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We consider the question of whether, and in what sense, Wardrop equilibria provide a good approximation for Nash equilibria in atomic unsplittable congestion games with a large number of small players. We examine two different definitions of small players. In the first setting, we consider games where each player's weight is small. We prove that when the number of players goes to infinity and their weights to zero, the random flows in all (mixed) Nash equilibria for the finite games converge in distribution to the set of Wardrop equilibria of the corresponding nonatomic limit game. In the second setting, we consider an increasing number of players with a unit weight that participate in the game with a decreasingly small probability. In this case, the Nash equilibrium flows converge in total variation towards Poisson random variables whose expected values are Wardrop equilibria of a different nonatomic game with suitably-defined costs. The latter can be viewed as symmetric equilibria in a Poisson game in the sense of Myerson, establishing a plausible connection between the Wardrop model for routing games and the stochastic fluctuations observed in real traffic. In both settings we provide explicit approximation bounds, and we study the convergence of the price of anarchy. Beyond the case of congestion games, we prove a general result on the convergence of large games with random players towards Poisson games., Mathematics of Operations Research, Forthcoming, 2022

CAC for trees is the statement asserting that any infinite subtree of $\mathbb{N}^{, 28 pages

We introduce a behavioral model that effectively predicts auctioneers' reserve choice patterns in English clock auctions across varying reserve price formats (whether reserve prices are set ex-ante or ex-post auction events), number of bidders and the distribution of suppliers' costs. In a two-parameter model, which we call Subjective Conditional Probability (SCP), auctioneers have subjective judgement of conditional probabilities. We theoretically show that the SCP explains two intuitive, but sub-optimal, reserve price setting patterns. It predicts and provides rationalizations for ex-ante reserve prices that decrease in the number of bidders and ex-post reserve prices that increase in the realized auction price. We conduct two experiments; one with a uniform cost distribution and another with a left-skewed cost distribution. We validate the SCP model internally and externally by comparing it to a wide range of models, including reduced form linear regression, risk aversion, anticipated regret model, and subjective probability judgement via in-sample and out-of-sample predictions. We conclude that the SCP has strong external validity across a variety of procurement environments.

Inspired by the path coordination problem arising from robo-taxis, warehouse management, and mixed-vehicle routing problems, we model a group of heterogeneous players responding to stochastic demands as a congestion game under Markov decision process dynamics. Players share a common state-action space but have unique transition dynamics, and each player's unique cost is a {function} of the joint state-action probability distribution. For a class of player cost functions, we formulate the player-specific optimization problem, prove the equivalence between the Nash equilibrium and the solution of a potential minimization problem, and derive dynamic programming approaches to solve the Nash equilibrium. We apply this game to model multi-agent path coordination and introduce congestion-based cost functions that enable players to complete individual tasks while avoiding congestion with their opponents. Finally, we present a learning algorithm for finding the Nash equilibrium that has linear complexity in the number of players. We demonstrate our game model on a multi-robot warehouse \change{path coordination problem}, in which robots autonomously retrieve and deliver packages while avoiding congested paths., 6 pages, 4 figures

Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, auction has been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this paper, we design a differentially private combinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time., 12 pages, 6 figures

Observable games are game situations that reach one of possibly many Nash equilibria. Before an instance of the game starts, an external observer does not know, a priori, what is the exact profile of actions that will occur; thus, he assigns subjective probabilities to players' actions. However, not all probabilistic assignments are coherent with a given game. We study the decision problem of determining if a given set of probabilistic constraints assigned a priori by the observer to a given game is coherent, which we call the Coherence of Probabilistic Constraints on Equilibria, or PCE-Coherence. We show several results concerning algorithms and complexity for PCE-Coherence when only pure Nash equilibria are considered. In this context, we also study the computation of maximal and minimal probabilistic constraints on actions that preserves coherence. Finally, we study these problems when mixed Nash equilibria are allowed., Preprint

Oblivious RAM (ORAM), first introduced in the ground-breaking work of Goldreich and Ostrovsky (STOC ’87 and J. ACM ’96) is a technique for provably obfuscating programs’ access patterns, such that the access patterns leak no information about the programs’ secret inputs. To compile a general program to an oblivious counterpart, it is well-known that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varOmega (\log N)$$\end{document}Ω(logN) amortized blowup is necessary, where N is the size of the logical memory. This was shown in Goldreich and Ostrovksy’s original ORAM work for statistical security and in a somewhat restricted model (the so called balls-and-bins model), and recently by Larsen and Nielsen (CRYPTO ’18) for computational security. A long standing open question is whether there exists an optimal ORAM construction that matches the aforementioned logarithmic lower bounds (without making large memory word assumptions, and assuming a constant number of CPU registers). In this paper, we resolve this problem and present the first secure ORAM with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(\log N)$$\end{document}O(logN) amortized blowup, assuming one-way functions. Our result is inspired by and non-trivially improves on the recent beautiful work of Patel et al. (FOCS ’18) who gave a construction with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(\log N\cdot \log \log N)$$\end{document}O(logN·loglogN) amortized blowup, assuming one-way functions. One of our building blocks of independent interest is a linear-time deterministic oblivious algorithm for tight compaction: Given an array of n elements where some elements are marked, we permute the elements in the array so that all marked elements end up in the front of the array. Our O(n) algorithm improves the previously best known deterministic or randomized algorithms whose running time is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(n \cdot \log n)$$\end{document}O(n·logn) or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(n \cdot \log \log n)$$\end{document}O(n·loglogn), respectively.

