Backward vs. Forward-oriented Decision Making in the Iterated Prisoners
Dilemma: A Comparison between Two Connectionist Models
E. Lalev, and M. Grinberg. Anticipatory Behavior in Adaptive Learning Systems: From Brains
to Individual and Social Behavior, Springer-Verlag, (2007)
Abstract
We compare the performance of two connectionist models developed
to account for some specific aspects of the decision making process
in the Iterated
Prisoners Dilemma Game. Both models are based on common recurrent
network architecture. The first of them uses a backward-oriented reinforcement
learning algorithm for learning to play the game while the second
one makes its
move decisions based on generated predictions about future games,
moves and
payoffs. Both models involve prediction of the opponent move and of
the expected
payoff and have an in-built autoassociator in their architecture aimed
at
more efficient payoff matrix representation. The results of the simulations
show
that the model with explicit anticipation about game outcomes could
reproduce
the experimentally observed dependence of the cooperation rate on
the so-called
cooperation index thus showing the importance of anticipation in modeling
the
actual decision making process in human participants. The role of
the models
building blocks and mechanisms is investigated and discussed. Comparisons
with experiments with human participants are presented.
%0 Book Section
%1 Lalev:2007ab
%A Lalev, Emilian
%A Grinberg, Maurice
%B Anticipatory Behavior in Adaptive Learning Systems: From Brains
to Individual and Social Behavior
%D 2007
%E Butz, Martin V.
%E Sigaud, Olivier
%E Pezzulo, Giovanni
%E Baldassarre, Gianluca
%I Springer-Verlag
%K anticipation, artificial cooperation, decision-making, learning network, neural recurrent reinforcement
%P 345-364
%T Backward vs. Forward-oriented Decision Making in the Iterated Prisoners
Dilemma: A Comparison between Two Connectionist Models
%X We compare the performance of two connectionist models developed
to account for some specific aspects of the decision making process
in the Iterated
Prisoners Dilemma Game. Both models are based on common recurrent
network architecture. The first of them uses a backward-oriented reinforcement
learning algorithm for learning to play the game while the second
one makes its
move decisions based on generated predictions about future games,
moves and
payoffs. Both models involve prediction of the opponent move and of
the expected
payoff and have an in-built autoassociator in their architecture aimed
at
more efficient payoff matrix representation. The results of the simulations
show
that the model with explicit anticipation about game outcomes could
reproduce
the experimentally observed dependence of the cooperation rate on
the so-called
cooperation index thus showing the importance of anticipation in modeling
the
actual decision making process in human participants. The role of
the models
building blocks and mechanisms is investigated and discussed. Comparisons
with experiments with human participants are presented.
@incollection{Lalev:2007ab,
abstract = {We compare the performance of two connectionist models developed
to account for some specific aspects of the decision making process
in the Iterated
Prisoners Dilemma Game. Both models are based on common recurrent
network architecture. The first of them uses a backward-oriented reinforcement
learning algorithm for learning to play the game while the second
one makes its
move decisions based on generated predictions about future games,
moves and
payoffs. Both models involve prediction of the opponent move and of
the expected
payoff and have an in-built autoassociator in their architecture aimed
at
more efficient payoff matrix representation. The results of the simulations
show
that the model with explicit anticipation about game outcomes could
reproduce
the experimentally observed dependence of the cooperation rate on
the so-called
cooperation index thus showing the importance of anticipation in modeling
the
actual decision making process in human participants. The role of
the models
building blocks and mechanisms is investigated and discussed. Comparisons
with experiments with human participants are presented.},
added-at = {2009-06-26T15:25:19.000+0200},
author = {Lalev, Emilian and Grinberg, Maurice},
biburl = {https://www.bibsonomy.org/bibtex/2ea26989867632dd7b178e72c8c2f1b6f/butz},
booktitle = {Anticipatory Behavior in Adaptive Learning Systems: {F}rom Brains
to Individual and Social Behavior},
description = {diverse cognitive systems bib},
editor = {Butz, Martin V. and Sigaud, Olivier and Pezzulo, Giovanni and Baldassarre, Gianluca},
interhash = {09d504281b74df961109352ee6b8e36d},
intrahash = {ea26989867632dd7b178e72c8c2f1b6f},
keywords = {anticipation, artificial cooperation, decision-making, learning network, neural recurrent reinforcement},
owner = {butz},
pages = {345-364},
publisher = {Springer-{V}erlag},
timestamp = {2009-06-26T15:25:44.000+0200},
title = {Backward vs. Forward-oriented Decision Making in the Iterated Prisoners
Dilemma: A Comparison between Two Connectionist Models},
year = 2007
}