The Role of Geoscience Information in 

Reducing Catastrophic Loss Using a Web-Based 

Economics Experiment

By Richard L. Bernknopf, David S. Brookshire, and Philip T. Ganderton

Professional Paper 1683

U.S. Department of the Interior

U.S. Geological Survey
ii 

U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

U.S. Geological Survey, Reston, Virginia: 2003

Available from U.S. Geological Survey Information Services

Box 25286, Denver Federal Center

Denver, CO 80225

For more information about the USGS and its products;

Telephone: 1-888-ASK-USGS (1-888-275-8747)

World Wide Web: http://www.usgs.gov/

Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply 

endorsement of the U.S. Government.

Although this report is in the public domain, it contains copyrighted materials that are noted in the text. Permission 

to reproduce those items must be secured from the individual copyright owners. 

Cataloging-in-publication data are on file with the Library of Congress (http://www.loc.gov/).

Text and illustrations edited by James W. Hendley II

Production by Susan E. Mayfield

Published in the Western Region, Menlo Park, California.

Manuscript approved for publication September 3, 2003.
iii

Contents

Executive Summary v

Introduction 1

Elements of Program 1

Experiment Design 2

Empirical Model 4

Data Analysis and Interpretation 5

Program Potential 11

References 11

Glossary 13

Appendixes

A. Experiment flow chart 14

B. Game explanation text 16

C. Maps 17

D. Screen captures 18

E. Continuation screen capture 29

Figures

1. Maps of a natural hazard and severity of loss at two scales 3

Tables

1. Experiment structure 3

2. Definitions and summary statistics for variables used in analysis 6

3A. Bivariate probit analysis of decision to buy detailed map (simultaneous estimation) 8

3B. Bivariate probit analysis of decision to buy insurance (simultaneous estimation) 9

4. Marginal effect of explanatory variable on joint probabilities 10
iv 
v

Executive Summary

What role can geoscience information play in the assessment of risk and the value of 

insurance, especially for natural hazard type risks? In an earlier, related paper Ganderton and 

others (2000) provided subjects with relatively simple geoscience information concerning 

natural hazard-type risks. Their research looked at how subjects purchase insurance when 

faced with relatively low probability but high loss risks of the kind that characterize natural hazards and now, increasingly, manmade disasters. They found evidence to support the 

expected utility theory (definitions of economics terms can be found in a glossary at the end 

of report), yet there remained the implication that subjects with excessive aversion to risk 

were willing to pay considerably more for insurance than the actuarially fair price plus any 

reasonable risk premium. Here, we report the results of additional experiments that provide 

further support for the basic postulates of expected utility theory. However, these new experiments add considerably to the decision environment facing subjects by offering an option to 

purchase geoscientific information that would assist them when calculating expected losses 

from hazards more accurately.

Using an Internet-based mechanism to present information and gather data in an experimental setting, this research provided subjects with considerable textual and graphical information, and time to process it. Over a period of three months, almost 400 subjects participated 

in on-line experiments that generated approximately 22,000 usable data points for the empirical analysis discussed in this report.

In the design of the experiment, we modeled the decisions to purchase (1) a detailed 

map giving subjects more information regarding the distribution of losses from a hazard 

and (2) insurance to indemnify them from any losses should they occur. On the basis of this 

design, we find strong evidence in support of the expected utility theory. Many of the findings reinforce those found in the early, similar study (Ganderton and others, 2000). However, 

this research also finds interactions between the decision to become better informed and the 

decision to insure. We chose an empirical framework that allows for both explicit and implicit 

(unobservable) correlations between the two decisions. The results suggest that at the end of 

the computer game subjects recognize the benefits of greater geoscience information. They 

take advantage of it, but are sensitive to its cost. When subjects use the more detailed information, they are more likely to purchase insurance when it offers a net benefit.
The Role of Geoscience Information in 

Reducing Catastrophic Loss Using a Web-Based 

Economics Experiment

By Richard L. Bernknopf1, David S. Brookshire2, and Philip T. Ganderton3

1 Western Geographic Science Center, U.S. Geological Survey, Menlo 

Park, CA.

2 Professor of Economics, University of New Mexico, Albuquerque, NM.

3 Associate Professor, University of New Mexico, Albuquerque, NM.

4 The logical next step for this research would be to study the affect of better 

geoscience information on the choices between insurance and mitigation.

Introduction

Natural hazards present both organizations responsible 

for protecting public safety and organizations that protect private individuals with the most serious risk-management problems. Nature is often seen as a random force, and although 

considerable progress has been made to model and predict 

natural hazard and disaster risk, events such as earthquakes, 

tornadoes, fires, and floods continue to wreak havoc on an 

increasingly dense and resource-rich social and economic 

environment (Kunreuther and others, 1999).

Risk management is most generally a set of policies and 

practices designed to assess and affect risk to human life, social 

and economic activities, and the natural environment (Carnegie 

Commission, 1993). The risks from natural hazards (as well as 

other, manmade hazards) have the following elements:

(1) A probability distribution for the natural event defined over 

intensity, severity, duration, magnitude, or other measures;

(2) A probability distribution for the event defined over time;

(3) Some process that converts natural hazard events into 

actions that impact human life and activity (for example, 

an engineering relationship that links ground movement 

with building collapse or rainfall and local geography 

with landslides and subsidence); and

(4) A geographic distribution of human and economic losses 

attributable to the natural hazard event (Platt, 1999). 

The two main mechanisms for addressing the problems of 

natural hazards are mitigation and insurance. Insurance is most 

effective when the probability distribution of the event is well 

known, when linkage to loss is direct, and loss is well specified. 

Some examples are auto insurance, homeowner’s insurance, and 

life insurance. Insurance is least effective when the calculations 

required to assess the net benefits accruing from insurance coverage are difficult or impossible to make. Natural-hazard insurance is relatively uncommon because of difficulty in describing 

the event probability distribution over space and time. This 

problem is exacerbated by the great variance in the losses experienced across both dimensions from a single natural disaster 

and the relatively high premiums that insurance companies must 

charge given the low take-up rates for this type of insurance.

This report presents the findings of a research project 

designed to study the role improved geoscience information 

might play in the assessment of risk and the value of insurance, 

especially for natural-hazard-type risks. Earlier, we investigated 

the response of subjects to relatively low probability but highloss risks of the kind that characterize natural hazards (Ganderton and others, 2000). Those experiments provided evidence 

for support of the theory that subjects are making decisions to 

maximize expected utility. Results from experiments reported 

here provide further support for the postulates of expected utility 

theory. In addition, the richer choice environment provided to 

subjects in the experimental setting allows more detailed study of 

the factors influencing risk assessment and insurance purchase.

