43 research outputs found
Incentive-aware Contextual Pricing with Non-parametric Market Noise
We consider a dynamic pricing problem for repeated contextual second-price
auctions with strategic buyers whose goals are to maximize their long-term time
discounted utility. The seller has very limited information about buyers'
overall demand curves, which depends on -dimensional context vectors
characterizing auctioned items, and a non-parametric market noise distribution
that captures buyers' idiosyncratic tastes. The noise distribution and the
relationship between the context vectors and buyers' demand curves are both
unknown to the seller. We focus on designing the seller's learning policy to
set contextual reserve prices where the seller's goal is to minimize his regret
for revenue. We first propose a pricing policy when buyers are truthful and
show that it achieves a -period regret bound of
against a clairvoyant policy that has full
information of the buyers' demand. Next, under the setting where buyers bid
strategically to maximize their long-term discounted utility, we develop a
variant of our first policy that is robust to strategic (corrupted) bids. This
policy incorporates randomized "isolation" periods, during which a buyer is
randomly chosen to solely participate in the auction. We show that this design
allows the seller to control the number of periods in which buyers
significantly corrupt their bids. Because of this nice property, our robust
policy enjoys a -period regret of , matching
that under the truthful setting up to a constant factor that depends on the
utility discount factor
Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions
Motivated by pricing in ad exchange markets, we consider the problem of
robust learning of reserve prices against strategic buyers in repeated
contextual second-price auctions. Buyers' valuations for an item depend on the
context that describes the item. However, the seller is not aware of the
relationship between the context and buyers' valuations, i.e., buyers'
preferences. The seller's goal is to design a learning policy to set reserve
prices via observing the past sales data, and her objective is to minimize her
regret for revenue, where the regret is computed against a clairvoyant policy
that knows buyers' heterogeneous preferences. Given the seller's goal,
utility-maximizing buyers have the incentive to bid untruthfully in order to
manipulate the seller's learning policy. We propose learning policies that are
robust to such strategic behavior. These policies use the outcomes of the
auctions, rather than the submitted bids, to estimate the preferences while
controlling the long-term effect of the outcome of each auction on the future
reserve prices. When the market noise distribution is known to the seller, we
propose a policy called Contextual Robust Pricing (CORP) that achieves a
T-period regret of , where is the dimension of {the}
contextual information. When the market noise distribution is unknown to the
seller, we propose two policies whose regrets are sublinear in .Comment: Accepted for publication in Operations Research Journal (An earlier
version of this paper accepted to NeurIPS 2019.
Contextual Dynamic Pricing with Strategic Buyers
Personalized pricing, which involves tailoring prices based on individual
characteristics, is commonly used by firms to implement a consumer-specific
pricing policy. In this process, buyers can also strategically manipulate their
feature data to obtain a lower price, incurring certain manipulation costs.
Such strategic behavior can hinder firms from maximizing their profits. In this
paper, we study the contextual dynamic pricing problem with strategic buyers.
The seller does not observe the buyer's true feature, but a manipulated feature
according to buyers' strategic behavior. In addition, the seller does not
observe the buyers' valuation of the product, but only a binary response
indicating whether a sale happens or not. Recognizing these challenges, we
propose a strategic dynamic pricing policy that incorporates the buyers'
strategic behavior into the online learning to maximize the seller's cumulative
revenue. We first prove that existing non-strategic pricing policies that
neglect the buyers' strategic behavior result in a linear regret
with the total time horizon, indicating that these policies are not better
than a random pricing policy. We then establish that our proposed policy
achieves a sublinear regret upper bound of . Importantly, our
policy is not a mere amalgamation of existing dynamic pricing policies and
strategic behavior handling algorithms. Our policy can also accommodate the
scenario when the marginal cost of manipulation is unknown in advance. To
account for it, we simultaneously estimate the valuation parameter and the cost
parameter in the online pricing policy, which is shown to also achieve an
regret bound. Extensive experiments support our theoretical
developments and demonstrate the superior performance of our policy compared to
other pricing policies that are unaware of the strategic behaviors
On the Interplay between Social Welfare and Tractability of Equilibria
Computational tractability and social welfare (aka. efficiency) of equilibria
are two fundamental but in general orthogonal considerations in algorithmic
game theory. Nevertheless, we show that when (approximate) full efficiency can
be guaranteed via a smoothness argument \`a la Roughgarden, Nash equilibria are
approachable under a family of no-regret learning algorithms, thereby enabling
fast and decentralized computation. We leverage this connection to obtain new
convergence results in large games -- wherein the number of players
