Evidence-backed explainers and research notes. The library will expand by topic, method, and evidence type.
What recursive self-improvement in AI could mean for humanity, and why welfare-economic reasoning offers a practical framework for managing the risk under deep uncertainty.
Read research note →Simple aggregation, prioritarianism, egalitarianism, and rights-based constraints — four different answers to how welfare gains and losses should be weighed against each other.
Read research note →Why artificial systems raise the question of moral status differently than animals do, and why the uncertainty itself is starting to matter for AI governance.
Read research note →Why sentience became the central criterion for animal welfare, and how welfare science tries to measure it empirically.
Read research note →Milton Friedman's shareholder primacy doctrine, Edward Freeman's stakeholder theory, and the institutional responses caught between them.
Read research note →Why income-based measures of welfare fall short, and how the Human Development Index and subjective well-being research try to fill the gap.
Read research note →How health economists operationalize welfare comparisons to allocate scarce medical resources, and the ethical objections the approach faces.
Read research note →Why turning individual welfare into a single social ranking runs into deep formal obstacles, from interpersonal comparability to Arrow's impossibility theorem.
Read research note →The two leading accounts of what welfare actually consists in, and the thought experiments used to test each one.
Read research note →Why Amartya Sen and Martha Nussbaum argued that welfare should be measured in capabilities rather than resources or utility.
Read research note →A careful introduction to the relationship between welfarism and utilitarianism.
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