Researcher and software engineer interested in applying economics to AI safety
The core focus of my research is using agent-based modelling to understand real-world complex-adaptive systems which are composed of interacting autonomous agents. The key research question that I am interested in is how, and if, these systems maintain macroscopic homeostatic behaviour despite the fact that their constituent agents often face an incentive to disrupt the rest of the system for their own gain. This question pervades the biological and social sciences, as well as many areas of engineering and computing. Accordingly, I work with a diverse range of collaborators in different disciplines. I am particularly interested in whether models of learning and cooperation can be validated against empirical studies, and I have had the opportunity to apply many different modelling techniques to a diverse range of data.
https://sphelps.net/
https://sphelps.substack.com/
https://github.com/phelps-sg
Further to my original comment, this idea has also been discussed in non-human animals in the context of biological markets (Noe & Hammerstein 1995). In nature, many forms of cooperation can be described in terms of trade, e.g. primate allo-grooming effort can be used as a medium of exchange to obtain not just reciprocal grooming but also can be traded for other goods and services (Barrett et al. 1999).
In artificial markets, counter-party risk can be mitigated through institutions which enforce contracts, but in biological markets this is not possible. Incremental increasing of “bids” has been proposed as one explanation of how large-scale cooperation can be bootstrapped in nature (c.f. Phelps & Russell 2015, Section 4 for a review).
Barrett, L., Henzi, S. P., Weingrill, T., Lycett, J. E., & Hill, R. A. (1999). Market forces predict grooming reciprocity in female baboons. Proceedings of the Royal Society B: Biological Sciences, 266(1420), 665–665. https://doi.org/10.1098/rspb.1999.0687
Noë, R., & Hammerstein, P. (1995). Biological markets. Trends in Ecology and Evolution, 10(8), 336–339. http://www.ingentaconnect.com/content/els/01695347/1995/00000010/00000008/art89123
Phelps, S., & Russell, Y. I. (2015). Economic drivers of biological complexity. Adaptive Behavior, 23(5), 315–326. https://sphelps.net/papers/ecodrivers-20150601-ab-final.pdf