first, we detail the characteristics of the study area and then a computational model on landslide risks that considers the role of both human factors and physical factors. Next, we detail the working of the ILS tool, i.e. based on the landslide model. Furthermore, we use the ILS tool in an experiment to evaluate the influence of differing strengths of experiential feedback and feedback's availability on people's decisions. Finally, we close this paper by discussing our results and detailing the benefits of using tools like ILS for communicating landslide risks in the real world.
In this paper, the study area was one involving the local communities living
in Mandi (31.58
3-D satellite view of Mandi and adjoining areas. The town is located in a valley around the Beas River with high mountains that are prone to landslides on both sides. Source: Google Maps.
Chaturvedi et al. (2017) proposed a computational model for simulating
landslide risks that was based upon the integration of human and physical
factors (see Fig. 2). Here, we briefly detail this model and use it in the
ILS tool for our experiment (reported ahead). As seen in Fig. 2, the
probability of landslides due to human factors in the ILS tool is adapted
from a model suggested by Hasson et al. (2010) (see box 1.1 in Fig. 2). In
the model of Hasson et al. (2010), the probability of a disaster (e.g. landslide)
due to human factors (e.g. investment) was a function of the cumulative
monetary contributions made by participants to avert the disaster from the
total endowment available to participants. Thus, investing against
disaster in mitigation measures reduces the probability of disaster, and
not investing in mitigation measures increases the probability of
disaster. However, by reducing the landslide risk, people also have less
ability to engage in other profitable investments due to loss in revenue.
Although we assume this model to incorporate human mitigation actions in the
ILS tool, there may also be other model assumptions possible where certain
detrimental human actions (e.g. deforestation) may increase the probability
of landslides or the risk of landslides (where risk
Furthermore, in the landslide model, the probability of landslides due to physical (natural) factors (see box 1.2 in Fig. 2) is a function of the prevailing rainfall conditions and the nature of geology in the area (Mathew et al., 2013). In this paper, we restrict our focus to considering only weather (rainfall)-induced landslides. As shown in Fig. 2, the ILS model focuses on calculation of total probability of landslide (due to physical and human factors) (box 1.3 in Fig. 2). This total probability of landslide is calculated as a weighted sum of probability of landslide due to physical factors and probability of landslide due to human factors. Furthermore, the model simulates different types of damages caused by landslides and their effects on people's earnings (box 1.4 in Fig. 2).
Probabilistic model of the Interactive Landslide Simulator tool. Figure adapted from Chaturvedi et al. (2017).
As described by Chaturvedi et al. (2017), the total probability of landslides
is a function of landslide probabilities due to human factors and physical
factors. This total probability of landslides can be represented as the
following:
As suggested by Chaturvedi et al. (2017), the probability
People's monetary investments (
Some of the physical factors impacting landslides include rainfall, soil
types, and slope profiles (Chaturvedi et al., 2017; Dai et al., 2002). These
factors can be categorized into two categories:
probability
of landslide due to rainfall ( probability
of landslide due to soil types and slope profiles (spatial
probability,
For the sake of simplicity, we have assumed that
Probability of landslide due to rainfall over days for the study area. The probability was generated by using Eqs. (4a) and (4b).
The second step is to evaluate the spatial probability of landslides,
First, from Table 1, the critical THED values (e.g. 3.5, 5.0, 6.5, and 8.0)
were converted into a probability value by dividing by the highest THED value
(
Total estimated hazard (THED) scale for evaluating the susceptibility of an area to landslides across different hazard classes.
In the ILS tool, using Fig. 4b, we used a randomly determined point value
of the
As suggested by Chaturvedi et al. (2017), the damages caused by landslides were classified into three independent categories: property loss, injury, and fatality. These categories have their own damage probabilities. When a landslide occurs, it can be harmless or catastrophic. A landslide becomes catastrophic with damage probability value of property loss, injury, and fatality. Thus, once a uniformly distributed random number is less than or equal to the probability of the corresponding damage, the corresponding damage is assumed to occur in the ILS tool. Landslide damages have different effects on the player's wealth and income, where damage to property affects one's property wealth and damages concerning injury and fatality affect one's income level. When the landslide is harmless, then there is no injury, no fatality, and no damages to one's property. For calculation of the damage probabilities due to landslides, data of 371 landslide events in India over a period of about 300 years were used (Parkash, 2011). If we consider the entire 300-year period, then one could expect very different socio-economic conditions to prevail over this period. However, it is to be noted that, in this paper, we vary this probability in the experiment. Thus, the exact value of the probability from the literature is not required in the simulation. The exact assumptions about damages are detailed ahead in this paper.
