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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

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Motivated Reasoning in an Explore-Exploit Task.

Zachary A Caddick¹, Benjamin M Rottman¹

¹Department of Psychology, University of Pittsburgh.

Cognitive Science

|August 11, 2021

Summary

Prior preferences bias causal learning, even with accuracy incentives. People tend to see policies aligning with their beliefs as effective, regardless of objective outcomes.

Keywords:

Causal learning Dynamic Economic decision making Explore-Exploit Motivated reasoning

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Area of Science:

Cognitive Psychology
Behavioral Economics
Causal Inference

Background:

Understanding how individuals learn cause-and-effect relationships is crucial.
Prior beliefs and preferences can significantly influence decision-making and judgment.

Purpose of the Study:

To investigate the impact of pre-existing preferences on causal learning.
To examine the role of motivated reasoning in ambiguous causal inference tasks.

Main Methods:

Participants engaged in an explore-exploit task, choosing policies to optimize economic output.
Causal relationships between policies and economic outcomes were inferred under challenging and ambiguous conditions.
Financial incentives for accuracy were provided to mitigate bias.

Main Results:

Evidence of motivated reasoning was found, where participants favored conclusions aligning with their prior preferences.
Beliefs about border security funding influenced participants' inferred causal effects.
Neither neutral preferences nor providing policy functional forms significantly reduced motivated reasoning bias.

Conclusions:

Prior preferences exert a strong influence on causal learning, overriding accuracy incentives.
Motivated reasoning persists even when individuals are incentivized to be accurate.
This research bridges causal learning and motivated reasoning through a novel task.