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Comparing input interfaces to elicit belief distributions

by Paolo Crosetto and Thomas De Haan

Aim of the paper

We think that a belief elicitation interface should:

  • be easy to understand;
  • allow for all sort of beliefs, from simple point estimates to bimodal distributions and more, without imposing any structure;
  • manage not to get in the way of subjects;
  • help subjects easily express what they believe;
  • be fast, responsive, and accurate.

There are several interfaces out there. Which one is the best according to the above criteria? We ran a test to find out.

The test

We test our newly developed Click-and-Drag interface against the state of the art in the experimental economics literature and in the online forecasting industry:

We asked 372 MTurkers to mimic a given distribution. It looks like this:

Subjects must simply try to mimic as close as possible the given target distribution. The closer they get, the more they earn.

They face screens with symmetric, asymmetric, bimodal, and complex distributions; with 7, 15 or 30 bins; and they are given 45 or 15 seconds per screen. Here are all the screens subjects had to mimic:

We also collect subjective data on ease of use, frustration and understanding.

We pre-registered the experiment (here, with a simple set of hypotheses: Click-and-Drag will outperform all other interfaces.


We recruit 372 MTurkers – on average middle-aged, mostly male, that on average fare fairly well on our control questions, and who earn between 2 and 3 euros on our 20-minute task:

% female
mean age (sd)
mean payoff (sd)
% no error in CQ
Click-and-Drag 95 41% 36.73 (9.69) 2.92 (0.75) 43%
Slider 91 42% 40.56 (10.93) 2.35 (0.7) 49%
Text 91 48% 37.07 (10.94) 2.17 (0.89) 46%
Distribution 95 37% 37.11 (11.17) 2.23 (0.43) 37%


The main indicator is accuracy: how good are subjects at mimicking the target distribution in the allotted time? It turns out Click-and-Drag sports the highest accuracy overall, and when breaking screens down by their type, granularity, and allotted time.


An interface is good if it allows for a good accuracy, but also if it does so in a limited amount of time. Is it easy to draw the main strokes of one’s beliefs or is it a long and painful exercise? How fast do subjects converge to the best accuracy?

It turns out Click-and-Drag allows for a faster convergence than all other shapes. In this plot we draw accuracy at each point in time for each belief elicitation interface. Distribution starts with an unfair advantage, as it does not start with a blank sheet but with a normal-looking distribution; still, its slope – the speed of convergnece – is the lowest of them all.

Self-reported assessment

But how do subjects themselves rate the interfaces? We asked three questions about the ease of understanding, ease of use, and generated frustration.

Click-and-Drag comes on top of all self-reported assessment (even if most of the differences are not significant).

Get the paper

You can find the paper, with all the details, more results, statistical tests, and further exploration of the interfaces’ performance here

Get the software

Find the oTree code for each of the four interfaces here

Try it out yourself on our oTree demo here

Comments? Questions on how to implement this for your own experiment? Contact us!