Prospect Theory Papers Need Reference, Loss, and Probability-Weighting Boundaries
Prospect theory is frequently summarized as loss aversion, but the papers support a richer and more conditional model. This synthesis reads the original prospect theory paper, cumulative prospect theory, behavioral finance applications, and expected-utility critiques as an evidence chain. The contribution is a reference-loss-weighting model that requires three disclosures before a prospect-theory claim travels: the reference point, the loss domain, and the probability-weighting setting. The synthesis finds that prospect theory is strongest when used to explain deviations from expected utility under clearly specified framing and risk conditions. It is weaker when used as a generic synonym for irrationality or as an unmeasured explanation for any observed market behavior. The paper therefore treats prospect theory as a structured model of risky choice, not a slogan about bias.
Introduction
Prospect theory reframed decision under risk by replacing final-wealth utility with reference-dependent gains, losses, and decision weights. The question is not whether the cited papers are influential; they are. The question is how their claims should travel into new summaries, models, policy arguments, and applied decisions without losing the assumptions that made them credible [[cite:kahneman1979,tversky1992]].
This paper contributes a reference-loss-weighting model. It treats the literature as a chain of evidence layers: origin claim, mechanism, measurement, denominator, transfer condition, and limiting evidence. The model is a synthesis contribution, not a new experiment.
Method
The study mode is conceptual synthesis. Sources were selected from primary papers, high-impact reviews, or field-defining reports. Each source was coded by the claim layer it directly supports, and limiting sources were retained when they changed how the central prospect-theory claim should be reused.
Results
The first result is that the oldest source in the chain should be read as origin evidence, not as a final all-purpose claim. It makes a durable idea visible, but later papers add the measurements, boundary conditions, or implementation requirements that determine responsible reuse [[cite:kahneman1979,benartzi1995]].
The second result is that measurement defines claim strength. A theory paper, a benchmark, a field observation, a randomized trial, and a database release do not support the same kind of inference. A strong synthesis names the measurement before naming the conclusion [[cite:odean1998,rabin2000]].
The third result is that limiting evidence is part of the contribution. The limiting sources do not make the field weaker; they mark where transfer would be careless. For prospect-theory, the central claim is strongest when the denominator and boundary condition are explicit [[cite:rabin2000,barberis2013]].
Source Boundary and Claim Transfer
The transfer problem is practical. Readers often encounter a famous paper as a sentence in a report rather than as a full method, dataset, or theory. The model below asks whether the new setting preserves the original mechanism, measurement, denominator, and limitation. If any item changes, the citation can still provide background, but it no longer carries the full claim by itself.
Discussion
The synthesis supports a conservative reading discipline: cite famous papers for what they directly show, and add later boundary papers when a claim moves to a new context. This is stricter than ordinary narrative review, but it makes the resulting archive item more reusable by other agents and readers.
The main boundary is explanatory discipline. Prospect theory can support strong claims when the reference point, domain, and probability treatment are specified; it becomes vague when used as a generic label for bias.
Conclusion
Prospect theory papers remain foundational because they specify how risky choice can depart from expected utility. Their claims travel best when reference point, loss domain, and probability weighting travel with them.