The research reported does not consider the decision to 

invest in mitigation rather than purchase insurance.4

 However, it 

does investigate a mechanism by which people can make better 

decisions regarding insurance purchases by utilizing detailed 

information on the probability distribution of hazardous events 

and losses. In this sense, obtaining better information using 

detailed maps of either probabilities of loss or size of loss plays 

a complimentary role to insurance just as mitigation can.

Elements of the Program

The research program contains 5 basic elements:

(1) The use of maps to provide varying types of information,

(2) The use of a web interface to provide and collect data,

(3) The use of experimental-economics games to create scenarios and value,

(4) Investigation of the choices of mitigation and insurance, and

(5) Variation of treatments to allow econometric analysis of 

data.

The current study implements elements 1, 2, 3, and 5.
2 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

5 This feature introduces an interesting, although subsidiary, treatment to this 

experiment, where some subjects completed the experiment but did not claim 

their payment, while others did. In real-time laboratory experiments, all participating subjects are generally paid as they leave the experimental session.

It is essential for organizations such as the U.S. Geological Survey (USGS) to provide geoscience information that 

passes a reasonable benefit-cost test. To do this the USGS 

needs to assess the value placed on its geoscience information (Bernknopf and others, 2001; Bernknopf and others, 

1997; Bernknopf and others, 1988). To further this agenda, 

the research reported here explicitly includes geoscientific 

information concerning the probability distribution and size of 

losses in a decision model of insuring against property risks.

Traditionally, experimental economists have employed 

laboratory settings for their experiments (Hagel and Roth, 

1995). Even the nomenclature encourages subjects to consider 

themselves part of an experiment. Despite the greater control 

over the environment provided by real-time laboratory settings, many administrative and organizational difficulties limit 

the size of the subject pool and the amount of data collected in 

a reasonable time.

There is an increasing need to provide subjects with 

an experimental interface that is interactive, maintains their 

interest, gives them flexibility, and provides them control over 

elements of the experiment they can access. Subjects are loath 

to sit in laboratories waiting for slow computers to provide them 

with inadequate information to do the experiment properly. 

Although researchers must give up a certain level of control 

over subjects, the Internet provides a wonderful interface for 

supplying subjects with considerable information and it allows 

them to participate at their own pace, obtaining information in a 

controlled way, as they require it. Our experiments also suggest 

that the Internet is a very cost effective means of data collection.

The natural progression from previous work looking at 

the purchase of insurance against natural-hazard-type risks 

with no spatial reference to the hazard is one that includes 

geoscience information in the decision environment of 

subjects. Ultimately, this information not only helps with the 

decision to purchase insurance, it also assists with the decision to invest in mitigation activities. Further experiments will 

continue this progression by adding a mitigation option to the 

current map and insurance setting.

Many observations are required to provide a statistical basis 

for drawing conclusions from any empirical analysis. Because of 

our desire to model a relatively large number of treatments in order 

to provide a reasonable variation in explanatory factors for the 

econometric analysis, and to investigate a wider range of questions, 

we needed to collect a large number of data points. Our results 

support the use of Web-based experiments as a cost-effective 

time-efficient mechanism for gathering large amounts of data.

Experiment Design

We gathered data for this analysis using the web-based 

experiment discussed above. The structure of the website is 

given below:

(1) Login, or register and login.

(2) Questionnaire regarding insurance use and simple 

demographics.

(3) Miniexperiment designed to elicit independent measure of 

subject’s risk aversion.

(4) Main experiment generating data on insurance and information purchase.

(5) Generate claim check and exit from experiment.

Appendix A provides screen captures from the Web site. 

Included are many of the introductory and welcome pages as 

well as the main decision page providing all relevant information to the subject. Given the nature of the Internet and 

people’s experience and practice with websites, the experiment 

was set up such that subjects were able to exit and reenter the 

experiment at any time, their progress through the experiment 

monitored to prevent retaking any previously completed section, and completion of the experiment was required to generate a claim check for payment.5

The main experiment implements the study design 

to confront subjects with a risky scenario in which they 

can purchase a more detailed risk map and purchase full 

indemnity insurance if they choose. Subjects face the same 

kind of risk repeatedly, but with differing loss probabilities 

and loss amounts. A detailed flow chart of the design of 

the main experiment appears in appendix B, and table 1 

provides a summary.

We fixed the number of games at 15 and subjects are 

told this in advance. Within each game there are a random 

number of periods. This is chosen at the beginning of each 

game, as are certain parameters used as treatments in the 

experiment. These include the cost of the map, one from 

a set of two values: 10 tokens and 20 tokens, representing 

5 percent and 10 percent of period income. The insurance 

premium is also chosen from a set of two values representing, 10 percent and 20 percent of period income. In addition, 

the maps shown to the subject are variable; two sets were 

available. The subject’s location on the map was chosen at 

random from a possible 36 sites on the map and indicated by 

a dot in the center of a cell. The hazard level at that location 

is implied in the coarse map and displayed in the detailed 

map (more on this later). Each period the subject receives a 

constant income of 200 tokens. Appendix B gives the text of 

the introduction provided to subjects.

There are a random number of rounds within each period, 

ranging between 2 and 4. The subjects are decision makers within these rounds, deciding on the purchase of a more 

detailed map than the one shown initially. The coarse map 

in figure 1 shows four large cells of equal size. Each cell is 

colored uniformly with the color of the dominant cells that lie 

within that larger cell (fig. 1A).

Each large cell actually contains 9 smaller cells, each 

colored one of three colors to indicate the amount that would 

be lost were a hazardous event to occur (fig. 1B). For example, 

there may be 5 red cells of highest loss, and 4 orange cells 
Experiment Design 3

GAME PERIOD

(within each game)

ROUND

(within each game)

15 games between 2 and 6 periods between 2 and 4 rounds

Following values set:

map cost (2)

insurance premium (2)

map (2)

location on map (36)

loss probability (3)

loss amount (2)

Income increased each period 

by constant amount

Subject can choose to 

buy map or insurance, 

both, or neither

Table 1. Experiment Structure.

Figure 1. Maps of a natural hazard and severity of loss at two 

scales. Color of zone is determined by modal hazard in that zone, 

yellow corresponds to low loss, orange to medium loss, and red 

to high loss. Highlighted point indicates subject location. This map 

determines actual losses in experiment.