-- under the well-documented property of full efficiency via smoothness in the
limit. Surprisingly, our framework unifies equilibrium computation in disparate
classes of problems including games with vanishing strategic sensitivity and
two-player zero-sum games, illuminating en route an immediate but overlooked
equivalence between smoothness and a well-studied condition in the optimization
literature known as the Minty property. Finally, we establish that a family of
no-regret dynamics attains a welfare bound that improves over the smoothness
framework while at the same time guaranteeing convergence to the set of coarse
correlated equilibria. We show this by employing the clairvoyant mirror descent
algortihm recently introduced by Piliouras et al.Comment: To appear at NeurIPS 202
A Combinatorial-Bandit Algorithm for the Online Joint Bid/Budget Optimization of Pay-per-Click Advertising Campaigns
Pay-per-click advertising includes various formats (e.g., search, contextual, and social) with a total investment of more than 140 billion USD per year. An advertising campaign is composed of some subcampaigns-each with a different ad-and a cumulative daily budget. The allocation of the ads is ruled exploiting auction mechanisms. In this paper, we propose, for the first time to the best of our knowledge, an algorithm for the online joint bid/budget optimization of pay-per-click multi-channel advertising campaigns. We formulate the optimization problem as a combinatorial bandit problem, in which we use Gaussian Processes to estimate stochastic functions, Bayesian bandit techniques to address the exploration/exploitation problem, and a dynamic programming technique to solve a variation of the Multiple-Choice Knapsack problem. We experimentally evaluate our algorithm both in simulation-using a synthetic setting generated from real data from Yahoo!-and in a real-world application over an advertising period of two months
Exploring the Tradeoff between Competitive Ratio and Variance in Online-Matching Markets
In this paper, we propose an online-matching-based model to study the
assignment problems arising in a wide range of online-matching markets,
including online recommendations, ride-hailing platforms, and crowdsourcing
markets. It features that each assignment can request a random set of resources
and yield a random utility, and the two (cost and utility) can be arbitrarily
correlated with each other. We present two linear-programming-based
parameterized policies to study the tradeoff between the \emph{competitive
ratio} (CR) on the total utilities and the \emph{variance} on the total number
of matches (unweighted version). The first one (SAMP) is to sample an edge
according to the distribution extracted from the clairvoyant optimal, while the
second (ATT) features a time-adaptive attenuation framework that leads to an
improvement over the state-of-the-art competitive-ratio result. We also
consider the problem under a large-budget assumption and show that SAMP
achieves asymptotically optimal performance in terms of competitive ratio.Comment: This paper was accepted to the 18th Conference on Web and Internet
Economics (WINE), 202
Peak reduction in decentralised electricity systems : markets and prices for flexible planning
In contemporary societies, industrial processes as well as domestic activities rely to a large degree on a well-functioning electricity system. This reliance exists both structurally (the system should always be available) and economically (the prices for electricity affect the costs of operating a business and the costs of living). After many decades of stability in engineering principles and related economic paradigms, new developments require us to reconsider how electricity is distributed and paid for.Twowell-known examples of important technological developments in this regard are decentralised renewable energy generation (e.g. solar and wind power) and electric vehicles. They promise to be highly useful, for instance because they allow us to decrease our CO2 emissions and our dependence on energy imports. However, a widespread introduction of these (and related) technologies requires significant engineering efforts. In particular, two challenges to themanagement of electricity systems are of interest to the scope of this dissertation. First, the usage of these technologies has significant effects on howwell (part of) supply and demand can be planned ahead of time and balanced in real time. Planning and balancing are important activities in electricity distribution for keeping the number of peaks low (peaks can damage network hardware and lead to high prices). It can become more difficult to plan and balance in future electricity systems, because supply will partly depend on intermittent sunshine and wind patterns, and demand will partly depend on dynamic mobility patterns of electric vehicle drivers. Second, these technologies are often placed in the lower voltage (LV) tiers of the grid in a decentralised manner, as opposed to conventional energy sources, which are located in higher voltage (HV) tiers in central positions. This is introducing bi-directional power flows on the grid, and it significantly increases the number of actors in the electricity systems whose day-to-day decisionmaking about consumption and generation (e.g. electric vehicles supplying electricity back to the network) has significant impacts on the electricity system.In this dissertation, we look into dynamic pricing and markets in order to achieve allocations (of electricity and money) which are acceptable in future electricity systems. Dynamic pricing and markets are concepts that are highly useful to enable efficient allocations of goods between producers