Theory of Mind in Learning to Forecast Experiments

Last registered on July 07, 2021


Trial Information

General Information

Theory of Mind in Learning to Forecast Experiments
Initial registration date
June 30, 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
July 07, 2021, 11:04 AM EDT

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



Primary Investigator

Radboud University

Other Primary Investigator(s)

PI Affiliation
Nanyang Technological University
PI Affiliation
Dongbei University of Finance and Economics

Additional Trial Information

In development
Start date
End date
Secondary IDs
Learning to forecast experiments aim to find evidence on whether subjects can learn a rational expectations equilibrium. Experimental results often report substantial swings in prices due to adaptive learning processes or trend extrapolation strategies. We aim to explore whether Theory of Mind - in our case, the degree to which participants correctly assess information and signals in markets - helps to explain deviations from rational expectations equilibrium and the existence of price swings. We hypothesize that groups consisting of subjects who score high in a Theory of Mind test get closer to the rational expectations equilibrium showing lower price swings than groups consisting of subjects who score low. In line with Corgnet et al. (2018), we assume that high Theory of Mind subjects better assess the precision of signals in the markets and thus are better at making forecasts. To test our research question, we compare the price deviation from the fundamental value and the coordination of price forecasts in markets with different Theory of Mind levels, elicited via the eye gaze test (Baron-Cohen et al. 1997)
External Link(s)

Registration Citation

Bao, Te, Sascha Füllbrunn and Jichuan Zong. 2021. "Theory of Mind in Learning to Forecast Experiments." AEA RCT Registry. July 07.
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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The independent variable ToM has levels 1) individual ToM score, 2) average ToM group score, and 3) categorization in top (=1), middle upper (=2), middle lower (=3), and bottom (=4). The main learning to forecast measurement (dependent variable) contains the deviation of prices from the rational expectation equilibrium measured by the relative absolute deviation (RAD) and the scale of price fluctuation, i.e. the amplitude of price. Further measures from the learning to forecast experiment literature will apply.
Primary Outcomes (explanation)
The dependent variables are taken from Hommes, C., Sonnemans, J., Tuinstra, J., & Velden, H. van de. (2005). Coordination of expectations in asset pricing experiments. Review of Financial Studies, 18(3), 955–980.

We test whether groups with higher ToM scores reduce price deviation from fundamental value, price amplitude, and standard deviations of price expectations.

Secondary Outcomes

Secondary Outcomes (end points)
In addition, we consider subjects characteristics as controls.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Experimental Design Details
We use a standard laboratory experiment.

Stage 1:We perform the ToM test (eye gaze test) on all subjects and divide them into top, middle upper, middle lower and bottom quartiles (for 24 people sessions) based on the ToM scores. Hence, each session has four groups.

Stage 2:We apply a learning-to-forecast experiment with six subjects in each group. The price determination functions are defined as follows p(t)=1/(1+r) (F(t) + d) + e(t), where p(t) is the realized period priced, d is the dividend of the asset, r is the risk-free interest rate, F(t) is the average price forecast for period t, and e(t)~N(0,1) is a small iid shock to the price. The rational expectations equilibrium (REE) of the economy is, therefore, p(t)=d/r. Find more information in Bao, T., Hommes, C., & Makarewicz, T. (2017). Bubble Formation and (In) Efficient Markets in Learning‐to‐forecast and optimise Experiments. The Economic Journal, 127(605), F581-F609.

Stage 3: We will collect background information about subjects. In addition, we perform numeracy and a CRT test to measure their cognitive ability used as controls.

Programming will take place in zTree.
Randomization Method
Students are invited from a subject pool and randomly register for a session.
Randomization Unit
With the student group, we apply the ToM test to every person. We rank the 24 student subjects by their ToM score and cluster them in for cohorts. We then compare the forecasting behaviour of such groups (see analysis plan) as a standard approach considered in other experimental studies on the LtF environment. Hence, we consider a so-called quasi-experiment.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
In total, we consider six sessions with 24 subjects, i.e. six times four groups.
Sample size: planned number of observations
four group level observation for each session
Sample size (or number of clusters) by treatment arms
6*24=144 student subjects
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Considering a Wilcoxon signed-rank test with six observations (Diff=Top - Bottom=0) the minimum detectable effect size is 1.22 (assuming a power of 80% and significance levels of 5%. However, even doubling the sample size would yield 0.79.

Institutional Review Boards (IRBs)

IRB Name
IRB of Dongbei University of Finance and Economics
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials