arXiv subjects

Marko Sysi-Aho

Publications and source records attributed to Marko Sysi-Aho.

6 recordsLinked to original sources

arXiv2005

Spatial snowdrift game with myopic agents

We have studied a spatially extended snowdrift game, in which the players are located on the sites of two-dimensional square lattices and repeatedly have to choose one of the two strategies, either cooperation (C) or defection (D). A player interacts with its nearest neighbors only, and aims at playing a strategy which maximizes its instant pay-off, assuming that the neighboring agents retain their strategies. If a player is not content with its current strategy, it will change it to the opposite one with probability $p$ next round. Here we show through simulations and analytical approach that these rules result in cooperation levels, which differ to large extent from those obtained using the replicator dynamics.

physics.bio-ph↗

arXiv2004

Invisible Hand Effect in an Evolutionary Minority Game Model

In this paper we study the properties of a Minority Game with evolution realized by using genetic crossover to modify fixed-length decision-making strategies of agents. Although the agents in this evolutionary game act selfishly by trying to maximize their own performances only, it turns out that the whole society will eventually be rewarded optimally. This "Invisible Hand" effect is what Adam Smith over two centuries ago expected to take place in the context of free market mechanism. However, this behaviour of the society of agents is realized only under idealized conditions, where all agents are utilizing the same efficient evolutionary mechanism. If on the other hand part of the agents are adaptive, but not evolutionary, the system does not reach optimum performance, which is also the case if part of the evolutionary agents form a uniformly acting "cartel".

cond-mat.other↗

arXiv2003

Searching good strategies in adaptive minority games

In this paper we introduce adaptation mechanism based on genetic algorithms in minority games. If agents find their performances too low, they modify their strategies in hope to improve their performances and become more successful. One aim of this study is to find out what happens at the system as well as at the individual agent level. We observe that adaptation remarkably tightens the competition among the agents, and tries to pull the collective system into a state where the aggregate utility is the largest. We first make a brief comparative study of the different adaptation mechanisms and then present in more detail parametric studies. These different adaptation mechanisms broaden the scope of the applications of minority games to the study of complex systems.

cond-mat.stat-mech↗

arXiv2003

Biology helps you to win a game

We present a game of interacting agents which mimics the complex dynamics found in many natural and social systems. These agents modify their strategies periodically, depending on their performances using genetic crossover mechanisms, inspired by biology. We study the performances of the agents under different conditions, and how they adapt themselves. In addition the dynamics of the game is investigated.

cond-mat.stat-mech↗

arXiv2002

Adaptation using hybridized genetic crossover strategies

We present a simple game which mimics the complex dynamics found in most natural and social systems. Intelligent players modify their strategies periodically, depending on their performances. We propose that the agents use hybridized one-point genetic crossover mechanism,inspired by genetic evolution in biology, to modify the strategies and replace the bad strategies. We study the performances of the agents under different conditions and investigate how they adapt themselves in order to survive or be the best, by finding new strategies using the highly effective mechanism we proposed.

cond-mat.stat-mech↗

arXiv2002

Intelligent Minority Game with genetic-crossover strategies

We develop a game theoretical model of $N$ heterogeneous interacting agents called the intelligent minority game. The ``intelligent'' agents play the basic minority game and depending on their performances, generate new strategies using the one-point genetic crossover mechanism. The performances change dramatically and the game moves rapidly to an efficient state (fluctuations in the number of agents performing a particular action, characterized by $σ^2$, reaches a low value). There is no ``phase transition'' when we vary $σ^2/N$ with $2^M/N$, where $M$ is the ``memory''of an agent.

cond-mat.stat-mech↗