15,891 research outputs found
Selling a Single Item with Negative Externalities
We consider the problem of regulating products with negative externalities to
a third party that is neither the buyer nor the seller, but where both the
buyer and seller can take steps to mitigate the externality. The motivating
example to have in mind is the sale of Internet-of-Things (IoT) devices, many
of which have historically been compromised for DDoS attacks that disrupted
Internet-wide services such as Twitter. Neither the buyer (i.e., consumers) nor
seller (i.e., IoT manufacturers) was known to suffer from the attack, but both
have the power to expend effort to secure their devices. We consider a
regulator who regulates payments (via fines if the device is compromised, or
market prices directly), or the product directly via mandatory security
requirements.
Both regulations come at a cost---implementing security requirements
increases production costs, and the existence of fines decreases consumers'
values---thereby reducing the seller's profits. The focus of this paper is to
understand the \emph{efficiency} of various regulatory policies. That is,
policy A is more efficient than policy B if A more successfully minimizes
negatives externalities, while both A and B reduce seller's profits equally.
We develop a simple model to capture the impact of regulatory policies on a
buyer's behavior. {In this model, we show that for \textit{homogeneous}
markets---where the buyer's ability to follow security practices is always high
or always low---the optimal (externality-minimizing for a given profit
constraint) regulatory policy need regulate \emph{only} payments \emph{or}
production.} In arbitrary markets, by contrast, we show that while the optimal
policy may require regulating both aspects, there is always an approximately
optimal policy which regulates just one
Sequential item pricing for unlimited supply
We investigate the extent to which price updates can increase the revenue of
a seller with little prior information on demand. We study prior-free revenue
maximization for a seller with unlimited supply of n item types facing m myopic
buyers present for k < log n days. For the static (k = 1) case, Balcan et al.
[2] show that one random item price (the same on each item) yields revenue
within a \Theta(log m + log n) factor of optimum and this factor is tight. We
define the hereditary maximizers property of buyer valuations (satisfied by any
multi-unit or gross substitutes valuation) that is sufficient for a significant
improvement of the approximation factor in the dynamic (k > 1) setting. Our
main result is a non-increasing, randomized, schedule of k equal item prices
with expected revenue within a O((log m + log n) / k) factor of optimum for
private valuations with hereditary maximizers. This factor is almost tight: we
show that any pricing scheme over k days has a revenue approximation factor of
at least (log m + log n) / (3k). We obtain analogous matching lower and upper
bounds of \Theta((log n) / k) if all valuations have the same maximum. We
expect our upper bound technique to be of broader interest; for example, it can
significantly improve the result of Akhlaghpour et al. [1]. We also initiate
the study of revenue maximization given allocative externalities (i.e.
influences) between buyers with combinatorial valuations. We provide a rather
general model of positive influence of others' ownership of items on a buyer's
valuation. For affine, submodular externalities and valuations with hereditary
maximizers we present an influence-and-exploit (Hartline et al. [13]) marketing
strategy based on our algorithm for private valuations. This strategy preserves
our approximation factor, despite an affine increase (due to externalities) in
the optimum revenue.Comment: 18 pages, 1 figur
The Economics of Privacy
This chapter reviews economic analyses of privacy. We begin by scrutinizing the âfree marketâ critique of privacy regulation. Welfare may be non-monotone in the quantity of information, hence there may be excessive incentive to collect information. This result applies to both non-productive and productive information. Over-investment is exacerbated to the extent that personal information is exploited across markets. Further, the âfree marketâ critique does not apply to overt and covert collection of information that directly causes harm. We then review research on property rights and challenges in determining their optimal allocation. We conclude with insights from recent empirical research and directions for future research.
Pricing in Social Networks with Negative Externalities
We study the problems of pricing an indivisible product to consumers who are
embedded in a given social network. The goal is to maximize the revenue of the
seller. We assume impatient consumers who buy the product as soon as the seller
posts a price not greater than their values of the product. The product's value
for a consumer is determined by two factors: a fixed consumer-specified
intrinsic value and a variable externality that is exerted from the consumer's
neighbors in a linear way. We study the scenario of negative externalities,
which captures many interesting situations, but is much less understood in
comparison with its positive externality counterpart. We assume complete
information about the network, consumers' intrinsic values, and the negative
externalities. The maximum revenue is in general achieved by iterative pricing,
which offers impatient consumers a sequence of prices over time.
We prove that it is NP-hard to find an optimal iterative pricing, even for
unweighted tree networks with uniform intrinsic values. Complementary to the
hardness result, we design a 2-approximation algorithm for finding iterative
pricing in general weighted networks with (possibly) nonuniform intrinsic
values. We show that, as an approximation to optimal iterative pricing, single
pricing can work rather well for many interesting cases, but theoretically it
can behave arbitrarily bad
Approaching Utopia: Strong Truthfulness and Externality-Resistant Mechanisms
We introduce and study strongly truthful mechanisms and their applications.
We use strongly truthful mechanisms as a tool for implementation in undominated
strategies for several problems,including the design of externality resistant
auctions and a variant of multi-dimensional scheduling
- âŠ