1,721,163 research outputs found
Using Priced Options to Solve the Exposure Problem in Sequential Auctions
No full textWe propose a priced options model for solving the exposure problem of bidders with valuation synergies participating in a sequence of online auctions. We consider a setting in which complementary-valued items are offered sequentially by different sellers, who have the choice of either selling their item directly or through a priced option. In our model, the seller fixes the exercise price for this option, and then sells it through a first-price auction. We analyze this model from a decision-theoretic perspective and we show, for a setting where the competition is formed by local bidders (which desire a single item), that using options can increase the expected profit for both sides. Furthermore, we derive the equations that provide minimum and maximum bounds between which the bids of the synergy buyer are expected to fall, in order for both sides of the market to have an incentive to use the options mechanism. Next, we perform an experimental analysis of a market in which multiple synergy buyers are active simultaneously. We show that, despite the extra competition, some synergy buyers may benefit, because sellers are forced to set their exercise prices for options at levels which encourage participation of all buyers
Flexibly Priced Options: A New Mechanism for Sequential Auctions with Complementary Goods
No full textIn this paper we present a novel option pricing mechanism for reducing the exposure problem encountered by bidders with complementary valuations when participating in sequential, second-price auction markets. Existing option pricing models have two main drawbacks: they either apply a fixed exercise price, which may deter bidders with low valuations, thereby decreasing allocative efficiency, or options are offered for free, in which case bidders are less likely to exercise them, thereby reducing seller revenues. Our novel mechanism with flexibly priced options addresses these problems by calculating the exercise price as well as the option price based on the bids in an auction. For this novel setting we derive the optimal strategies for a bidding agent with complementary preferences. Furthermore, to compare our approach to existing ones, we derive, for the first time, the bidding strategies for a fixed price mechanism, in which exercise prices for options are fixed by the seller. Finally, we use these strategies to empirically evaluate the proposed option mechanism and compare it to existing ones, both in terms of the seller revenue and the social welfare. We show that our new mechanism achieves higher market efficiency, while still ensuring higher revenues for the seller than direct sale auctions (without options)
Constructing smart portfolios from data driven quantitative investment models
No full textIn this paper we present a smart portfolio management methodology, which advances existing portfolio management techniques at two distinct levels. First, we develop a set of investment models that target regimes found in the data over different time horizons. We then build a meta-model which uses the Kelly criterion to determine an optimal allocation over these investment strategies, thus simultaneously capturing regimes operating in the data over different time horizons. Finally, in order to detect changes in the relevant data regime, and hence investment allocations, we use a forecasting algorithm which relies on a Kalman filter. We call our combined method, that uses both the Kelly criterion and the Kalman filter, the K2 algorithm. Using a large-scale historical dataset of both stocks and indices, we show that our K2 algorithm gives better risk adjusted returns in terms of the Sharpe ratio, better average gain to average loss ratio and higher probability of success compared to existing benchmarks, when measured in out-of-sample test
Prediction-of-use games: a cooperative game theory approach to sustainable energy tariffs
Full text linkData for "Online Mechanism Design for Vehicle-to-Grid Car Parks" in Proc. IJCAI-16
No full textThis archive contains data from our simulations, as described in the paper.
Please see the included README file for further information.</span
Efficient buyer groups for prediction-of-use electricity tariffs
Full text linkCopyright © 2014, Association for the Advancement of Artificial Intelligence.Current electricity tariffs do not reflect the real cost that customers incur to suppliers, as units are charged at the same rate, regardless of how predictable each customers consumption is. A recent proposal to address this problem are prediction-of-use tariffs. In such tariffs, a customer is asked in advance to predict her future consumption, and is charged based both on her actual consumption and the deviation from her prediction. Prior work (Vinyals et al. 2014) studied the cost game induced by a single such tariff, and showed customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. In this work we study the efficient (i.e. cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing prediction-of-use tariffs are available. We propose a polynomial time algorithm to compute efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic electricity consumers in the UK
Redistribution in Online Mechanisms
Full text linkFollowing previous work on payment redistribution in static mechanisms, we develop the theory of redistribution in online mechanisms (e.g., [2, 10, 8]). In static mechanisms, redistribution is important as it increases social welfare in scenarios with no residual claimant. Many online scenarios also do not have a natural residual claimant, and redistribution there is equally important. In this work, we adopt a fundamental online mechanism design model where a single expiring item is allocated in each of T periods. Agents with unit demand are present in the market between their arrival and departure periods, which are private information along with the value an agent attributes to the item. For this model, we derive a number of properties characterizing redistribution in online mechanisms (including revenue monotonicity properties, and quantifying the effect an agent can have on the total revenue). We then design two redistribution functions. The first one generalizes the static redistribution proposed by Cavallo [2] making redistribution after the departure of the last agent. For this redistribution function we provide theoretical worst-case guarantees. The second function is truly online making redistribution to each agent on her departure. The performance of both functions is evaluated using numerical simulations. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved
Online mechanism design for vehicle-to-grid car parks
Full text linkVehicle-to-grid (V2G) is a promising approach whereby electric vehicles (EVs) are used to store excess electricity supply (e.g., from renewable sources), which is sold back to the grid in times of scarcity. In this paper we consider the setting of a smart car park, where EVs come and go, and can be used for V2G while parked. We develop novel allocation and payment mechanisms which truthfully elicit the EV owners' preferences and constraints, including arrival, departure, required charge, as well as the costs of discharging due to loss of efficiency of the battery. The car park will schedule the charging and discharging of each EV, ensuring the constraints of the EVs are met, and taking into consideration predictions about future electricity prices. Optimally solving the global problem is intractable, and we present three novel heuristic online scheduling algorithms. We show that, under certain conditions, two of these satisfy monotonicity and are therefore truthful. We furthermore evaluate the algorithms using simulations, and we show that some of our algorithms benefit significantly from V2G, achieving positive benefit for the car park even when agents do not pay for using it
Emergence of consensus and shared vocabularies in collaborative tagging systems
No full textThis article uses data from the social bookmarking site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail. Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other websites that employ tagging
A model-based online mechanism with pre-commitment and its application to electric vehicle charging
No full textWe introduce a novel online mechanism that schedules the allocation of an expiring and continuously-produced resource to self-interested agents with private preferences. A key application of our mechanism is the charging of pure electric vehicles, where owners arrive dynamically over time, and each owner requires a minimum amount of charge by its departure to complete its next trip. To truthfully elicit the agents' preferences in this setting, we introduce the new concept of pre-commitment: Whenever an agent is selected, our mechanism pre-commits to charging the vehicle by its reported departure time, but maintains flexibility about when the charging takes place and at what rate. Furthermore, to make effective allocation decisions we use a model-based approach by modifying Consensus, a well-known online optimisation algorithm. We show that our pre-commitment mechanism with modified Consensus incentivises truthful reporting. Furthermore, through simulations based on real-world data, we show empirically that the average utility achieved by our mechanism is 93% or more of the offline optimal
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