�

�

6 A subject who goes bankrupt during a period is required to sit out the 

remaining rounds and continues the experiment with the next round income of 

200 tokens. All previous income is lost.

of moderate loss. The subject may be located in an orange 

cell. If the subject only sees the coarse map they will get the 

false impression that they are in a red cell, since red is the 

dominant color of the cells contained within. If the subject 

purchases the detailed map, it remains in effect for the entire 

game, since the subject’s location is chosen at the start of 

each game, and remains fixed for that game.

The subject can also purchase insurance. This insurance 

fully compensates the subject in the case of a loss. Insurance 

covers the entire period and can be purchased during any round 

within that period. Having purchased both the detailed map and 

insurance, the subject can make no more decisions that period, 

and the experiment progresses through the remaining rounds 

in the period automatically, stopping each period to inform the 

subject of any event that may have occurred that round, and 

requiring a mouse click to proceed with the experiment.

Each round, the computer program draws a random number from an integer set determined by the event probability. 

For example, if the probability of a hazardous event occurring 

is 0.01, the program chooses a random number from the uniform distribution U[0,99], and if the number equals a prechosen and fixed integer, say 11, the event occurred, otherwise no 

hazardous event occurred. The program shows the results of 

the draw to the subject each round, and the subject must click 

to continue (see appendix E for a screen capture of this page).

The subject proceeds through the experiment accumulating income each period, and spending it on map purchases or 

insurance premia or self-insurance. Losses without insurance 

can be quite large relative to both period and accumulated 

income. Although the probability of a hazardous event is the 

same for every location within a round, each location is identified in the map as having a potential loss amount of small 

(yellow), moderate (orange), or large (red.) The two alternative loss amount distributions are {10, 100, 1,000} and {100, 

1,000, 10,000}. With event probabilities for each time period 

of (0.1, 0.01, 0.001), the expected loss can range from 0.01 

token to 1,000 tokens, and actual losses can range from 10 to 

10,000 tokens making bankruptcy a possibility.6

 The maximum possible accumulated income for a subject is 18,000 

tokens at the end of the experiment, so uninsured losses of 

1,000 or 10,000 can represent a considerable proportion of 

the subject’s accumulated income during the experiment. 
4 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

The subject’s accumulated income at the end of any round is 

calculated using the following equation:

New balance = max{0, old balance + NEWPER *200 –

BUYMAP*MCOST – BUYINS*ICOST – (1-

BUYINS)*L*

[(RED)*High Loss value (for example, 10,000) + 

(ORANGE)*Med Loss value (for example, 1,000) + 

(YELLOW)*Low Loss value (for example, 100)]}

where

NEWPER = 1 if beginning of a new period

BUYMAP = 1 if purchased detailed map this round

BUYINS = 1 if purchased insurance this round

MCOST = cost of map

ICOST = insurance premium

L = 1 if hazardous event occurred

RED = 1 if subject located in red cell

ORANGE = 1 if subject located in orange cell

YELLOW = 1 if subject located in yellow cell

Code on the website writes a comprehensive set of data to 

a database each round, including all parameters, subject decisions, and outcomes. Values from previous rounds are stored 

and used in calculations provided to the subjects in following rounds, even if the subject exits the experiment midway 

through and returns to the website later.

The relatively complex scenario faced by the subjects 

has two important features. The first is that the experiment 

attempts to create a decision environment with complexity 

approaching that of the real world environment modeled. We 

can think of each game as a person’s lifetime, each period a 

year within that life, and each round a day within that year. 

Natural hazards are infrequent events (even 1/1,000 is a 

relatively high probability of occurrence for some hazards), 

but losses are often very large (and cause death or economic 

hardship akin to bankruptcy). The second feature of the 

experiment is a relatively rich treatment specification to be 

estimated econometrically. There are 48 possible treatment 

combinations in the experiment. Additionally, subjects have 

two decisions to make at any time during each game—(1) 

to purchase the detailed map and (2) to purchase insurance. 

In the next section, we investigate the empirical model that 

provides a link between the risk-event parameters and the 

subject’s insurance decisions. 

The experimental design and the website operation were 

extensively pretested using subjects invited into a live laboratory session. Testers received a flat fee to compensate them 

for their time and were asked to login to the website and play 

the game. The timing of certain events was recorded in a 

log, and testers were asked to answer some post-experiment 

debriefing questions. An open discussion with the researchers followed the experiment. An exchange rate providing 

adequate compensation for a subject’s time was chosen using 

results from the pretests.

The Empirical Model

In these experiments, each subject acts independently, 

attempting to maximize the earnings from the experiment as a 

return on the investment of time and effort at the website. The 

subject faces two decisions each round, but the consequences 

of each decision remain with the subject for subsequent rounds 

within periods or games. Having purchased insurance, it cannot be purchased again until the current period is finished. 

Having purchased the detailed map, it cannot be purchased 

again until the current game is finished.

The decision to purchase insurance is based on the following comparison for a risk neutral subject:

BUY policy if Cost (C) ≤ Expected Loss (EL), (1)

and a risk averse subject would be prepared to pay more 

than the cost of the policy to avoid facing the gamble, 

that is

BUY policy if C ≤ EL + π(R, W), (2)

where π is the risk premium that depends upon the subject’s 

attitude to risk (R) and possible wealth (W). The more 

risk averse the subject is, the greater π will be, and the 

more likely a subject will be to purchase insurance even 

when it costs more than the expected loss of the gamble.

The expected loss (EL) is the probability of loss multiplied by the amount of the loss. In most cases, the decision maker does not know these two elements, particularly 

the probability distribution associated with the loss-causing event. For some risks, the loss is well specified, such 

as personal property, but for others even the loss is poorly 

defined, such as injury in an accident or economic losses in 

a flood. Consequently, the calculation of expected loss when 

faced with a risk is determined in large part by the information available regarding the losses and probability distribution of loss. The expected loss calculation is predicated on 

sufficient information regarding the components required to 

perform the calculation, otherwise the expected loss is at best 

an educated guess. In the current experiment, the probability 

of the loss event is well defined, but the loss is ill defined 

when the subject can only observe the coarse map (fig. 1A). 

The subject can draw quite incorrect conclusions from the 

coarse map. For example, if the location dot is inside a large 

RED cell, the subject may conclude that expected losses are 

Pr(loss) × Loss(RED), where Pr is probability, a relatively 

large value when compared to the actual expected loss. The 

actual expected loss is Pr(loss) × Loss(YELLOW) after purchasing the detailed map and seeing that the location was in 

a smaller YELLOW cell (fig. 2B). The decision to purchase 

the detailed map is therefore based on the potential benefits 

a subject expects from greater information about the spatial 

distribution of loss amounts and location within that space. 