The ILS tool (Chaturvedi et al., 2017) is a Web-based tool, and it is based
upon the ILS model described above. The ILS tool was coded in open-source
programming languages PHP and MySQL and is freely available for use at the
following URL:
The goal in the ILS tool is to maximize one's total wealth, where this wealth is influenced by one's income, property wealth, and losses experienced due to landslides. Landslides and corresponding losses are influenced by physical factors (spatial and temporal probabilities of landslides) and human factors (i.e. the past contributions made by a participant for landslide mitigation). The total wealth may decrease (by damages caused by landslides, like injury, death, and property damage) or increase (due to daily income). While interacting with the tool, the repeated feedback on the positive or negative consequences of their decisions on their income and property wealth enables participants to revise their decisions and learn landslide risks and dynamics over time.
Figure 5 represents the graphical user interface of the ILS tool's investment screen. On this screen, participants are asked to make monetary mitigation decisions up to their daily income upper bound (see panel a). The total wealth is a sum of income not invested for landslide mitigation, property wealth, and total damages due to landslides (see panel b). As shown in panel b, participants are also shown the different probabilities of landslide due to human and physical factors as well as the probability weight used to combine these probabilities into the total probability. Furthermore, as shown in panel c, participants are graphically shown the history of total probability of landslide, total income not invested in landslides, and their remaining property wealth across different days. As part of the instructions, the players are told that the mitigation measures will be taken close to the places where they reside in the district in the ILS tool.
The ILS tool's investment screen.
As described above, participants, i.e. common people residing in the study
area, could invest between zero (minimum) and player's current daily income
(maximum). Once the investment is made, participants need to click the
“invest” button. Upon clicking the invest button, participants enter the
experiential feedback screen, where they can observe whether a landslide
occurred or not and whether there were changes in the daily income, property
wealth, and damages due to the landslide (see Fig. 6). As discussed above,
the landslide occurrence was determined by the comparison of a uniformly
distributed random number in [0, 1] with
The ILS tool's feedback screens.
To test the effectiveness of strength and availability of feedback, we performed a laboratory experiment involving human participants where we compared performance in the ILS tool in the presence or absence of experiential feedback about different damage probabilities. Based upon the prior literature (Baumeister et al., 2007; Dutt and Gonzalez, 2012; Finucane et al., 2000; Knutti, 2005; Reis and Judd, 2013; Wagner, 2007), we expected the proportion of investments to be higher in the presence of experiential feedback compared to those in the absence of experiential feedback. Furthermore, we expected higher investments against landslides when feedback was more damaging in ILS compared to when it was less damaging (Chaturvedi et al., 2017; Dutt and Gonzalez, 2011; Gonzalez and Dutt, 2011a).
Eighty-three participants were randomly assigned across four between-subject
conditions in the ILS tool, where the conditions differed in the strength of
experiential feedback (high damage (
The proportion of damage (in terms of daily income and property wealth) that
occurred in the event of fatality, injury, or property damage was kept
constant over 30 days. The property wealth decreased to half of its value
every time property damage occurred in the event of a landslide. The daily
income was reduced by 10 % of its latest value due to a landslide-induced
injury and 20 % of its latest value due to a landslide-induced fatality.
The initial property wealth was fixed to EC 20 million, which is the expected
property wealth in the Mandi area. To avoid the effects of currency units on
people's decisions, we converted Indian National Rupees (INR) to a fictitious
currency called “Electronic Currency (EC)”, where EC 1
Simulation of total probability of landslides in ILS for different
values of
Participants were recruited from Mandi via an online advertisement. The
research was approved by the Ethics Committee at the Indian Institute of
Technology Mandi. Informed consent was obtained from each participant, and
participation was completely voluntary. All participants were from science,
technology, engineering, and mathematics (STEM) backgrounds, and their ages
ranged between 21 and 28 years (mean
Experimental sessions were about 30 min long per participant. Participants were given instructions on the computer screen and were encouraged to ask questions before starting their study (see Appendix A for text of instructions used). Once participants had finished their study, they were asked questions related to what information and decision strategy they used on the investment screen and the feedback screen to make their decisions. Once participants ended their study, they were thanked and paid for their participation.
