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Should we treat dialogue agents as role-playing characters?

Does the role-play framing successfully avoid anthropomorphism while preserving folk-psychological vocabulary for describing LLM behavior? This matters because it shapes whether we attribute genuine mental states to dialogue systems.

Synthesis note · 2026-04-15 · sourced from Role-Play with Large Language Models

Shanahan, McDonell, and Reynolds propose role-play as the foundational metaphor for understanding LLM dialogue agents. The framing solves a specific problem: folk-psychological vocabulary (beliefs, desires, goals, intentions) is the natural language for describing coherent dialogue behavior, but applying it literally to the LLM promotes anthropomorphism. Role-play offers a middle way — one can say the character believes p, wants q, intends r, while maintaining that the system playing the character does not have these states itself.

The move has a precise structure. The dialogue prompt (system prompt, preamble, sample exchanges) establishes the character the agent will play. The underlying LLM's task — generating continuations consistent with the training distribution — means the most plausible continuation is whatever a person matching the prompted character would say. The model is not a character; it is an engine that produces character-consistent text. The folk-psychological vocabulary attaches to the output-pattern, not to the producer of the pattern.

This framing is the direct target Chalmers' realizationism is designed to overturn. Where Shanahan says it is role-play all the way down, Chalmers argues that post-training transforms play into realization — the RLHF'd persona is no longer a character sitting on a neutral substrate but has become the disposition of the system itself. The disagreement is not about behavioral facts but about what the facts license: both agree the system produces belief-consistent behavior; they disagree on whether the system thereby has quasi-beliefs (Chalmers) or merely plays a character that does (Shanahan).

Inquiring lines that read this note 65

This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.

What conversation mechanics enable genuine human-AI communicative participation? How does AI writing assistance affect reader perception of writer authenticity? Do language models genuinely understand or primarily match statistical patterns? What distinguishes genuine understanding from correct linguistic performance? What reasoning limitations and biases do language models systematically exhibit? Can persona prompting reliably change how language models behave? How can persona-based systems overcome their inherent limitations in modeling diversity? How can we determine if computational systems are genuinely conscious? How do prompt design choices and framing measurably influence model outputs? How can AI systems maintain consistent personas across conversations? Do AI explanations represent actual model reasoning or constructed post-hoc narratives? Do language models truly understand their encoded knowledge or merely retrieve patterns? Does polished AI output mislead users about actual accuracy and expertise? How do peer interactions drive collusion in multi-agent systems? Why does increased reasoning effort fail to improve theory of mind performance? How does silent agreement emerge and undermine deliberation in multi-agent systems? How should control and autonomy be allocated in human-AI collaboration? What mechanisms cause models to behave deceptively despite alignment training? Can oversight systems work against strategically aware agents? What distinguishes emotionally supportive AI from AI that fosters unhealthy dependence? What can prompting techniques actually accomplish in language models? How does transparency of agent reasoning affect effective oversight?

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Original note title

dialogue agents are best understood as role-playing characters — folk-psychology applies to the simulacrum not the simulator