Market-Based Task Allocation Mechanisms for Limited-Capacity Suppliers

Abstract

Abstract-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.Index Terms-Decision theory, distributed decision making, market-based control (MBC), multiagent systems.

Continuous Double Auctions with Execution Uncertainty

“…In particular, Dash et al adapt the CDA for a scenario where sellers have a limited capacity and a complex cost structure, defined by a fixed overhead cost and a constant marginal cost (Dash et al 2007). In that work, the extension of the CDA is empirically compared to a centralized mechanism that is known to find the optimal allocation, an approach we also adopt in this paper.…”

“…Specifically, in adopting a decentralized approach, we also want to ensure a good system-wide allocation of resources, i.e., we aim to maximize social welfare. Given that we need to deal with large numbers of agents (buyers and sellers in the market-based paradigm), who may enter or leave the market at any time and who want to be able to trade at any time, the Continuous Double Auction (CDA) is an appropriate choice (Dash et al 2007). Indeed, the CDA has already been shown to be a highly efficient mechanism for such cases (Gode and Sunder 1993).…”

Software Agents in Multiple Adaptive Double Auction Markets

“…Besides its prevalence in financial markets, many variants of the DA have been successfully applied as a solution to a multitude of resource allocation problems [1], [2]. The importance of DAs lies in the fact that they exhibit a high allocative efficiency (ratio of traders' actual profit to their theoretical maximum profit) with the implementation of very simple rules.…”

Computational Service Economies: Design and Applications

“…In the former case, we have made advances in the areas of auctions (Dash et al, 2007Rogers et al, 2007a;Vetsikas et al, 2007;Gerding et al, 2008), coalition formation (Dang and Jennings, 2006;Fatima et al, 2009;Rahwan and Jennings, 2007;Chalkiadakis et al, 2008;, automated negotiation (Fatima et al, 2006;Karunatillake et al, 2009;Ramchurn et al, 2007;Fatima et al, 2004), trust and reputation , flexible reasoning strategies for workflows (Stein et al, 2009a) and decentralized coordination (Rogers et al, 2007b, Farinelli et al, 2008. In the latter case, we have built applications using these techniques in areas such as: virtual organizations (Norman et al, 2004), emergency response (Chapman et al, 2009), sensor networks (Padhy et al, 2006;Kho et al, 2009;, mobile sensors (Stranders et al, 2009), computational grids (Stein et al, 2009b) and personalized recommendations (Wei et al, 2005;Payne et al, 2006).…”