Anonymous network access using the digital marketplace

With increasing usage of mobile telephony, and the trend towards additional mobile Internet usage, privacy and anonymity become more and more important. Previously-published anonymous communication schemes aim to obscure their users' network addresses, because real-world identity can be easily be derived from this information. We propose modifications to a novel call-management architecture, the digital marketplace, which will break this link, therefore enabling truly anonymous network access

User evaluation of a market-based recommender system

Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommende

Challenges for the comprehensive management of cloud services in a PaaS framework

The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

Setting Fees in Competing Double Auction Marketplaces: An Equilibrium Analysis

In this paper, we analyse competing double auction marketplaces that vie for traders and need to set appropriate fees to make a profit. Specifically, we show how competing marketplaces should set their fees by analysing the equilibrium behaviour of two competing marketplaces. In doing so, we focus on two different types of market fees: registration fees charged to traders when they enter the marketplace, and profit fees charged to traders when they make transactions. In more detail, given the market fees, we first derive equations to calculate the marketplaces' expected profits. Then we analyse the equilibrium charging behaviour of marketplaces in two different cases: where competing marketplaces can only charge the same type of fees and where competing marketplaces can charge different types of fees. This analysis provides insights which can be used to guide the charging behaviour of competing marketplaces. We also analyse whether two marketplaces can co-exist in equilibrium. We find that, when both marketplaces are limited to charging the same type of fees, traders will eventually converge to one marketplace. However, when different types of fees are allowed, traders may converge to different marketplaces (i.e. multiple marketplaces can co-exist)

Market-based Recommendation: Agents that Compete for Consumer Attention

The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the 'consumer attention space', or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of 'consumer attention space' for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains

Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

Exogenous coalition formation in the e-marketplace based on geographical proximity

This paper considers a model for exogenous coalition formation in e-marketplaces. Using the informational advantage e-retailer creates coalitions of customers based on geographical proximity. Most of the literature regards this process as endogenous: a coalition leader bundles eventual purchases together in order to obtain a better bargaining position. In contrast - and in response to what is being observed in business practice - we analyse a situation in which an existing e-retailer exogenously forms customers' coalitions. Results of this study are highly encouraging. Namely, we demonstrate that even under highly imperfect warehouse management schemes leading to contagion eects, suggested combined delivery service may oer signifficant efficiency gains as well as opportunities for Pareto-improvement.Coalition formation, e-commerce, multi-agent systems, consumer satisfaction, demand planning, warehouse management.