153,650 research outputs found
On Design and Analysis of Cyber-Physical Systems with Strategic Agents
In contrast to traditional CPS where a designer can specify an action plan for each agent, in CPS with strategic agents, every agent acts selfishly and chooses his strategy privately so as to maximize his own objective.
In this dissertation, we study problems arising in the design and analysis of CPSs with strategic agents.
We consider two classes of design problems. In the first class, the designer utilizes her control over decisions and resources in the system to incentivize the agents via monetary incentive mechanisms to reveal their private information that is crucial for the efficient operation of the system. In particular, we consider market mechanism design for the integration of renewable energy and flexible loads into power grids. We consider a model that captures the dynamic and intermittent nature of these resources, and demonstrate the advantage of dynamic market mechanism over static market mechanisms that underly the existing architecture of the electricity markets.
In the second class of design problems, the designer utilizes her informational advantage over the agents and employ informational incentive mechanisms to disclose information selectively to the agents so as to influence the agents' decisions. Specifically, we consider the design of public and private information disclosure mechanisms in a transportation system so as to improve the overall congestion.
We also study the analysis of CPS with strategic agents as a stochastic dynamic game of asymmetric information. We present a set of conditions sufficient to characterize an information state for each agent that effectively compresses his private and common information over time. This information state provides a sufficient statistic for decision-making purposes in strategic and non-strategic settings. Accordingly, we provide a sequential decomposition of the dynamic game over time, and formulate a dynamic program that enables us to determine a set of equilibria of the game. The proposed approach generalizes and unifies the existing results for dynamic teams with non-classical information structure and dynamic games with asymmetric information.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140850/1/tavaf_1.pd
Markets are Dead, Long Live Markets
Researchers have long proposed using economic approaches to resource
allocation in computer systems. However, few of these proposals became
operational, let alone commercial. Questions persist about the economic
approach regarding its assumptions, value, applicability, and relevance to
system design. The goal of this paper is to answer these questions. We find
that market-based resource allocation is useful, and more importantly, that
mechanism design and system design should be integrated to produce systems that
are both economically and computationally efficient.Comment: Fix rotation of figure
Learning from Semantic Inconsistencies as the Origin of Dynamic Capabilities in MNCs: Evidence from Pharmaceutical MNCs
This paper focuses on origins of dynamic capabilities in multinational corporations (MNCs). Building on literature in the area of organizational memory and organizational learning, we investigate factors that contribute to subsidiaries of MNCs ability to detach themselves from obsolete knowledge and practices. To construct the theoretical framework, 11 extensive interviews with marketing and sales executives from three pharmaceutical MNCs operated in Iran were conducted. We test our hypotheses using statistical quantitative analysis of data related to 459 observations from subsidiaries of 51 pharmaceutical MNCs during years 2005-2009. We examine the quality of corrective actions taken by subsidiaries of pharmaceutical MNCs subsequent to subsidiaries failing to meet expected performance objectives. Our findings confirm a moderating role for internationalization, span, and the composition of human resources on the quality of corrective actions pursued
Designing Scalable Business Models
Digital business models are often designed for rapid growth, and some relatively young companies have indeed achieved global scale. However despite the visibility and importance of this phenomenon, analysis of scale and scalability remains underdeveloped in management literature. When it is addressed, analysis of this phenomenon is often over-influenced by arguments about economies of scale in production and distribution. To redress this omission, this paper draws on economic, organization and technology management literature to provide a detailed examination of the sources of scaling in digital businesses. We propose three mechanisms by which digital business models attempt to gain scale: engaging both non- paying users and paying customers; organizing customer engagement to allow self- customization; and orchestrating networked value chains, such as platforms or multi-sided business models. Scaling conditions are discussed, and propositions developed and illustrated with examples of big data entrepreneurial firms
What is a networked business?
Due to increasing competitive pressure in their market, many enterprises are implementing changes to the way they conduct business. These changes range from implementing new IT, to redesigning the structure of the organization and entering into all kinds of cooperations with other enterprises, forming what we call a ‘networked business’. In this paper, we try to explain the origin of the networked business from three different, but related, perspectives: resource dependence, transaction cost and IT impact. We also explore some terms that are used to describe interorganizational structures to find their principal components in an attempt to determine relationships between them and find a broad and precise, new definition of the term ‘networked business’
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms
Opportunities for Price Manipulation by Aggregators in Electricity Markets
Aggregators are playing an increasingly crucial role in the integration of
renewable generation in power systems. However, the intermittent nature of
renewable generation makes market interactions of aggregators difficult to
monitor and regulate, raising concerns about potential market manipulation by
aggregators. In this paper, we study this issue by quantifying the profit an
aggregator can obtain through strategic curtailment of generation in an
electricity market. We show that, while the problem of maximizing the benefit
from curtailment is hard in general, efficient algorithms exist when the
topology of the network is radial (acyclic). Further, we highlight that
significant increases in profit are possible via strategic curtailment in
practical settings
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