Summary

Large Language Models (LLMs) seem well-suited to ‘psychological testing’, because you can get lots of data quickly and conditions are easy to fix. I'm curious to what extent LLMs display the same cognitive biases documented in humans.
I carried out a quick and dirty test with a Chat-GPT model for ‘scope insensitivity’, a cognitive bias where the perceived importance of a problem isn’t influenced by its scale. You can find the results in the post below.
I think there are two obvious reasons why models might display cognitive biases – either because they are ‘inherited’ from the data they’re trained on, or because they emerge as useful heuristics that the model independently ‘discovers’.

**In one chart: the scope insensitivity of gpt-3.5-turbo.** As the size of a problem affecting a company increases, the model’s suggested spend to solve the problem doesn’t increase proportionally.

Background

It seems like LLM's are well-suited to ‘psychological testing’, because you can get lots of responses much more easily and quickly than you can from humans, and the conditions are much more easily controllable.

I’m curious to what extent LLM’s show the same cognitive biases that have been documented in humans. It’s been pointed out before that AI models display remarkably human-like biases (see Import AI newsletter #319), but I’m not aware of much other work on this (apart from this paper).

I carried out a quick and dirty test with a chat-gpt model for ‘scope insensitivity’, the well-known cognitive bias documented in humans in which the perceived importance of a problem isn’t influenced by its scale.

I did these tests super quickly and roughly in the process of trying to understand OpenAI’s evals framework, so I’m sure there are lots of ways they could be improved and there might be errors.

Test design

To test for scope insensitivity, I wrote a question format to use as a prompt to LLM’s:

“A company is considering how much money to spend to solve a problem. This problem affects X% of their workforce, and for each employee it affects is equivalent to them losing 1 day of work a week.

What might be a reasonable percentage of revenue to spend on this problem? Give me just a numerical range and no text or anything else. Don't do any reasoning -- I want your quick 'gut' reaction"

I then varied ‘X’ and measured the model’s response.

I used this question rather than the ty...

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