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To investigate how differing amounts of experiential feedback and feedback’s availability in an interactive simulation tool influences people’s decision-making against landslide risks. Feedback via simulation tools is likely to help people improve their decisions against disasters; however, currently little is known on how differing amounts of experiential feedback and feedback’s availability in simulation tools influences people’s decisions against landslides. We tested the influence of differing amounts of experiential feedback and feedback’s availability on people’s decisions against landslide risks in an Interactive Landslide Simulation (ILS) tool. In an experiment, 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 condition, 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. Experience gained in ILS enables people to improve their decision-making against landslide risks. Simulation tools seem appropriate for landslide risk communication and for performing what-if analyses.
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Learning in an Interactive Simulation Tool against Landslide
Risks: The Role of Strength and Availability of Experiential
Feedback 3
Pratik Chaturvedi1, 2, Akshit Arora1, 3, and Varun Dutt1
1Applied Cognitive Science Laboratory, Indian Institute of Technology, Mandi- 175005, India
2Defence Terrain Research Laboratory, Defence Research and Development Organization, Delhi 6
-110054, India
3Computer Science and Engineering Department, Thapar University, Patiala - 147004, India 8
Correspondence to: Pratik Chaturvedi (
Abstract. 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 11
feedback and feedback’s availability in simulation tools influences people’s decisions against 12
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 feedbacks availability (present or absent) were varied
across four between-subject conditions in an interactive landslide simulation (ILS) tool: high-
damage feedback-present, high-damage feedback-absent, low-damage feedback-present, and low-
damage 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 19
feedback-present conditions, experiential feedback was provided in numeric, text, and graphical 20
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 24
produced learning in ILS. Simulation tools like ILS seem appropriate for landslide risk
communication and for performing what-if analyses.
1 Introduction
Landslides cause massive damages to life and property worldwide (Chaturvedi and Dutt, 2015; 28
Margottini et al., 2011). Imparting knowledge about landslide causes-and-consequences as well as
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 32
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-35
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). 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). 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 susceptibilities
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
Promising recent research has shown that experiential feedback in simulation tools likely 63
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-66
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) influences 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 feedbacks availability influences 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 93
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 96
due to landslides at two levels: low probability and high probability. The high probability was set
about 10-times higher compared to 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 compared to when this probability was low. However, Chaturvedi et
al. (2017) did not fully evaluate the effectiveness of experiential feedback of damages in 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, 106
where these decisions and strategies would be indicative of learning of landslide dynamics in the 107
Prior literature on learning from experiential feedback (Baumeister et al., 2007; Dutt and
Gonzalez, 2012; Finucane et al., 2000; Knutty, 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 113
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 117
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 rationale individuals while facing landslide disasters (Bossaerts and Murawski,
2015; Neumann and Morgenstern, 1947), where one disregards people’s bounded rationality, risk
perceptions, attitudes, and behaviours (De Martino, Kumaran, Seymour, and Dolan; 2005;
Gigerenzer and Selten, 2002; Kahneman and Tversky, 1979; Simon, 1959; Slovic, Peters, 124
Finucane, and MacGregor, 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 127
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 130
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 132
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. 135
In what follows, first, we detail the characteristics of the study area, and then a 136
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. 142
2 Study area
In this paper, the study area was one involving the local communities living in the Mandi town
(31.58° N, 76.91° E), a township located in the state of Himachal Pradesh, India (see Figure 1).
The Mandi town has an average elevation of 850m above mean-sea level, 23 square km area, and 146
a population of 26,422 people (Census, 2011). Literacy rate in Mandi town is 81.5% and most of
the population are Hindus by religion. Mandi is a highly religious place with a huge number of
Hindu temples all around the town (Census, 2011). Geologically, Mandi town is located on the
folds of the lesser Himalayan mountains and it lies in the earthquake Zone IV and V, the highest
earthquake zones in the world (Hpsdma, 2017). Apart from inherent geological weaknesses that
may cause landslides in Mandi town, other anthropogenic activities such as road construction,
deforestation of hill slopes, building construction on slopes, and debris dumping may also trigger
landslides in the area surrounding the town (Hpsdma, 2017). As per Kahlon, Chandel, and Brar 154
(2014), around 90% of the Mandi town is prone to landslides, where 25% of this area falls under
the severe landslide hazard risk category. Landslide occurrences during the past 39 years (from
1971 to 2009) exhibit Mandi to account for 99 landslide events (11%) out of a total 919 landslide 157
events in Himachal Pradesh, forming the 4th highest ranked district in terms of number landslides
behind Shimla, Solan, and Kinnaur (Kahlon et al., 2014). The problem of landslides is accelerated
in the monsoon season (mid-June to mid-September) in the town. The per-capita income of people 160
in the Mandi town is close to INR 292 per day (Census, 2011). In addition, as per the tenancy laws
of Himachal Pradesh, most people own land, which cannot be sold to people from outside the state 162
(Himachal, 2012). The average per-capita property value in the state would be close to INR 20
million (Census, 2011). These values of per-capita daily income and property wealth were used in
the ILS tool and these values have been detailed ahead in this paper. Furthermore, the prevailing 165
rainfall pattern and the landslide hazard zonation map of Mandi town, which were used in the ILS 166
tool, have also been detailed ahead in this paper.
Figure 1. The 3D satellite view of Mandi town and adjoining areas. The town is located in a valley around river Beas
with high mountains that are prone to landslides on both sides. Source: Google Maps.
3 Computational model of landslide risk
Chaturvedi et al. (2017) had proposed a computational model for simulating landslide risks that
was based upon the integration of human and physical factors (see Figure 2). Here, we briefly 174
detail this model and use it in the ILS tool for our experiment (reported ahead). As seen in Figure
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 Figure 2). In Hasson et al. (2010)’s model, the 177
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 the disaster in mitigation measures 180
reduces the probability of the disaster and not investing in mitigation measures increases the
probability of the disaster. However, by reducing the landslide risk, people also have lesser ability 182
in investing 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 185
of landslides or the risk of landslides (where, risk = probability (hazard) * consequence). We plan 186
to consider such model assumptions as part of our future research. In addition, there may be
contributions made by the national, regional, and local governments for providing protection
measures against landslides in addition to the investments made by people residing in the area
(Hpsdma, 2017). Such investments may be made based upon the past occurrences of landslides in
the study area. Furthermore, people may also be able to buy insurance that covers for the damages
caused by landslides. However, in India, in the absence of assistance from the government, mostly 192
people tend to rely on their own wealth for adaptation to landslide occurrence. Thus, purchasing
insurance against disasters is less common and unpopular as insurance companies mostly do not
pay insured amounts in the event of natural disasters like landslides (ICICI, 2018). In this paper,
we restrict our analyses to only people’s own investments influencing landslides. We plan to 196
consider the role of government contributions for mitigation and adaptation (mostly after landslide
events) and partial insurance payments as part of our future research.
