Learning task performance in market-based task allocation

© 2012 Springer-Verlag. The original publication is available at www.springerlink.com.Presented at the 12th International Conference on Intelligent Autonomous Systems (IAS-12) held June 26-29, 2012, Jeju Island, Korea.DOI: 10.1007/978-3-642-33932-5_57Auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents can be trusted to effectively perform assigned tasks. However, reliable performance of team members may not always be a valid assumption. An approach to learning team member performance is presented, which enables more efficient task assignment. A policy gradient reinforcement learning algorithm is used to learn a cost factor that can be applied individually to auction bids. Experimental results demonstrate that agents that model team member performance using this approach can more efficiently distribute tasks in multi-agent auctions

Resource Allocation through Auction-based Incentive Scheme for Federated Learning in Mobile Edge Computing

openMobile Edge Computing (MEC) combinedly with Federated Learning is con- sidered as most capable solutions to AI-driven services. Most of the studies focus on Federated Learning on security aspects and performance, but the re- search is lacking to establish an incentive mechanism for the devices that are connected with a server to perform different task. In MEC, edge nodes would not participate voluntarily in learning process, nodes differ in the accusation of multi-dimensional resources, which also affects the performance of federated learning. In a competitive market scenario, the auction game theory has been widely popular for designing efficient resource allocation mechanisms, as it particularly focuses on regulating the strategic interactions among the self-interested play- ers.In this thesis, I investigate auction-based approach that based on incentive mechanism and encourage nodes to share their resources and take part in train- ing process as well as to maximize the auction revenue. To achieve this research goal, I developed auction mechanism considering the network dynamics and neglecting the devices computation and design a novel generalized first price auction mechanism to encourage participation of connected devices. Furthermore, I studied the K top best-response bidding strategies that maximize the profits of the resource sellers and guarantee the stability and effectiveness of the auction by satisfying desired economic properties. To this end, I validate the performance of the proposed auction mechanisms and bidding strategies through numerical result analysis.Mobile Edge Computing (MEC) combinedly with Federated Learning is con- sidered as most capable solutions to AI-driven services. Most of the studies focus on Federated Learning on security aspects and performance, but the re- search is lacking to establish an incentive mechanism for the devices that are connected with a server to perform different task. In MEC, edge nodes would not participate voluntarily in learning process, nodes differ in the accusation of multi-dimensional resources, which also affects the performance of federated learning. In a competitive market scenario, the auction game theory has been widely popular for designing efficient resource allocation mechanisms, as it particularly focuses on regulating the strategic interactions among the self-interested play- ers.In this thesis, I investigate auction-based approach that based on incentive mechanism and encourage nodes to share their resources and take part in train- ing process as well as to maximize the auction revenue. To achieve this research goal, I developed auction mechanism considering the network dynamics and neglecting the devices computation and design a novel generalized first price auction mechanism to encourage participation of connected devices. Furthermore, I studied the K top best-response bidding strategies that maximize the profits of the resource sellers and guarantee the stability and effectiveness of the auction by satisfying desired economic properties. To this end, I validate the performance of the proposed auction mechanisms and bidding strategies through numerical result analysis

Beyond product architecture: Division of labour and competence accumulation in complex product development

This paper considers the trade-off between leveraging external sources of innovation by outsourcing design and engineering activities and the ability to develop internal product development competences. The trade-off arises because the division of labor within and across firms' boundaries has a crucial role in shaping competence development processes, especially because the division of labor also influences opportunities for learning by doing. In new product development projects, learning by doing appears to be both a key determinant of competence development and a difficult-to-substitute form of learning. While the division of development tasks is often considered as guided by product architecture, we show that by decoupling the decisions concerning the product architecture and the allocation of development tasks, firms can realize the benefits of outsourcing such tasks while developing new internal competences. Drawing on a longitudinal case study in the automotive industry, we also identify a new organizational lever for shaping competence development paths and for designing firm boundaries. This lever consists in alternating different task allocation schemes over time for different types of development projects. We show why this is a novel solution, what its underlying logic is, and how it enables alleviating the trade-off between the benefits of leveraging external sources of innovation and the opportunities for competence development provided by in-house design and engineering. We discuss implications for theories of organizational boundary design and innovation management.innovation management; organizational boundaries; outsourcing; product architecture; modularity; new product development; template process; automotive industry; Fiat

How and Why Decision Models Influence Marketing Resource Allocations

We study how and why model-based Decision Support Systems (DSSs) influence managerial decision making, in the context of marketing budgeting and resource allocation. We consider several questions: (1) What does it mean for a DSS to be "good?"; (2) What is the relationship between an anchor or reference condition, DSS-supported recommendation and decision quality? (3) How does a DSS influence the decision process, and how does the process influence outcomes? (4) Is the effect of the DSS on the decision process and outcome robust, or context specific? We test hypotheses about the effects of DSSs in a controlled experiment with two award winning DSSs and find that, (1) DSSs improve users' objective decision outcomes (an index of likely realized revenue or profit); (2) DSS users often do not report enhanced subjective perceptions of outcomes; (3) DSSs, that provide feedback in the form of specific recommendations and their associated projected benefits had a stronger effect both on the decision making process and on the outcomes.Our results suggest that although managers actually achieve improved outcomes from DSS use, they may not perceive that the DSS has improved the outcomes. Therefore, there may be limited interest in managerial uses of DSSs, unless they are designed to: (1) encourage discussion (e.g., by providing explanations and support for the recommendations), (2) provide feedback to users on likely marketplace results, and (3) help reduce the perceived complexity of the problem so that managers will consider more alternatives and invest more cognitive effort in searching for improved outcomes.marketing models;resource allocation;DSS;decision process;decision quality

Neural signature of fictive learning signals in a sequential investment task

Reinforcement learning models now provide principled guides for a wide range of reward learning experiments in animals and humans. One key learning (error) signal in these models is experiential and reports ongoing temporal differences between expected and experienced reward. However, these same abstract learning models also accommodate the existence of another class of learning signal that takes the form of a fictive error encoding ongoing differences between experienced returns and returns that "could-have-been-experienced" if decisions had been different. These observations suggest the hypothesis that, for all real-world learning tasks, one should expect the presence of both experiential and fictive learning signals. Motivated by this possibility, we used a sequential investment game and fMRI to probe ongoing brain responses to both experiential and fictive learning signals generated throughout the game. Using a large cohort of subjects (n = 54), we report that fictive learning signals strongly predict changes in subjects' investment behavior and correlate with fMRI signals measured in dopaminoceptive structures known to be involved in valuation and choice