Self-Organized Routing For Wireless Micro-Sensor Networks

In this paper we develop an energy-aware self-organized routing algorithm for the networking of simple battery-powered wireless micro-sensors (as found, for example, in security or environmental monitoring applications). In these networks, the battery life of individual sensors is typically limited by the power required to transmit their data to a receiver or sink. Thus effective network routing algorithms allow us to reduce this power and extend both the lifetime and the coverage of the sensor network as a whole. However, implementing such routing algorithms with a centralized controller is undesirable due to the physical distribution of the sensors, their limited localization ability and the dynamic nature of such networks (given that sensors may fail, move or be added at any time and the communication links between sensors are subject to noise and interference). Against this background, we present a distributed mechanism that enables individual sensors to follow locally selfish strategies, which, in turn, result in the self-organization of a routing network with desirable global properties. We show that our mechanism performs close to the optimal solution (as computed by a centralized optimizer), it deals adaptively with changing sensor numbers and topology, and it extends the useful life of the network by a factor of three over the traditional approach

Computational Mechanism Design: A Call to Arms

Game theory has developed powerful tools for analyzing decision making in systems with multiple autonomous actors. These tools, when tailored to computational settings, provide a foundation for building multiagent software systems. This tailoring gives rise to the field of computational mechanism design, which applies economic principles to computer systems design

Forming efficient agent groups for completing complex tasks

In this paper we produce complexity and impossibility results and develop algorithms for a task allocation problem that needs to be solved by a group of autonomous agents working together. In particular, each task is assumed to be composed of several subtasks and involves an associated predetermined and known overall payment (set by the task’s owner) for its completion. However, the division of this payment among the corresponding contributors is not predefined. Now to accomplish a particular task, all its subtasks need to be allocated to agents with the necessary capabilities and the agents’ corresponding costs need to fall within the preset overall task payment. For this scenario, we first provide a cooperative agent system designer with a practical solution that achieves an efficient allocation. However, this solution is not applicable for non-cooperative settings. Consequently, we go on to provide a detailed analysis where we prove that certain design goals cannot be achieved if the agents are self interested. Specifically, we prove that for the general case, no protocol achieving the efficient solution can exist that is individually rational and budget balanced. We show that although efficient protocols may exist in some settings, these will inevitably be setting-specific

Market-Based Task Allocation Mechanisms for Limited Capacity Suppliers

This paper reports on the design and comparison of two economically-inspired mechanisms for task allocation in environments where sellers have finite production capacities and a cost structure composed of a fixed overhead cost and a constant marginal cost. Such mechanisms are required when a system consists of multiple self-interested stakeholders that each possess private information that is relevant to solving a system-wide problem. Against this background, we first develop a computationally tractable centralised mechanism that finds the set of producers that have the lowest total cost in providing a certain demand (i.e. it is efficient). We achieve this by extending the standard Vickrey-Clarke-Groves mechanism to allow for multi-attribute bids and by introducing a novel penalty scheme such that producers are incentivised to truthfully report their capacities and their costs. Furthermore our extended mechanism is able to handle sellers' uncertainty about their production capacity and ensures that individual agents find it profitable to participate in the mechanism. However, since this first mechanism is centralised, we also develop a complementary decentralised mechanism based around the continuous double auction. Again because of the characteristics of our domain, we need to extend the standard form of this protocol by introducing a novel clearing rule based around an order book. With this modified protocol, we empirically demonstrate (with simple trading strategies) that the mechanism achieves high efficiency. In particular, despite this simplicity, the traders can still derive a profit from the market which makes our mechanism attractive since these results are a likely lower bound on their expected returns

Graviton Emission into Non-Z_2 Symmetric Brane World Spacetimes

The equations for the evolution of a homogeneous brane world that emits gravitons at early times, and into a non-Z_2 symmetric bulk, are derived using an AdS-Vaidya spacetime approximation. The behaviour of the black hole mass parameters either side of the brane is analysed, and it is found that in general graviton emission leads to a decrease in the non-Z_2 symmetry. However, the behaviour of the dark radiation term in the Friedmann equation is more complex: it is shown that this term can increase or decrease due to the non-Z_2 symmetry, and can become negative in some cases, leading to H=0 and the brane universe collapsing. Constraints on the initial (nonzero) sizes of the mass parameters are therefore derived