Furthermore, in the landslide model, the probability of landslides due to physical (natural)
factors (see box 1.2) 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 Figure 2, the ILS model focuses on calculation of total
probability of landslide (due to physical and human factors) (box 1.3). 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 205
damages caused by landslides and their effects on people’s earnings (box 1.4).
3.1 Total probability of landslides
As described by Chaturvedi et al. (2017), the total probability of landslides is a function of 211
landslide probabilities due to human factors and physical factors. This total probability of
landslides can be represented as the following:!213
" # $ ! % & "! ' ( ! ) * % & "! + (1)
Where W is a free weight parameter in [0, 1]. The total probability formula involves calculation
of two probabilities, probability of landslide due to human investments (P(I)) and probability of 216
landslide due to physical factors (P(E)). These probabilities have been defined below. According 217
to Equation 1, the total probability of landslides will change based upon both human decisions and
environmental factors over time. In the ILS model, we simulate the total probability of landslides
P(T), where a landslide occurs when a uniformly distributed random number (~ U(0, 1)) is less
Figure 2. Probabilistic model of the Interactive Landslide Simulator tool. Figure adapted from Chaturvedi et al. (2017).
than or equal to P(T) on a certain day. If a uniformly distributed random number in [0, 1] (U (0,
1)) is less than or equal to a point probability value, then it simulates this point probability value.
For example, if U (0, 1) 30%, then U (0, 1) will be less than or equal to the 30% value exactly 223
30% of the total number of times it is simulated; and, thus this random process will simulate a 30%
probability value.
3.1.1 Probability of landslide due to human investments (P(I))
As suggested by Chaturvedi et al. (2017), the probability P(I) is calculated using the probability
model suggested by Hasson et al. (2010). In this model, P(I) is directly proportional to the amount 229
of money invested by participants for landslide mitigation. The probability of landslide due to
human investments is: 231
" ' $ ) * !,&! -.
2&3 (2)
B = Budget available towards addressing landslides for a day (if a person earns an income or salary, 234
then B is the same as this income or salary earned in a day). 235
n = Number of days.
xi = Investments made by a person for each day i to mitigate landslides; xi B.
M = Return to Mitigation, which is a free parameter and captures the lower bound probability of
P(I), i.e., P (I) = 1- M when a person puts her entire budget B into landslide mitigation ( 45
567 =
8 & 9); 0 M 1.
People’s monetary investments (xi) are for mitigation measures like building retaining walls or 241
planting long root crops.
3.1.2 Probability of landslide due to physical factors (P(E))
Some of the physical factors impacting landslides include rainfall, soil types, and slope profiles 245
(Chaturvedi et al., 2017; Dai et al., 2002). These factors can be categorized into two parts:
1. Probability of landslide due to rainfall (P(R))
2. Probability of landslide due to soil types and slope profiles (spatial probability,
For the sake of simplicity, we have assumed that P(S) is independent of P(R). Thus, given P(R)
and P(S), the probability of landslide due to physical factors, P(E), is defined as:
" + $ !" : & !" ; (3) 252
In the first step, P(R) is calculated based upon a logistic-regression model (Mathew et al., 2013)
as follows:
"<:= $ 7
7>?@A (4a) 255
B! $ ! *CDE)F! ( ! G: & !HDHFF! (! CGI: & !HDHJE! ( ! CHGK: & !HDHHL!
BM!<*!NO (N= (4b) 258
Where, the G:, CGI:, and CHGK: is the daily rainfall, the 3-day cumulative rainfall, and the 30-
day antecedent rainfall in the study area. This model in equations 4a and 4b was developed for the 260
study area by Mathew et al. (2013) and we have used the same model in this paper. The rainfall
parameters in the model were calculated from the daily rain data from the Indian Metrological
Department (IMD). We compared the shape of the P(R) distribution by averaging rainfall data 263
over the past five years with the shape of the P(R) distribution by averaging rainfall data over the 264
past 30-years. This comparison revealed that were no statistical differences between these two
distributions. Thus, we used the daily rainfall data averaged over the past 5-years (2010-14) to find
the average rainfall values on each day out of the 365-days in a year. Next, these averaged rainfall
values were put into equations 4a and 4b to generate the landslide probability due to rainfall (P(R))
over an entire year. Figure 3 shows the resulting shape of P(R) distribution as a function of days
in the year for the study area. Due to the monsoon period in India during mid-June mid-270
September, there is a peak in the P(R) distribution curve during these months. Depending upon the
start date in the ILS tool, one could read P(R) values from Figure 3 as the probability of landslides
due to rainfall on a certain day in the year. This P(R) function was assumed to possess the same
shape across all participants in the ILS tool. 274
Figure 3. Probability of landslide due to rainfall over days for the study area. The probability was generated by
using equations 4a and 4b. 278
The second step is to evaluate the spatial probability of landslides, P(S). The determination
of P(S) is done from the landslide hazard zonation (LHZ) map of the study area (see Figure 4A;
Anbalagan, 1992; Chaturvedi et al., 2017; Clerici et al., 2002), which are based on various 281
causative factors of landslides in the study area (e.g., geology, geometry, and geomorphology). As
shown in Figure 4A, we computed the spatial probability of landslides in the study area based upon
the Total Estimated Hazard (THED) rating of different locations on a LHZ map (see legend) and 284
their surface area of coverage (the maximum possible value of THED is 11.0 and its minimum
possible value is 0.0). Table 1 provides the THED scale to report the susceptibility of an area to 286
landslides (Anbalagan, 1992).
Figure 4 (A): Landslide hazard map of study area. (B): The cumulative density function of the spatial probability of
landslides (P(S)). The P(S) is shaped by geological and other causative factors in the study area. 292
Table 1. Total Estimated Hazard (THED) scale for evaluating the susceptibility of an area to
landslides across to different hazard classes
Hazard Zone
Range of corrected THED
Hazard class
THED < 3.5
Very low hazard (VLH) zone
3.5 THED < 5.0
Low hazard (LH) zone
5.0 THED 6.5
Moderate hazard (MH) zone
6.5 < THED 8.0
High Hazard (HH) zone
THED > 8.0
Very high hazard (VHH) zone
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 with the highest THED value (= 11.0). Next, we used the LHZ map
of the study area (Figure 4A) to find the surface area that was under a hazard class (very low, low, 298
moderate, high, and very high) and used this area to determine the cumulative probability density
function for P(S). For example, if a THED of 3.5 (low hazard class) has a 20% coverage area on 300
LSZ (Figure 4A), then the spatial probability is less than equal to 0.32 (=3.5/11.0) with a 20%
chance. Similarly, if a THED of 5.0 (moderate hazard class) has a 30% coverage area on LSZ, then
the then the spatial probability is less than equal to 0.45 (=5.0/11.0) with a 50% chance (30% + 303
20%). Such calculations enabled us to develop a cumulative density function for P(S) (see Figure 304
4B). As shown in Figure 4B (the cumulative density function of P(S)), 1.94% area belonged to the
very low hazard class (P(S) from 0/11 to 3.5/11), 46.61% area belonged to the low hazard class
(P(S) from 3.5/11 to 5.0/11), 30.28% area belonged to the moderate hazard class (P(S) from 5.0/11
to 6.5/11), and 13.71% area belonged to the high hazard class (P(S) from 6.5/11 to 8.0/11), and
7.43% area belonged to the very high hazard class (P(S) from 8.0/11 to 11/11).
