Extent and Modulation of LLM Behavioral Consistency with Human Decision-Making

Determine the extent to which large language models exhibit behavior consistent with human decision-making, and ascertain whether their behavior can be modulated through targeted interventions.

Background

The paper examines whether LLMs can replicate core properties of human decision-making, particularly the stochastic variability and adaptive behavior observed in dynamic tasks. Although LLMs often match or exceed human performance on standard reasoning benchmarks, their ability to reproduce human-like noise and process-level dynamics is uncertain.

To address this, the authors propose a process-oriented evaluation framework with progressive interventions (Intrinsicality, Instruction, Imitation) and validate it on two classic economic tasks (second-price auction and newsvendor problem). The open question frames the central goal: assessing behavioral fidelity and the potential to modulate LLM behavior through targeted interventions.