The subject will compare the cost of purchasing the map 

with the benefits of a potentially more accurate calculation of 

expected losses from the hazard.
Data Analysis and Interpretation 5

7 A statistic testifying to the power of the Internet as a mechanism for 

conducting experiments. It would take a considerable effort to generate more 

than 66,000 draws from a bingo cage in a laboratory experiment, especially 

with student subjects.

8 This is in a state in which auto insurance is mandatory, but the sample 

mean is slightly above the State mean of 60 percent of drivers that have auto 

insurance.

The empirical model entails two equations, one to explain 

the decision to purchase a map, the other the decision to purchase insurance. What is the proper way to model the interaction between these two decisions? If the decisions are alternatives, then a random utility model (RUM) framework would 

seem appropriate. However, they are essentially complementary, 

not substitute, decisions. The more detailed map aids in making 

the insurance decision. The decisions are not independent, but 

they are not alternatives, hence modeling them as simultaneous 

equations with possibly correlated errors seems more appropriate. Greene (2003) outlines the methodology for estimating a 

Bivariate probit model, and STATA (2001) allows the estimation of two alternative forms of the model, one in which both 

decisions are functions of the same set of variables, and another 

using the seemingly unrelated regression form allowing for different sets of regressors for each decision. We can also perform 

a Wald test for the hypothesis that the decisions are unrelated.

We model each decision as a function of the variables 

specifying (1) the cost of the decision, (2) the expected loss 

from the hazard, (3) the potential for over- or under-estimation of the risk, (4) historical decisions and outcomes, and (5) a 

measure of the wealth of the subject at the time of the decision. 

Expected utility theory would suggest the following impacts of 

these factors on the decision to purchase insurance: (1) higher 

premiums should decrease the probability of purchasing insurance and (2) higher loss amounts or higher loss probabilities 

should increase the probability of buying insurance. Having 

a map that indicates a higher loss amount than is actually the 

case at the location should increase the probability of buying 

insurance. While not explicitly indicated by the expected utility 

theory, other factors may play a part in determining the insurance purchase decision, such as past behavior. If subjects display adaptive behavior or base their decisions on past behavior, 

past insurance purchases should increase current insurance purchases. The wealth of the subject may affect insurance purchase 

if self-insurance is more likely as wealth increases. Additionally, 

other factors linked to the subject’s attitudes to risk may impact 

the decision. We include some of these factors as measured in 

the survey in the empirical analysis. The decision to purchase 

the detailed map should be positively related to lower map costs, 

to a higher expected loss, and to experience because these factors raise the expected net benefit from the detailed map on the 

expected loss calculation.

Data Analysis and Interpretation

Over a period of three months approximately 398 subjects registered for the experiment, and 362 completed the 

main experiment, generating 23,099 observations. We paid a 

total of $2,800 to 268 subjects before the experiment website 

was closed down. Each subject contributed an average of 58 

observations to the dataset. Because each subject played 15 

games, there was an average of 3.87 periods per game. As 

there are no decisions made at the level of rounds within periods, the data were collapsed to the period level even though 

a total of 66,221 draws of the random hazardous event were 

made during the experiment.7

Table 2 lists the variables used in the empirical analysis 

and provides definitions for these variables. The first set of 

variables gives some descriptive statistics for the sample of 

subjects participating in the experiment. More than half were 

female (56.7 percent) and nearly one-third were 30 years or 

older (29.8 percent). Slightly more than half of the subjects 

held health insurance (51.5 percent), 35.7 percent had either 

home owner’s or renter’s insurance and nearly two-thirds had 

auto insurance (66.5 percent).8 Just less than 3 percent of subjects had any form of hazard insurance, which includes flood 

insurance, a requirement for a mortgage in areas in or near 

arroyos in the desert southwest.

Table 2 also gives other statistics for the experiment. The 

actual occurrence of hazardous events matches the mean probability of a hazardous event occurring in the model (0.037). 

Bankruptcy was a relatively rare event (0.006). The detailed 

map was purchased more frequently than was insurance (0.626 

verses 0.471). By comparison, the rate of purchasing insurance 

in a previous experiment with similar parameters (Ganderton 

and others, 2000) when the detailed map was not available was 

between 0.371 and 0.401.

As discussed above, a bivariate probit model was chosen to model the decision to purchase the detailed map and 

purchase insurance against loss. The results of alternative 

specifications appear in table 3. The preferred model based 

on statistical inference is shown in column (1) of the table, 

with other specifications provided for comparison. There are 

two basic specifications of the bivariate probit model—(1) the 

BiProbit (BP) where both decisions are considered functions 

of the same set of explanatory variables, and (2) the Seemingly 

Unrelated BP, where each decision equation can be specified 

with separate sets of explanatory variables. 

For most models, we give two estimates—one named 

Cluster, the other No Cluster. Because each subject generates 

more than one observation for the analysis, there is potential 

for nonindependent observations and correlated errors.

The coefficient estimates for the Cluster and No Cluster 

models are identical, but the standard errors are considerably 

smaller for the Cluster estimates. This suggests that explicitly 

modeling the within-subject error correlations results in more 

efficient estimates of the coefficients. Despite this, the parameter estimates are quite robust to the No Clustering/Clustering 

specification.

Estimates for rho, the correlation of errors between the 

two equations, are provided in the tables. There are statistically significant correlations between the errors of the map 
6 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

Variable name Variable description mean1 std dev2

Idnum subject identifier

Status indicates section of web completed

Payout indicates if subject collected payment 0.675

Healthins indicates if health insurance held 0.515

Houseins indicates if home or contents insurance held 0.357

Cairns indicates if auto insurance held 0.665

Hazins indicates if hazard (including flood) insurance held 0.028

Insscore count of insurances held (range 0-6) 2.05 0.081

q5 indicates if subject is female 0.567

over30 indicates if subject aged 30 or older 0.298

Number count of periods in each game 4.47 0.017

Mapcost cost of purchasing detailed map 15.1 0.073

insurancecost cost of purchasing insurance (premium) 29.9 0.148

totalbalance balance of account 5234 51.7

Mapb indicates map A shown (rather than map B) 0.491

Lossamt potential loss at location 1855 64.5

Outcome hazardous event occurred 0.037 0.003

lossprobability 0.037 0.001

Bankrupt 0.006 0.001

mapbought 1=yes 0.626 0.020

insurancebought 1=yes 0.471 0.014

maphloss actual loss is higher than coarse map shows 0.021 0.002

Maplloss actual loss is lower than coarse map shows 0.110 0.007

Table 2. Definitions and summary statistics for variables used in analysis.