Combinatorial auctions have seen limited applications in large-scale consumer-oriented marketplaces, partly due to the substantial complexity to keep track of auction status and formulate informed bidding strategies. We study the bidder support problem for the general multi-item multi-unit (MIMU) combinatorial auctions, where multiple heterogeneous items are being auctioned and multiple homogeneous units are available for each item. Under two prevalent bidding languages (OR bidding and XOR bidding), we derive theoretical results and design efficient algorithmic procedures to calculate important bidder support information, such as the winning bids of an auction and the minimum bidding value for a bid to win an auction either immediately or potentially in the future. Our results unify the theoretical insights on bidder support problem for different bidding languages as well as different special cases of general MIMU auctions, namely the single-item multi-unit (SIMU) auctions and the multi-item single-unit (MISU) auctions. We also consider auctions with additional bidding constraints, including batch-based combinatorial auctions and hierarchical combinatorial auctions, as well as the combinatorial reverse auctions, all of which have relevant practical applications (e.g., industrial procurements). Our results can be readily extended to solve the bidder support problems in these auction mechanisms.

Combinatorial auctions are auctions in which bids can be submitted on sets of items, rather than just on individual items. These auctions are generally beneficial to both auctioneers and bidders, as they allow bidders to express their synergies for sets of items. In recent years, we have seen the advent of combinatorial auctions as well as the emergence of online market platforms with competing auctioneers. However, combinatorial auctions have largely been absent from these platforms. Our article provides an explanation for this absence by demonstrating that competition between auctioneers can reduce the attractiveness of offering combinatorial auctions. Specifically, we show that auctioneers can limit competitive pressure between themselves by allowing bids only on specific packages, where these packages differ between auctioneers. This results in market segmentation, which increases bidder competition, and consequently increases auctioneer revenues. These findings have implications for market design. In particular they imply that, for an online market platform having multiple sellers offering auctions to the same set of buyers, it might not be advantageous to offer combinatorial auctions as a design option to the competing sellers.

In a model with multiple Pareto-ranked equilibria we add trade in assets that pay based on the realization of a sunspot. Asset trading restricts the equilibrium set in a way that raises welfare by eliminating equilibria with a high likelihood of disasters. When the probability of a disaster is high enough, the coordination game becomes like a prisoner’s dilemma situation in which the high-output equilibrium disappears because the portfolios that agents choose induce them to produce less. We derive an upper bound on the disaster probability, we derive asset pricing implications including the disaster premium, and we study the effect on stock prices of news shocks to beliefs.

We use a unique feature of California’s property tax system to empirically identify the causal effect of selling homeowners’ past property tax payments on their choice of listing price. Although past property taxes are sunk costs, we find that they have a significant positive effect on the sellers’ choice of listing price, which is inconsistent with rational models of decision making. This effect is stronger when sellers expect to sell at a loss relative to their purchase price and for properties whose value is harder to assess. The effect of property taxes on listing price is mostly transmitted to the selling price, which is consistent with the idea that buyers use listing prices as anchors to assess property values. Overall, our results suggest that sunk costs affect prices in the housing market. This paper was accepted by Tomasz Piskorski, finance. Supplemental Material: Data files are available at https://doi.org/10.1287/mnsc.2021.4285 .

Mature internet advertising platforms offer high-level campaign management tools to help advertisers run their campaigns, often abstracting away the intricacies of how each ad is placed and focusing on aggregate metrics of interest to advertisers. On such platforms, advertisers often participate in auctions through a proxy bidder, so the standard incentive analyses that are common in the literature do not apply directly. In this paper, we take the perspective of a budget management system that surfaces aggregated incentives—instead of individual auctions—and compare first and second price auctions. We show that theory offers surprising endorsement for using a first price auction to sell individual impressions. In particular, first price auctions guarantee uniqueness of the steady-state equilibrium of the budget management system, monotonicity, and other desirable properties, as well as efficient computation through the solution to the well-studied Eisenberg–Gale convex program. Contrary to what one can expect from first price auctions, we show that incentives issues are not a barrier that undermines the system. Using realistic instances generated from data collected at real-world auction platforms, we show that bidders have small regret with respect to their optimal ex post strategy, and they do not have a big incentive to misreport when they can influence equilibria directly by giving inputs strategically. Finally, budget-constrained bidders, who have significant prevalence in real-world platforms, tend to have smaller regrets. Our computations indicate that bidder budgets, pacing multipliers, and regrets all have a positive association in statistical terms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: D. Panigrahi was supported in part by the National Science Foundation [Awards CCF 1535972, CCF 1750140, and CCF 1955703]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4310 .

