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In-Depth Information
Fig. 1.
Illustration of our idea
desirable one gains the highest score. So, there is a time lag between the rapid change
of a price and the adjustment of an agent's behavior.
To improve the time lag, we allow each agent another action that satisfies the payoff
function with some probability. If the price rises/falls rapidly and the difference between
the price and agent
i
's mean asset value exceeds some threshold, the agent
i
may take
the action according to the payoff function (regardless of the strategy). By tuning up the
threshold, etc., we can reproduce the real market dynamics. We call the variant of AG
an
extended asset value game
, denoted by ExAG.
Contributions.
Our contributions in this paper are summarized as follows:
-
We present a new variant of the MG, called an asset value game.
-
To improve the problem of AG, we further consider an extended AG.
-
We investigate the behavior of AG and ExAG in detail.
The rest of this paper is organized as follows. Section 2 states our model, which contains
MG, MJ, AG and ExAG. Section 3 presents an analysis of AG. Section 4 describes a
simulation model and shows some experimental results. Finally, Section 5 concludes
the paper.
2Mod s
In this section, we first describe MG and MJ in Section 2.1, then the difference between
MG and AG in Section 2.2. Finally, we describe the difference between AG and ExAG
in Section 2.3.
2.1
Previous Model — MG and MJ
At the beginning of the game, each agent
i
∈{
1
,...,N
}
is randomly given
s
strategies
R
i,a
for
a
. The number of agents,
N
, is assumed to be odd in order to
break a tie. Any strategy
R
i,a
(
μ
)
∈{
1
,...,s
}
∈
R
i,a
maps an
m
-length binary string
μ
into a
decision
−
1
or
1
,thatis,
R
i,a
:
m
{−
1
,
1
}
−→ {−
1
,
1
}
,
(1)
