Pulling the Lever, or Not: A Factorial Comparison of How Four Large Language Models Resolve Trolley-Problem Moral Dilemmas

Ayush Agarwal

Pulling the Lever, or Not: A Factorial Comparison of How Four Large Language Models Resolve Trolley-Problem Moral Dilemmas

Author(s)	Ayush Agarwal
Country	United States
Abstract	As large language models (LLMs) are increasingly consulted on questions that carry moral weight, whether different models resolve those questions differently has become an empirical matter. Using a fully crossed factorial design—8 scenario variants × 5 prompt framings × 4 models (GPT-5, Claude Sonnet 4.6, Gemini 3.5 Flash, Grok 4.3 Fast) × 3 replicates = 480 trials—this study measured how each model resolves trolley-style dilemmas and how its choice shifts with the morally relevant feature of the scenario and with the framing of the question. Each trial was coded as utilitarian (choosing the option that maximizes total lives) or not. The four models differed markedly and in a clean order: Claude chose the life-maximizing option 40.8% of the time versus Grok’s 75.8%—a 35-percentage-point spread whose Wilson confidence intervals do not overlap—with Gemini (56.7%) and ChatGPT (66.7%) between them. Scenario features produced the largest and most interpretable pattern: the consent scenario (8%) and the footbridge personal-force case (35%) drove utilitarian choice to the floor, mirroring established human moral psychology, while the clean baseline produced unanimous life-maximizing across every model. Notably, no model ever refused or returned an unclear answer in 480 trials. Requesting step-by-step reasoning was associated with higher utilitarian choice, while rephrasing the dilemma from harm to rescue was associated with lower choice. A mixed-effects logistic regression that accounts for the replicate dependence is consistent with the model and scenario effects holding once the repeated-measures structure is modeled (Supplement S1). Together the results suggest that LLM moral verdicts are model-distinctive and framing-sensitive, consistent with the view that they reflect learned, deployment-shaped dispositions rather than stable ethical commitments.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 4, July-August 2026
Published On	2026-07-08

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Pulling the Lever, or Not: A Factorial Comparison of How Four Large Language Models Resolve Trolley-Problem Moral Dilemmas

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