In the ILS tool, using Figure 4B, we used a randomly determined point value of the P(S) 310
from its cumulative density function for each participant in the ILS tool (see Figure 4B). This P(S)
value stayed the same for this participant across her performance in the ILS tool. Please note that
this exercise was not meant to accurately determine the spatial probability of landslide in the area
of interest, where more accurate and advanced methods could be used. Rather, the primary
objective of this exercise was to develop an approximate model that could account for the spatial 315
probability in the ILS based upon the LHZ map and THED scale (the ILS tool was primarily meant
to improve people’s understanding about landslide risks and not for physical modeling of
landslides). 318
3.1.3 Damages due to landslides
As suggested by Chaturvedi et al. (2017), the damages caused by landslides were classified into 321
three independent categories: property loss, injury, and fatality. These categories have their own
damage probabilities. When a landslide occurs, it could be benign or catastrophic. A landslide 323
becomes catastrophic with damage probability value of property loss, injury, and fatality. Thus,
once a uniformly distributed random number is less or equal to the probability of the corresponding
damage, then the corresponding damage is assumed to occur in ILS tool. Landslide damages have 326
different effects on the player’s wealth and income, where damage to property affects one’s 327
property wealth and damages concerning injury and fatality affect one’s income level. When the
landslide is benign, 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 was used (Parkash, 2011). 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 literature
is not required in the simulation. The exact assumptions about damages are detailed ahead in this 333
4 Interactive Landslide Simulator (ILS) tool
The ILS tool (Chaturvedi et al., 2017) is a web-based tool and it is based upon the ILS model 337
described above. The ILS tool was coded in open-source programming languages PHP and
MySQL and it is freely available for use at the following URL: The ILS
tool allows participants to make repeated monetary investment decisions for landslide risk-
mitigation, observe the consequences of their decisions via feedback, and try new investment
decisions. This way, ILS helps to improve people’s understanding about the causes and
consequences of landslides. The ILS tool can run for different time periods, which could be from
days to months to years. This feature can be customized in the ILS tool. However, in this paper,
we have assumed a daily time-scale to make it match the daily probability of landslides computed
in equations 4a and 4b. 346
The goal in 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 349
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 352
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 354
dynamics over time.
Figure 5 represents graphical user interface of ILS tool’s investment screen. On this screen,
participants are asked to make monetary mitigation decisions up to their daily income upper bound 357
(see Box A). The total wealth is a sum of income not invested for landslide mitigation, property 358
wealth, and total damages due to landslides (see Box B). As shown in Box 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 Box 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 were told that the mitigation measures will be taken close 364
to the places where they reside in the district in the ILS tool.
Figure 5. ILS tool’s Investment Screen. Box (A): The text box where participants made investments against landslides. Box (B): The tool’s different parameters
and their values. Box (C): Line graphs showing the total probability of landslide, the total income not invested in landslides, and the property wealth ov er days.
Horizontal axes in these graphs represents number of days. The goal was to maximize Total Wealth across a number of days of performance in the ILS tool. This
figure is adapted from Chaturvedi et al. (2017).
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 371
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 Figure 6). As discussed above, the landslide occurrence was determined by the 374
comparison of a uniformly distributed random number in [0, 1] with P(T). If a uniformly
distributed random number in [0, 1] was less than or equal to P(T), then a landslide occurred;
otherwise, the landslide did not occur. Furthermore, if the landslide occurred, then three uniformly 377
distributed random numbers in [0, 1] were compared with the probability of injury, fatality, and
property damage, respectively. If the values of any of these random numbers were less than or 379
equal to the corresponding injury, fatality, or property-damage probabilities, then the landslide was
catastrophic (i.e., causing injury, fatality, or property damage; all three events could occur
simultaneously). In contrast, if the random numbers were more than the corresponding injury, 382
fatality, and property-damage probabilities, then the landslide was benign (i.e., it did not cause 383
injury, fatality, and property damage). As shown in Figure 6A, feedback information is presented
in three formats: monetary information about total wealth (box I), messages about different losses
(box I), and imagery corresponding to losses (box II). Injury and fatality due to landslides causes
a decrease in the daily income and damage to property causes a loss of property wealth (the exact
loss proportions are detailed ahead). If a landslide does not occur in a certain trial, a positive
feedback screen is shown to the decision maker (see Figure 6B). The user can get back to 389
investment decision screen by clicking on “Return to Game” button on the feedback screen.
(A) Negative Feedback
(B) Positive Feedback 402
Figure 6. ILS tool’s feedback screens. (A) Negative feedback when a landslide occurred. Box (I) contains the loss in
terms of magnitude and messages and Box (II) contains associated imagery. (B) Positive feedback when a landslide
did not occur. 406
5 Methods 409
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 412
prior literature (Baumeister et al., 2007; Dutt and Gonzalez, 2012; Finucane et al., 2000; Knutty,
2005; Reis and Judd, 2013; Wagner, 2007), we expected the proportion of investments to be higher 414
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 417
Gonzalez, 2011; Gonzalez and Dutt, 2011a). 418
5.1 Experimental Design
Eighty-three participants were randomly assigned across four between-subjects conditions in the
ILS tool, where the conditions differed in the strength of experiential feedback (high-damage (N=
40) or low-damage (N= 43)) and availability of feedback (feedback-present (N= 43) or feedback-
absent (N= 40)) provided after every mitigation decision. An experiment involving the high-424
damage feed-present condition (N = 20) and the low-damage feedback-present condition (N = 23)
in the ILS tool was reported by Chaturvedi et al. (2017). This data has been included in this paper
with two more conditions, the high-damage feedback-absent (N = 20) and the low-damage
feedback-absent (N = 20). Data in all four conditions was collected simultaneously. They were 428
asked to invest repeatedly against landslides across 30-days. In feedback-present conditions,
participants made investment decisions on the investment screen and then they received feedback
about the occurrence of landslides or not on the feedback screen. Participants were also provided
graphical displays showing the total probability of landslides, the total income not invested in
landslides, and the property wealth over days. Figures 5 and 6 show the investment and feedback
screen that were shown to participants in the feedback-present conditions. In feedback-absent
conditions, participants were given a text description and they made an investment decision,
however, neither they were shown the feedback screen nor they were shown the graphical displays
on the investment screen. Thus, in the feedback-absent condition, although participants were
provided with the probability of damages due to landslides and the results of 0% and 100%
investments as a text description, however, they were not shown the feedback screen as well as the 439
graphical displays on the investment screen. The text description and investment screen shown to
participants in the feedback-absent conditions is given as Appendix ‘A’. In high-damage
conditions, the probability of property damage, fatality and injury on any trial were set at 30%, 442
9%, and 90%, respectively, over 30-days. In low-damage conditions, the probability of property
damage, fatality and injury on any trial were set at 3%, 1%, and 10%, respectively, over 30-days
(i.e., about 1/10th of its values in the high-damage condition). Across all conditions, participants 445
made one investment decision per trial across 30-days (this end-point was unknown to
participants). Participants’ goal was to maximize their total wealth over 30-days. Across all 447
conditions, only 1-landslide could occur on a particular day. The nature of functional forms used
for calculating different probabilities in ILS were unknown to participants.