How to generate more revenues is crucial to cloud providers. Evidences from the Amazon cloud system indicate that ``dynamic pricing'' would be more profitable than ``static pricing.'' The challenges are: How to set the price in real-time so to maximize revenues? How to estimate the price dependent demand so to optimize the pricing decision? We first design a discrete-time based dynamic pricing scheme and formulate a Markov decision process to characterize the evolving dynamics of the price-dependent demand. We formulate a revenue maximization framework to determine the optimal price and theoretically characterize the ``structure'' of the optimal revenue and optimal price. We apply the Q-learning to infer the optimal price from historical transaction data and derive sufficient conditions on the model to guarantee its convergence to the optimal price, but it converges slowly. To speed up the convergence, we incorporate the structure of the optimal revenue that we obtained earlier, leading to the VpQ-learning (Q-learning with value projection) algorithm. We derive sufficient conditions, under which the VpQ-learning algorithm converges to the optimal policy. Experiments on a real-world dataset show that the VpQ-learning algorithm outperforms a variety of baselines, i.e., improves the revenue by as high as 50% over the Q-learning, speedy Q-learning, and adaptive real-time dynamic programming (ARTDP), and by as high as 20% over the fixed pricing scheme.

This study examines the question of selling agricultural commodities by auction or directly. Hedonic price analysis using transaction data from the sale of frozen cod in Norway shows that buyer–seller matches explain 32.4 and 13.6 per cent of the price variation in direct sales and auctions, respectively, indicating that direct sales are more informationally efficient than auctions. Meanwhile, auctions gain a price premium of 2.6 per cent over direct sales, holding other variables constant. However, a substantial increase in the use of direct sales indicates that their information efficiency is more important to sellers than the small price premium provided by auctions.

The notion of abstract convex structure generalizes the standard notion of convexity in linear spaces. We consider abstract convex structures that are combinatorial objects studied in various areas of mathematics and convex algebras as introduced in [8] and we study a general definition of convex preferences. Relations defined by aggregation of orderings are considered.

A third party developer designs and sells a pricing algorithm that enhances a firm’s ability to tailor prices to a source of demand variation, whether high-frequency demand shocks or market segmentation. The equilibrium pricing algorithm is characterized that maximizes the third party’s profit given firms’ optimal adoption decisions. Outsourcing the pricing algorithm does not reduce competition but does make prices more sensitive to the demand variation, and this is shown to decrease consumer welfare and increase industry profit. This effect is larger when products are more substitutable. This paper was accepted by Joshua Gans, business strategy.

Problem definition: This paper addresses the question whether or not self-learning algorithms can learn to collude instead of compete against each other, without violating existing competition law. Academic/practical relevance: This question is practically relevant (and hotly debated) for competition regulators, and academically relevant in the area of analysis of multi-agent data-driven algorithms. Methodology: We construct a price algorithm based on simultaneous-perturbation Kiefer–Wolfowitz recursions. We derive theoretical bounds on its limiting behavior of prices and revenues, in the case that both sellers in a duopoly independently use the algorithm, and in the case that one seller uses the algorithm and the other seller sets prices competitively. Results: We mathematically prove that, if implemented independently by two price-setting firms in a duopoly, prices will converge to those that maximize the firms’ joint revenue in case this is profitable for both firms, and to a competitive equilibrium otherwise. We prove this latter convergence result under the assumption that the firms use a misspecified monopolist demand model, thereby providing evidence for the so-called market-response hypothesis that both firms’ pricing as a monopolist may result in convergence to a competitive equilibrium. If the competitor is not willing to collaborate but prices according to a strategy from a certain class of strategies, we prove that the prices generated by our algorithm converge to a best-response to the competitor’s limit price. Managerial implications: Our algorithm can learn to collude under self-play while simultaneously learn to price competitively against a ‘regular’ competitor, in a setting where the price-demand relation is unknown and within the boundaries of competition law. This demonstrates that algorithmic collusion is a genuine threat in realistic market scenarios. Moreover, our work exemplifies how algorithms can be explicitly designed to learn to collude, and demonstrates that algorithmic collusion is facilitated (a) by the empirically observed practice of (explicitly or implicitly) sharing demand information, and (b) by allowing different firms in a market to use the same price algorithm. These are important and concrete insights for lawmakers and competition policy professionals struggling with how to respond to algorithmic collusion.

A watchman path is a path such that a direct line of sight exists between each point in some region and some point along the path. Here, we study the online watchman path problem outside a convex polygon, i.e., in $\mathbb{R}^2\setminus \Omega$, where $\Omega$ is a convex polygon that is not known in advance. We present an algorithm for the exploration of the region outside the polygon. We prove that the presented algorithms guarantees a $\approx 22.77$ competitive ratio compared to the optimal offline watchman path.