Optimal Design Of English Auctions With Discrete Bid Levels

This paper considers a form of ascending price English auction widely used in both live and online auctions. This discrete bid auction requires that the bidders submit bids at predetermined discrete bid levels, and thus, there exists a minimal increment by which the bid price may be raised. In contrast, the academic literature of optimal auction design deals almost solely with continuous bid auctions. As a result, there is little practical guidance as to how an auctioneer, seeking to maximize its revenue, should determine the number and value of these discrete bid levels, and it is this omission that is addressed here. To this end, a model of a discrete bid auction from the literature is considered, and an expression for the expected revenue of this auction is derived. This expression is used to determine both numerical and analytical solutions for the optimal bid levels, and uniform and exponential bidder’s valuation distributions are compared. Finally, the limiting case where the number of discrete bid levels is large is considered. An analytical expression for the distribution of the optimal discrete bid levels is derived, and an intuitive understanding of how this distribution maximizes the revenue of the auction is developed

The Impact of Augmenting Traditional Instruction with Technology-based, Experiential Exercise

Traditional instruction, under an experiential learning model, is neither the sole nor a sufficient means of learning. Learning systems that engage students as contributors to learning offer opportunities for educators. The availability of technology to support experiential exercises represents one opportunity to augment traditional instruction. In a quasi-experiment, traditional-only instruction was compared with traditional instruction augmented with a technology-based, experiential exercise. The two instructional methods were evaluated based on student learning, intended behavior, and satisfaction. Our findings suggest that augmented instruction significantly increased student learning and satisfaction. The implications for students are clear. However, there are also noteworthy implications for faculty

Online mechanism design for electric vehicle charging

The rapid increase in the popularity of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) is expected to place a considerable strain on the existing electricity grids, due to the high charging rates these vehicles require. In many places, the limited capacity of the local electricity distribution network will be exceeded if many such vehicles are plugged in and left to charge their batteries simultaneously. Thus, it will become increasingly important to schedule the charging of these vehicles, taking into account the vehicle owners’ preferences, and the local constraints on the network. In this paper, we address this setting using online mechanism design and develop a mechanism that incentivises agents (representing vehicle owners) to truthfully reveal their preferences, as well as when the vehicle is available for charging. Existing related online mechanisms assume that agent preferences can be described by a single parameter. However, this is not appropriate for our setting since agents are interested in acquiring multiple units of electricity and can have different preferences for these units, depending on factors such as their expected travel distance. To this end, we extend the state of the art in online mechanism design to multi-valued domains, where agents have non-increasing marginal valuations for each subsequent unit of electricity. Interestingly, we show that, in these domains, the mechanism occasionally requires leaving electricity unallocated to ensure truthfulness. We formally prove that the proposed mechanism is dominant-strategy incentive compatible, and furthermore, we empirically evaluate our mechanism using data from a real-world trial of electric vehicles in the UK. We show that our approach outperforms any fixed price mechanism in terms of allocation efficiency, while performing only slightly worse than a standard scheduling heuristic, which assumes non-strategic agents

Intergenerational Transmission of Reproductive Behavior during the Demographic Transition

New evidence from the Utah Population Database (updp) reveals that at the onset of the fertility transition, reproductive behavior was transmitted across generations—between women and their mothers, as well as between women and their husbands\u27 family of origin. Age at marriage, age at last birth, and the number of children ever born are positively correlated in the data, most strongly among first-born daughters and among cohorts born later in the fertility transition. Intergenerational ties, including the presence of mothers and mothers-in-law, influenced the hazard of progressing to a next birth. The findings suggest that the practice of parity-dependent marital fertility control and inter-birth spacing behavior derived, in part, from the previous generation and that the potential for mothers and mothers-in-law to help in the rearing of children encouraged higher marital fertility

Distinct Quantum States Can Be Compatible with a Single State of Reality

Perhaps the quantum state represents information about reality, and not reality directly. Wave function collapse is then possibly no more mysterious than a Bayesian update of a probability distribution given new data. We consider models for quantum systems with measurement outcomes determined by an underlying physical state of the system but where several quantum states are consistent with a single underlying state---i.e., probability distributions for distinct quantum states overlap. Significantly, we demonstrate by example that additional assumptions are always necessary to rule out such a model.Comment: 5 pages, 2 figure