104 research outputs found
On Financial Markets Trading
Starting from the observation of the real trading activity, we propose a
model of a stockmarket simulating all the typical phases taking place in a
stock exchange. We show that there is no need of several classes of agents once
one has introduced realistic constraints in order to confine money, time, gain
and loss within an appropriate range. The main ingredients are local and global
coupling, randomness, Zipf distribution of resources and price formation when
inserting an order. The simulation starts with the initial public offer and
comprises the broadcasting of news/advertisements and the building of the book,
where all the selling and buying orders are stored. The model is able to
reproduce fat tails and clustered volatility, the two most significant
characteristics of a real stockmarket, being driven by very intuitive
parameters.Comment: 18 pages, submitte
A game theoretic approach to a peer-to-peer cloud storage model
Classical cloud storage based on external data providers has been recognized
to suffer from a number of drawbacks. This is due to its inherent centralized
architecture which makes it vulnerable to external attacks, malware, technical
failures, as well to the large premium charged for business purposes. In this
paper, we propose an alternative distributed peer-to-peer cloud storage model
which is based on the observation that the users themselves often have
available storage capabilities to be offered in principle to other users. Our
set-up is that of a network of users connected through a graph, each of them
being at the same time a source of data to be stored externally and a possible
storage resource. We cast the peer-to-peer storage model to a Potential Game
and we propose an original decentralized algorithm which makes units interact,
cooperate, and store a complete back up of their data on their connected
neighbors. We present theoretical results on the algorithm as well a good
number of simulations which validate our approach.Comment: 10 page
Finite-time influence systems and the Wisdom of Crowd effect
Recent contributions have studied how an influence system may affect the
wisdom of crowd phenomenon. In the so-called naive learning setting, a crowd of
individuals holds opinions that are statistically independent estimates of an
unknown parameter; the crowd is wise when the average opinion converges to the
true parameter in the limit of infinitely many individuals. Unfortunately, even
starting from wise initial opinions, a crowd subject to certain influence
systems may lose its wisdom. It is of great interest to characterize when an
influence system preserves the crowd wisdom effect. In this paper we introduce
and characterize numerous wisdom preservation properties of the basic
French-DeGroot influence system model. Instead of requiring complete
convergence to consensus as in the previous naive learning model by Golub and
Jackson, we study finite-time executions of the French-DeGroot influence
process and establish in this novel context the notion of prominent families
(as a group of individuals with outsize influence). Surprisingly, finite-time
wisdom preservation of the influence system is strictly distinct from its
infinite-time version. We provide a comprehensive treatment of various
finite-time wisdom preservation notions, counterexamples to meaningful
conjectures, and a complete characterization of equal-neighbor influence
systems
De Gustibus Disputandum
We propose a simple method to predict individuals' expectations about
products using a knowledge network. As a complementary result, we show that the
method is able, under certain conditions, to extract hidden information at
neural level from a customers' choices database
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