We define and analyze the notion of variational Wardrop equilibrium for nonatomic aggregative games with an infinity of players types. These equilibria are characterized through an infinite-dimensional variational inequality. We show, under monotonicity conditions, a convergence theorem enables to approximate such an equilibrium with arbitrary precision. To this end, we introduce a sequence of nonatomic games with a finite number of players types, which approximates the initial game. We show the existence of a symmetric Wardrop equilibrium in each of these games. We prove that those symmetric equilibria converge to an equilibrium of the infinite game, and that they can be computed as solutions of finite-dimensional variational inequalities. The model is illustrated through an example from smart grids: the description of a large population of electricity consumers by a parametric distribution gives a nonatomic game with an infinity of different players types, with actions subject to coupling constraints., arXiv admin note: substantial text overlap with arXiv:1806.06230

We present a polynomial-time online algorithm for maximizing the conditional value at risk (CVaR) of a monotone stochastic submodular function. Given $T$ i.i.d. samples from an underlying distribution arriving online, our algorithm produces a sequence of solutions that converges to a ($1-1/e$)-approximate solution with a convergence rate of $O(T^{-1/4})$ for monotone continuous DR-submodular functions. Compared with previous offline algorithms, which require $\Omega(T)$ space, our online algorithm only requires $O(\sqrt{T})$ space. We extend our online algorithm to portfolio optimization for monotone submodular set functions under a matroid constraint. Experiments conducted on real-world datasets demonstrate that our algorithm can rapidly achieve CVaRs that are comparable to those obtained by existing offline algorithms., Comment: Full version of our paper in IJCAI 2021

Experimental work in monetary economics is usually based on theory that incorporates an infinite horizon. Yet, hard constraints on laboratory sessions lead to finite times when the game must (with probability 1) end, and then simple backward induction implies monetary equilibria cannot exist. Hence, these experiments cannot evaluate subjects’ ability to settle on the use of money as a medium of exchange, that ameliorates trading frictions, as an equilibrium outcome. To address this, we present some finite-horizon games where monetary exchange is an equilibrium outcome, and report some experimental results using these games.

We consider a single-item single-period joint inventory management and pricing problem of a retailer selling an item that has selling price uncertainties. Unlike most of the literature on the newsvendor problem, we assume that price-dependent demand arrives randomly according to a stochastic arrival process whose rate depends on the fluctuating market input price process. The retailer’s problem is to choose the order quantity and a proportional price markup over the input price to maximize the expected profit. This setting is mostly encountered by retailers that trade in different currencies or have to purchase and convert commodities for seasonal sales. For this setting, we characterize both the optimal inventory and markup levels. We present monotonicity properties of the expected profit function with respect to each decision variable. We also show that more volatile input price processes lead to lower expected profits.

Based on the research and development (R&D) state of prefabricated buildings, this paper establishes an evolutionary game model involving such three parties as latecomer prefabricated building firm (LPBF), related firm, and university-research institute, and simulates the evolutionary results of the game to determine the evolutionarily stable strategy (ESS) of the game. The results show that: In the tripartite system evolution, the evolution and trend of each party are closely related with the strategies chosen by the other two parties; eventually, the LPBF strategy always converges to R&D, while the strategies of related firm, and university-research institute always converge to cooperation in R&D. Whether the game system converges to the desired equilibrium state hinges on the R&D profit and cost of, and opportunistic profit from subsequent cooperation with the LPBF. During the development of prefabricated buildings, the game parties should promote the high-quality development of the industry by stepping up technological R&D, forging a complete industrial chain, and implementing industry-university-research cooperation.

Dynamic pricing is a well-known revenue management technique used by firms to maximize their revenues. In industries such as fashion retail and airlines, it is observed in practice that firms vary the prices of their products, through time, only among certain pre-fixed discrete price points; for example, in fashion retail, products are first sold at a base price and then later at, say, 10%, 20%, 30% discounted prices. In such discrete-price practices, when the number of products offered by a firm is large, it is mathematically challenging to determine the optimal pricing decisions. In “Multiproduct Pricing with Discrete Price Sets,” Manchiraju, Dawande, and Janakiraman design pricing algorithms that are fast and result in near-optimal revenues.

We introduce risk-averse bidders in a security-bid auction to analyze how the security design affects bidders’ equilibrium behavior and, as a result, the revenue and efficiency of the auction. We show that steeper securities provide more insurance because they allow bidders to smooth payoffs across realizations. Such insurance levels the playing field for more-risk-averse bidders, inducing them to bid more aggressively. As a consequence, the auction’s allocative efficiency weakly increases when the seller switches from a flatter to a steeper security. Furthermore, we prove that when bidders are homogeneously and sufficiently risk averse, the only security that guarantees Pareto efficiency is the steepest, that is, a call option. We also determine the relationship between the security design and the auction format. In particular, we show that for convex and superconvex families of securities, the first-price auction yields higher expected revenues, provided a technical condition, whereas for subconvex families, the second price yields higher expected revenues, provided that bidders are moderately risk averse. Finally, we show that steeper securities also attract higher entry from an ex ante perspective, when entry is costly, and discuss the effects that the presence of risk aversion has on informal auctions. This paper was accepted by Gustavo Manso, finance.

