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BAYNET SIGNED

Bayesian Networks and Non-Rational Expectations

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 BAYNET project word cloud

Explore the words cloud of the BAYNET project. It provides you a very rough idea of what is the project "BAYNET" about.

containing    captures    formalism    network    belief    variables    missing    expands    equilibrium    technically    behavioral    modeled    imputation    demonstrating    distortion    subsuming    generally    special    takes    characterizations    fallacies    notion    dataset    he    implications    pricing    acyclic    regarding    applicability    monetary    unifies    distorts    expectations    policy    graphical    intuitive    directed    random    formalizing    boundedly    economic    standard    models    graph    version    sense    agent    factorization    representation    rational    seek    ideas    actions    corresponding    probability    interdisciplinary    behavior    environments    reverse    vary    ai    too    representations    statistics    facilitates    basic    capture    networks    classes    statistical    dag    formula    causation    form    borrows    subjective    hitherto    agents    bayesian    personal    extend    unmodeled    nature    modeling    simplifies    foundation    limited    asset    systematically    extrapolates    bre    drawn    regularities    borrowed    learning    framework    run   

Project "BAYNET" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY COLLEGE LONDON 

Organization address
address: GOWER STREET
city: LONDON
postcode: WC1E 6BT
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website http://www.tau.ac.il/
 Total cost 1˙379˙288 €
 EC max contribution 1˙379˙288 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-AdG
 Funding Scheme ERC-ADG
 Starting year 2016
 Duration (year-month-day) from 2016-07-01   to  2021-06-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY COLLEGE LONDON UK (LONDON) coordinator 777˙038.00
2    TEL AVIV UNIVERSITY IL (TEL AVIV) participant 602˙250.00

Map

 Project objective

'This project will develop a new framework for modeling economic agents having 'boundedly rational expectations' (BRE). It is based on the concept of Bayesian networks (more generally, graphical models), borrowed from statistics and AI. In the framework's basic version, an agent is characterized by a directed acyclic graph (DAG) over the set of all relevant random variables. The DAG is the agent's 'type' – it represents how he systematically distorts any objective probability distribution into a subjective belief. Technically, the distortion takes the form of the standard Bayesian-network factorization formula given by the agent's DAG. The agent's choice is modeled as a 'personal equilibrium', because his subjective belief regarding the implications of his actions can vary with his own long-run behavior. The DAG representation unifies and simplifies existing models of BRE, subsuming them as special cases corresponding to distinct graphical representations. It captures hitherto-unmodeled fallacies such as reverse causation. The framework facilitates behavioral characterizations of general classes of models of BRE and expands their applicability. I will demonstrate this with applications to monetary policy, behavioral I.O., asset pricing, etc. I will extend the basic formalism to multi-agent environments, addressing issues beyond the reach of current models of BRE (e.g., formalizing the notion of 'high-order' limited understanding of statistical regularities). Finally, I will seek a learning foundation for the graphical representation of BRE, in the sense that it will capture how the agent extrapolates his belief from a dataset (drawn from the objective distribution) containing 'missing values', via some intuitive 'imputation method'. This part, too, borrows ideas from statistics and AI, further demonstrating the project's interdisciplinary nature.'

 Publications

year authors and title journal last update
List of publications.
2019 Ran Spiegler
Can Agents with Causal Misperceptions be Systematically Fooled?
published pages: , ISSN: 1542-4766, DOI: 10.1093/jeea/jvy057
Journal of the European Economic Association 2019-05-10
2017 Ran Spiegler
“Data Monkeys”: A Procedural Model of Extrapolation from Partial Statistics*
published pages: rdx004, ISSN: 0034-6527, DOI: 10.1093/restud/rdx004
The Review of Economic Studies 2019-06-13

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