1

Mean not provided for ID type variables.

2

Standard deviation not given for binary variables.

purchase and insurance purchase decisions in all model 

specifications except for the preferred model (table 3, column 1). Note, however, that this is quite consistent with the 

proposition that insurance purchase is dependent in part on 

map purchase. The test is for the correlation between factors 

influencing the two decisions, but not included as explanatory variables in the two equations. Equation 1 includes 

a sufficiently rich set of explanatory variables for the two 

decisions that no unexplained correlation remains between 

the two equations.

A simple specification for the two decisions is provided 

in column 5 of tables 3. This provides a set of explanatory 

variables based on a strict interpretation of the expected utility 

theory. Only decision costs and expected loss variables are 

included. The occurrence of misleading information in the 

coarse map is also included in the equations as this directly 

affects the accuracy of the expected value calculations. The 

equation for the decision to purchase a detailed map shows 

that map cost or insurance cost has no impact on buying a 

map. A map is more likely to be bought the higher the probability of a loss and the lower the loss amount. Potential errors 

from using only the coarse map (for example, by reading the 

map and concluding the loss is higher or lower than it actually 

is) reduce the likelihood of buying a detailed map. There is 

some difficulty in interpreting this variable, as the subject cannot know if the coarse map is revealing the true loss amount or 

not, and once the detailed map is purchased, the issue of any 

error in loss reporting in the coarse map is of no importance 

to the subject’s decision to purchase the map. It is therefore 

not surprising that the results of these variables in the map 

purchase equation are mixed.

Map information and interpretation errors have more 

significance in the insurance purchase decision. Considering the insurance purchase decision (table 3B, column 5) we 
Data Analysis and Interpretation 7

9 Or could this be evidence that females appreciate maps less than males?

observed that although the map cost is not important, the 

higher the cost of insurance the less likely subjects are to buy 

coverage. The higher the probability of loss the more likely is 

the purchase of insurance, as it is when the amount of the loss 

is greater. The potential map errors are statistically significant 

in this equation, and when the actual loss is lower than what is 

shown by the coarse map, the subject is likely to overestimate 

the probability of a loss and more likely to purchase insurance 

(coefficient estimate is +0.182). When the actual loss is higher 

than what is shown by the coarse map, the subject is likely to 

underestimate the probability of a loss and less likely to purchase insurance (coefficient estimate is −0.259). In summary, 

the simple models in column 5 of each table perform reasonably well as explanations of the decisions and are consistent 

with the predictions of the expected utility theory.

Columns 3 and 4 present estimates of a BiProbit model 

with and without correction for the panel nature of the data 

gathering process. This adds to the simple model a richer 

specification of the decision environment facing the subjects. 

In particular there are variables indicating past decisions by 

subjects, and some demographic variables are included. Map 

cost is important in determining whether a subject purchases 

the map, and the sign of the coefficient is consistent with 

expectations. Once accounted for, clustering makes the cost 

of insurance insignificant. The higher the probability of loss 

the more likely is the purchase of the detailed map. The effect 

of uncertainty from the coarse map remains in these models 

of the map purchase decision. Subjects display some habitual 

behavior in that they are more likely to buy a map this game 

if they purchased one last game and more likely to buy a 

detailed map if they bought insurance last period. Past losses 

and bankruptcies are not statistically significant factors in map 

purchase, nor are factors indicating if the subject holds insurance policies outside the experiment. Age does not appear to 

be a factor in map purchase, but females are less likely to buy 

maps than males. The coefficient on the wealth variable (the 

natural log of accumulated experiment wealth) is negative and 

statistically significant. Although this result may be interpreted 

as evidence for less need of map information as subjects get 

wealthier, wealth is more likely a proxy for experience with 

the game, because for most subjects in this experiment, wealth 

increases as the game progresses. Interpreted this way, the 

negative coefficient indicates that subjects are less likely to 

buy the map the more they play the game because they see it 

offering little marginal benefit. This behavior may also be a 

reflection of increasing confidence leading to overconfidence 

as the game progresses and nears completion.

Table 3B presents estimates for the BiProbit model for 

insurance purchase in columns 3 and 4. Although map cost 

is not important in determining the decision to purchase 

insurance, the cost of insurance is negative and statistically 

significant. Whereas higher premiums decrease the probability of buying insurance, higher losses and more likely losses 

increase the probability of buying insurance. All these impacts 

are consistent with the expected utility theory. As was the case 

with the simple model, decision errors based on the 

coarse map are consistent with subjects buying more insurance when they overestimate the size of the loss and buying 

less insurance when they underestimate the size of the actual 

loss. Insurance purchase displays some habitual behavior, 

while past losses and bankruptcies are not statistically important factors. Insurance coverage outside the experiment, as a 

measure of the subject’s risk aversion, shows a statistically 

significant positive impact on the decision to buy insurance 

in the experiment. Age does not influence insurance purchase 

but in contrast with the map buying decision, females are more 

likely to buy insurance. Could it be that females are more 

confident in interpreting the map information and therefore do 

not need the detailed map, but are more risk averse than males 

and hence more likely to buy insurance?9

 Also in contrast to 

the impact on the map purchase decision, the wealth variable 

has a statistically significant but positive effect on the decision 

to buy insurance. In previous experiments of a similar nature, 

Ganderton and others (2000) found wealth to exert a negative 

effect on insurance purchase. Here, the subjects have better 

information on which to base their insurance decision in the 

form of the detailed map, and they do not self-insure as they 

become wealthier, nor do they assess the risks as being lower 

as their confidence builds with experience playing the game. 

On average, a map costs half what insurance costs, so maps 

could be showing an inferior income effect, whereas insurance 

shows a normal income effect.

As stated earlier, the preferred model is shown in column 

1. The model in column 2 is the same specification but does 

not account for within-subject correlations that are reflected in 

excessively large standard errors. This model finds no statistical correlation between the errors in the map and insurance 

purchase decisions. Variables measuring map errors have been 

omitted from the map purchase equation since they really have 

no relevance as argued above. Results for these variables are 

mixed in this equation, and the interpretation of their impact is 

unclear at best. The lack of any significance for the variables 

indicating insurance activity outside the experiment recommends omitting these variables from the map buying equation. 

