# The Challenge and Reward of Fair Play in Narrative: A Computational Approach

arXiv: https://arxiv.org/abs/2507.13841 · HTML: https://mlsanity.com/paper/2507.13841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 81 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [The Complexity of Constrained Min-Max Optimization](https://mlsanity.com/paper/2009.09623/) (2020)
- [Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities](https://mlsanity.com/paper/2210.12187/) (2022)
- [Detecting Mode Collapse in Language Models via Narration](https://mlsanity.com/paper/2402.04477/) (2024)
- [Finding Flawed Fictions: Evaluating Complex Reasoning in Language Models via Plot Hole Detection](https://mlsanity.com/paper/2504.11900/) (2025)
- [Creating Suspenseful Stories: Iterative Planning with Large Language Models](https://mlsanity.com/paper/2402.17119/) (2024)
- [True Detective: A Deep Abductive Reasoning Benchmark Undoable for GPT-3 and Challenging for GPT-4](https://mlsanity.com/paper/2212.10114/) (2022)
- [Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation](https://mlsanity.com/paper/2004.14905/) (2020)
- [CoheSentia: A Novel Benchmark of Incremental versus Holistic Assessment of Coherence in Generated Texts](https://mlsanity.com/paper/2310.16329/) (2023)
- [Whodunnit? Crime Drama as a Case for Natural Language Understanding](https://mlsanity.com/paper/1710.11601/) (2017)
- [Characterizing the Investigative Methods of Fictional Detectives with Large Language Models](https://mlsanity.com/paper/2505.07601/) (2025)
- [Can LLMs Generate Good Stories? Insights and Challenges from a Narrative Planning Perspective](https://mlsanity.com/paper/2506.10161/) (2025)

## Abstract (arXiv)

Good storytelling involves surprise -- unpredictability in how the story unfolds -- and sense-making, the requirement that the story forms a coherent sequence. However, to date, these two qualities have largely been addressed in isolation. We formalize these qualities and their relationship in an information-theoretic framework, using detective fiction as a paradigm case of narratives in which a hidden truth is discovered through reasoning. Our central theoretical result shows that surprise and coherence must trade off for any *single* reader model, but can coexist when two reader modes are distinguished: a pre-revelation mode that forms expectations while the ending is unknown, and a post-resolution hindsight mode that re-evaluates the story after the culprit is revealed. The balance of these two dimensions is realized in the common requirement of *fair play*, giving the reader a chance to solve the mystery while maintaining a challenge. We operationalize the framework using large language models as simulated readers, and define reference-less evaluation metrics for surprise, coherence, and fair play. Experiments on LLM-generated stories validate our theoretical predictions: while models generally succeed in creating surprise or coherence, achieving fair play poses a challenge even for strong models. Moreover, surprise and coherence do not positively correlate across stories, resisting reduction to a single latent quality. A human study validates the metrics, confirming they capture aspects of narrative quality that matter to readers. Our metrics also reproduce established literary intuitions, finding Christie's stories more surprising and more fair-playing than Conan Doyle's.