UCL Research Papers
Author
Neuroeconomics appears to offer welfare economics an evidential supplement, since neural data may help distinguish choices that express genuine preferences from choices distorted by addiction, framing or self-control failure. This paper asks when such evidence can legitimately inform welfare judgements for policy. It develops a conceptual framework linking three levels of analysis, namely neural signals, computational decision models and normative welfare criteria. The central argument is that neural data can constrain welfare analysis only under four conditions, covering the validity of the neural measure, the identifiability of the decision model, the explicit statement of the welfare criterion, and the institutional context of any policy use. Applying this framework to addiction, neuromarketing and environmental policy, and working through the United Kingdom’s Tobacco and Vapes Act 2026, a major intervention justified without any neural measurement, the paper develops a Neuroeconomic Welfare Inference Checklist for neural evidence. Neural data can improve behavioural welfare economics by clarifying the mechanisms behind choice, and they cannot replace normative argument about what counts as welfare. The same reasoning applies to artificial agents trained on human feedback, whose internal reward signals are computational quantities and not welfare measures.
Keywords: Neuroeconomics, Behavioural Welfare Economics, Reinforcement Learning, NeuroAI, Human-Inspired AI, Addiction, Policy Evaluation
How to Cite: Zhu, L. Y. (2026) “Neural Evidence and Behavioural Welfare Economics: When Can Neuroeconomics Inform Policy?”, UCL Journal of Economics. 5(1). doi: https://doi.org/10.14324/111.444.2755-0877.2257
None