AUTHOR=Undheim Trond Arne , Ahmad Taimur TITLE=Quantitative scenarios for cascading risks in AI, climate, synthetic bio, and financial markets by 2075 JOURNAL=Frontiers in Complex Systems VOLUME=2 YEAR=2024 URL=https://www.frontiersin.org/journals/complex-systems/articles/10.3389/fcpxs.2024.1323321 DOI=10.3389/fcpxs.2024.1323321 ISSN=2813-6187 ABSTRACT=

Humanity faces a myriad of existential technology, geopolitical, and ecological risks. The paper analyzes the possibility that negative shocks superimpose and multiply their effects, leading to catastrophic macro-dynamics. Methodologically, this paper presents a rare, quantitative scenario model superimposed upon narrative scenarios where the cascading economic effects of 19 quantitative indicators of growth or decline are projected into 2075. These indicators map onto five narrative scenarios, and are subsequently re-combined to study effects of plausible cascading risk events coming to pass in the 50 years period between 2025 and 2075. Results indicate that even in the case of deeply catastrophic singular events, the world would eventually recover within 25 years, as has historically been the case. The exception is that in the event of several catastrophic events in short order around the midpoint of the 50-year scenario timeline, the cascading risk escalation would create formidable negative cascades. The possibility of a protracted depression and no visible recovery within 25 years is the result. However, if we assume a modest interaction effect, even with just 3-5 co-occurring catastrophes, the result becomes a path towards humanity’s extinction based on economic decline alone. The implications are that humanity either needs to avoid significant cascading effects at all costs or needs to identify novel ways to recover compared to previous depressions. Given the amount of model assumptions they rely upon, these projections carry a degree of uncertainty. Further study should therefore be conducted with a larger set of indicators and impacts, including mortality modeling, to assess how much worse plausible real-world outcomes might be compared to the simplified economic model deployed here.