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R. Emilio Muniz-Langle

Publications and source records attributed to R. Emilio Muniz-Langle.

2 recordsLinked to original sources

Catastrophic Attention Preferences

This paper provides an axiomatic foundation for catastrophic thinking, a form of pessimism in which an agent evaluates uncertain alternatives by attending only to a subset of adverse outcomes. We introduce Catastrophic Attention Preferences (CAP), under which an act is evaluated by its subjective expected utility conditional on the worst outcomes, up to a subjectively determined probability threshold $q$. The resulting functional is a subjective counterpart of Expected Shortfall: both the agent's belief $μ$ and her threshold $q$ are derived from preferences rather than assumed, without a probability distribution given as a primitive. Our main result is a complete behavioral characterization: six axioms, one of which, Catastrophic Complementarity, carries the behavioral content of catastrophic thinking, together with two standard richness conditions, are equivalent to the existence of a CAP representation, and the parameters $(μ, q)$ are unique. The parameters are fully identified from probability equivalents of events, simple binary bets that can be elicited experimentally. We characterize comparative ambiguity aversion within the class: with common beliefs, ambiguity aversion is completely ordered by $q$; with different beliefs, we provide a necessary and sufficient condition on the two belief-threshold pairs. The model admits an equivalent multiple priors representation with a closed-form set of priors, nests subjective expected utility at $q = 1$, and converges to maxmin expected utility as $q \rightarrow 0$.

econ.TH↗

Belief Identification in Populations

We study the identification of belief distributions in a population of Bayesian agents from anonymous aggregate belief data. While a single Bayesian agent's full belief can be recovered from beliefs over a suitable collection of binary events, this principle need not extend to populations: event-by-event distributions of beliefs may fail to identify the underlying distribution of priors. We study when this failure is generic and when it is exceptional. Identification is governed by the graph-theoretic structure induced by the observed family of events on the state space. Among $n$-agent distributions, identification is generic if the induced graph is nonseparable, while non-identification is generic if the graph is separable. The results establish both limits and design principles for recovering belief heterogeneity from aggregate belief data.

econ.TH↗