zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Convergence and asymptotic agreement in distributed decision problems. (English) Zbl 0535.90006
The team decision problem is considered, and in particular the distributed problem in which each agent has an objective cost function and a prior probability distribution. In contrast to other schemes for distributed decision making (or computation) the authors are interested in consensus and in common decisions quite independently of implementation issues, where each agent does not specialize in updating some components of the decision vector assigned to him, but updates the entire decision vector. It is supposed that each agent obtains a different stochastic measurement possibly at different random times, which is related to the same uncertain random vector from the environment. Conditions for asymptotic convergence of each agent’s decision sequence and asymptotic agreement of all agents’ decisions are derived. The appealing and weak features of the suggested model are discussed.
Reviewer: F.V.Burshtein
91B10Group preferences
91A35Decision theory for games
90B50Management decision making, including multiple objectives