When Should Neural Data Inform Welfare? A Critical Framework for Policy Uses of Neuroeconomics

TL;DR

This paper develops a framework linking neural data, computational models, and welfare criteria to determine when neural evidence can legitimately inform policy decisions, emphasizing validation and normative clarity.

Contribution

It introduces a formal, model-based framework for assessing the legitimacy of using neural data in welfare policy, including a practical checklist for regulators.

Findings

Neural signals constrain welfare judgments only with validated neural-computational mappings.

The framework emphasizes the importance of identifying true interests versus mistakes.

A Neuroeconomic Welfare Inference Checklist is derived for policy and AI system design.

Abstract

Neuroeconomics promises to ground welfare analysis in neural and computational evidence about how people value outcomes, learn from experience and exercise self-control. At the same time, policy and commercial actors increasingly invoke neural data to justify paternalistic regulation, "brain-based" interventions and new welfare measures. This paper asks under what conditions neural data can legitimately inform welfare judgements for policy rather than merely describing behaviour. I develop a non-empirical, model-based framework that links three levels: neural signals, computational decision models and normative welfare criteria. Within an actor-critic reinforcement-learning model, I formalise the inference path from neural activity to latent values and prediction errors and then to welfare claims. I show that neural evidence constrains welfare judgements only when the neural-computational mapping is well validated, the decision model identifies "true" interests versus context-dependent mistakes, and the welfare criterion is explicitly specified and defended. Applying the framework to addiction, neuromarketing and environmental policy, I derive a Neuroeconomic Welfare Inference Checklist for regulators and for designers of NeuroAI systems. The analysis treats brains and artificial agents as value-learning systems while showing that internal reward signals, whether biological or artificial, are computational quantities and cannot be treated as welfare measures without an explicit normative model.