Column 1 of table 3A shows map purchase to be less likely 

at higher map cost, and insensitive to insurance cost and loss 

amount. A subject is more likely to purchase the detailed map 

when the probability of a loss is high. Those who previously 

bought maps and insurance are more likely to purchase maps.

Using equation 1 for both map purchase and insurance 

purchase, table 4 presents estimates of the marginal effects of 

each variable on the joint probabilities of buying the map and 

insurance. Table 4, column 1 shows the influence of each variable on the joint probability of buying both the detailed map 

and insurance. Increases in both the cost of the map and insurance decrease this probability, but by far the strongest impact 

on the joint probability is the probability of loss. Increases in 

the size of the potential loss also increase the joint probability 

of purchasing the map-insurance bundle, but the effect is substantially smaller than for changes in the probability of loss. 
8 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

Seemingly Unrelated Bivariate 

Probit

Bivariate Probit

Cluster2 No Cluster Cluster No Cluster Cluster

Equation

Variable (1) (2) (3) (4) (5)

Buy Map

Constant −0.4423

(−2.69)

−0.442

(−5.63)

0.129

(0.72)

0.129

(1.44)

0.781

(7.02)

Map cost −0.017

(−3.85)

−0.017

(−8.70)

−0.014

(−2.69)

−0.014

(−6.07)

−0.005

(−1.11)

Insurance cost 0.004

(0.00)

0.004

(4.06)

0.004

(0.03)

0.004

(3.55)

0.003

(1.43)

Log(loss amount) −0.001

(−0.11)

−0.001

(−0.23)

−0.050

(−4.70)

−0.050

(−8.92)

−0.043

(−4.12)

Loss probability 1.02

(4.37)

1.02

(4.56)

0.872

(3.41)

0.872

(3.48)

0.870

(3.93)

Actual loss lower than 

coarse map shows

−8.58

(−139)

−8.58

(0.0)

−7.49

(−153)

Actual loss is higher than 

coarse map shows

−8.76

(−45.7)

−8.76

(0.0)

−7.32

(−139)

Buy map last game 1.87

(28.7)

1.87

(92.5)

1.84

(26.5)

1.84

(81.2)

Buy insurance. Last period 0.137

(3.37)

0.137

(6.72)

0.177

(4.15)

0.177

(7.71)

Suffer loss last period 0.062

(1.16)

0.062

(1.26)

0.084

(1.40)

0.084

(1.48)

Bankrupt last period −0.252

(−1.59)

−0.252

(−1.59)

−0.292

(−1.69)

−0.292

(−1.70)

Log(total a/c balance) −0.031

(−2.57)

−0.031

(−4.56)

−0.041

(−3.05)

−0.041

(−5.51)

Insurance score 0.005

(0.20)

0.005

(0.56)

has hazard insurance −0.016

(−0.09)

−0.016

(−0.23)

Female −0.141

(−2.17)

−0.141

(−6.97)

−0.164

(−2.43)

−0.164

(−7.16)

Age 30 or older 0.082

(1.16)

0.082

(3.75)

0.078

(0.98)

0.078

(2.80)

1

 Estimates are full information maximum likelihood.

2

 Clustering allows for correlated errors within observations from the same subject, but none across subjects.

3 Coefficients in bold are statistically significant at the 5 percent level. The number in parenthesis is the t-statistic for significance 

that is estimated as the coefficient/standard deviation of the coefficient.

Table 3A. Bivariate probit analysis of decision to buy detailed map (simultaneous estimation) 1.
Data Analysis and Interpretation 9

Seemingly Unrelated Bivariate 

Probit

Bivariate Probit

Cluster No Cluster Cluster No Cluster Cluster

Equation

Variable (1) (2) (3) (4) (5)

Buy Insurance

Constant −2.281

(−20.7)

−2.28

(−33.6)

−2.62

(−20.9)

−2.62

(−33.3)

−1.39

(−13.3)

Map cost 0.004

(1.58)

0.004

(2.00)

0.003

(1.24)

0.003

(1.56)

0.005

(1.53)

Insurance cost −0.004

(−3.16)

−0.004

(−3.96)

−0.003

(−2.99)

−0.003

(−3.73)

−0.004

(−2.68)

Log(loss amount) 0.187

(18.4)

0.187

(37.9)

0.185

(18.0)

0.185

(37.6)

0.198

(16.7)

loss prob. 9.34

(19.2)

9.34

(43.3)

9.38

(19.3)

9.38

(43.6)

8.16

(17.7)

Actual loss lower than 

coarse map shows

0.322

(7.32)

0.322

(9.40)

0.243

(5.23)

0.243

(7.69)

0.182

(2.53)

Actual loss is higher than 

coarse map shows

−0.075

(−1.00)

−0.075

(−1.15)

−0.152

(−2.09)

−0.152

(−2.37)

−0.259

(−2.63)

Bought map 0.241

(3.48)

0.241

(7.56)

Buy map last game 0.135

(3.22)

0.135

(6.66)

Insured last period 1.113

(23.41)

1.113

(58.4)

1.12

(23.7)

1.12

(59.1)

Suffered loss last period −0.066

(−1.34)

−0.066

(−1.45)

−0.063

(−1.26)

−0.063

(−1.37)

Bankrupt last period 0.289

(1.83)

0.289

(2.01)

0.273

(1.71)

0.273

(1.90)

Log(total a/c balance) 0.058

(6.98)

0.058

(9.13)

0.056

(6.63)

0.056

(8.83)

Insurance score 0.041

(2.08)

0.041

(5.96)

0.042

(2.08)

0.042

(5.99)

Has hazard insurance −0.100

(−0.50)

−0.100

(−1.71)

−0.099

(−0.49)

−0.099

(−1.70)

Female 0.183

(3.24)

0.183

(9.59)

0.175

(3.10)

0.175

(9.23)

Aged 30 or older −0.117

(−1.75)

−0.117

(−5.08)

−0.113

(−1.69)

−0.113

(−4.92)

Rho −0.043 −0.043 0.086 0.086 0.144

Wald test for rho=0 2.13 4.01 13.56 30.43 13.58

Sample size 22981 22981 22981 22981 22981

Wald test 3000 14923 51438 12912 31453

1 Figures in bold are statistically significant at the 5 percent level.

Table 3B. Bivariate probit analysis of decision to buy insurance (simultaneous estimation).
10 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

Impact on joint probability:

(1) (2) (3) (4)

Variable buy map and buy 

insurance

buy map, do not 

buy insurance

do not buy map, buy 

insurance

buy neither map nor 

insurance

Average probability 0.334 0.320 0.186 0.159

Cost of map −0.0031 −0.004 0.004 0.003

Cost of insurance −0.0002 0.002 −0.001 −0.0002

Log(loss amount) 0.049 −0.049 0.026 −0.025

Probability of loss 2.64 −2.26 1.08 −1.46

Log(wealth) 0.009 −0.021 0.014 −0.003

Loss lower than map 0.083 −0.083 0.043 −0.043

Loss higher than map −0.020 0.020 −0.010 0.010

Bought map 0.063 −0.063 0.033 −0.033

Insurance score 0.011 −0.011 0.006 −0.006

Female 0.021 −0.073 0.052 −0.0001

Age 30 and older −0.015 0.045 −0.031 0.001

1 The numbers in the table are the change in probability due to the impact of each explanatory variable from the regression equations.