The proportion of damage (in terms of daily income and property wealth) that occurred in an event 450
of fatality, injury, or property damage was kept constant across 30-days. The property wealth 451
decreased to half of its value every time property damage occurred in an 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 20
million EC, which is the expected property wealth in 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 1 EC = 1 INR. The initial per-trial income was kept at 457
292 EC (taking into account the GDP and per-capita income of Himachal state where Mandi is
located). Overall, there was a large difference between the initial income earned by a participant
and the participant’s initial property wealth. In this scenario, the optimal strategy dictates
participants to invest their entire income in landslide protection measures, since participants’ goal 461
was to maximize total wealth. The weight (W) parameter in the equation 1 of the ILS model was
fixed at 0.7 across all conditions. This high value of the W parameter ensured that participants’
investment decisions played a dominant role in influencing the total landslide probability as per
the equation 1. To understand the effect of the W parameter on the total probability of landslide in
ILS, a Monte-Carlo simulation was performed in the ILS model for different investment conditions
over time (see Figure 7A and 7B). It can be seen from both Figures 7A and 7B, in both the extreme
investment conditions over 30-days (i.e., zero investments and full investments from human
players), the value of W determined the range of possible values of the total probability of
landslides, P(T). For example, with a W = 1.0, zero human investments over a 30-day period 470
caused P(T) = 1.0 (a sure landslide) and full investments caused P(T) ~ 0.20 (landslides to be 20%
likely to occur). Thus, by keeping a higher W value, we could ensure that there was a large possible
change in the P(T) due to human actions, giving human participant salient feedback on how their 473
decisions changed P(T). The W value was set to be 0.70 in the ILS tool and it was shown to
participants through the investment screen on the ILS tool’s interface (see Figures 5). Furthermore,
the return to mitigation free parameter (M) was set at 0.8. Again the value of the M parameter 476
ensured that probability of landslides reduced to 20% (= 1 – M from equation 2) when participants
invested their daily income in full. Participants performed in the ILS for 30-days, starting in mid-478
July and ending in mid-August. This period coincided with the period of heavy monsoon rainfall
in Mandi area (see the P(R) peaks in Figure 3). Thus, participants performing in ILS experienced
an increasing probability of landslides due to environmental factors (due to an increasing amount 481
of rainfall over days). We used the investment ratio as a dependent variable for the purpose of data 482
analyses. The investment ratio was defined as the ratio of investment made in a trial to total
investment that could have been made up to the same trial. This investment ratio was averaged
across all participants in one case and averaged over all participants and days in another case. We
expected the average investment ratio to be higher in the feedback-present and high-damage
conditions compared to feedback-absent and low-damage conditions. We took an alpha-level (the
probability of rejecting the null hypothesis when it is true) to be 0.05 (or 5%). 488
(A) (B)
Figure 7. Simulation of total probability of landslides in ILS for different values of W in zero investment scenario
(A) and full investment scenario (B).
5.2 Participants 494
Participants were recruited from Mandi town via an online advertisement. The research was
approved by the Ethics Committee at Indian Institute of Technology Mandi. Informed consent was
obtained from each participant and participation was completely voluntary. All participants were 497
from Science, Technology, Engineering, and Mathematics (STEM) backgrounds and their ages
ranged in between 21 and 28 years (Mean = 22 years; Standard Deviation = 2.19 years). The
following percentage of participants were pursuing or had completed different degrees: 6.0% high-500
school degrees; 54.3% undergraduate degrees; 33.7% Master’s degrees; and, 6.0% Ph.D. degrees.
The Mandi area is prone to landslides and most participants self-reported to be knowledgeable or 502
possess basic understanding about landslides. The literacy rate in Mandi and surrounding area is
quite high (81.5%) (Census, 2011) and our sample was representative of the population residing
in this area. When asked about their previous knowledge about landslides, 2.4% claimed to be 505
highly knowledgeable, 16.8% claimed to be knowledgeable, 57.8% claimed to have basic 506
understanding, 18.2% claimed to have little understanding, and 4.8% claimed to have no idea. All
participants received a base payment of INR 50 (~ USD 1). In addition, there was a performance
incentive based upon a lucky draw. Top-10 performing participants based upon total wealth
remaining at the end of the study were put in a lucky draw and one of the participants was randomly
selected and awarded a cash prize of INR 500. Participants were told about this performance
incentive before they started their experiment. 512
5.3 Procedure
Experimental sessions were about 30-minutes long per participant. Participants were given
instructions on the computer screen and were encouraged to ask questions before starting their 516
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.
6 Results
6.1 Investment Ratio Across Conditions
The data were subjected to a 2 × 2 repeated-measures analyses of variance. As shown in Figure
8A, there was a significant main effect of feedback’s availability: the average investment ratio was
higher in feedback-present conditions (0.53) compared to that in feedback-absent conditions (0.37) 526
(F (1, 79) = 8.86, p < 0.01, η2 = 0.10). We performed analysis of variance statistical tests for
evaluating our expectations. The F-statistics is the ratio of between-group variance and the within-
group variance. The numbers in brackets after the F-statistics are the degrees of freedom (K-1, N 529
- K), where K are the total number of groups compared and N is the overall sample size. The p-
value indicates the evidence in favor of the null-hypothesis when it is true. We reject the null-
hypothesis when p-value is less than the alpha-level (0.05). The η2 is the proportion of variance 532
associated with one or more main effects. It is a number between 0 and 1 and a value of 0.02, 0.13,
and 0.26 measures a small, medium, or large correlation between the dependent and independent 534
variables given a population size. The bracket values are indicative of the F-value, its significance
and effect size. This result is as per our expectation and shows that the presence of experiential
feedback in ILS tool helped participants increase their investments against landslides compared to 537
investments in the absence of this feedback. 538
As shown in Figure 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) (F (1, 79) = 5.46, p < 0.05, η2 = 0.07). Again, this result is
as per our expectation and shows that high-damaging feedback helped participants increase their
investments against landslides compared low-damaging feedback.
