18,613 research outputs found
A new approach for estimating northern peatland gross primary productivity using a satellite-sensor-derived chlorophyll index
No full textCarbon flux models that are largely driven by remotely sensed data can be used to estimate gross primary productivity (GPP) over large areas, but despite the importance of peatland ecosystems in the global carbon cycle, relatively little attention has been given to determining their success in these ecosystems. This paper is the first to explore the potential of chlorophyll-based vegetation index models for estimating peatland GPP from satellite data. Using several years of carbon flux data from contrasting peatlands, we explored the relationships between the MERIS terrestrial chlorophyll index (MTCI) and GPP, and determined whether the inclusion of environmental variables such as PAR and temperature, thought to be important determinants of peatland carbon flux, improved upon direct relationships. To place our results in context, we compared the newly developed GPP models with the MODIS (Moderate Resolution Imaging Spectrometer) GPP product. Our results show that simple MTCI-based models can be used for estimates of interannual and intra-annual variability in peatland GPP. The MTCI is a good indicator of GPP and compares favorably with more complex products derived from the MODIS sensor on a site-specific basis. The incorporation of MTCI into a light use efficiency type model, by means of partitioning the fraction of photosynthetic material within a plant canopy, shows most promise for peatland GPP estimation, outperforming all other models. Our results demonstrate that satellite data specifically related to vegetation chlorophyll content may ultimately facilitate improved quantification of peatland carbon flux dynamics
A mechanism for multiple goods and interdependent valuations
No full textThis paper reports on the design of an auction mechanism for allocating multiple goods when the buyers have interdependent valuations. We cast the problem as a multi-agent system consisting of selfish, rational agents and develop an auction mechanism which is efficient, incentive compatible and individually rational. We first discuss the necessary assumptions that any mechanism developed for this scenario should satisfy so as to achieve the aforementioned properties. We then present our mechanism and show how it is a generalisation of the Vickrey-Clarke-Groves mechanism
Constrained Bandwidth Allocation in Multi-Sensor Information Fusion: A Mechanism Design Approach
No full textSensor networks are increasingly seen as a solution for a large number of environmental, security and military monitoring tasks. Typically, in these networks, noisy data from a number of local sensors is fused to reduce the uncertainty in the global picture. A central issue in this information fusion is the decision of what data should be shared between sensors, in order to maximise the global gain in information, when the bandwidth of the communication network is limited. In this paper, we study the problem from a selfish agent perspective. We show how the uncertainty in the measurement of an event can be cast as a utility function derived from the Kalman filter. We then use the tools of mechanism design to engineer an incentive-compatible mechanism that allows rational selfish agents to individually maximise their own utility, whilst ensuring that the overall utility of the system is also maximised. We apply the mechanism to multi-sensor target detection and consider the complexity of finding an efficient solution with broadcast communication protocols
Trust-Based Mechanism Design
No full textWe define trust-based mechanism design as an augmentation of traditional mechanism design in which agents take into account the degree of trust that they have in their counterparts when determining their allocations. To this end, we develop an efficient, individually rational, and incentive compatible mechanism based on trust. This mechanism is embedded in a task allocation scenario in which the trust in an agent is derived from the reported performance success of that agent by all the other agents in the system. We also empirically study the evolution of our mechanism when iterated and show that, in the long run, it always chooses the most successful and cheapest agents to fulfill an allocation and chooses better allocations than other comparable models when faced with biased reporting
Optimal bidding strategies for simultaneous Vickrey auctions with perfect substitutes
No full textIn this paper, we derive optimal bidding strategies for a global bidder who participates in multiple, simultaneous second-price auctions with perfect substitutes. We first consider a model where all other bidders are local and participate in a single auction. For this case, we prove that, assuming free disposal, the global bidder should always place non-zero bids in all available auctions, irrespective of the local bidders’ valuation distribution. Furthermore, for nondecreasing valuation distributions, we prove that the problem of finding the optimal bids reduces to two dimensions. These results hold both in the case where the number of local bidders is known and when this number is determined by a Poisson distribution. In addition, by combining analytical and simulation results, we demonstrate that similar results hold in the case of several global bidders, provided that the market consists of both global and local bidders. Finally, we address the efficiency of the overall market, and show that information about the number of local bidders is an important determinant for the way in which a global bidder affects efficiency
A Framework for Designing Strategies for Trading Agents
No full textIn this paper, we present a novel multi-layered framework for designing strategies for trading agents. The objective of this work is to provide a framework that will assist strategy designers with the different aspects involved in designing a strategy. At present, such strategies are typically designed in an ad-hoc and intuitive manner with little regard for discerning best practice or attaining reuseability in the design process. Given this, our aim is to put such developments on a more systematic engineering footing. After we describe our framework, we then go on to illustrate its use for a particular type of market mechanism (namely the Continuous Double Auction)
Market-Based Task Allocation Mechanisms for Limited Capacity Suppliers
No full textThis 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
Optimal strategies for bidding agents participating in simultaneous Vickrey auctions with perfect substitutes
No full textWe derive optimal strategies for a bidding agent that participates in multiple, simultaneous second-price auctions with perfect substitutes. We prove that, if everyone else bids locally in a single auction, the global bidder should always place non-zero bids in all available auctions, provided there are no budget constraints. With a budget, however, the optimal strategy is to bid locally if this budget is equal or less than the valuation. Furthermore, for a wide range of valuation distributions, we prove that the problem of finding the optimal bids reduces to two dimensions if all auctions are identical. Finally, we address markets with both sequential and simultaneous auctions, non-identical auctions, and the allocative efficiency of the market
Computational Mechanism Design for Information Fusion within Sensor Networks
No full textConventional centralised information fusion and control architectures will be challenged by developments in sensor networks that allow sophisticated autonomous sensors, owned by different stakeholders with individual goals, to interact and share information. Given this, we advocate the use of tools and techniques from computational mechanism design (CMD), a field at the intersection of computer science, game theory and economics, to address the challenges posed by these networks. In particular, CMD allows us to engineer networks with desirable system-wide properties, in which sensors act as rational selfish agents, each attempting to fulfil their own individuals goals through the exchange of observations and information. In this paper, we present our work developing such networks. Specifically, we discuss our development of a generic and principled information valuation metric for sensor networks and we report our experiences applying it within a real world information fusion sensor network scenario
WhiteDolphin: A TAC travel agent.
No full textIn this paper, we detail our WhiteDolphin agent that was designed for the Trading Agent Competition (TAC) Travel game. Specifically, we employed the multi-layered IKB framework to design our strategy, and describe the intricate cogs involved at the different layers in this complex decision-making process. We focus, in particular, on WhiteDolphin’s strategic behaviour when bidding in the different types of auctions involved in the game, and how the information and knowledge required to support the complex decisions made is gathered and inferred respectively. Finally, we empirically analyse our agent by considering its performance in the 2006 competition where it ranked third
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