Strategic avoidance of lying costs - Mind Game

Last registered on February 17, 2021


Trial Information

General Information

Strategic avoidance of lying costs - Mind Game
Initial registration date
February 16, 2021

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
February 17, 2021, 10:35 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.


Primary Investigator

WZB Berlin Social Science Center

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
I study whether people take advantage of privileged positions to have someone lying on their behalf without explicit delegation. I use a sequential cheating game in dyads in which if at least one member of the dyad lie, both benefit from it. Consequently, the first-mover can avoid the cost of lying by reporting truthfully if he or she expects that the second-mover will lie. I use a mind game where only participants know the state of nature, so I cannot detect individual liars. I use treatment variations to eliminate the belief of the second-mover lying, control for the positive externality of the first-mover lie, and control for simultaneous decisions.
External Link(s)

Registration Citation

Parra, Daniel. 2021. "Strategic avoidance of lying costs - Mind Game." AEA RCT Registry. February 17.
Sponsors & Partners


Experimental Details


I study whether people take advantage of privileged positions to have someone lying on their behalf without explicit delegation. I first conducted an experiment where I can identify whether participants lie or not. In this second experiment, the state of nature is in participants' minds so I can only compare distributions of groups based on the theoretical distribution, but cannot identify whether an individual lies or not.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Report of first-mover (Participant A). Report of the Second mover (Participant B).
Primary Outcomes (explanation)
I will compare whether the distributions of the reports are statistically different across treatments.

Secondary Outcomes

Secondary Outcomes (end points)
Beliefs about the second mover report; Gender; Religiosity.
Secondary Outcomes (explanation)
I will use this information to understand whether participants understand that in some treatments the other participant has incentives to lie on their behalf. I will also use the information on gender and religiosity to see whether these groups are significantly different.

Experimental Design

Experimental Design
In a first experiment, I used a variation of the observed game presented by Gneezy et al. (2018). However, the lying rates of the experiment conducted online were too low. To check whether the results vary when I deliver more privacy, I used this second experiment.

In this experiment, two participants play sequentially. In the beginning, the first-mover (P1) chooses a color out of five in their head. Then, she/he clicks on a box on the computer's screen, and a color (one of the five) is revealed. Therefore, the probability that both colors match is 0.2. After the first-mover observes the draw, they are asked to report to the second-mover (P2) whether the colors match. Once the second-mover learns this report, they follow the same process as P1.

Participants' payoffs depend on both the reports of the two members of a dyad. In particular, if any of the two participants report that the colors match, both of them get 2.5 pounds. Otherwise, both gain 0.3 pounds. I use this payoff structure because I am interested in lying at the extensive margin.

In the Baseline, I hypothesized that the first-movers will take advantage of their position and, therefore, will avoid the cost of lying. To attribute truth-telling to the possibility of having another participant lying on their behalf, I make the report of the second mover a random variable instead of a decision made by P2. Specifically, in the treatment No Avoidance, after the second-mover learns the report of P1, she/he chooses a color and reveals the selected color. Then, the computer reports whether the selected color is the same as the drawn color. Thus, in No Avoidance, the first-mover can not rely on second-mover incentives to lie.

In No Avoidance, the only thing that changes compared with the Baseline is the second-mover report's beliefs. However, I am interested in knowing whether this result will hold even when dropping the positive externality generated by the first-mover's report. With the previous treatments, I do not know whether the report in No Externality is purely because it is not possible to use P2 as an implicit agent or because P1 is using the handy excuse of causing a benefit on P2. In other words, it may be the case that in the Baseline, the first-mover is not using the positive externality as a justification because both participants generate positive externalities and then take full advantage of their position. In contrast, in No Avoidancethe positive impact of P1's report is more salient.

Hence, in the treatment No Externality, I modify the payoff scheme to eliminate the positive externality generated by P1 in previous treatments. In No Externality, the game sequence and main game features, including the pecuniary payoffs for the first-mover, are identical to those in No Avoidance. However, the payoff for P2 depends only on the report made by the computer.

In No Avoidance and No Externality, I control for different components that can explain why the first-mover decides to report truthfully in Baseline. However, there is another component that worth ruling out: the fact that participants play sequentially. In this treatment, Simultaneous, participants decide at the same time. The impact of this treatment on first-mover strategy is similar to No Avoidance. However, it allows me to understand the role of the moral signal that the first mover sends about whether it is correct to lie or not. Thus, this treatment makes it possible to confirm the first-mover's willingness to avoid the cost of lying and the impact of the moral signal on second-mover behavior.
Experimental Design Details
Hypothesis 1(Strategic Cost Avoidance).In the Baseline, the first-mover's report will be similar to the theoretical distribution.
Hypothesis 2 (No cost avoidance). The first-mover will lie more in No Avoidance compared with the Baseline.
Hypothesis 3 (Beliefs about second-mover). The first-mover will believe that the second-mover will report that the colors match significantly more in Baseline than in No Avoidance.
Hypothesis 4 (Positive externality justification). The first-mover will report that the colors match significantly more in No Avoidance than those reported in No Externality.
Hypothesis 5 (Delegation controlling for Externality). The difference between No Externality and the Baseline will be significant once we account for the difference between No Externality and No Avoidance.
Hypothesis 6 (No cost avoidance simultaneous). The first-mover will lie more in Simultaneous compared with the Baseline.
Hypothesis 7 (Moral signaling). The second-mover will lie more in Simultaneous compared with Baseline.

*I will analyze participants in Simultaneous as if all are P1 and P2 at the same time.
Randomization Method
The code of the experiment randomly assigns treatment.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
140 dyads for sequential treatments and 70 for simultaneous.

I assume that in Simultaneous, given that participants decide without knowing what the other has decided, I can use the report of both participants in the group as identical. Then, I will only need to collect half of the dyads compared with other treatments.
Sample size: planned number of observations
980 participants
Sample size (or number of clusters) by treatment arms
140 dyads Baseline
140 dyads No avoidance
140 dyads No externality
70 dyads Simultaneous
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
I calculated the power using computer simulations. I used a minimum detectable effect size of 0.15 percentual points from people detected as liars. The power reached with my sample size of 120 observations by treatment is 0.8 when simulating 1500 Fisher tests.

Institutional Review Boards (IRBs)

IRB Name
WZB Research Ethics Review
IRB Approval Date
IRB Approval Number


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