Furthermore, as shown in Figure 8C, the interaction between the strength of feedback and 544
feedback’s availability was significant (F (1, 79) = 8.98, p < 0.01, η2 = 0.10). There was no
difference in the investment ratio between the high-damage condition (0.35) and low-damage
condition (0.38) when experiential feedback in ILS was absent, however, the investment ratio was
much higher in the high-damage condition (0.67) compared to the low-damage condition (0.38) 548
when experiential feedback in ILS was present (Chaturvedi et al., 2017). Thus, feedback needed
to be damaging in ILS to cause an increase in investments in mitigation measures against
Figure 8. (A) Average investment ratio in Feedback-present and Feedback-absent conditions. (B) Average 554
investment ratio in low- and high-damage conditions. (C) Average investment ratio in low- and high-damage
conditions with Feedback-present and absent. The error bars show 95% Confidence Interval (CI) around the point
estimate. 557
6.2 Investment Ratio Across Days 560
The average investment ratio increased significantly over 30-days (see Figure 9A; F (8.18, 646.1)
= 8.35, p < 0.001, η2 = 0.10). As shown in Figure 9B, the average investment ratio increased rapidly 562
over 30-days in feedback-present conditions, however, the increase was marginal in feedback-
absent conditions (F (8.18, 646.1) = 3.98, p < 0.001, η2 = 0.05). Furthermore, in feedback-present
conditions, the average investment ratio increased rapidly over 30-days in high-damage conditions, 565
however, the increase was again marginal in the low-damage conditions (see Figure 9C; F (8.18, 566
646.1) = 6.56, p < 0.001, η2 = 0.08). Lastly, as seen in Figure 9D, although there were differences
in the increase in average investment ratio between low-damage and high-damage conditions when
experiential feedback was present, however, such differences were non-existent between the two
damage conditions when experiential feedback was absent (F (8.18, 646.1) = 4.16, p < 0.001, η2 =
0.05). Overall, ILS performance helped participants increase their investments for mitigating
landslides when damage feedback was high compared to low in ILS. 572
Figure 9. (A) Average investment ratio over days. (B) Average investment ratio over days in Feedback-present and Feedback-absent conditions. (C) Average 574
investment ratio over days in low- and high-damage conditions. (D) Average investment ratio over days in low- and high- damage conditions with Feedback-575
present or absent. The error bars show 95% CI around the point estim
However, in feedback’s absence in ILS, participants were unable to increase their investments for 578
mitigating landslides, even when damages were high compared to low. 579
6.3 Participant Strategies 580
We analyzed whether an “invest-all” strategy (i.e., investing the entire daily income in mitigating 581
landslides) was reported by participants across different conditions. As mentioned above, the invest-all 582
strategy was an optimal strategy and this strategy’s use indicated learning in the ILS tool. Figure 10 shows 583
the proportion of participants reporting the use of the invest-all strategy. Thus, many participants learnt 584
to follow the invest-all strategy in conditions where experiential feedback was present and it was highly 585
damaging compared to participants in the other conditions. 586
Figure 10. The proportion of reliance on the invest-all strategy across different conditions. 588
8 Discussion 592
In this paper, we used an existing ILS tool for evaluating the effectiveness of feedback in influencing 593
people’s decisions against landslide risks. We used the ILS tool in an experiment involving human 594
participants and tested how the strength and availability of experiential feedback in ILS helped increase 595
people’s investment decisions against landslides. Our results agree with our expectations: Experience 596
gained in ILS enabled improved understanding of processes governing landslides and helped participants 597
improve their investments against landslides. 598
First, the high-damaging feedback helped increase people’s investments against landslides over 599
time compared to the low-damaging feedback. Furthermore, the feedback’s presence helped participants 600
increase their investments against landslides over time compared to feedback’s absence. These results can 601
be explained by the previous lab-based research on use of repeated feedback or experience (Chaturvedi 602
et al., 2017; Dutt and Gonzalez, 2010, 2011; Finucane et al., 2000; Gonzalez and Dutt, 2011a). Repeated 603
experiential feedback likely enables learning by repeated trial-and-error procedures, where bounded-604
rational individuals (Simon, 1959) try different investment values in ILS and observe their effects on the 605
occurrence of landslides and their associated consequences. The negative consequences due to landslides 606
are higher in conditions where the damages are more compared to conditions where the damages are less. 607
This difference in landslide consequences influences participants’ investments against landslides. 608
According to Slovic et al. (2005), loss-averse individuals tend to increase their contribution against a risk 609
over time. In our case, similar to Slovic et al. (2005), participants started contributing slowly against 610
landslides and, with the experience of landslide losses over time, they started contributing larger amounts 611
to reduce landslide risks. 612
We also found that the reliance on invest-all strategy was higher in the high-damage and feedback-613
present condition compared to the low-damage and feedback-absent condition. The invest-all strategy 614
was the optimal strategy in the ILS tool. This result shows that participants learned the underlying system 615
dynamics (i.e., how their actions influenced the probability of landslides) in ILS better in the feedback-616
rich condition compared to the feedback-poor condition. As participants were not provided with exact 617
equations governing the ILS tool and they had to only learn from trial-and-error feedback, the saliency of 618
the feedback due to messages and images likely helped participants’ learning in the tool. In fact, we 619
observed that the use of the optimal invest-all strategy was maximized when the experiential feedback 620
was highly damaging. One likely reason for this observation could be the high educational levels of 621
participants residing in the study area, where the literacy rate was more than 80%. Thus, it seems that 622
participants’ education levels helped them make the best use of damaging feedback. 623
We believe that the ILS tool can be integrated in teaching courses on landslide sustainable 624
practices in schools from kindergarten to standard 12th. These courses could make use of the ILS tool and 625
focus on educating students about causes, consequences, and risks of hazardous landslides. We believe 626
that the use of ILS tool will make teaching more effective as ILS will help incorporate experiential 627
feedback and other factors in teaching in interactive ways. The ILS tool’s parameter settings could be 628
customized to a certain geographical area over a certain time period of play. In addition, the ILS tool 629
could be used to show participants the investment actions other participants (e.g., society or neighbours). 630
The presence of investment decisions of opponents in addition to one’s own decisions will likely enable 631
social norms to influence people’s investments and learning in the tool (Schultz et al., 2007). These 632
features makes ILS tool very attractive for landslide education in communities in the future. 633
Furthermore, the ILS tool holds a great promise for policy-research against landslides. For 634
example, in future, researchers may vary different system-response parameters in ILS (e.g. weight of 635
one’s decisions and return to mitigation actions) and feedback (e.g. numbers, text messages and images 636
for damage) in order to study their effects on people’s decisions against landslides. Here, researchers 637
could evaluate differences in ILS’s ability to increase public contributions in the face of other system-638
response parameters and feedback. In addition, researchers can use the ILS tool to do “what-if” analyses 639
related to landslides for certain time periods and for certain geographical locations. The ILS tool has the 640
ability to be customized to certain geographical area as well as certain time periods, where spatial 641
parameters (e.g., soil type and geology) as well as temporal parameters (e.g., daily rainfall) can be defined 642
for the study area. Once the environmental factors have been accounted for, the ILS tool enables 643