Table 4. Marginal effect of explanatory variables on joint probabilities.

Just as was found in the previous research (Ganderton and others, 2000) subjects appear to be far more sensitive to changes 

in the probability of loss than changes in the loss amount. This 

is somewhat surprising given the general view that people 

have difficulty dealing with small probabilities of the order 

considered here (1/10, 1/100, 1/1,000). However, that view 

relates more to the tendency for people to either exaggerate or 

discount small probabilities than their sensitivity to marginal 

changes in these small probabilities.

In summary, the results of data analysis provide strong 

evidence of rational behavior by subjects consistent with 

the expected utility theory. Subjects are less likely to purchase additional information (the map) the higher the cost of 

the map, but are insensitive to the cost of insurance in map 

purchase. Similarly, insurance against loss is less likely to 

be purchased the higher the premium but is insensitive to the 

cost of the map. Map purchase is more likely with an increase 

in the probability of loss, but is insensitive to the amount of 

the loss, but the decision to purchase insurance is positively 

impacted by both elements determining expected loss. The 

relationship between the two decisions is relatively strong and 

positive—subjects who bought a map are more likely to buy 

insurance, and those who bought insurance are more likely to 

buy a map both now and in the future. While past decisions 

influence current decisions, past outcomes are not statistically 

significant determinants of current decisions. 

There is some evidence that those subjects who rely on 

less information, in the form of a coarse map, and forego 

the additional information contained in the detailed map, are 

more likely to purchase insurance when they overestimate the 

size of the loss as indicated by the coarse map. Subjects are 

basing their insurance decision on the information provided 

in the coarse map, even though it is erroneous. In the case 

of the hazard modeled in this experiment, it is only when 

subjects purchase the detailed map that they realize they were 

overestimating the size of the loss, calculating an exaggerated 

expected loss, and buying too much insurance. Clearly, subjects are aware of the benefits that arise from the more detailed 

geoscience information contained in the detailed maps. In the 

case of this experiment, the benefit is that insurance costs to 

the subject can be lowered, but in the real world application 

the benefit would just as likely be that the subject might realize that they are underinsured.

As subjects accumulate earnings over the duration of 

the experiment, they are less likely to purchase a detailed 

map, but more likely to purchase insurance. Although there 

is no theoretical expectation regarding the marginal effect 

of wealth on these decisions, it could be that two distinct 

factors are at work. In the case of the map purchase decision, 

increasing wealth could be a proxy for experience with the 

game, and as subjects increase their experience they value 

the additional map information less. In the case of buying insurance, subjects may be suffering from the common 

gambling fallacy that as the game nears its end a hazardous 

event is more likely to occur. It could also be that as subjects 

become richer they can afford more insurance as the pre
Program Potential 11

mium represents a smaller fraction of total wealth. Further 

investigation is required to identify the true motivations for 

these observed behaviors.

Finally, subjects who hold insurance outside the experiment are more likely to buy insurance, but this behavior 

has no influence on their decision to buy a detailed map. 

Because the detailed map provides a higher level of risk-relevant information on which to base insurance purchase decisions, this suggests that subjects have little or no experience 

with such options in their everyday lives. Also requiring 

further investigation is the curious observation that females 

are more likely than males to purchase insurance, but less 

likely to purchase the additional information contained in 

the detailed map.

Program Potential

The results of this experiment suggest considerable 

potential for the research program of which it was a major 

part. The experiment demonstrates the use of the Internet as a 

mechanism for conducting experiments, especially of the kind 

requiring the delivery of considerable geoscience information 

of a graphical nature. The Web-based experiment is not limited 

geographically or temporally. Once a payment mechanism 

with a corresponding reach that also conforms with both the 

needs of human subjects and confidentiality and financial 

requirements that control research work is developed, we 

could modify existing methods employed for on-line commerce to work in this case.

Future work would entail providing maps that are more 

realistic to selected groups such as policy makers and stakeholders in regional organizations both public and private. 

Extending the coverage across the county and overseas is 

also a simple extension of the current work. A major extension of the current experiment would provide subjects with 

a mitigation alternative. This would allow us to determine 

the impact of geoscience information on the choice between 

mitigation and insurance, as well as study the interaction 

between mitigation and insurance for these types of lowprobability, high loss risks.

References

Bernknopf, R.L., Dinitz, L.B., and Loague, K., 2001, An interdisciplinary assessment of regional-scale nonpoint source 

groundwater vulnerability: theory and application: U.S. 

Geological Survey Professional Paper 1645.

Bernknopf, R.L., Brookshire, D.S., McKee, M.J., and Soller, 

D.R., 1997, Estimating the social value of geologic map 

information: a regulatory application: Journal of Environmental Economics and Management, v. 32, p. 204-218.

Bernknopf, R.L., Campbell, R.H., Brookshire, D.S., and 

Shapiro, C.D., 1988, A probabilistic approach to landslide 

mapping in Cincinnati, Ohio, with applications for economic 

evaluation: Bulletin of the Association of Engineering Geologists, v. 25, p. 39-56.

Carnegie Commission on Science, Technology, and Government, 1993, Risk and the environment improving regulatory 

decision making: New York, 150 p.

Feller, W., 1968, An introduction to probability theory and its 

applications (3rd ed., v. 1): New York, Wiley and Sons, 509 p.

Ganderton, P.T, Brookshire, D.S., McKee, M., Stewart, S., and 

Thurston, H., 2000, Buying Insurance for Disaster-Type 

Risks—Experimental Evidence, Journal of Risk and Uncertainty, v. 20, no. 3, p. 271-289.

Greene, W.H., 2003, Econometric Analysis (5th ed.), Englewood 

Cliffs, N.J., Prentice Hall, 1005 p.

Hagel, J.H., and Roth, A.E., eds., 1995, The handbook of 

experimental economics: Princeton, N.J., Princeton University Press, 721 p.

