Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
BDPD (Be Different Play Differential) is an open-source computational laboratory for the study of common-pool resource dilemmas, governance, Seneca-type collapse dynamics, and the role of heterogeneous generative agents. This deposit archives the source code at tag v1.1.5 as a self-contained snapshot, intended for permanent citation and reproducibility of the five-paper BDPD research series, the ten-lecture mini-course, the research notes, the annotated bibliography, and the fifteen-slide overview pitch — all of which are deposited as siblings on Zenodo and linked back to this software record. Since v1.1.4, this release repairs the governed substrate — a turn that resolves nothing is scored at harvest 0, a declared announcement always reaches the record, and an unattributable fine is fixed — each guarded by a two-sided invariant gate; retires the abandoned GT1 experiment; and adds BDPD⁴ (A Fully Grown Forest of Humbaba), which extends the card game to carry the platform's governance, polycentric and Seneca surfaces together with a reproducible experimental apparatus. Components: a Node.js multi-agent simulation engine (the BDPD Arena) with a polycentric World layer for cross-arena treaties and meta-agent governance, three swappable physics engines (logistic, ladder-perturbation, Bardi/Seneca three-variable ODE); Python heuristic and LLM agents on a uniform message bus; The Forest of Humbaba, a physical card game and its AI-playable digital twin; an experiment runner with sweep, perturbation, and OFAT-robustness pipelines; an annotated documentation site (bdpd.gitlab.io/bdpd); and the rendered PDFs of every BDPD deliverable for offline review. The source repository lives at gitlab.com/bdpd/bdpd. Reproducibility instructions: see docs/experiments/reproduce.md in the archive. Substrate-engine determinism is guaranteed for heuristic players; LLM players require external API keys (deposit ships scripts but not credentials).
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
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MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026