The data were subjected to a 2
As shown in Fig. 8b, there was a significant main effect of strength of
feedback: the average investment ratio was significantly higher in
high-damage conditions (0.51) compared to that in low-damage conditions
(0.38) (
Furthermore, as shown in Fig. 8c, the interaction between the strength of
feedback and feedback's availability was significant (
The average investment ratio increased significantly over 30 days (see
Fig. 9a;
However, in feedback's absence in ILS, participants did not increase their investments for mitigating landslides, even when damages were high compared to low.
We analysed whether an “invest-all” strategy (i.e. investing the entire daily income in mitigating landslides) was reported by participants across different conditions. As mentioned above, the invest-all strategy was an optimal strategy, and this strategy's use indicated learning in the ILS tool. Figure 10 shows the proportion of participants reporting the use of the invest-all strategy. Thus, many participants learnt to follow the invest-all strategy in conditions where experiential feedback was present and damage was high, as opposed to participants in the other conditions.
The proportion of reliance on the invest-all strategy across different conditions.
In this paper, we used an existing ILS tool for evaluating the effectiveness of feedback in influencing people's decisions against landslide risks. We used the ILS tool in an experiment involving human participants and tested how the strength and availability of experiential feedback in ILS helped increase people's investment decisions against landslides. Our results agree with our expectations: experience gained in ILS enabled improved understanding of processes governing landslides and helped participants improve their investments against landslides.
First, the high-damage feedback helped increase people's investments against landslides over time compared to the low-damage feedback. Furthermore, the feedback's presence helped participants increase their investments against landslides over time compared to feedback's absence. These results can be explained by the previous lab-based research on use of repeated feedback or experience (Chaturvedi et al., 2017; Dutt and Gonzalez, 2010, 2011; Finucane et al., 2000; Gonzalez and Dutt, 2011a). Repeated experiential feedback likely enables learning by repeated trial-and-error procedures, where bounded-rational individuals (Simon, 1959) try different investment values in ILS and observe their effects on the occurrence of landslides and their associated consequences. The negative consequences due to landslides are higher in conditions where the damages are more compared to conditions where the damages are less. This difference in landslide consequences influences participants' investments against landslides. According to Slovic et al. (2005), loss-averse individuals tend to increase their contribution against a risk over time. In our case, similar to Slovic et al. (2005), participants started contributing slowly against landslides and, with the experience of landslide losses over time, they started contributing larger amounts to reduce landslide risks.
We also found that the reliance on invest-all strategy was higher in the high-damage and feedback-present conditions than in the low-damage and feedback-absent conditions. The invest-all strategy was the optimal strategy in the ILS tool. This result shows that participants learned the underlying system dynamics (i.e. how their actions influenced the probability of landslides) in ILS better in the feedback-rich conditions compared to the feedback-poor conditions. As participants were not provided with exact equations governing the ILS tool and they had to only learn from trial-and-error feedback, the saliency of the feedback due to messages and images likely helped participants' learning in the tool. In fact, we observed that the use of the optimal invest-all strategy was maximized when the experiential feedback was highly damaging. One likely reason for this observation could be the high educational levels of participants residing in the study area, where the literacy rate was more than 80 %. Thus, it seems that participants' education levels helped them make the best use of damaging feedback.
We believe that the ILS tool can be integrated in teaching courses on sustainable landslide practices in schools from kindergarten to standard 12th. These courses could make use of the ILS tool and focus on educating students about causes, consequences, and risks of hazardous landslides. We believe that the use of the ILS tool will make teaching more effective as ILS will help incorporate experiential feedback and other factors in teaching in interactive ways. The ILS tool's parameter settings could be customized to a certain geographical area over a certain time period of play. In addition, the ILS tool could be used to show participants the investment actions of other participants (e.g. society or neighbours). The presence of investment decisions of opponents in addition to one's own decisions will likely enable social norms to influence people's investments and learning in the tool (Schultz et al., 2007). These features make the ILS tool very attractive for landslide education in communities in the future.
Furthermore, the ILS tool holds a great promise for policy research against landslides. For example, in future, researchers may vary different system-response parameters in ILS (e.g. weight of one's decisions and return to mitigation actions) and feedback (e.g. numbers, text messages and images for damage) in order to study their effects on people's decisions against landslides. Here, researchers could evaluate differences in ILS's ability to increase public contributions in the face of other system-response parameters and feedback. In addition, researchers can use the ILS tool to do “what-if” analyses related to landslides for certain time periods and for certain geographical locations. The ILS tool has the ability to be customized to a certain geographical area as well as certain time periods, where spatial parameters (e.g. soil type and geology) as well as temporal parameters (e.g. daily rainfall) can be defined for the study area. Once the environmental factors have been accounted for, the ILS tool enables researchers to account for assumptions on human factors (contribution against landslides) with real-world consequences (injury, fatality, and infrastructure damage). Such assumptions may help researchers model human decisions in computational cognitive models, which are based upon influential theories of how people make decisions from feedback (Dutt and Gonzalez, 2012; Gonzalez and Dutt, 2011b). In summary, these features make the ILS tool apt for policy research, especially for areas that are prone to landslides. This research will also help test the ILS tool and its applicability in different real-world settings.