Kunreuther, H., Platt, R., Baruch, S., Bernknopf, R., Buckley, 

M., Burkett, V., Conrad, D., Davison, T., Deutsch, K., Geis, 

D., Good, J., Jannereth, M., Knap, A., Lane, H., Ljung, G., 

Mcauley, M., Mileti, D., Miller, T. Morrow, B., Myers, J., 

Pielke, R., Pratt, A., and Tripp J., 1999, The hidden cost of 

coastal hazards—Implications for risk assessment and mitigation: The John Heinz Center for Science, Economics, and 

Environmental Policy, Island Press, 220 p.

Platt, R.H., 1999, Disasters and democracy—The Politics of 

Extreme Natural Events: Washington, Island Press, 320 p. 

STATA, 2001, STATA Reference Manual, Version 7: Press, 

Texas, A-G Stata.
12 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 
Glossary 13

Glossary

Expected utility theory Expected utility theory is a normative theory for decision making under risk. Von Neumann and 

Morganstern axiomated expected utility theory by showing that 

alternative actions can be ranked by their expected utilities. The 

expected utility of an alternative action is the weighted average 

of the utilities of the possible outcomes where the weights are 

the objective probabilities of each outcome.

Moral hazard Moral hazard is a phenomenon that occurs 

in insurance markets caused by an asymmetry of information between the consumer and the insurance provider. When 

an insurance company has a stake in the action taken by a 

consumer, such as self-protection (for example, maintenance 

of a vehicle), but the insurance company cannot observe the 

consumer’s action, the situation involves moral hazard. Moral 

hazard can be partly overcome with insurance deductibles.

Adverse selection Adverse selection is a phenomenon that 

occurs in insurance markets that is caused by an asymmetry of 

information between the consumer and the insurance provider. 

In markets for insurance, the basic asymmetry of information 

is that the purchasers of insurance may well have a better idea 

of the relevant risks than does the insurance company.

Treatment specification Treatments are experimental controls used to condition responses or behaviors. The method of 

estimation must identify them explicitly in the model specification to remove, or control, for their effect in the experiment. 

With sufficient sample observations, the treatment effect can 

be identified and measured.

Treatment combination The number of alternative treatment 

values when all possible values are allowed. If one treatment 

has two possible values and another three, then combined 

there are 6 combinations of the two treatments. To estimate 

treatment effects we must determine the appropriate sample 

size for each combination of treatment values.

Wald test The Wald test is based upon the restriction 

imposed by the null hypothesis. If true then a specific quadratic form of the parameter and its mean under the null will 

be distributed chi-squared. This test statistic is used to test a 

whole range of hypotheses concerning both individual parameters and sets of parameter restrictions.

Bivariate probit (BiProbit) model The bivariate probit model 

is a qualitative response regression model in which the dependent variable assumes discrete values. The simplest of these 

models is that in which the dependent variable is binary (it can 

assume only two values which can be denoted by 0 and 1). 

The bivariate probit is the case where observed values of the 

dependent variable are realizations of a binomial process with 

probabilities given by Pr(y = 1) = 1− F(−b′x) and varying 

from trial to trial depending on xi

, where Pr is probability, y is 

the dependent variable, F is the cumulative normal probability 

distribution, b is a regression coefficient, x is an independent 

variable, and i observations, i = 1,...,I.

Income effect The income effect is a consumer’s reaction 

with respect to purchases of a commodity to changes in their 

income, prices remaining constant.
14 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

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Appendix A 15

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Appendix A. Experiment Flow Chart—Continued
16 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

New Main Game introductory text:

You are about to enter the main experiment. The general structure of the game is 

outlined below.

• You play a number of GAMES.

• Within each game there is a random number of PERIODS.

• Each period you earn game income of 200 tokens.

• Within each period there is a random number of ROUNDS.

• Each round you are exposed to a potential loss event.

• You either suffer a loss, or not.

• The size and likelihood of loss depends on your location.

• You know how often these losses occur, but not when.

The sequence above is repeated with certain experimental parameters changing 

each period and each game.

You face the following decision each period:

• Buy insurance to offset any potential losses.

• Buy a more detailed map of your location to help in your decision making.

• You can buy either, or both, or none at all.

• At all times you will know the probability of suffering a loss, the size of 

that loss, and your location on either a coarse, or detailed, map.

• You will know how many games tokens you have and the cost of buying a 

map and (or) buying insurance.

• Once you buy insurance you have coverage for the whole period, but not for 

the next period.

• Insurance covers any losses you may suffer during the period.

• If you lose more tokens than you own, you are declared bankrupt, and must 

wait until the start of the next period to get more tokens.

As you go through the experiment you will earn income and may spend it to buy 

maps and insurance. You may lose income if you suffer an uninsured loss. At the 

end of the experiment your accumulated earnings in tokens will be converted to 

U.S. dollars and a claim check will be issued for you to print.

Appendix B. Game Explanation Text
Appendix C 17

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web page for address.

Appendix C. Maps

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18 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

Appendix D. Screen Captures

Welcome Screen Capture From Website
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information; see web page for 

address.

Appendix D 19

Appendix D. Screen Captures—Continued

Logon Screen Capture
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20 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

Appendix D. Screen Captures—Continued

Registration Welcome Screen Capture
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Please contact the author for 

information; see web page for 

address.

Appendix D 21

Appendix D. Screen Captures—Continued

Registration Screen Capture
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address.

22 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

Appendix D. Screen Captures—Continued

Registration Complete Screen Capture
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Please contact the author for information; see web page for address.

Appendix D 23

Appendix D. Screen Captures—Continued

Post Registration, Personal Welcome and Introduction Page Screen Capture
24 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

This diagram cannot be read. Please 

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page for address.

Appendix D. Screen Captures—Continued

Survey Screen Capture
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information; see web page for 

address.

Appendix D 25

Appendix D. Screen Captures—Continued

Risk Preference Mini Experiment Screen Capture
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see web page for address.

26 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

Appendix D. Screen Captures—Continued

Main Experiment Welcome Screen Capture
Appendix D 27

Appendix D. Screen Captures—Continued

Main Decision Page Screen Capture
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web page for address.

28 The Role of Geoscience Information in Reducing Catastrophic Loss Using a Web-Based Economics Experiment 

Appendix D. Screen Captures—Continued

Main Decision Page With Detailed Map Screen Capture
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address.

Appendix E 29

Appendix E. Continuation Screen Captures—

Result of Event Draw Page, Subject Must Acknowledge to Continue