Real-World Open-Ended Evolution: A League of Legends Adventure

Real-World Open-Ended Evolution: A League of Legends Adventure

Alyssa M. Adams Sara I. Walker 

Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, USA

Algorithmic Nature Group, LABORES, Paris, France

School of Earth and Space Exploration, Arizona State University, Tempe, USA

ASU-SFI Center for Biosocial Complex Systems, Arizona State University, Tempe, USA

Blue Marble Space Institute of Science, Seattle, USA

1 February 2018
| Citation



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.


Complexity, Open-ended evolution, social systems, theoretical biology, video games


[1] Chalmers, D.W, et al. High-content words in patent records reflect key innovations in the evolution of technology. Alife XII Proceedings, pp. 838–845, 2010.

[2] Buchanan, A., Packard, N.H. & Bedau, M.A., Measuring the evolution of the drivers of technological innovation in the patent record. Artificial Life, 17(2), pp. 109–122, 2011.

[3] DeDeo, S. Collective phenomena and non-finite state computation in a human social system. PLoS ONE, 9(6), e101511, 2013.

[4] Oka, M. & Ikegami, T. Exploring default mode and information flow on the web. PLoS ONE, 8(4), e60398, 2013.

[5] Oka, M., Hashimoto, Y. & Ikegami, T. Open-ended evolution in a web system. Late Breaking Papers at Artificial Life, 2015.

[6] Bringing big data to the enterprise, What is big data? IBM, available at:, accessed March 2017.

[7] Calude, C.S. & Longo, G., The deluge of spurious correlations in big data. Foundations of Science, pp. 1–18, 2016.

[8] Player Numbers. Riot Games, available at:, accessed February 2016.

[9] Riot Manifesto. Riot Games, available at:, accessed March 2017.

[10] Banzhaf, W. et al. Defining and simulating open-ended novelty: requirements, guidelines, and challenges. Theory in Biosciences, 135(3), pp. 131–161, 2016.

[11] Ruiz-Mirazo, K., Peretó, J. & Moreno, A., A universal definition of life: autonomy and open-ended evolution. Origins of Life and Evolution of the Biosphere, 34(3), pp. 323–346, 2004.

[12] Adams, A.M., Zenil, H., Davies, P.C.W. & Walker, S.I. Formal definitions of unbounded evolution and innovation reveal universal mechanisms for open-ended evolution in dynamical systems. Scientific Reports, 7(1), 2016.

[13] Stats straight from the source. Riot Games, available at: (accessed March 2017).

[14] Cassiopeia. Merkai-Analytics, available at: (accessed March 2017).

[15] Datreant: Persistent, pythonic trees for heterogeneous data. ReadtheDocs, available at: (accessed March 2017).

[16] Allen, J. A. & Clarke, B. C., Frequency-dependent selection- homage to Poulton. Biological Journal of the Linnean Society, 23, pp. 15–18, 1984.

[17] Austin, D., How google finds your needle in the Web’s Haystack. AMS Feature Column, available at:, accessed March 2017.

[18] Szalay, K.Z. & Csermely, P., Perturbation centrality and turbine: a novel centrality measure obtained using a versatile network dynamics tool. PLoS ONE, 8(10), e78059, 2013.