Sequential Posted Price Mechanisms with Correlated Valuations

We study the revenue performance of sequential posted price mechanisms and some natural extensions, for a general setting where the valuations of the buyers are drawn from a correlated distribution. Sequential posted price mechanisms are conceptually simple mechanisms that work by proposing a take-it-or-leave-it offer to each buyer. We apply sequential posted price mechanisms to single-parameter multi-unit settings in which each buyer demands only one item and the mechanism can assign the service to at most k of the buyers. For standard sequential posted price mechanisms, we prove that with the valuation distribution having finite support, no sequential posted price mechanism can extract a constant fraction of the optimal expected revenue, even with unlimited supply. We extend this result to the the case of a continuous valuation distribution when various standard assumptions hold simultaneously. In fact, it turns out that the best fraction of the optimal revenue that is extractable by a sequential posted price mechanism is proportional to ratio of the highest and lowest possible valuation. We prove that for two simple generalizations of these mechanisms, a better revenue performance can be achieved: if the sequential posted price mechanism has for each buyer the option of either proposing an offer or asking the buyer for its valuation, then a Omega(1/max{1,d}) fraction of the optimal revenue can be extracted, where d denotes the degree of dependence of the valuations, ranging from complete independence (d=0) to arbitrary dependence (d=n-1). Moreover, when we generalize the sequential posted price mechanisms further, such that the mechanism has the ability to make a take-it-or-leave-it offer to the i-th buyer that depends on the valuations of all buyers except i's, we prove that a constant fraction (2-sqrt{e})/4~0.088 of the optimal revenue can be always be extracted.Comment: 29 pages, To appear in WINE 201

I Did Not Accept That: Demonstrating Consent in Online Collection of Personal Data

Privacy in online collection of personal data is currently a much debated topic considering, amongst other reasons, the incidents with well known digital organisations, such as social networks and, in Europe, the recent EU/GDPR regulation. Among other required practices, explicit and simply worded consent from individuals must be obtained before collecting and using personal information. Further, individuals must also be given detailed information about what, how and what for data is collected. Consent is typically obtained at the collection point and, at a single point in time (ignoring updates), associated with Privacy Policies or End-User Agreements. At any moment, both the user and the organization should be able to produce evidence of this consent. This proof should not be disputable which leads us to strong cryptographic properties. The problem we discuss is how to robustly demonstrate such consent was given. We adapt fair-exchange protocols to this particular problem and, upon an exchange of personal data, we are able to produce a cryptographic receipt of acceptance that any party can use to prove consent and elicit non-repudiation. We discuss two broad strategies: a pure peerto-peer scheme and the use of a Trusted Third Party

Learning and innovative elements of strategy adoption rules expand cooperative network topologies

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3 Tables, 12 Figures and 116 reference

Special Agents Can Promote Cooperation in the Population

Cooperation is ubiquitous in our real life but everyone would like to maximize her own profits. How does cooperation occur in the group of self-interested agents without centralized control? Furthermore, in a hostile scenario, for example, cooperation is unlikely to emerge. Is there any mechanism to promote cooperation if populations are given and play rules are not allowed to change? In this paper, numerical experiments show that complete population interaction is unfriendly to cooperation in the finite but end-unknown Repeated Prisoner's Dilemma (RPD). Then a mechanism called soft control is proposed to promote cooperation. According to the basic idea of soft control, a number of special agents are introduced to intervene in the evolution of cooperation. They comply with play rules in the original group so that they are always treated as normal agents. For our purpose, these special agents have their own strategies and share knowledge. The capability of the mechanism is studied under different settings. We find that soft control can promote cooperation and is robust to noise. Meanwhile simulation results demonstrate the applicability of the mechanism in other scenarios. Besides, the analytical proof also illustrates the effectiveness of soft control and validates simulation results. As a way of intervention in collective behaviors, soft control provides a possible direction for the study of reciprocal behaviors

Coalitions among computationally bounded agents

This paper analyzes coalitions among self-interested agents that need to solve combinatorial optimization problems to operate e ciently in the world. By colluding (coordinating their actions by solving a joint optimization prob-lem) the agents can sometimes save costs compared to operating individually. A model of bounded rationality is adopted where computation resources are costly. It is not worthwhile solving the problems optimally: solution quality is decision-theoretically traded o against computation cost. A normative, application- and protocol-independent theory of coalitions among bounded-rational agents is devised. The optimal coalition structure and its stability are signi cantly a ected by the agents ' algorithms ' performance pro les and the cost of computation. This relationship is rst analyzed theoretically. Then a domain classi cation including rational and bounded-rational agents is in-troduced. Experimental results are presented in vehicle routing with real data from ve dispatch centers. This problem is NP-complete and the instances are so large that|with current technology|any agent's rationality is bounded by computational complexity.

Adaptive multiagent reinforcement learning with non-positive regret

LNAI 9992We propose a novel adaptive reinforcement learning (RL) procedure for multi-agent non-cooperative repeated games. Most existing regret-based algorithms only use positive regrets in updating their learning rules. In this paper, we adopt both positive and negative regrets in reinforcement learning to improve its convergence behaviour. We prove theoretically that the empirical distribution of the joint play converges to the set of correlated equilibrium. Simulation results demonstrate that our proposed procedure outperforms the standard regret-based RL approach and a well-known state-of-the-art RL scheme in the literature in terms of both computational requirements and system fairness. Further experiments demonstrate that the performance of our solution is robust to variations in the total number of agents in the system; and that it can achieve markedly better fairness performance when compared to other relevant methods, especially in a large-scale multiagent system.Duong D. Nguyen, B, Langford B. White, and Hung X. Nguye

David Leany, Tom Reaume : Light Impressions

The short text in this fold-out pamphlet compares the artists' contrasting photographic techniques and styles: Reaume's use of light to create bold abstract images and Leaney's use in infra-red film to expose a romantic gestural quality in his subjects. Artists' statements and technical procedures included. Biographical notes