Mitigating Catastrophic Risk

Last registered on March 19, 2024


Trial Information

General Information

Mitigating Catastrophic Risk
Initial registration date
March 15, 2024

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
March 19, 2024, 5:39 PM EDT

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


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Primary Investigator

Uni Essex

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Public Good Games played in the lab have been used widely to study the trade-off between mitigation and adaption as a response to challenges posed by the climate crisis. In this experiment we introduce a new paradigm to study mitigation. See Experimental Design for details.
External Link(s)

Registration Citation

Kurian, Dilip Richie, Friederike Mengel and Dennie van Dolder. 2024. "Mitigating Catastrophic Risk." AEA RCT Registry. March 19.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Survival rate: percentage of games where climate catastrophe is avoided.
Primary Outcomes (explanation)
We will measure at the group level the fraction of games where the climate catastrophe was avoided, i.e. where the group reached the final round of the game.
This outcome will depend both on participant choices as well as externally set parameters (and these will interact), so we will also compare the actual survival rate in any given treatment with counterfactuals based on the same parameters but with choices as in the other treatments.

Secondary Outcomes

Secondary Outcomes (end points)
- Decisions to invest in mitigation
- Fraction of groups reaching “doom probability threshold”
Secondary Outcomes (explanation)
Decisions to invest in mitigation: the decisions of participants to invest or not to invest in mitigation.
Fraction of groups reaching the “doom probability threshold”: This threshold is defined as p=(1+gamma)/gamma * Delta N. It is the probability threshold that if reached means that the probability of disaster will keep increasing even under maximal mitigation efforts.

Experimental Design

Experimental Design
In our game participants receive an endowment of 20 tokens and play for 15 periods. In each round there is a probability p that the game ends and the endowment is lost. In our game p increases round-by-round using the following formula
p^{t+1}=(1+\gamma) p^{t}.
Participants can invest into reducing the probability. Specifically participants can pay in each round 1 GBP to reduce the probability by \Delta.

Our treatments will vary
- the group size (either N=3 or N=12)
- the growth path of p.
There are three exogenous parameters affecting the growth-path: the initial probability (p^{0}), the round-on-round percent increase (\gamma) and the effectiveness of individual action (\Delta). Variation in those “visible” parameters then induces variation in “hidden” parameters such as the doom probability threshold or the number of stages needed to reach that threshold. We will vary these parameters across sessions.

Participants will play this 15-period game eight times with random re-matching in between. They will be paid for one randomly selected round.

Prior to the experiment, participants are asked to complete an online survey. This survey consists of four tasks: one to measures risk aversion, one to measure cooperative preferences, one to measure participants’ understanding of exponential growth, and one to measure participants understanding of compound lotteries. In addition, we obtain participants’ age, gender, nationality, ethnicity, and student status.
Experimental Design Details
Not available
Randomization Method
Treatments are assigned to sessions by researchers randomly in an office by a computer.
Randomization Unit
experimental sessions
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
6912 observations at the individual level (and correspondingly fewer at the group level in the N=3 and N=12 groups).
Sample size (or number of clusters) by treatment arms
For each group size we plan to have 3456 observations or 18 clusters.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We will have 36 sessions with 24 participants each who will play for 8 rounds. This amounts to 6912 observations at the individual level. At the group level (main test) this will be 1152 observations in the small groups (N=3) and 288 observations for the big groups (N=12). We will test how our main (as well as secondary outcomes) depend on parameters related to the growth path (e.g. the percent increase in each round or the number of stages needed to reach the doom probability threshold), the effectiveness of individual action (\Delta) as well as group size. We will use OLS regressions to test for these differences. Standard errors will be clustered at the session level. Our sample sizes allow us to detect an increase or decrease of 5 (10) percentage points in the survival rate between the conditions with N=3 and N=12 with 80 percent power at the 5% level. (This is based on a t-test with hypothesized standard deviation of 0.25 (0.5) and a mean in the small groups of 0.5). There are no prior studies in this setting but this power analysis gives us confidence to detect economically meaningful differences in our main outcomes with our chosen sample sizes. We will focus both on mature behaviour after learning has occurred as well as behaviour in early stages. The latter is interesting in this context as in the context of the climate crisis learning is often not possible or very limited. When explaining individual donation decisions, some models will include demographics and the measures for risk preferences, cooperative preferences, understanding of exponential growth, and understanding of compound lotteries as controls. Furthermore, we will also explore which of these four measures best explain donation decisions, as all these four characteristics ay be relevant in this setting.

Institutional Review Boards (IRBs)

IRB Name
University of Essex Social Sciences Ethics Subcommittee
IRB Approval Date
IRB Approval Number
ETH2223-1713 and ETH2324-0881