We study a multi-player stochastic differential game, where agents interact through their joint price impact on an asset that they trade to exploit a common trading signal. In this context, we prove that a closed-loop Nash equilibrium exists if the price impact parameter is small enough. Compared to the corresponding open-loop Nash equilibrium, both the agents' optimal trading rates and their performance move towards the central-planner solution, in that excessive trading due to lack of coordination is reduced. However, the size of this effect is modest for plausible parameter values., 41 pages, 5 figures, supplementary appendix

International audience; A common practice in many auctions is to offer bidders an opportunity to improve their bids, known as a best and final offer stage. This improved bid can depend on new information either about the asset or about the competitors. This paper examines the effects of new information regarding competitors, seeking to determine what information the auctioneer should provide assuming the set of allowable bids is discrete. The rational strategy profile that maximizes the revenue of the auctioneer is the one where each bidder makes the highest possible bid that is lower than his valuation of the item. This strategy profile is an equilibrium for a large enough number of bidders, regardless of the information released. We compare the number of bidders needed for this profile to be an equilibrium under different information structures. We find that it becomes an equilibrium with fewer bidders when less additional information is made available to the bidders regarding the competition. It follows that when the number of bidders is a priori unknown, there are some advantages to the auctioneer not revealing information and conducting a one-stage auction instead.

With the prevailing use of smartphones and location-based services, vast amounts of trajectory data are collected and used for many applications. When trajectory data are sent to a third-party research institute for analytical applications, the privacy of users would be severely disclosed. For example, the relationship between users will be revealed from the correlation between trajectories. In this paper, we propose a method for releasing trajectory datasets without revealing correlation between trajectories, called RDPT. In RDPT, we first quantify the trajectory correlation and convert the problem of protecting trajectory correlations into reducing the trajectory similarities of users and preserving the utility of the perturbed trajectories. Based on the insight, we model a multi-objective optimization problem and solve the problem with the particle swarm optimization algorithm modified to satisfy differential privacy. Then we generate synthetic trajectories and the correlations between trajectories are reduced. We conduct extensive experiments on three real trajectory datasets. The experimental results show that RDPT achieves almost equivalent data utility to and better privacy than the existing methods.

This paper provides a comprehensive convergence analysis of the PoA of both pure and mixed Nash equilibria in atomic congestion games with unsplittable demands., 57 pages

The development of e-commerce has formulated the hybrid platform mode for retail enterprises. We studied how the differences in product distribution cost, unit retail price, and competition conflict affect the business model decision making. The theoretical model shows the following results: (1) When the hybrid platform sells complementary products with third-party sellers, the profit of choosing the hybrid mode is always the best. (2) When the hybrid platform competes with third-party sellers, if the unit retail price is in a higher range, the merchant mode is the best choice; when the unit retail price is in the lower range, the hybrid platform mode is the best choice. (3) Competition between the hybrid platform and third-party sellers determines the profit level of the operating enterprise. The excessive price competition between the self-operated business and the third-party sellers is magnified by the existence of cross-network externalities, resulting in a strong anti-competitive effect, and affecting the profits of the hybrid platform’s two businesses. These findings guide retail enterprises to design their business model as well as address competition conflict.

In recent years, a trend in retail pricing has been to give consumers greater autonomy in setting their own prices, be it through auctions or other forms of participative pricing. Such consumer pricing autonomy often requires the seller to set limits in the form of price floors and price ceilings. Price floors and ceilings in our auction settings are referred to as reserve prices (RP) and Buy It Now (BIN) prices, respectively. We examine the effect of RP and BIN presence and magnitude on the number of bidders and ending price. Using auctions, we uncover consumers’ willingness to pay (WTP) through bids. WTP is malleable through reference cues. Our focus is on two such cues: BINs and RPs. Results of two field studies, augmented with a laboratory study, show that both BINs and RPs result in lower bidder entry, but have an overall positive effect on ending price. Furthermore, results show that RP is more effective than a comparable BIN magnitude and that these two pricing cues are substitutes. The study design allows the authors to rule out alternative explanations. Open RP and BIN's effect on ending price is due to a reference point effect rather than a price truncation effect. Thus, retailers can increase WTP through changing these reference cues and exploit a richer choice set over which to shape a malleable WTP. The quantification of the interaction between RP and BIN gives managers the ability to jointly take advantage of both RP and BIN.

International audience; In this paper we introduce a definition of approximate Pareto efficient solution as well as a necessary condition for such solutions in the multiobjective setting on Riemannian manifolds. We also propose an inexact proximal point method for nonsmooth multiobjective optimization in the Riemannian context by using the notion of approximate solution. The main convergence result ensures that each cluster point (if any) of any sequence generated by the method is a Pareto critical point. Furthermore, when the problem is convex on a Hadamard manifold, full convergence of the method for a weak Pareto efficient solution is obtained. As an application, we show how a Pareto critical point can be reached as a limit of traps in the context of the variational rationality approach of stay and change human dynamics.

