Cultural Evolution Society Conference 2021

Many features of Human cultural evolution, such as technology (Kolodny et al., 2015), language (Carr et al., 2016), and scientific knowledge (Lehman, 1947) seem to exhibit unbounded evolutionary dynamics; growing in complexity, introducing new innovations, and having no obvious bounds. Evolutionary systems exhibiting unbounded evolutionary dynamics are commonly referred to as being open-ended (Bedau et al., 1998). The creation and analysis of systems that exhibit open-ended evolutionary dynamics is an open problem in the field of Artificial Life (Bedau et al., 2000). Motivated by the observations that nature exhibits unbounded evolution, with the ongoing generation of adaptive novelty and complexity, Artificial Life researchers want to create open-ended evolutionary systems in artificial media (e.g. computer simulations, robots, ... ). The goal of achieving open-endedness in artificial evolutionary systems has led to the formalisation of measurements for unbounded evolutionary dynamics - these measurements take the form of evolutionary activity statistics, and are often called the “ALife Test” for unbounded evolutionary dynamics (Bedau et al., 1998). This test has been applied to the fossil record (Bedau et al., 1998), the patent record (Bedau et al., 2019) and artificial evolutionary systems (Channon, 2003). The activity statistics enable researchers to take any evolving system over time and assess changes to diversity, alongside measuring the introduction and adaptive persistence of new components in the system. Despite having numerous datasets, models, and evaluation methods, the field of cultural evolution is yet to effectively determine whether any given component of a culturally evolving system is bounded or unbounded. Determining what species exhibit open-ended cultural evolution, and in which behavioural domains, should be a key focus of cultural evolution going forward, and the ALife test provides us with a consistent approach for doing this.

References:

Bedau MA, Gigliotti N, Janssen T, Kosik A, Nambiar A and Packard N. (2019). Open-Ended Technological Innovation. Artificial Life, 25(1), 33–49. https://doi.org/10.1162/artl_a_00279

Bedau MA, McCaskill JS, Packard NH, Rasmussen S, Adami C, Green DG, Ikegami T, Kaneko K, and Ray TS. (2000). Open Problems in Artificial Life. Artificial Life, 6(4), 363–376. https://doi.org/10.1162/106454600300103683

Bedau MA, Snyder E and Packard NH. (1998). A Classification of Long-Term Evolutionary Dynamics. In C. Adami, R. K. Belew, H. Kitano, & C. E. Taylor (Eds.), Artificial Life VI: Proceedings of the Sixth International Conference on Artificial Life (pp. 228-237). MIT Press.

Carr JW, Smith K, Cornish H and Kirby S. (2016). The Cultural Evolution of Structured Languages in an Open‐Ended, Continuous World. Cognitive Science, 41(4), 892-923. https://doi.org/10.1111/cogs.12371

Channon AD. (2003). Improving and still passing the ALife test: Component-normalised activity statistics classify evolution in Geb as unbounded. In R. Standish, M. A. Bedau, & H. A. Abbass (Eds.), Artificial Life VIII: proceedings of the Eighth International Conference on Artificial Life (pp. 173 -181). MIT Press. http://alife8.alife.org/proceedings/sub2118.pdf

Kolodny O, Creanza N and Feldman MW. (2015). Evolution in leaps: The punctuated accumulation and loss of cultural innovations. Proceedings of the National Academy of Sciences, 112(49), E6762-E6769. https://doi.org/10.1073/pnas.1520492112

Lehman HC. (1947). The exponential increase of man's cultural output. Social Forces, 25(3), 281-290. https://doi.org/10.2307/3005665

Taylor T, Bedau M, Channon A, Ackley D, Banzhaf W, Belson G, Dolson E, Froese T, Hickinbotham S, Ikegami T, McMullin B, Packard N, Rasmussen S, Virgo N, Agmon E, Clark E, McGregor S, Ofria C, Ropella G, … Wiser M. (2016). Open-Ended Evolution: Perspectives from the OEE Workshop in York. Artificial Life, 22(3), 408–423. https://doi.org/10.1162/ARTL_a_00210