Asset Value Game and Its Extension: Taking Past Actions into Consideration - Agents and Artificial Intelligence - page 313

Asset Value Game and Its Extension: Taking Past
Actions into Consideration

Jun Kiniwa 1 , Takeshi Koide 2 , and Hiroaki Sandoh 3

1 Department of Applied Economics, University of Hyogo, Kobe, Japan
2 Department of Intelligence and Informatics, Konan University, Kobe, Japan
3 Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan
[e-mail], [e-mail],
[e-mail]

Abstract. In 1997, a minority game (MG) was proposed as a non-cooperative
iterated game with an odd population of agents who make bids whether to buy
or sell. Since then, many variants of the MG have been proposed. However, the
common disadvantage in their characteristics is to ignore the past actions beyond
a constant memory. So it is difficult to simulate actual payoffs of agents if the past
price behavior has a significant influence on the current decision. In this paper we
present a new variant of the MG, called an asset value game (AG), and its exten-
sion, called an extended asset value game (ExAG). In the AG, since every agent
aims to decrease the mean acquisition cost of his asset, he automatically takes the
past actions into consideration. The AG, however, is too simple to reproduce the
complete market dynamics, that is, there may be some time lag between the price
and his action. So we further consider the ExAG by using probabilistic actions,
and compare them by simulation.

Keywords: Multiagent, Minority game, Mean asset value, Asset value game,
Contrarian, Trend-follower.

1 Introduction

Background. A minority game (MG) has been extensively studied since it was origi-
nally proposed [7]. It is considered as a model for financial markets or other applications
in physics. It is a non-cooperative iterated game with an odd population size N of agents
who make bids whether to buy or sell. Since each agent aims to choose the group of mi-
nority population, he is called a contrarian . Every agent makes a decision at each step
based on the prediction of a strategy according to the sequence of the m most recent
outcomes of winners, where m is said to be the memory size of the agents. Though MG
is a very simple model, it captures some of the complex macroscopic behavior of the
markets.
It is also known that the MG cannot capture large price drifts such as bubble/crash
phenomena, but just can do the stationary state of the markets. This can be intuitively
explained as follows. Suppose that a group of buyers can keep a majority for a long time.
Then a group of sellers must continuously win in the bubble phenomenon. However,
since every agent wants to win and thus joins the group of sellers one after another,