In this article, we study the distribution, ordering, and pricing strategies when the supplier has a limited output and the retailer has substitute suppliers in a fresh agricultural product supply chain. The Stackelberg game method is adopted to study the optimal strategy of suppliers and retailers with suppliers as the leader and retailers as the follower. The results show that the wholesale price and selling price of suppliers are related to the price of substitutes and the substitution rate, the wholesale price and online selling price increase when the retailer introduces high-quality substitutes. When substitutes exist, the supplier is willing to distribute more fresh agricultural products to retailers. As the substitution rate increases, suppliers are willing to distribute more quantities. Therefore, retailers should choose more suppliers and fresh agricultural products with a higher substitution rate.

In this paper, we present a resource allocation mechanism for the problem of incentivizing filtering among a finite number of strategic social media platforms. We consider the presence of a strategic government and private knowledge of how misinformation affects the users of the social media platforms. Our proposed mechanism incentivizes social media platforms to filter misleading information efficiently, and thus indirectly prevents the spread of fake news. In particular, we design an economically inspired mechanism that strongly implements all generalized Nash equilibria for efficient filtering of misleading information in the induced game. We show that our mechanism is individually rational, budget balanced, while it has at least one equilibrium. Finally, we show that for quasi-concave utilities and constraints, our mechanism admits a generalized Nash equilibrium and implements a Pareto efficient solution.

This study considers a duopoly market in which two competitors operate their own service-inventory systems. Both competitors determine their prices to maximize their profit while considering the inventory holding cost, ordering cost, and cost incurred by lost sales. Customers are price sensitive, and customer attractiveness is expressed by arrival rates. We use a game theory approach to formulate and analyze three types of pricing games: (i) a parallel pricing game, (ii) a sequential pricing game, and (iii) a unified pricing game. The uniqueness of equilibrium prices is analytically proven, after which, a solution procedure for obtaining equilibrium prices is outlined.

We study a competitive partial equilibrium in markets where risk-averse agents solve multistage stochastic optimization problems formulated in scenario trees. The agents trade a commodity that is produced from an uncertain supply of resources. Both resources and the commodity can be stored for later consumption. Several examples of a multistage risked equilibrium are outlined, including aspects of battery and hydroelectric storage in electricity markets, distributed ownership of competing technologies relying on shared resources, and aspects of water control and pricing. The agents are assumed to have nested coherent risk measures based on one-step risk measures with polyhedral risk sets that have a nonempty intersection over agents. Agents can trade risk in a complete market of Arrow-Debreu securities. In this setting, we define a risk-trading competitive market equilibrium and establish two welfare theorems. Competitive equilibrium will yield a social optimum (with a suitably defined social risk measure) when agents have strictly monotone one-step risk measures. Conversely, a social optimum with an appropriately chosen risk measure will yield a risk-trading competitive market equilibrium when all agents have strictly monotone risk measures. The paper also demonstrates versions of these theorems when risk measures are not strictly monotone.

In this paper, we conduct a structural analysis of multi-attribute auctions of contracts with a general allocation rule when private information is multidimensional. Upon modeling bidders’ contract value that accounts for their endogenous ex post actions, we nonparametrically identify bidders’ private information from their bids and estimate their joint distribution. Analyzing cash-royalty auctions of Louisiana oil leases, we find government revenue worse and development rates no better than in a cash auction with a fixed royalty in view of adverse selection and moral hazard. Our findings revise conventional wisdom on the optimality of multi-attribute auctions. (JEL D44, D82, D86, H82, Q35)

With the diversity of manipulator grasping methods and the complexity of the unstructured environment, the grasping planning of the target object is very complicated. However, the external factor for the manipulator grasping is primarily the external shape of the target object. Therefore, it is of great significance to establish a grasping system based on target shape analysis. This paper proposes a method for determining the grasping posture of manipulator based on shape analysis and force closure. The irregular or complex objects are reduced to a combination of some basic shapes. The 3D data points of the object are split into blocks and each part is fitted to sphere, cylinder or rectangle by a best-fit algorithm. This allows the grasping posture of the manipulator to be determined quickly. The grasping characteristics of the object are analyzed and the grasping surface is described qualitatively by means of a superellipse. The grasping of objects is simplified according to the stable grasping condition of force closure. The force spiral space for grasping defines the grasping quality of the manipulator. The best grasping posture is obtained by evaluating the indicators. This method eliminates complex training processes, reduces the complexity of robotic grasping and highly universal.

