Real-world open-ended evolution: A league of legends adventure
Bibliographic record
Abstract
A prominent feature of life on Earth is the evolution of biological complexity: over evolutionary history the biosphere has displayed continual adaptation and innovation, giving rise to an apparent open-ended increase in complexity.The capacity for open-ended evolution has been cited as a hallmark feature of life and also characterizes human and technological systems.Yet, the underlying drivers of open-ended evolution remain poorly understood.League of Legends (League) is an online team-based strategy game that has become immensely popular over the last 6 years.Because new characters (called 'champions') are regularly added and the game is updated every few weeks by the game's developer Riot Games, the game never settles into an equilibrium distribution of player strategies.Innovative strategies are required for players to succeed, just as innovation is required to outcompete other organisms in open-ended biological systems.Although understanding open-endedness is crucial to understanding how living systems operate, it is often difficult or impossible to collect sufficient data to study the mechanisms driving open-ended evolution in natural systems.Online social systems, particularly games, offer ideal laboratories for studying open-ended evolutionary dynamics because of the rich data archived on statistics of users and their interactions.We focus on using data from North America's top 200 players to determine how dominance hierarchies emerge from player strategies and how they evolve in time after an external perturbation.This is a microcosm for studying, in detail, how external and internal mechanisms can drive a real-world open-ended system.Our goal is to provide general insights that can be applied to a wide range of fields, including astrobiology and evolutionary systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".