researchers to account for assumptions on human factors (contribution against landslides) with real-world 644
consequences (injury, fatality, and infrastructure damage). Such assumptions may help researchers model 645
human decisions in computational cognitive models, which are based upon influential theories of how 646
people make decisions from feedback (Dutt and Gonzalez, 2012; Gonzalez and Dutt, 2011b). In summary, 647
these features make ILS tool apt for policy research, especially for areas that are prone to landslides. This 648
research will also help test the ILS tool and its applicability in different real-world settings. 649
9. Limitations 650
Although the ILS tool causes the use of optimal invest-all strategies among people in conditions 651
where experiential feedback is highly damaging, more research is needed on investigating the nature of 652
learning that the tool imparts among people. As people’s investments for mitigating landslides in ILS 653
directly influences the risk of landslides due to human and environmental factors, investments indeed 654
have the potential of educating people about landslide risks. Still, it is important to investigate how 655
investing money in the ILS tool truly educates people about landslides. We would like to investigate this 656
research question as part of our future research. 657
Currently, in the ILS model, we have assumed that damages from fatality and injury to influence 658
participants’ daily-income levels. The reduced income levels do create adverse consequences, but one 659
could also argue that they would be much less of concern for most people compared to the injury and 660
fatality itself. Furthermore, people could also choose to migrate from an area when the landslide 661
mitigation costs are too high, and adaptation becomes impossible, especially due to the differences 662
between the landslide hazard and other hazards such as flood, drought, and general climate risks. As part 663
of our future research, we plan to investigate the influence of feedback that causes only injuries or 664
fatalities in ILS compared to the feedback that causes economic losses due to injuries and fatalities. Also, 665
as part of our future research in the ILS tool, we plan to investigate people’s migration decisions when 666
the landslide mitigation costs are too high and adaptation to landslides is not possible. 667
In this paper, our primary objective was not to accurately predict rainfall or other landslide 668
parameters; rather, to educate people about landslide disasters. Thus, we have used approximate models 669
of real landslide phenomena in the ILS simulation tool. This use of approximate models is in line with a 670
large body of literature on using simulation tools for improving people’s understanding about natural 671
processes like climate change and other natural disasters (Dutt and Gonzalez, 2010, 2011; Finucane et al., 672
2000). As part of our abstraction, we may have missed certain aspects related to the sensitivity of the 673
different social classes to their economic and cultural resources. In future, we would like to compare the 674
proportion of investments in different experimental conditions to people’s likely socio-economic cost 675
thresholds given that people may need to spend their wealth in other areas beyond landslide mitigation. 676
Furthermore, we used a linear model to compute the probability of landslides due to human factors 677
in the ILS tool. Also, the probabilistic equations governing the physical factors in the ILS model were not 678
disclosed to participants, who seemed to possess high education levels. One could argue that there are 679
several other linear and non-linear models that could help compute the probability of landslides due to 680
human factors. Some of these models could not only influence the probability of landslides, but also the 681
severity of consequences (damages) caused by landslides. Also, other generic models could account for 682
the physical factors in the ILS tool. We plan to try these possibilities as part of our future work in the ILS 683
tool. Specifically, we plan to assume different models of investments in the ILS tool and we plan to test 684
them against participants with different education levels. 685
In the current experiment, we assumed a large disparity between a participant’s property wealth 686
and her daily income. In addition, as part of the ILS model, we did not consider support from governments 687
or insurance companies against damages from landslides. In India, people mostly use their own finances 688
to overcome the challenges put by natural disasters as insurance or other public methods have only shown 689
limited success (ICICI, 2018). However, in certain cases, especially in developing countries, mitigation 690
of landslide risks may often be financed by government or international agencies. As part of our future 691
work, we plan to extend the ILS model to include assumptions of contributions from government and 692
international agencies. Such assumptions will help us determine the willingness of common people to 693
contribute against landslide disasters, which is important as the developing world becomes more 694
developed over time. 695
To test our hypotheses, we presented participants with a high damage scenario and a low damage 696
scenario, where the probabilities of property damage, injury, and fatality were high and low, respectively. 697
However, such scenarios may not be realistic, where people may want to migrate from both low and 698
damage areas in even the least developed countries. In future research with ILS, we plan to calibrate the 699
probability of damages, injury, and fatality to realistic values and test the effectiveness of ILS in 700
improving the participants’ investment decision making. 701
Furthermore, in our experiment, when landslide did not occur and experiential feedback was 702
present, people were presented with a smiling face followed by a message. The message and emoticon 703
were provided to connect the cause-and-effect relationships for participants in the ILS tool. However, it 704
could also be that the landslide did not occur on a certain trial due to the stochasticity in the simulation 705
rather than participants’ investment actions. Although such situations are possible over shorter time-706
periods, over longer time-periods increased investments from people will only reduce the probability of 707
landslides. 708
In this paper, the experiment used a daily investment setting in the ILS tool. However, the ILS 709
tool can easily be customized to different time periods ranging from seconds, minutes, hours, days, 710
months, and years. As part of our future research, we plan to extend the daily assumption by considering 711
people making decisions on longer time-scales ranging from months to years. In addition, in the 712
experiment, we assumed a value of 0.7 and 0.8 for the weight (W) and return to mitigation (M) parameters. 713
These W and M values indicated that landslide risks could largely be mitigated by human actions. 714
However, this assumption may not be the case always, especially for mitigation measures like tree 715
plantations. For example, afforestation alone may not help in reducing deep-seated landslides in hilly 716
areas (Forbes, 2013). Thus, it would be worthwhile investigating as part of future research on how 717
people’s decision-making evolves in conditions where investments likely influence the landslide 718
probability (higher values of W and M parameters) compared to conditions where investments unlikely 719
influence the landslide probability (lower values of W and M parameters). Some of these ideas form the 720
immediate next steps in our ongoing research program on landslide risk communication. 721
10. Conclusions 722
It can be concluded from this preliminary research study that simulation tools like ILS that provide 723
feedback about the outcomes of landslides influenced certain people’s investment decisions agianst 724
landslides in the study area. Given our results, we believe that ILS could potentially be used as a landslide-725
education tool for increasing public understanding about landslides. The ILS tool can also be used by 726
policymakers to do what-if analyses in different scenarios concerning landslides. 727
Data availability. Data used in this article have not been deposited to respect the privacy of users. The 728
data can be provided to readers upon request. 729
Author contributions. AA designed the website, administered the account, PC wrote the first draft of 730
website articles and collected data. VD supervised the website contents. AA provided technical support 731