and consumers. Currently, they are being used to allocate electricity between wholesale traders. In recent years, the roles of the wholesale producer and the retailer have been unbundled in many countries of the world, which is often referred to as “market liberalisation”. This is supposed to increase competition and give end consumers more choice in contracts. Market liberalisation creates opportunities to design markets and dynamic pricing approaches that can tackle the aforementioned challenges in future electricity systems. However, they also introduce new challenges themselves, such as the acceptance of price fluctuations by consumers.The research objective of this dissertation is to develop market mechanisms and dynamic pricing strategies which can deal with the challenges mentioned above and achieve acceptable outcomes. To this end, we formulate three major research questions:First, can we design pricing mechanisms for electricity systems that support two necessary featureswell, which are not complementary—namely to encourage adaptations in electricity consumption and generation on short notice (by participants who have this flexibility), but also to enable planning ahead of electricity consumption and generation (for participants who can make use of planning)?Second, the smart grid vision (among others) posits that in future electricity systems, outcomeswill be jointly determined by a large number of (possibly) small actors and allocations will be mademore frequently than today. Which pricing mechanisms do not require high computational capabilities from the participants, limit the exposure of small participants to risk and are able to find allocations fast?Third, automated grid protection against peaks is a crucial innovation step for network operators, but a costly infrastructure program. Is it possible for smart devices to combine the objective of protecting network assets (e.g. cables) from overloading with applying buying and selling strategies in a dynamic pricing environment, such that the devices can earn back parts of their own costs?In order to answer the research questions, our methods are as follows: We consider four problems which are likely to occur in future electricity systems and are of relevance to our research objective. For each problem, we develop an agent-based model and propose a novel solution. Then, we evaluate our proposed solution using stochastic computational simulations in parameterised scenarios. We thus make the following four contributions:In Chapter 3,we design a market mechanism in which both binding commitments and optional reserve capacity are explicitly represented in the bid format, which can facilitate price finding and planning in future electricity systems (and therefore gives answers to our first research question). We also show that in this mechanism, flexible consumers are incentivised to offer reserve capacity ahead of time, whichwe prove for the case of perfect competition and showin simulations for the case of imperfect competition. We are able to show in a broad range of scenarios that our proposed mechanism has no economic drawbacks for participants. Furthermore (giving answers to our second research question), the mechanism requires less computational capabilities in order to participate in it than a contemporary wholesale electricitymarket with comparable features for planning ahead.In Chapter 4, we consider the complexity of dynamic pricing strategies that retailers could use in future electricity systems (this gives answers to our first, but foremost to our second research question). We argue that two important features of pricing strategies are not complementary—namely power peak reduction and comprehensibility of prices—and we propose indicators for the comprehensibility of a pricing strategy from the perspective of consumers. We thereby add a novel perspective for the design and evaluation of pricing strategies.In Chapter 5, we consider dynamic pricing mechanisms where the price is set by a single seller. In particular, we develop pricing strategies for a seller (a retailer) who commits to respect an upper limit on its unit prices (this gives answers to both our first and second research question). Upper price limits reduce exposure of market participants to price fluctuations. We show that employing the proposed dynamic pricing strategies reduces consumption peaks, although their parameters are being simultaneously optimised for themaximisation of retailer profits.In Chapter 6, we develop control algorithms for a small storage device which is connected to a low voltage cable. These algorithms can be used to reach decisions about when to charge and when to discharge the storage device, in order to protect the cable from overloading as well as to maximise revenue from buying and selling (this gives answers to our third research question). We are able to show in computational simulations that our proposed strategies perform well when compared to an approximated theoretical lower cost bound. We also demonstrate the positive effects of one of our proposed strategies in a laboratory setupwith real-world cable hardware.The results obtained in this dissertation advance the state of the art in designing pricing mechanisms and strategies which are useful for many use cases in future decentralised electricity systems. The contributions made can provide two positive effects: First, they are able to avoid or reduce unwanted extreme situations, often related to consumption or production peaks. Second, they are suitable for small actors who do not have much computation power but still need to participate in future electricity systems where fast decision making is needed