121,274 research outputs found
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
It is widely believed that one's peers influence product adoption behaviors.
This relationship has been linked to the number of signals a decision-maker
receives in a social network. But it is unclear if these same principles hold
when the pattern by which it receives these signals vary and when peer
influence is directed towards choices which are not optimal. To investigate
that, we manipulate social signal exposure in an online controlled experiment
using a game with human participants. Each participant in the game makes a
decision among choices with differing utilities. We observe the following: (1)
even in the presence of monetary risks and previously acquired knowledge of the
choices, decision-makers tend to deviate from the obvious optimal decision when
their peers make similar decision which we call the influence decision, (2)
when the quantity of social signals vary over time, the forwarding probability
of the influence decision and therefore being responsive to social influence
does not necessarily correlate proportionally to the absolute quantity of
signals. To better understand how these rules of peer influence could be used
in modeling applications of real world diffusion and in networked environments,
we use our behavioral findings to simulate spreading dynamics in real world
case studies. We specifically try to see how cumulative influence plays out in
the presence of user uncertainty and measure its outcome on rumor diffusion,
which we model as an example of sub-optimal choice diffusion. Together, our
simulation results indicate that sequential peer effects from the influence
decision overcomes individual uncertainty to guide faster rumor diffusion over
time. However, when the rate of diffusion is slow in the beginning, user
uncertainty can have a substantial role compared to peer influence in deciding
the adoption trajectory of a piece of questionable information
The effect of competition among brokers on the quality and price of differentiated internet services
Price war, as an important factor in undercutting competitors and attracting customers, has spurred considerable work that analyzes such conflict situation. However, in most of these studies, quality of service (QoS), as an important decision-making criterion, has been neglected. Furthermore, with the rise of service-oriented architectures, where players may offer different levels of QoS for different prices, more studies are needed to examine the interaction among players within the service hierarchy. In this paper, we present a new approach to modeling price competition in (virtualized) service-oriented architectures, where there are multiple service levels. In our model, brokers, as the intermediaries between end-users and service providers, offer different QoS by adapting the service that they obtain from lower-level providers so as to match the demands of their clients to the services of providers. To maximize profit, players, i.e. providers and brokers, at each level compete in a Bertrand game while they offer different QoS. To maintain an oligopoly market, we then describe underlying dynamics which lead to a Bertrand game with price constraints at the providers' level. Numerical simulations demonstrate the behavior of brokers and providers and the effect of price competition on their market shares.This work has been partly supported by National Science Foundation awards: CNS-0963974, CNS-1346688, CNS-1536090 and CNS-1647084
Random Access Game in Fading Channels with Capture: Equilibria and Braess-like Paradoxes
The Nash equilibrium point of the transmission probabilities in a slotted
ALOHA system with selfish nodes is analyzed. The system consists of a finite
number of heterogeneous nodes, each trying to minimize its average transmission
probability (or power investment) selfishly while meeting its average
throughput demand over the shared wireless channel to a common base station
(BS). We use a game-theoretic approach to analyze the network under two
reception models: one is called power capture, the other is called signal to
interference plus noise ratio (SINR) capture. It is shown that, in some
situations, Braess-like paradoxes may occur. That is, the performance of the
system may become worse instead of better when channel state information (CSI)
is available at the selfish nodes. In particular, for homogeneous nodes, we
analytically present that Braess-like paradoxes occur in the power capture
model, and in the SINR capture model with the capture ratio larger than one and
the noise to signal ratio sufficiently small.Comment: 30 pages, 5 figure
Time and volume based optimal pricing strategies for telecommunication networks
In the recent past, there have been several initiatives by major network providers such as Turk Telekom lead the industry towards network capacity distribution in Turkey. In this study, we use a monopoly pricing model to examine the optimal pricing strategies for “pay-per-volume” and “pay-per-time” based leasing of data networks. Traditionally, network capacity distribution includes short/long term bandwidth and/or usage time leasing. Each consumer has a choice to select volume based pricing or connection time based pricing. When customers choose connection time based pricing, their optimal behavior would be utilizing the bandwidth capacity fully therefore it can cause network to burst. Also, offering pay-per-volume scheme to the consumer provides the advantage of leasing the excess capacity for other potential customers for network provider.
We examine the following issues in this study:
(i) What are the extra benefits to the network provider for providing the volume based pricing scheme? and
(ii) Does the amount of demand (number of customers enter the market) change?
The contribution of this paper is to show that pay-per-volume is a viable alternative for a large number of customers, and that judicious pricing for pay-per-volume is profitable for the network provider
Suboptimal solutions to network team optimization problems
Smoothness of the solutions to network team optimization problems with statistical information structure is investigated. Suboptimal solutions expressed as linear combinations of elements from sets of basis functions containing adjustable parameters are considered. Estimates of their accuracy are derived, for basis functions represented by sinusoids with variable frequencies and phases and
Gaussians with variable centers and widthss
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