for website maintenance. PC and VD analysed the data and prepared the manuscript. PC and VD revised 732
the manuscript. 733
Competing interests. The authors declare that they have no conflict of interest. 735
Acknowledgements. This research was partially supported by a grant from Himachal Pradesh State 737
Council for Science, Technology and Environment to Varun Dutt (grant number: IITM / HPSCSTE / VD 738
/ 130). We thank Akanksha Jain and Sushmita Negi, Centre for Converging Technologies, University of 739
Rajasthan, India for providing preliminary support for data collection in this project. 740
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Appendix A 868
Instructions of the Experiment 869
Welcome! 870
You are a resident of Mandi district of Himachal Pradesh, India, a township in the lap of Himalayas. You 871
live in an area that is highly prone to landslides due to a number of environmental factors (e.g., the 872
prevailing geological conditions and rainfall). During the monsoon season, due to high intensity and 873
prolonged period of rainfall, a number of landslides may occur in the Mandi district. These landslides 874
may cause fatalities and injuries to you, your family, and to your friends, who reside in the same area. In 875
addition, landslides may also damage your property and cause loss to your property wealth. 876
This study consists of a task, where you will be making repetitive decisions to invest money in order to 877
mitigate landslides. Every trial, you’ll earn certain money between 0 and 10 points. This money is 878
available to you to invest against landslides. You may invest certain amount from the money available to 879
you; however, if you do not wish to invest anything, you may invest 0.0 against landslides on a particular 880
trial. Based upon your investment against landslides, you’ll get feedback on whether a landslide occurred 881
and whether there was an associated loss of life, injury, or property damage (all three events are 882
independent and they can occur at the same time). 883
Your total wealth at any point in the game is the following: sum of the amounts you did not invest 884
against landslides across days + your property wealth - damages to you, your family, your friends, 885
and to your property due to landslides. Your property wealth is assumed to be 100 points at the start 886
of the game. The amount of money not invested against landslides increases your total wealth. Your 887
goal is to maximize your total wealth in the game. 888
Whenever a landslide occurs, if it causes fatality, then your daily earnings will be reduced by 5% of its 889
present value at that time and if landslide causes injury to someone, then the daily earnings willbe reduced 890
by 2.5% of its present value at that time. Thus, the amount available to you to invest against landslides 891
will reduce with each fatality and injury due to landslides. Furthermore, if a landslide occurs and it causes 892
property damage, then your property wealth will be reduced by 80% of its present value at that time; 893
however, the money available to you to invest against landslides due to your daily earnings will remain 894
unaffected. 895
Generally, landslides are triggered by two main factors: environmental factors (e.g., rainfall; outside one’s 896
control) and investment factors (money invested against landslides; within one’s own control). The total 897
probability of landslide is a weighted average of probability of landslide due to environment factors and 898
probability of landslide due to investment factors. The money you invest against landslides reduces the 899
probability of landslide due to investment factors and also reduces the total probability of landslides. 900
However, the money invested against landslides is lost and it cannot become a part of your total wealth. 901
At the end of the game, we’ll convert your total wealth into INR and pay you for your effort. For this 902
conversion, a ratio of 100 total wealth points = INR 1 will be followed. In addition, you will be paid INR 903
30 as base payment for your effort in the task. Please remember that your goal is to maximize your total 904
wealth in the game. 905
Starting Game Parameters 906
Your wealth: 20 Million 907
When a landslide occurs: 908
If a death occurs, your daily income will be reduced by 50% of its current value. 909
If an injury takes place, your daily income will be reduced by 25% of its current value. 910
If a property damage occurs, your wealth will be reduced by 50% of your property wealth. 911
Best of Luck! 912
... Catastrophic and disastrous effects of landslides cause extensive damage to life, property, and public-utility services [1]. Landslides and associated debris flows are a major concern for disaster-prevention groups in regions with steep terrain, especially in Himalayan mountains [2][3][4]. Several physical factors like ground slope, soil depth, and rainfall may precipitate landslides. Besides these physical causes of the landslide, land development and other man-made activities also aggravate landslide disasters [2][3][4][5]. ...
... Several physical factors like ground slope, soil depth, and rainfall may precipitate landslides. Besides these physical causes of the landslide, land development and other man-made activities also aggravate landslide disasters [2][3][4][5]. The consequences of extreme natural events like landslides are a combination of both physical factors as well as the actions taken by people before landslides occur (human factors). ...
... The ILS tool is an interactive dynamic system for studying people's decisions against landslide risks [2,3]. Details about the ILS tool were already discussed by [2,3], and here we briefly cover the tool's working. ...
Full-text available
Landslides cause extensive damages to property and life and there is an urgent need to increase community awareness against landslide risks. Interactive simulations help to provide people with experience of landslide disasters and increase community awareness. However, it would be interesting to evaluate the influence of contextual feedback via messages and images in people's decision-making in these simulations. The main objective of this paper was to evaluate the role of contextual feedback in an interactive landslide simulator (ILS) tool. ILS considers both human and environmental factors to influence landslide risks. Fifty participants randomly participated across two between-subject conditions in the experiment: feedback-rich (messages and images present) and feedback-poor (numeric feedback only; messages and images absent). Participants made repeated monetary decisions against landslides in ILS. Investments were greater in the feedback-rich condition compared to feedback-poor condition. We highlight the implications of our results for awareness against landslide risks.
Full-text available
Landslides are destructive geological processes that have globally caused deaths and destruction to property worth billion dollars. Landslide occurrences are widespread and prolific in India covering more than 15 per cent of the total area. These are mostly concentrated in the Himalayan belt, parts of Meghalaya Plateau, Nilgiri Hills, Western and Eastern Ghats. The slope failure in the hilly terrain is due to geological processes and events. The frequency and magnitude of slope failure also increased due to anthropogenic activities such as road construction, deforestation and urban expansion. Keeping all these problems in mind research focuses on the Lesser Himalaya of Himachal Himalaya as it falls under very high risk zone in case of landslides and comprise of three objectives. They are: a) to analyse the spatial pattern of landslides in the Lesser Himalaya, b) to assess the causes of landslides vulnerability in the study region and c) to suggests some preventive measures to mitigate landslides. In this work an attempt has been made to collect data on landslides incidences and damage from the secondary sources like Geological Survey of India, Building Material and Technology Promotion council from Ministry of Urban Affairs. The methodologies adopted for data analysis are simple tabulations, bar diagrams, statistical and mapping techniques to represent the Landslide vulnerability of the Lesser Himalaya. The analysis of the study reveals that there is increase in the number of landslides. The spatial pattern of landslide shows linear patterns, viz. along roads, rivers or lineaments/ faults. Besides, heavy rainfall, floods and earthquakes enhance the vulnerability condition. The landslides may be part and parcel of the Himalayan landscape, but they can be mitigated by some suitable measures. Few methods of landslide prevention in the study region have been suggested.
