The Doubly-Bounded Rationality of an Artificial Agent and its Ability to Represent the Bounded Rationality of a Human Decision-Maker in Policy-Relevant Situations
Journal of Experimental & Theoretical Artificial Intelligence
This article introduces two tools aimed at improving our understanding of the relationship between human and artificial rationality and helping us identify agents that are false positives or negatives. The first is a framework that systematically exposes where and how discrepancies between human and artificial rationalities can arise. The second is a test that utilises the insight gained from applying the framework in testing the ability of an artificial agent to represent human decision-making. To demonstrate the usefulness of the test, the article describes its application in testing the ability of a set of Individual Evolutionary Learning agents to represent human decision-making in a social psychology experiment, called the Voluntary Contributions Mechanism. In contrast to the results of a prior test that relied on a behaviour-based method, the results of this test show that the ability of these artificial agents to replicate the behaviour of their human counterparts is not a reliable indicator of their ability to represent their decision-making. The article then uses insight from the test to suggest how to improve the ability of Individual Evolutionary Learning agents to represent human decision-making in the Voluntary Contributions Mechanism.
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Sotnik, Garry (2019). The Doubly-Bounded Rationality of an Artificial Agent and its Ability to Represent the Bounded Rationality of a Human Decision-Maker in Policy-Relevant Situations, Journal of Experimental & Theoretical Artificial Intelligence.