2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911467571249152 |
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| author | Nyberg, Otto Carcassi, Fausto Cinà, Giovanni |
| author_facet | Nyberg, Otto Carcassi, Fausto Cinà, Giovanni |
| contents | Across a growing number of fields, human decision making is supported by predictions from AI models. However, we still lack a deep understanding of the effects of adoption of these technologies. In this paper, we introduce a general computational framework, the 2-Step Agent, which models the effects of AI-assisted decision making. Our framework uses Bayesian methods for causal inference to model 1) how a prediction on a new observation affects the beliefs of a rational Bayesian agent, and 2) how this change in beliefs affects the downstream decision and subsequent outcome. Using this framework, we show by simulations how a single misaligned prior belief can be sufficient for decision support to result in worse downstream outcomes compared to no decision support. Our results reveal several potential pitfalls of AI-driven decision support and highlight the need for thorough model documentation and proper user training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21889 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | 2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support Nyberg, Otto Carcassi, Fausto Cinà, Giovanni Artificial Intelligence Machine Learning Across a growing number of fields, human decision making is supported by predictions from AI models. However, we still lack a deep understanding of the effects of adoption of these technologies. In this paper, we introduce a general computational framework, the 2-Step Agent, which models the effects of AI-assisted decision making. Our framework uses Bayesian methods for causal inference to model 1) how a prediction on a new observation affects the beliefs of a rational Bayesian agent, and 2) how this change in beliefs affects the downstream decision and subsequent outcome. Using this framework, we show by simulations how a single misaligned prior belief can be sufficient for decision support to result in worse downstream outcomes compared to no decision support. Our results reveal several potential pitfalls of AI-driven decision support and highlight the need for thorough model documentation and proper user training. |
| title | 2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.21889 |