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Introduction: The occurrence of landslides and floods in East Africa has increased over the past decades with enormous Public Health implications and massive alterations in the lives of those affected. In Uganda, the Elgon region is reported to have the highest occurrence of landslides and floods making this area vulnerable. This study aimed at understanding both coping strategies and the underlying causes of vulnerability to landslides and floods in the Mt. Elgon region. Methods: We conducted a qualitative study in three districts of Bududa, Manafwa and Butalejja in the Mt. Elgon region in eastern Uganda. Six Focus Group Discussions (FGDs) and eight Key Informant Interviews (KIIs) were conducted. We used trained research assistants (moderator and note taker) to collect data. All discussions were audio taped, and were transcribed verbatim before analysis. We explored both coping strategies and underlying causes of vulnerability. Data were analysed using latent content analysis; through identifying codes from which basis categories were generated and grouped into themes. Results: The positive coping strategies used to deal with landslides and floods included adoption of good farming methods, support from government and other partners, livelihood diversification and using indigenous knowledge in weather forecasting and preparedness. Relocation was identified as unsustainable because people often returned back to high risk areas. The key underlying causes of vulnerability were; poverty, population pressure making people move to high risk areas, unsatisfactory knowledge on disaster preparedness and, cultural beliefs affecting people’s ability to cope. Conclusion: This study revealed that deep rooted links to poverty, culture and unsatisfactory knowledge on disaster preparedness were responsible for failure to overcome the effects to landslides and floods in disaster prone communities of Uganda. However, good farming practices and support from the government and implementation partners were shown to be effective in enabling the community to lessen the negative effects disasters. This calls for high impact innovative interventions focused in addressing these underlying causes as well as involvement of all stakeholders in scaling the effective coping strategies in order to build resilience in this community and other similarly affected areas. Key words: Coping, Underlying causes, Floods, Landslides, Mt. Elgon, Uganda
Full-text available
Understanding landslide risks is important for people living in hilly areas in India. A promising way of communicating landslide risks is via simulation tools, where these tools integrate both human factors (e.g., public investments to mitigate landslides) and environmental factors (e.g., spatial geology and rainfall). In this paper, we develop an interactive simulation model on landslide risks and use it to design a web-based Interactive Landslide Simulator (ILS) microworld. The ILS microworld is based on the assumption that landslides occur due to both environmental factors (spatial geology and rainfall) as well as human factors (lack of monetary investments to mitigate landslides). We run a lab-based experiment involving human participants performing in ILS and we show that the ILS performance helps improve public understanding of landslide risks. Overall, we propose ILS to be an effective tool for doing what-if analyses by policymakers and for educating public about landslide risks.
Conference Paper
Full-text available
Understanding landslide risks is important for people living in hilly areas in India. A promising way of communicating landslide risks is via simulation tools, where these tools integrate both human factors (e.g., public investments to mitigate landslides) and environmental factors (e.g., spatial geology and rainfall). In this paper, we develop an interactive simulation model on landslide risks and use it to design a web-based Interactive Landslide Simulator (ILS) microworld. The ILS microworld is based on the assumption that landslides occur due to both environmental factors (spatial geology and rainfall) as well as human factors (lack of monetary investments to mitigate landslides). We run a lab-based experiment involving human participants performing in ILS and we show that the ILS performance helps improve public understanding of landslide risks. Overall, we propose ILS to be an effective tool for doing what-if analyses by policy-makers and for educating public about landslide risks.
Conference Paper
Full-text available
Landslide experts have developed very detailed landslide hazard maps for different parts of Himalayas in India. These maps indicate the types of damage possible and the probabilities of adverse events. Six categories of risk severity are defined on the maps, ranging from severe risk to very low risk. Based on the existing maps, we selected respondents for a survey, some from areas high in risk and others from low-risk regions. Respondents answered several questions related to landslide risk perception and preparedness. Survey results showed a lack of awareness about the scientific causes of landslides among Mandi residents. Most of the respondents were of the belief that they lived at a safe place. Survey results suggested that many inhabitants did not know that landslide hazard maps existed for their region and most of them were not able to understand them. People overestimated the risks associated with landslides. Consequently, some people were more worried of landslides than was justified by the facts. Another important finding was that since catastrophic landslides are rare events, most of the people were risk averse. These people did not show prevention behaviour, and they were not well prepared for an adverse event. Furthermore, results suggest that respondents’ experiences with landslides were positively related to their perceptions of landslide risk. Thus, findings of the present study comply with the concept of availability heuristic.
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Here, we briefly review the evolution of research on human decision-making over the past few decades. We discern a trend whereby biology moves from subserving economics (neuroeconomics), to providing the data that advance our knowledge of the nature of human decision-making (decision neuroscience). Examples illustrate that the integration of behavioural and biological models is fruitful especially for understanding heterogeneity of choice in humans.
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Landslides are common in Himalaya due to high relief, weak, tectonised& highly weathered rocks, glacial debris and man-made activities like road construction and step cultivation. The triggering of landslides are caused mainly due to prolonged or high intensity rainfall. Losses caused due to landslide are over 200 deaths and Rs. 550 crore annually in Himalaya. It is therefore a necessity to assess Landslide Risk. Remote sensing and GIS is an effective tool for the assessment of landslide susceptibility at regional scale. The area for current research study is Chamoli- Joshimath region. Satellite data, toposheets, digital elevation model data, field observations and satellite based rainfall data are used as input data in this study. Various thematic layers, i.e., lithology, fault, lineament, geomorphology, drainage, slope angle, slope aspect, landuse /land cover, soil texture, and soil depth are generated by manual remote sensing based interpretations. Subsequently these thematic layers are integrated based on predefined rankings and weightages calculated using map algebra in GIS environment for generation of Landslide Susceptibility maps.The results show that this approach for the susceptibility evaluation is fairly accurate and precise after field validation.Landslide susceptibility map that gives spatial probability of landslides in conjunction with empirical rainfall thresholds can be used to warn the residents and local authorities about the hazard early on. In future, we plan to use the generated landslide susceptibility maps for risk perceptions studies in the area of interest.
With the increasing need to take an holistic view of landslide hazard and risk, this book overviews the concept of risk research and addresses the sociological and psychological issues resulting from landslides. Its integrated approach offers understanding and ability for concerned organisations, landowners, land managers, insurance companies and researchers to develop risk management solutions. Global case studies illustrate a variety of integrated approaches, and a concluding section provides specifications and contexts for the next generation of process models. © 2005 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester. All rights reserved.
This study refers to numerous worldwide natural disasters cause of many tragedies in the past and especially now, by the immensity of the damage they produce and the contribution of insurance companies in the financial coverage of such damages. In Romania natural disasters caused by earthquakes and floods and protection of personal property homes by these risks are treated by Law 260/2010- compulsory insurance of personal property homes against earthquakes, landslides and floods.