arXiv:2606.13201v1 Announce Type: new Abstract: Human decision-making often involves choosing between multi-attribute alternatives, yet classical models assume fully compensatory utility aggregation despite evidence that people reject options with poor performance on critical attributes. We propose a bounded trade-off reasoning framework in which decisions are governed by a screening process that evaluates the balance between gains and losses across attributes. The model introduces a trade-off tolerance parameter that controls acceptable imbalance and can vary across contexts. Through simulati
The continuous evolution of AI and cognitive science research is leading to more sophisticated models of human decision-making, which are crucial for developing more effective AI agents.
Understanding human decision-making, especially compensatory utility and screening processes, is fundamental for designing AI that interacts more intuitively and effectively with human users and complex environments.
This research introduces a 'bounded trade-off reasoning framework' which allows for the development of AI systems that can incorporate context-dependent 'trade-off tolerance' in decision-making, moving beyond simplistic utility aggregation.
- · AI developers
- · Cognitive science researchers
- · AI systems in decision support
- · AI models relying solely on classical utility theory
Improved AI models for complex, multi-attribute decision tasks.
More robust and human-aligned AI agents capable of nuanced negotiations and resource allocation.
Enhanced automation in fields requiring subjective judgment and flexible trade-offs, reducing friction in human-AI collaboration.
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Read at arXiv cs.AI