Although the ILS tool causes the use of optimal invest-all strategies among people in conditions where experiential feedback is highly damaging, more research is needed on investigating the nature of learning that the tool imparts among people. As people's investments for mitigating landslides in ILS directly influences the risk of landslides due to human and environmental factors, investments indeed have the potential of educating people about landslide risks. Still, it is important to investigate how investing money in the ILS tool truly educates people about landslides. We would like to investigate this research question as part of our future research.
Currently, in the ILS model, we have assumed that damages from fatality and injury to influence participants' daily-income levels. The reduced income levels do create adverse consequences, but one could also argue that they would be much less of concern for most people compared to the injury and fatality itself. Furthermore, people could also choose to migrate from an area when the landslide mitigation costs are too high, and adaptation becomes impossible, especially due to the differences between the landslide hazard and other hazards such as flood, drought, and general climate risks. As part of our future research, we plan to investigate the influence of feedback that causes only injuries or fatalities in ILS compared to the feedback that causes economic losses due to injuries and fatalities. Also, as part of our future research in the ILS tool, we plan to investigate people's migration decisions when the landslide mitigation costs are too high and adaptation to landslides is not possible.
In this paper, our primary objective was not to accurately predict rainfall or other landslide parameters; rather, it was to educate people about landslide disasters. Thus, we have used approximate models of real landslide phenomena in the ILS simulation tool. The use of approximate models is in line with a large body of literature on using simulation tools for improving people's understanding about natural processes like climate change and other natural disasters (Dutt and Gonzalez, 2010, 2011; Finucane et al., 2000). As part of our abstraction, we may have missed certain aspects related to the sensitivity of the different social classes to their economic and cultural resources. In future, we would like to compare the proportion of investments in different experimental conditions to people's likely socio-economic cost thresholds given that people may need to spend their wealth in other areas beyond landslide mitigation.
Furthermore, we used a linear model to compute the probability of landslides due to human factors in the ILS tool. Also, the probabilistic equations governing the physical factors in the ILS model were not disclosed to participants, who seemed to possess high education levels. One could argue that there are several other linear and non-linear models that could help compute the probability of landslides due to human factors. Some of these models may also influence the probability of landslides and the severity of consequences (damages) caused by landslides. Also, other more generic models could account for the physical factors in the ILS tool. We plan to try these possibilities as part of our future work in the ILS tool. Specifically, we plan to assume different models of investments in the ILS tool and we plan to test them with participants possessing different education levels.
In the current experiment, we assumed a large disparity between a participant's property wealth and his/her daily income. In addition, as part of the ILS model, we did not consider support from governments or insurance companies against landslide damages. In India, people mostly use their own finances to overcome the challenges put by natural disasters as insurance or other public methods have only shown limited success (ICICI, 2018). However, in certain cases, especially in developing countries, mitigation of landslide risks may often be financed by the government or international agencies. As part of our future work, we plan to extend the ILS model to include assumptions of contributions from government and other international agencies. Such assumptions will help us determine the willingness of common people to contribute against landslide disasters, which is important as the developing world becomes more developed over time.
To test our hypotheses, we presented participants with a high-damage scenario and a low-damage scenario, where the probabilities of property damage, injury, and fatality were high and low, respectively. However, such scenarios may not be realistic, where people may want to migrate from both low- and high-damage areas in even the least developed countries. In future research with ILS, we plan to calibrate the probability of damages, injury, and fatality to realistic values and then test the effectiveness of ILS in improving decision-making.
Furthermore, in our experiment, when a landslide did not occur and experiential feedback was present, people were presented with a smiling face followed by a message. The message and emoticon were provided to connect the cause-and-effect relationships for participants in the ILS tool. However, it could also be that a landslide did not occur in a certain trial due to the stochasticity in the simulation rather than participants' investment actions. Although such situations are possible over shorter time periods, over longer time periods increased investments from people will only reduce the probability of landslides. Also, there is a possibility that the participant demographics in the experiment may not be representative of the study area. Thus, as part of future research, we plan to control the participant sample in different ways and test the effects that demographics have on people's investments.
