This paper presents methods for improving the attention span of workers in tasks that heavily rely on their attention to the occurrence of rare events. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. The proposed approach is an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly.

This paper studies a new paradigm for improving the attention span of workers in tasks that heavily rely on user's attention to the occurrence of rare events. Such tasks are highly common, ranging from crime monitoring to controlling autonomous complex machines, and many of them are ideal for crowdsourcing. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. This, as an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly. We use extensive experimentation to compare the methods with the traditional approach of inducing attention through rewarding the identification of the event of interest and within the three. The analysis of the results indicates that with the use of dummy events a substantially more favorable tradeoff between the detection (of the event of interest) probability and the expected expense can be achieved, and that among the three proposed method the one that decides on dummy events on the fly is (by far) the best.

The main challenge in developing a good Intelligent Tutoring System (ITS) is, surely, the question of how to adapt the hardness level of questions and tasks to the current student's capabilities, assuming these are dynamically changing over the tutoring period. According to state of art, most ITS systems make use of the Q-learning algorithm for this adaptation task. Our paper presents innovative results that compare performances of several methods not applied before for ITS to handle the above challenge. In particular as far as we know this is the first attempt to apply the Bayesian inference algorithm for question level matching in ITS. Next, as this is a groundwork research done to identify the best adaptation scheme, we propose to use an artificial environment with simulated students for the evaluation phase. The results were benchmarked with the optimal performance of the system assuming the user model (abilities) is completely known to the ITS. The results show that the method that outperforms the other is based on a Bayesian Inference which achieves more than 90% of the optimal performance. Our conclusion is that it may be worthwhile to integrate Bayesian inference based algorithms to adapt to student's level in the ITS. Future work is required in to confront these empirical results with those of real students.

This article considers a canonical auction protocol that forms the basis of nearly all current online auctions. Such discrete bid auctions require 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 an ascending price English auction with discrete bid levels is considered. An expression for the expected revenue of this auction is derived and used to determine numerical and analytical solutions for the optimal bid levels in the case of uniform and exponential bidder's valuation distributions. Finally, in order to develop an intuitive understanding of how these optimal bid levels are distributed, the limiting case where the number of discrete bid levels is large is considered, and an analytical expression for their distribution is derived.

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 systemwide problem. Against this background, we first develop a computationally tractable centralized 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 multiattribute bids and by introducing a novel penalty scheme such that producers are incentivized 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 centralized, we also develop a complementary decentralized 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

We present a mathematical model of the eBay auction protocol and perform a detailed analysis of the effects that the eBay proxy bidding system and the minimum bid increment have on the auction properties. We first consider the revenue of the auction, and we show analytically that when two bidders with independent private valuations use the eBay proxy bidding system there exists an optimal value for the minimum bid increment at which the auctioneer's revenue is maximized. We then consider the sequential way in which bids are placed within the auction, and we show analytically that independent of assumptions regarding the bidders' valuation distribution or bidding strategy the number of visible bids placed is related to the logarithm of the number of potential bidders. Thus, in many cases, it is only a minority of the potential bidders that are able to submit bids and are visible in the auction bid history (despite the fact that the other hidden bidders are still effectively competing for the item). Furthermore, we show through simulation that the minimum bid increment also introduces an inefficiency to the auction, whereby a bidder who enters the auction late may find that its valuation is insufficient to allow them to advance the current bid by the minimum bid increment despite them actually having the highest valuation for the item. Finally, we use these results to consider appropriate strategies for bidders within real world eBay auctions. We show that while last-minute bidding (sniping) is an effective strategy against bidders engaging in incremental bidding (and against those with common values), in general, delaying bidding is disadvantageous even if delayed bids are sure to be received before the auction closes. Thus, when several bidders submit last-minute bids, we show that rather than seeking to bid as late as possible, a bidder should try to be the first sniper to bid (i.e., it should “snipe before the snipers”).

In this paper we consider a common form of the English auction that is widely used in online Internet auctions. This discrete bid auction requires that the bidders may only submit bids which meet some predetermined discrete bid levels and, thus, there exists a minimal increment with which a bidder may raise the current price. In contrast, the academic literature of optimal auction design deals almost solely with continuous bid auctions, and, as a result, there is little practical guidance as to how an auctioneer, who is seeking to maximise his revenue, should determine the number and value of these discrete bid levels. Consequently, in current online auctions, a fixed bid increment is commonly implemented, despite this having been shown to be optimal in only limited cases.Given this background, in this paper, our aim is to provide the optimal auction design for an English auction with discrete bid levels. To this end, we derive an expression that relates the expected revenue of the auction, to the actual discrete bid levels im-plemented, the number of bidders participating, and the distribution from which the bidders draw their private independent valuations. We use this expression to derive numerical and analytical solutions for the optimal bid levels in the general case. To compare these results with previous work, we apply these solutions to an example, where bidders' valuations are drawn from a uniform distribution. In this case, we prove that when there are more than two bidders, a decreasing bid increment is optimal and we show that the optimal reserve price of the auction increases as the number of bidders increases. Finally, we compare the properties of an auction in which optimal bid levels are used, to the standard auction approach which implements a fixed bid increment. In so doing, we show that the optimal bid levels result in improvements in the revenue, duration and allocative efficiency of the auction.

In this paper, we develop an energy-aware self-organized routing algorithm for the networking of simple battery-powered wireless microsensors (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.