Economic Decision-Making Under Deep Uncertainty About AI's Trajectory
Mentors: Pavel Kocourek, Wim Howson Creutzberg
Project area: Economics Theory / Game Theory
Project Language
Minimum Time Commitment
12 hours per week.
Project Abstract
The future of AI could unfold in very different ways. In one scenario, AI automates most cognitive work and the economy grows explosively. In another, AI brings steady but modest productivity gains, much like earlier waves of IT adoption. These futures have radically different implications for how much people should save, what they should invest in, and which skills will retain their value. Yet the question of how to make such decisions when you genuinely do not know which future is coming has received almost no formal attention.
On the modeling side, Trammell & Korinek (2023) lay out a useful taxonomy of transformative AI (TAI) growth scenarios, and other important contributions — Aghion, Jones & Jones (2018), Acemoglu (2024), Benzell & Ye (2024) — work out the economic consequences of specific AI futures. On the empirical side, Andrews & Farboodi (2025) study what financial markets currently believe about TAI. What is missing is the normative question: given genuine uncertainty over which scenario will materialize, how should a forward-looking decision-maker allocate resources? That is the gap this project aims to fill.
Mentees will build a tractable model in which an investor faces uncertainty over whether AI leads to moderate or explosive growth, and chooses how much to save and how to split wealth across assets — broad equity, AI-intensive capital, human-capital-linked claims, and a safe asset — whose payoffs depend on which future arrives. The project will study how optimal choices shift with the perceived likelihood of explosive TAI, risk aversion, and ambiguity aversion (discomfort with poorly defined probabilities). The approach combines analytical work on a stylized model with numerical illustrations. The intended outputs are a research blog post and a technical working paper, with potential for coauthorship on a subsequent publication.
Theory of Change
Bad frameworks produce bad decisions. The question of machine moral status will increasingly affect AI development and governance. Currently, most people reasoning about it lack adequate conceptual tools. This matters for catastrophic risk in several ways.
Under-reaction: if AI systems develop welfare-relevant internal states and we lack frameworks to recognize this, we may create systems with misaligned interests while dismissing their signals as "mere computation." A system that experiences something like suffering under certain conditions, and whose operators dismiss this, is a system with reason to deceive.
Over-reaction: anthropomorphizing systems that lack morally relevant properties wastes attention and resources, and may constrain beneficial AI development without corresponding benefit.
Poor discourse: without shared conceptual foundations, public debate about AI consciousness polarizes between dismissive and credulous positions. Neither serves good governance.
The primer addresses these by training researchers and practitioners to reason carefully across multiple frameworks, recognize what each assumes, and navigate uncertainty without false confidence. The German focus (incorporating European philosophical traditions, piloting with German-speaking users) builds SAIGE's national infrastructure while contributing to the broader field.
Conceptual clarity is infrastructure. This project builds it.
Desired Mentee Background
Maths, Economics, Finance.
Desired Mentee Level of Education
Masters and above.
Other Mentee Requirements
Familiarity with intertemporal optimization (e.g., consumption-saving problems, Euler equations) and basic probability theory is required. Exposure to decision theory under uncertainty (expected utility, risk aversion) is strongly preferred. Interest in or familiarity with the macroeconomics of AI or transformative technology is a plus but not strictly necessary. No programming is required, though the ability to implement numerical comparative statics in Python or MATLAB would be a significant bonus.