Magnitude-sensitive preference formation`

Abstract

Our understanding of the neural computations that underlie the ability of animals to choose among options has advanced through a synthesis of computational mod-eling, brain imaging and behavioral choice experiments. Yet, there remains a gulf between theories of preference learning and accounts of the real, economic choices that humans face in daily life, choices that are usually between some amount of money and an item. In this paper, we develop a theory of magnitude-sensitive preference learning that permits an agent to rationally infer its prefer-ences for items compared with money options of different magnitudes. We show how this theory yields classical and anomalous supply-demand curves and pre-dicts choices for a large panel of risky lotteries. Accurate replications of such phenomena without recourse to utility functions suggest that the theory proposed is both psychologically realistic and econometrically viable. 1

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 6 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.