ForecastBench-Sim: A Simulated-World Forecasting Benchmark
Researchers introduce ForecastBench-Sim, a simulated-world forecasting benchmark built on Freeciv, a turn-based strategy game modeled on Civilization. It overcomes real-world benchmark limitations by providing quickly resolvable tasks, rare event examples, and counterfactual scenarios through game rollouts.
[2606.18686] ForecastBench-Sim: A Simulated-World Forecasting Benchmark
[Submitted on 17 Jun 2026]
Title:ForecastBench-Sim: A Simulated-World Forecasting Benchmark
View a PDF of the paper titled ForecastBench-Sim: A Simulated-World Forecasting Benchmark, by Jaeho Lee and Nick Merrill and Ezra Karger
View PDF HTML (experimental)
Abstract:Forecasting benchmarks for general-purpose AI systems usually inherit the constraints of the real world: outcomes resolve slowly, tail events are rare, and counterfactual questions are difficult to score. We introduce ForecastBench-Sim, a simulated-world forecasting benchmark built on game rollouts from Freeciv, a turn-based strategy game modelled on the Civilization series. Forecasters receive a fixed world report (a structured snapshot of the current game state) and answer questions about hidden future states; the benchmark then continues the simulation and scores forecasts. Because the world is simulated, the same setup can generate continuous or binary forecasting questions at arbitrary time horizons, paired intervention worlds for conditional or causal questions, and resolved examples of rare or disruptive outcomes. We describe the benchmark pipeline, question families, scoring protocol, and release artifacts, and report validation slices from model evaluations and an anonymized human pilot. ForecastBench-Sim is intended to complement real-world forecasting benchmarks by providing controlled, immediately resolvable tasks for studying probabilistic reasoning under dynamic world states.
Comments: 15 pages, 5 main figures, 6 appendix figures. Spotlight presentation at Forecasting as a New Frontier of Intelligence / Workshop on AI Forecasting, ICML 2026
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2606.18686 [cs.AI]
(or arXiv:2606.18686v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.18686
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jaeho Lee [view email] [v1] Wed, 17 Jun 2026 04:52:41 UTC (1,986 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled ForecastBench-Sim: A Simulated-World Forecasting Benchmark, by Jaeho Lee and Nick Merrill and Ezra Karger
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.AI
new | recent | 2026-06
Change to browse by:
cs cs.CL cs.LG
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)