TITLE:
A Network Analysis of Skill Game Dynamics
AUTHORS:
Rohit Raturi, Kumar Attangudi Perichiappan Perichappan
KEYWORDS:
Network Analysis, Game Dynamics, Mathematical Framework, Simulations
JOURNAL NAME:
Journal of Computer and Communications,
Vol.6 No.4,
April
28,
2018
ABSTRACT: Casino games can be classified in two main categories, i.e. skill games and
gambling. Notably, the former refers to games whose outcome is affected
by the strategies of players, the latter to those games whose outcome is
completely random. For instance, lotteries are easily recognized as pure
gambling, while some variants of Poker (e.g. Texas Hold’em) are usually
considered as skill games. In both cases, the theory of probability constitutes
the mathematical framework for studying their dynamics, despite their classification.
Here, it is worth to consider that when games entail the competition
between many players, the structure of interactions can acquire a relevant
role. For instance, some games as Bingo are not characterized by this
kind of interactions, while other games as Poker, show a network structure,
i.e. players interact each other and have the opportunity to share or exchange
information. In this paper, we analyze the dynamics of a population
composed of two species, i.e. strong and weak agents. The former represents
expert players, while the latter beginners, i.e. non-expert ones. Here,
pair-wise interactions are based on a very simple game, whose outcome is
affected by the nature of the involved agents. In doing so, expert agents have
a higher probability to succeed when playing with weak agents, while the
success probability is equal when two agents of the same kind face each other.
Numerical simulations are performed considering a population arranged
in different topologies like regular graphs and in scale-free networks. This
choice allows to model dynamics that we might observe on online game
platforms. Further aspects as the adaptability of agents are taken into account,
e.g. the possibility to improve (i.e. to becomean expert). Results show
that complex topologies represent a strong opportunity for experts and a risk
for both kinds of agents.