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The Law of Large Demand for Information
Author(s) -
Moscarini Giuseppe,
Smith Lones
Publication year - 2002
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.1111/j.1468-0262.2002.00442.x
Subject(s) - constant (computer programming) , bayesian probability , value of information , asymptotically optimal algorithm , falling (accident) , quality (philosophy) , value (mathematics) , signal (programming language) , economics , mathematics , mathematical economics , econometrics , mathematical optimization , computer science , statistics , physics , medicine , environmental health , quantum mechanics , programming language
An unresolved problem in Bayesian decision theory is how to value and price information. This paper resolves both problems assuming inexpensive information. Building on Large Deviation Theory, we produce a generically complete asymptotic order on samples of i.i.d. signals in finite–state, finite–action models. Computing the marginal value of an additional signal, we find it is eventually exponentially falling in quantity, and higher for lower quality signals. We provide a precise formula for the information demand, valid at low prices: asymptotically a constant times the log price, and falling in the signal quality for a given price.