Evolutionary games and population dynamics are finding increasing applications in design learning and control protocols for a variety of resource allocation problems. The implicit requirement for full communication has been the main limitation of the evolutionary game dynamic approach in engineering tasks with various information constraints. This article intends to build population games and dynamics with both static and dynamical graphical communication structures. To this end, we formulate a population game model with graphical strategy interactions and derive its corresponding population dynamics. In particular, we first introduce the concept of generalized Nash equilibria for population games with graphical strategy interactions, and establish the equivalence between the set of generalized Nash equilibria and the set of rest points of its distributed population dynamics. Furthermore, the conditions for convergence to generalized Nash equilibrium and particularly to Nash equilibrium are obtained for the distributed population dynamics with both static and dynamical graphical structures. These results provide a new approach to design distributed Nash equilibrium seeking algorithms for population games with both static and dynamical communication networks, and hence, expand the applicability of the population game dynamics in the design of learning and control protocols under distributed circumstances.

Intellectual Property Rights (IPRs) are property rights that originate or are born as a result of human intellectual capabilities. With the emergence of use values and economic rewards, a thinking or conception regarding the need of legal protection for human outcomes or intellectual works develops. The term "law of confidence" refers to the legislation that evolved as a framework for protecting secret information (confidence). Trade secret violations occur when someone deliberately divulges trade secrets, breaches agreements, or violates written or unwritten responsibilities to maintain trade secrets. Owners of economically valuable technology or company knowledge are more likely to register their rights as trade secrets than patents or copyright protection. Article 5 paragraph (1), letter d of the Trade Secrets Law expressly provides for the protection of trade secrets based on an agreement.

Determination of the cost of production is a way to take into account the elements of cost into the cost of production. Cost of Production is a collection of production costs consisting of direct materials, direct labor and factory overhead costs plus inventories of products in the initial process and less inventories of products in the final process. And the selling price is an estimate of the exchange rate of the product for money. The selling price is the price obtained from the sum of the total production costs plus the mark-up used to cover factory overhead costs. The purpose of this study is to analyze the comparison of the determination of the cost of production to determine the selling price of the product using the Full Costing and Variable Costing method. Based on the calculation of the cost of production to determine the selling price of the product using the Full Costing method and the calculation of the cost of production using the variable costing method Determination of the selling price of the method of calculating the cost of goods manufactured Full Costing is higher than the variable costing methode and the calculation of the cost of production of the variable costing method The difference in the unit selling price of this product occurs because the calculation of the cost of production is also different.

In recent years internet auctions have attracted much attention. This paper discusses the "last minute bidding" (sniping) phenomenon, first investigated by Roth and Ockenfels (American Economic Review 92, 1093-1103) in eBay, fixed-end, auctions. Unlike standard auctions where each offer is processed by the auctioneer due to system traffic, and connection time, in eBay auctions very late bids may be left unprocessed. In the paper we consider a simple two-players, two-periods sealed-bid auction model, inspired by eBay auctions, with private values and complete information. The main difference with respect to e-Bay auctions is given by the first period. Indeed, players can submit at most one bid per period. In the first stage bids are processed with certainty by the system while in the second stage, with positive probability, bids may not be processed due to system congestion. Unlike eBay we show how in this model last minute, that is second stage only, bidding may have low plausibility, as it can be a Nash equilibrium only under very specific circumstances and is never a unique best reply. Intuitively this is because the first stage is sealed-bid, with at most one offer per bidder, and players have no guarantee that they could outbid the opponent in the second period, once after the first round they realise that their bid is not the best.

The price of anarchy and price of stability are two well-studied performance metrics that seek to characterize the inefficiency of equilibria in distributed systems. The distinction between these two performance metrics centers on the equilibria that they focus on: the price of anarchy characterizes the quality of the worst-performing equilibria, while the price of stability characterizes the quality of the best-performing equilibria. While much of the literature focuses on these metrics from an analysis perspective, in this work we consider these performance metrics from a design perspective. Specifically, we focus on the setting where a system operator is tasked with designing local agent utility functions to optimize these performance metrics in a class of games termed covering games. Our main result characterizes a fundamental trade-off between the price of anarchy and price of stability in the form of a fully explicit Pareto frontier. Within this setup, we observe that optimizing the price of anarchy comes directly at the expense of the price of stability (and vice versa). Our second result demonstrates how a system-operator could incorporate an additional piece of system-le

6D grasping in cluttered scenes is a longstanding problem in robotic manipulation. Open-loop manipulation pipelines may fail due to inaccurate state estimation, while most end-to-end grasping methods have not yet scaled to complex scenes with obstacles. In this work, we propose a new method for end-to-end learning of 6D grasping in cluttered scenes. Our hierarchical framework learns collision-free target-driven grasping based on partial point cloud observations. We learn an embedding space to encode expert grasping plans during training and a variational autoencoder to sample diverse grasping trajectories at test time. Furthermore, we train a critic network for plan selection and an option classifier for switching to an instance grasping policy through hierarchical reinforcement learning. We evaluate our method and compare against several baselines in simulation, as well as demonstrate that our latent planning can generalize to real-world cluttered-scene grasping tasks. Our videos and code can be found at https://sites.google.com/view/latent-grasping .

