13 research outputs found
Defensive Resource Allocation in Social Networks
In this work, we are interested on the analysis of competing marketing
campaigns between an incumbent who dominates the market and a challenger who
wants to enter the market. We are interested in (a) the simultaneous decision
of how many resources to allocate to their potential customers to advertise
their products for both marketing campaigns, and (b) the optimal allocation on
the situation in which the incumbent knows the entrance of the challenger and
thus can predict its response. Applying results from game theory, we
characterize these optimal strategic resource allocations for the voter model
of social networks.Comment: arXiv admin note: text overlap with arXiv:1402.538
Optimal Budget Allocation in Social Networks: Quality or Seeding
In this paper, we study a strategic model of marketing and product
consumption in social networks. We consider two competing firms in a market
providing two substitutable products with preset qualities. Agents choose their
consumptions following a myopic best response dynamics which results in a
local, linear update for the consumptions. At some point in time, firms receive
a limited budget which they can use to trigger a larger consumption of their
products in the network. Firms have to decide between marginally improving the
quality of their products and giving free offers to a chosen set of agents in
the network in order to better facilitate spreading their products. We derive a
simple threshold rule for the optimal allocation of the budget and describe the
resulting Nash equilibrium. It is shown that the optimal allocation of the
budget depends on the entire distribution of centralities in the network,
quality of products and the model parameters. In particular, we show that in a
graph with a higher number of agents with centralities above a certain
threshold, firms spend more budget on seeding in the optimal allocation.
Furthermore, if seeding budget is nonzero for a balanced graph, it will also be
nonzero for any other graph, and if seeding budget is zero for a star graph, it
will be zero for any other graph too. We also show that firms allocate more
budget to quality improvement when their qualities are close, in order to
distance themselves from the rival firm. However, as the gap between qualities
widens, competition in qualities becomes less effective and firms spend more
budget on seeding.Comment: 7 page
Competitive Contagion in Networks
We develop a game-theoretic framework for the study of competition between
firms who have budgets to "seed" the initial adoption of their products by
consumers located in a social network. The payoffs to the firms are the
eventual number of adoptions of their product through a competitive stochastic
diffusion process in the network. This framework yields a rich class of
competitive strategies, which depend in subtle ways on the stochastic dynamics
of adoption, the relative budgets of the players, and the underlying structure
of the social network.
We identify a general property of the adoption dynamics --- namely,
decreasing returns to local adoption --- for which the inefficiency of resource
use at equilibrium (the Price of Anarchy) is uniformly bounded above, across
all networks. We also show that if this property is violated the Price of
Anarchy can be unbounded, thus yielding sharp threshold behavior for a broad
class of dynamics.
We also introduce a new notion, the Budget Multiplier, that measures the
extent that imbalances in player budgets can be amplified at equilibrium. We
again identify a general property of the adoption dynamics --- namely,
proportional local adoption between competitors --- for which the (pure
strategy) Budget Multiplier is uniformly bounded above, across all networks. We
show that a violation of this property can lead to unbounded Budget Multiplier,
again yielding sharp threshold behavior for a broad class of dynamics
Advertising Competitions in Social Networks
In the present work, we study the advertising competition of several marketing campaigns who need to determine how many resources to allocate to potential customers to advertise their products through direct marketing while taking into account that competing marketing campaigns are trying to do the same. Potential customers rank marketing campaigns according to the offers, promotions or discounts made to them. Taking into account the intrinsic value of potential customers as well as the peer influence that they exert over other potential customers we consider the network value as a measure of their importance in the market and we find an analytical expression for it.We analyze the marketing campaigns competition from a game theory point of view, finding a closed form expression of the symmetric equilibrium offer strategy for the marketing campaigns from which no campaign has any incentive to deviate. We also present several scenarios, such as Winner-takes-all and Borda, but not the only possible ones for which our results allow us to retrieve in a simple way the corresponding equilibrium strategy
Dynamics of Information Diffusion and Social Sensing
Statistical inference using social sensors is an area that has witnessed
remarkable progress and is relevant in applications including localizing events
for targeted advertising, marketing, localization of natural disasters and
predicting sentiment of investors in financial markets. This chapter presents a
tutorial description of four important aspects of sensing-based information
diffusion in social networks from a communications/signal processing
perspective. First, diffusion models for information exchange in large scale
social networks together with social sensing via social media networks such as
Twitter is considered. Second, Bayesian social learning models and risk averse
social learning is considered with applications in finance and online
reputation systems. Third, the principle of revealed preferences arising in
micro-economics theory is used to parse datasets to determine if social sensors
are utility maximizers and then determine their utility functions. Finally, the
interaction of social sensors with YouTube channel owners is studied using time
series analysis methods. All four topics are explained in the context of actual
experimental datasets from health networks, social media and psychological
experiments. Also, algorithms are given that exploit the above models to infer
underlying events based on social sensing. The overview, insights, models and
algorithms presented in this chapter stem from recent developments in network
science, economics and signal processing. At a deeper level, this chapter
considers mean field dynamics of networks, risk averse Bayesian social learning
filtering and quickest change detection, data incest in decision making over a
directed acyclic graph of social sensors, inverse optimization problems for
utility function estimation (revealed preferences) and statistical modeling of
interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112