Adaptive Proposer for Ultimatum Game
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00462888" target="_blank" >RIV/67985556:_____/16:00462888 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-44778-0_39" target="_blank" >http://dx.doi.org/10.1007/978-3-319-44778-0_39</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-44778-0_39" target="_blank" >10.1007/978-3-319-44778-0_39</a>
Alternative languages
Result language
angličtina
Original language name
Adaptive Proposer for Ultimatum Game
Original language description
Ultimate Game serves for extensive studies of various aspects of human decision making. The current paper contribute to them by designing proposer optimising its policy using Markov-decision-process (MDP) framework combined with recursive Bayesian learning of responder’s model. Its foreseen use: i) standardises experimental conditions for studying rationality and emotion-influenced decision making of human responders; ii) replaces the classical game-theoretical design of the players’ policies by an adaptive MDP, which is more realistic with respect to the knowledge available to individual players and decreases player’s deliberation effort; iii) reveals the need for approximate learning and dynamic programming inevitable for coping with the curse of dimensionality; iv) demonstrates the influence of the fairness attitude of the proposer on the game course; v) prepares the test case for inspecting exploration-exploitation dichotomy.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA13-13502S" target="_blank" >GA13-13502S: Fully Probabilistic Design of Dynamic Decision Strategies for Imperfect Participants in Market Scenarios</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks and Machine Learning – ICANN 2016
ISBN
978-3-319-44777-3
ISSN
0302-9743
e-ISSN
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Number of pages
8
Pages from-to
330-338
Publisher name
Springer
Place of publication
Cham
Event location
Barcelona
Event date
Sep 6, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000389086300039