Opendata, web and dolomites
  1. home
  2. trasparenza
  3. open h2020
  4. baynet

BAYNET SIGNED

Bayesian Networks and Non-Rational Expectations

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0
 Outcomes and results

 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.

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

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

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "BAYNET" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "BAYNET" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.1.)

CHIPTRANSFORM (2018)

On-chip optical communication with transformation optics

Read More  

QUAMAP (2019)

Quasiconformal Methods in Analysis and Applications

Read More  

OAlipotherapy (2018)

Long-retention liposomic drug-delivery for intra-articular osteoarthritis therapy

Read More