In this paper, the experiment used a daily investment setting in the ILS
tool. However, the ILS tool can easily be customized to different time
periods ranging from seconds to minutes, hours, days, months, and years. As
part of our future research, we plan to extend the daily assumption by
considering people making decisions on longer timescales ranging from months
to years. In addition, in the experiment, we assumed a value of 0.7 and 0.8
for the weight (
It can be concluded from this preliminary research that simulation tools like ILS that provide feedback about the outcomes of landslide disasters influence people's investment decisions against landslides. Given our results, we believe that ILS could potentially be used as a landslide-education tool for increasing public understanding about landslides among the adult population.
This work forms a good preliminary example for researchers involved in gamification and participative processes in the case of landslide disasters. However, this research work is preliminary in nature, and we plan to deepen it in the near future. To examine the full potential of ILS in influencing people's perceptions of landslide risk, a lot of experiments manipulating system variables, feedback strengths, and severity of damages need to be conducted on a bigger population across several study areas. Another line of research could be to understand the people's behaviour or decision-making style in landslide scenarios by fitting computational cognitive models to the human data. The ILS tool can also be used by policymakers to do what-if analyses in different scenarios concerning landslides. However, the assumptions in the ILS tool should be evaluated in the study area before it is released for policy research.
Data used in this article have not been deposited to respect the privacy of users. The data can be provided to readers upon request.
Welcome!You are a resident of Mandi district of Himachal Pradesh, India, a township in the lap of the Himalayas. You live in an area that is highly prone to landslides due to a number of environmental factors (e.g. the prevailing geological conditions and rainfall). During the monsoon season, due to high intensity and a prolonged period of rainfall, a number of landslides may occur in the Mandi district. These landslides may cause fatalities and injuries to you, your family, and your friends who reside in the same area. In addition, landslides may also damage your property and cause loss to your property wealth.
This study consists of a task where you will be making repetitive decisions to invest money in order to mitigate landslides. Every trial, you will earn certain money between 0 and 10 points. This money is available to you to invest against landslides. You may invest a certain amount from the money available to you; however, if you do not wish to invest anything, you may invest 0.0 against landslides in a particular trial. Based upon your investment against landslides, you will get feedback on whether a landslide occurred and whether there was an associated loss of life, injury, or property damage (all three events are independent and can occur at the same time).
Whenever a landslide occurs, if it causes fatality, then your daily earnings will be reduced by 5 % of its present value at that time; if landslide causes injury to someone, then the daily earnings will be reduced by 2.5 % of its present value at that time. Thus, the amount available to you to invest against landslides will reduce with each fatality and injury due to landslides. Furthermore, if a landslide occurs and it causes property damage, then your property wealth will be reduced by 80 % of its present value at that time; however, the money available to you to invest against landslides due to your daily earnings will remain unaffected.
Generally, landslides are triggered by two main factors: environmental factors (e.g. rainfall – outside one's control) and investment factors (money invested against landslides – within one's own control). The total probability of landslide is a weighted average of probability of landslide due to environment factors and probability of landslide due to investment factors. The money you invest against landslides reduces the probability of landslide due to investment factors and also reduces the total probability of landslides. However, the money invested against landslides is lost and cannot become a part of your total wealth.
At the end of the game, we will convert your total wealth into INR and pay you
for your effort. For this conversion, a ratio of 100 total wealth
points to INR 1 will be followed. In addition, you will be paid INR 30
as base payment for your effort in the task. Please remember that your goal
is to maximize your total wealth in the game.
Starting Game ParametersYour wealth:
AA developed the ILS tool under the guidance of PC and VD. AA and PC collected the data in the study. PC and VD analysed the data and prepared the manuscript. PC and VD revised the manuscript as per referee comments.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception”. It does not belong to a conference.
This research was partially supported by the following grants to Varun Dutt: a grant from the Himachal Pradesh State Council for Science, Technology and Environment (grant number: IITM/HPSCSTE/VD/130); a grant from the National Disaster Management Authority (grant number: IITM/NDMA/VD/184); and a grant from the Defence Terrain Research Laboratory, Defence Research and Development Organization (grant number: IITM/DRDO-DTRL/VD/179). We thank Akanksha Jain and Sushmita Negi, Centre for Converging Technologies, University of Rajasthan, India, for providing preliminary support for data collection in this project. We also thank the anonymous reviewers for their useful comments and suggestions which contributed to the improvement of the manuscript. Edited by: Stefano Luigi Gariano Reviewed by: four anonymous referees