The driving simulator task is designed to mimic a real-life commuting experience. The
subjects drive in a simulator environment installed on a laptop that is equipped with a steering
wheel, gas and brake pedals, and views everything from the perspective of sitting in the driver’s
seat. They drive from a simulated home origin to a simulated work destination as they make a
binary choice between a route that has free-flow traffic and another route that could be congested
with some probability. Each drive is referred to as a work day. If the subjects choose to take the
free-flow route there is a toll charge that varies across subjects but is stationary across the drives.
The drive takes approximately 2 to 4 minutes, depending on which route they take, which
scenario they are in, and how they drive. To increase the realism of the setting, simulated
vehicles are added to the road and subjects are required to follow general traffic rules, such as
speed limits.
The number of variables that are assigned to the subjects include: a wage that serves as a
monetary endowment for each drive, a time limit within which they have to arrive to work, a
monetary penalty if they arrive to work late, a toll charge when taking the risk-free route, and an
unknown probability of congestion on the risky road. These variables are adjusted on a between-
subject basis. The wage can be a high wage of $5.00 or a low wage of $2.50. If travel time
exceeds a certain time threshold, a discrete penalty amount will be subtracted from the wage.
Table A1 shows the ranges of tolls, penalties and time thresholds. Tolls range from $0.50 to
$2.00 if wage if $2.50, and from $0.50 to $4.00 if wage is $5.00. The range of toll is in 10-cent
increments. Penalties range from $0.50 to $2.00 if the wage is $2.50, and from $0.50 to $4.00 if
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2 minutes and 10 seconds to 2 minutes and 45 seconds in 5-second increments. These
assignments are constant across drives.
Each task is paid sequentially to avoid the issues that arise with random payment
protocols.6 Across the driving periods the cumulative earnings may present a wealth effect on
risk aversion, such that an increase in earnings could reduce risk aversion in the following
periods. However, an increase in earnings theoretically should not affect the belief of delay, and
it should only affect the belief estimate indirectly through its effect on risk aversion. Cox,
Sadiraj and Schmidt (2015) report that the PAS protocol did not induce a significant wealth
effect; the same result is reported in Cox and Epstein (1989) and Cox and Grether (1996) who
also use the PAS protocol. In contrast, Dixit, Harb, Martinez and Rutström (2015), who use the
PAS protocol in a driving simulator task with exogenous delay probabilities, report that
cumulative wealth significantly reduce risk aversion (p-value <1%). Here the cumulative wealth
effect is assumed to be negligible on the belief estimate.
An aerial view of the simulated city where the subjects drive is shown in Figure A1. In
the simulation, 7th Avenue is the express route that is risk free, and 9th Avenue is the alternate,
local road that is congested with some probability. Before driving, subjects are shown a deck of
6 The payment protocol that is used to elicit lottery choices must be compatible with the decision model in order to be incentive compatible. The pay-one-randomly (POR) protocol implicitly assumes that subjects view each outcome in each binary choice independently of each other, such that their behavior is in accordance with the Compound Independence Axiom (CIA). This payment protocol is incentive compatible under EUT (see Harrison and Swarthout (2014) and Cox, Sadiraj and Schmidt (2015)). However, it is incompatible with non-EUT models that are not based on the CIA, including Rank Dependent Utility (RDU).
This essay models choices over risky lotteries using both EUT and RDU, which necessitates the use of a payment protocol that is incentive compatible under both. The Pay-All-Sequentially (PAS) protocol does not rely on the CIA and is thus incentive compatible with both. However, PAS is not problem-free since it may induce a cumulative wealth effect. An alternative approach is to assume that there is one CIA that applies to the evaluation of a given lottery (in our case the evaluation of each route) and another CIA that applies to the payment protocol. One can then relax the former CIA and estimate the RDU model, while maintaining the assumption of the latter CIA. It is then possible to use the POR payment protocol and legitimately estimate the RDU model.
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toll cards (face-down) and are asked to draw one card that will determine their toll fee if they
were to take 7th Avenue. On 9th Avenue congestion is induced using a school bus that makes
frequent stops on the road causing delay. The objective probability of congestion, or the
probability of a school bus being present, takes four possible values, 0.2, 0.4, 0.6, or 0.8, and is
varied across subjects but constant within subjects. Subjects are not told what probability
treatment that they are assigned to nor are they told that these are the four possible congestion
levels. Subjects are told that the congestion level stay the same across the ten drives.
To implement the random congestion process, at the start of each period subjects are presented with a deck of cards, where some of the cards have the word “bus” on them and others have the words “no bus” on them. They choose a card without seeing if the card says “bus” or “no bus”. Next, the research assistant loads up the scenario stated on the chosen card. To ensure that subjects can trust that the research assistant actually loads the scenario indicated by the card
drawn, the cards selected are saved in an envelope and revealed at the end of the ten drive tasks.
Subjects do not know if a bus card is drawn unless they choose to drive on 9th Avenue in which
case they will find out by experience. Thus, the information obtain on 9th Avenue will only be
obtained if the route is selected. If the subjects drive very slowly, then late arrival is possible
even when a bus does not come.7 Prior to starting the drive task, subjects draw ten cards from
the deck of bus cards with replacement, allowing them to form prior beliefs.
Earnings are recorded after each drive and tracked, along with cumulative earnings,
throughout the drive periods in a transparent way.
7 These cases are not common. Pooling across all the drives and across all subjects, there are only 20 out of a total of 479 drives where the subjects take 9th Avenue, do not see a bus, and are still late (relative to the assigned time threshold). These are the subjects who are assigned the lowest time threshold and are late by less than 10 seconds; many of them are late by only 2 or 3 seconds.
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