137 research outputs found
Efficient Mechanisms for the Supply of Services in Multi-Agent Environments
No full textAuctions provide an efficient way of resolving one-to-many negotiations. This is particularly true for automated agents where delays and long communications carry negative externalities. A properly designed auction, tailored to the specific needs of the relevant multi-agent system, can significantly improve its performance. In this paper, we focus on the specific problem of service allocation among autonomous, automated agents, within the context of the ADEPT project, which concerns the BT (British Telecom) business process of providing a quote for designing a network for a customer. The main contributions of this paper are threefold: First, we show how an English auction can be modified for services, which are multi-dimensional private value objects. Second, we show how, under certain conditions, auctions can be arranged by the service providing agents, in the cases where the service seeking agents fail to do so. We consider the incentives of all participants, and show how such an arrangement can be in their best interest. Finally, by examining our results for what is, essentially, an application of game-theory and mechanism design to an existing application, we draw some general conclusions on how such concepts can be operationalized in automated agents
Uncertainty and endogenous selection of economic equilibria
No full textThis paper presents a model of co-ordination failures based on market power and local oligopoly. The economy exhibits a multiplicity of Pareto-ranked equilibria. The introduction of uncertainty generates an endogenous equilibrium selection process, due to a strategic use of information by firms. The economy is more likely to settle on some equilibria than on others. We argue that a full understanding of these robustness criteria is needed before any policy which is intended to help co-ordinate the level of activity to a Pareto dominant outcome can be successfully implemented
The Economics of E-Commerce. A Strategic Guide to Understanding and Designing the Online Marketplace. (Nir Vulkan, Princeton University Press, Princeton and Oxford, 2003)
No full textThe article reviews the book "The Economics of E-Commerce. A Strategic Guide to Understanding and Designing the Online Marketplace," by Vulkan Nir
Essays on consumer behaviour and pricing
No full textThis dissertation is a collection of five essays examining different aspects of consumer and firm behavior in dynamic markets. The first essay combines clickstreams of users at a major news website with Facebook activity data, to study if social networks complement or compete for online browsing time. This is the first empirical study to show that Facebook activity increases time spent on news sites. Online news consumption is a shared experience, as the activity of social network friends strongly influences the behavior of other network members. We also find that visitors’ own browsing patterns are important predictors of online content consumption. The second essay examines consumer attitudes to risk and uncertainty vis-a-vis their purchase and search decisions for air tickets online. Using a two-stage model of purchase incidence and carrier choice, we find that browsing experience, search costs and product characteristics are important predictors of purchase incidence. Implications for website managers are also discussed. The third essay provides insights on the impact of customer heterogeneity and preference stochasticity on behavior based price discrimination. While customer heterogeneity intensifies competition, resulting in greater price discrimination, preference stochasticity reduces the incidence of price discrimination. Overall, the effect of preference stochasticity is more salient. The fourth essay presents models of strategic interaction to analyze the impact of dominance and concentration on pricing strategies. We show that lack of market dominance is a sufficient condition for discounts to existing customers. We further test our predictions via an experiment with pricing professionals. The behavior of professionals confirms that price discrimination increases with market dominance and concentration; however, lack of dominance is not a sufficient condition for loyalty discounts. We contend that increasing competition is a more effective means of improving consumer welfare compared to regulating dominant firms. The fifth essay considers the role of identity and customer type recognition in influencing pricing behavior in dynamic markets with symmetric and asymmetric players. When customer identity is detectable firms charge higher prices to repeat customers while new customers are offered lower prices. However, pricing behavior changes when information on customer type is available and this behavior varies with market structure. Age, education and experience of managers are also found to significantly influence pricing behavior
Comparative performance analysis of vulkan implementations of computational applications
No full textThe recent introduction of the Vulkan API and the SPIR-V intermediate-level language by the Khronos Group provides a new GPU programming model in an effort to combine the advantages of its predecessors, OpenGL for 3D graphics and OpenCL for computing. Vulkan’s low-level and more direct control over the underlying GPU hardware as well as its support for explicit multi-threaded execution offers opportunities for better performance at the cost of higher programming effort. Most of the previous work associated with Vulkan has targeted the graphics pipeline. The fact that Vulkan also supports the compute pipeline has motivated us to examine it from the GPGPU perspective, by porting a number of realistic applications to a desktop GPU and evaluating their Vulkan implementations in terms of performance and programmability. Specifically, we consider the Laplacian filter which is used in image processing to detect areas of rapid change (edges) in images. Also, we consider a Visual Odometry (VO) application used to track the position and pose of a robot by analyzing a sequence of camera frames. VO is part of a Simultaneous Localization and Mapping (SLAM) application used in autonomous navigation systems to build a map of surrounding environments and to determine the location of a moving robot inside this map. These applications require advanced pixel-level processing at different levels of pyramid-based granularity, and may even require real-time performance (when, for example, SLAM is used in a robot navigation system). We ported the original implementations (written in C for Laplacian filter and in CUDA for SLAM) to OpenCL, OpenGL and Vulkan and evaluated their performance on a desktop NVIDIA GPGPU. We show that Vulkan performance is comparable (within 10%) with the performance attained by OpenCL and higher than the performance attained by OpenGL compute shader implementations. By exploiting Vulkan synchronization primitives using the command buffer, we can eliminate the overhead of launching multiple kernel invocations in iterative applications and improve performance of Vulkan implementations by up to 30%. However, the OpenCL compiler seems to be more mature than the SPIR-V compiler used in Vulkan implementations resulting in slightly faster OpenCL kernel execution. On the other hand, the low-level semantics of Vulkan demand higher programming effort compared with OpenCL/OpenGL which can be a burden if Vulkan is to be used as a GPGPU programming model. Most of the additional effort, however, is boilerplate code that can be reused in more than one Vulkan applications. Our work is one of the first to consider Vulkan compute as an implementation language for larger scale applications (and not just for small kernels as in previous work). © 2019 Copyright is held by the owner/author(s)