Rebate policies are widely used by manufacturers to stimulate sales. This paper analyzes a supply chain with revenue sharing and wholesale price contracts under manufacturer rebate policy, where the stochastic multiplicative demand function depends on both retail price and manufacturer rebate. We identify Stackelberg equilibrium for the supply chain under each contract. It is our finding that under the manufacturer’s respective optimal decisions for revenue sharing and wholesale price contracts with rebates, the retailer’s optimal retail prices are the same. We recognize sufficient conditions under which a revenue sharing contract and a wholesale price contract with rebate can achieve Pareto improvement, i.e., both the manufacturer and the retailer obtain a greater expected profit, compared with the cases without rebate. A sufficient condition is found, under which a revenue sharing contract with rebate policy can achieve Kaldor–Hicks improvement, i.e., yielding a greater expected profit for the manufacturer, a lower expected profit for the retailer, and a greater expected total profit, over pure revenue sharing contract without rebate. Further, it is shown that with rebate, a revenue sharing contract can achieve Kaldor–Hicks improvement over a wholesale price contract. Finally, our findings are demonstrated using numerical studies.

A data broker sells market segmentations created by consumer data to a producer with private production cost who sells a product to a unit mass of consumers with heterogeneous values. In this setting, I completely characterize the revenue-maximizing mechanisms for the data broker. In particular, every optimal mechanism induces quasi-perfect price discrimination. That is, the data broker sells the producer a market segmentation described by a cost-dependent cutoff, such that all the consumers with values above the cutoff end up buying and paying their values while the rest of consumers do not buy. The characterization of optimal mechanisms leads to additional economically relevant implications. I show that the induced market outcomes remain unchanged even if the data broker becomes more active in the product market by gaining the ability to contract on prices; or by becoming an exclusive retailer, who purchases both the product and the exclusive right to sell the product from the producer, and then sells to the consumers directly. Moreover, vertical integration between the data broker and the producer increases total surplus while leaving the consumer surplus unchanged, since consumer surplus is zero under any optimal mechanism for the data broker.

In this paper, the generalized Nash equilibrium problem over a fuzzy domain is considered. An existence result of the solution is given. Then, on the basis of the variational inequality approach, an algorithm is developed to solve the fuzzy Nash equilibrium problem. In addition, a stability analysis of the solution set is provided while the membership function is perturbed by a parameter. Finally, the fuzzy Nash equilibrium formulation is applied to the study of the conflicts faced by the fashion and textile industry: production and pollution.

A quantum secret sharing scheme is a method to share a quantum secret among participants in such a way that only certain specified subsets of the set of participants can combine to recover the secret. A quantum evolving secret sharing scheme is a recently introduced variant of secret sharing schemes where the sets of participants is not fixed and an unbounded and unspecified number of participants arrive one by one with time. The number of participants can be potentially infinite and and a quantum secret is shared and protected among the participants. The existing construction has some major drawbacks such as 1) the exponential quantum memory requirement and 2) the very high dimensions of the shares which makes the scheme difficult to implement. In this paper we overcome these drawbacks by constructing a scheme which uses quantum memory linear in the number of participants and significantly improves on the dimensions of the shares of the participants. This construction uses quantum secret redistribution and trap codes. The construction is flexible and can be modified to different types of access structures(subsets of participants which can recover the secret). Certain ramp properties can also be incorporated in the scheme.

The value of trajectory data lies mainly in the spatio-temporal correlation. However, the existing privacy protection methods ignore the spatio-temporal correlation of trajectory data, resulting in a large error in trajectory proportion estimation and Top-K classification. For the privacy of truck trajectory in intelligent logistics, the location and trajectory data perturbation method based on quadtree indexing is proposed, which leverages location generalization and local differential privacy techniques. Our proposed algorithms are suitable for datasets with a large sample space and can protect the trajectory privacy of truck drivers while preserving the strong correlation between adjacent spatio-temporal nodes in the trajectory. The results of simulation on a real trajectory dataset show that the proposed methods not only meet the trajectory privacy requirements of users but also have a good performance in trajectory proportion estimation and Top-K classification.

Wireless virtualization has become a key concept in future cellular networks which can provide virtualized wireless networks for different mobile virtual network operators (MVNOs) over the same physical infrastructure. Resource allocation is a main challenging issue in wireless virtualization for which auction approaches have been widely used. However, for most existing auction-based allocation schemes, the objective is to maximize the social welfare due to its simplicity. While in reality, MVNOs are more interested in maximizing their own revenues. However, the revenue-maximization auction problem is much more complex since the price is unknown before calculation. In this paper, we give a first attempt for designing a revenue-optimal auction mechanism for resource allocation in wireless virtualization. Considering the complexity in revenue maximization, deep learning techniques are applied. Specifically, we construct a multi-layer feed-forward neural network based on the analysis of optimal auction design. The neural network adopts users' bids as the input and the allocation rule and conditional payment rule as the output. The training set of this neural network is the users' valuation profiles. The proposed auction mechanism possesses several satisfactory properties, e.g., individual rationality, incentive compatibility and budget constraint. Finally, simulation results demonstrate the effectiveness of the proposed scheme.