Design of online reputation systems: an economic perspective
No full textOnline reputation systems are certainly the most overlooked 'heroes' of today's social Web. While these mechanisms are a vital element of every online transaction, they have received less consideration than some of their more well-known cousins, such as recommender systems or social networks, whose success would often not have been possible and tenable without their discrete but active backing. It then follows that despite their value and importance, the implementation of current reputation mechanisms has mostly been the result of trial-and-error. Resting on an economic perspective, this thesis regroups three chapters whose frameworks and findings aim at helping mechanism designers and researchers understand key mechanisms at play and develop more efficient online reputation systems. The first chapter examines the optimal number of ratings a reputation mechanism must make publicly available within an online marketplace in order to minimize cheating and maximize Pareto efficiency. I develop a moral hazard stage game featuring fictitious players which has the compelling property to prevent reputation effects from disappearing in the long run. I show that the number of ratings displayed by a reputation system is a fundamental predictor of market efficiency, and that the latter number should be kept minimal in order to maximize social welfare in the market – especially for economies proposing interactions with a high profit margin. The second chapter studies how different classes of reporting behaviours commonly found online affect the reliability of a reputation mechanism. I develop an iterative stochastic approximation model which I use to construct a behavioural measure of efficiency, so-called 'reporting bias'. I demonstrate that reporting bias tends towards its maximum when raters comply with the reports left by their predecessors. Following this result, I recommend to keep the rating interface separated from the rest of the reputation system. I also find that fake ratings are particularly harmful when one type of behaviour is present in the economy and suggest to counterbalance sybil attacks by displaying pairs of contrasted ratings. Finally, I defend the use of the arithmetic mean against the median as a way to compute reputation scores. The third chapter analyses how 5-star rating scales can lead to the formation of bimodal distributions of ratings within online marketplaces. Using a 2-time period model featuring altruistic raters, I identify the existence of a 'blind spot' of unrated transactions whose magnitude increases in the cost of rating and decreases in the number of buyers inhabiting the economy. Developing an additional model featuring Bayesian agents suffering from confirmatory bias, I show that non-binary rating scales can leave space to ambiguity and possibly wrong posteriors, even in the long run. Overall, results of the chapter hint that fine-grained rating scales best suit signalling reputation systems while coarse-grained scales should be preferred for sanctioning mechanisms
Matching and Bargaining with Deadlines: An Experimental Investigation
No full textWe describe an experiment where buyers and sellers, endowed with heterogeneous deadlines, are randomly matched and attempt to reach agreement over the division of a fixed surplus. The theoretical models that provide the background for this experiment have been developed in recent papers by Hurkens and Vulkan. Like those papers we consider both the case where deadlines are private and common information –that is, when a trader can or cannot see the deadline of the person she is matched with.
Observed behaviour in the experiment is largely consistent with the theory: when the deadline of the responder is known, offers made are increasing in the responder’s deadline while when the deadline of the responder is unknown, offers made are decreasing in the proposer’s deadline. However, in contradiction to the theory, the experimental evidence indicates that individuals reject positive offers and prefer to receive zero payoffs. This supports previous empirical findings of ultimatum game effects in bargaining
An economist's perspective on probability matching
No full textThe experimental phenomenon known as ‘probability matching’ is often offered as evidence in support of adaptive learning models and against the idea that people maximise their expected utility. Recent interest in dynamic-based equilibrium theories means the term re-appears in Economics. However, there seems to be conflicting views on what is actually meant by the term and about the validity of the data.
The purpose of this paper is therefore threefold: First, to introduce today’s readers to what is meant by probability matching, and in particular to clarify which aspects of this phenomenon challenge the utility-maximisation hypothesis. Second, to familiarise the reader with the different theoretical approaches to behaviour in such circumstances, and to focus on the differences in predictions between these theories in light of recent advances. Third, to provide a comprehensive survey of repeated, binary choice experiments
Economic implications of agent technology and e-commerce
No full textIn the light of the exponential growth of the Internet and World Wide Web, this paper describes, in some detail, existing agents and multiagents applications in the context of e-commerce, and suggests a research agenda for economists in response to these changes in technology and lifestyle. First, several ways where economic theory and, in particular, implementation theory can be used to design and improve the efficiency of e-commerce systems are described. Second, the paper discusses the impact on markets of using software agents. Finally, the paper discusses how economic theory can be used towards the design of the interactions between agents and their users
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