A Systematic Approach to Optimise Management in Global Sourcing Relationships

Despite a large number of studies, global IT sourcing projects are, in practice, performed ad-hoc and rely mostly on the manager’s experience. Outsourcing software development presents extra challenges because development is performed in an inter-organisational network. Although organisations collaborate by transferring knowledge from the customer to the vendor (e.g., requirements) and from the vendor to the client (e.g., product and status reports), each has its own interests and needs; which often conflict. Tacit requirements, conflicting interests and knowledge-domain gaps contribute to generate final solutions that cost more in terms of resources than what was originally planned or that do not help customers to meet their ambitions. In this work we propose eStudio, our semi-automatic quantitative-data based framework to provide guidance to reason about obstacles in, for instance, knowledge co-ordination

Bounded Rationality in the Economics of Organization Present Use and (Some) Future Possibilities

The way in which bounded rationality enters contemporary organizational economics theorizing is examined. It is argued that, as it is being used, bounded rationality is neither necessary nor sufficient for producing the results of organizational economics. It is at best a rhetorical device, used for the purpose of loosely explaining incomplete contracts. However, it is possible to incorporate much richer notions of bounded rationality, founded on research in cognitive psychology, and to illuminate the study of economic organization by means of such notions. A number of examples are provided.Varieties of bounded rationality, incomplete contracts, economic organization, cognitive psychology

Can bounded and self-interested agents be teammates? Application to planning in ad hoc teams

Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of self-interested decision-making frameworks. Agents engaged in individual decision making in multiagent settings face the task of having to reason about other agents’ actions, which may in turn involve reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models. For the purposes of this study, individual, self-interested decision making in multiagent settings is modeled using interactive dynamic influence diagrams (I-DID). These are graphical models with the benefit that they naturally offer a factored representation of the problem, allowing agents to ascribe dynamic models to others and reason about them. We demonstrate that an implication of bounded, finitely-nested reasoning by a self-interested agent is that we may not obtain optimal team solutions in cooperative settings, if it is part of a team. We address this limitation by including models at level 0 whose solutions involve reinforcement learning. We show how the learning is integrated into planning in the context of I-DIDs. This facilitates optimal teammate behavior, and we demonstrate its applicability to ad hoc teamwork on several problem domains and configurations

A Framework for Assessing Knowledge Sharing Risks in Interorganizational Networks

Collaboration technologies are making it easier for organizations and knowledge workers to collaborate across organizational boundaries. However, it is necessary for organizations to monitor, regulate and build appropriate security mechanisms in collaboration systems to prevent loss of strategic knowledge and competitive advantage. In this paper, we present a risk assessment framework that can help organizations identify valuable knowledge assets that can be exposed through collaboration technologies, and help prioritize security strategies that can be used to secure the collaboration systems to prevent the loss of valuable knowledge assets. We present an illustrative case to demonstrate the feasibility of the framework, and discuss issues for future research

Improving User Involvement Through Live Collaborative Creation

Creating an artifact - such as writing a book, developing software, or performing a piece of music - is often limited to those with domain-specific experience or training. As a consequence, effectively involving non-expert end users in such creative processes is challenging. This work explores how computational systems can facilitate collaboration, communication, and participation in the context of involving users in the process of creating artifacts while mitigating the challenges inherent to such processes. In particular, the interactive systems presented in this work support live collaborative creation, in which artifact users collaboratively participate in the artifact creation process with creators in real time. In the systems that I have created, I explored liveness, the extent to which the process of creating artifacts and the state of the artifacts are immediately and continuously perceptible, for applications such as programming, writing, music performance, and UI design. Liveness helps preserve natural expressivity, supports real-time communication, and facilitates participation in the creative process. Live collaboration is beneficial for users and creators alike: making the process of creation visible encourages users to engage in the process and better understand the final artifact. Additionally, creators can receive immediate feedback in a continuous, closed loop with users. Through these interactive systems, non-expert participants help create such artifacts as GUI prototypes, software, and musical performances. This dissertation explores three topics: (1) the challenges inherent to collaborative creation in live settings, and computational tools that address them; (2) methods for reducing the barriers of entry to live collaboration; and (3) approaches to preserving liveness in the creative process, affording creators more expressivity in making artifacts and affording users access to information traditionally only available in real-time processes. In this work, I showed that enabling collaborative, expressive, and live interactions in computational systems allow the broader population to take part in various creative practices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145810/1/snaglee_1.pd

Simplifying Context-Aware Agent Coordination Using Context-Sensitive Data Structures

Context-aware computing, an emerging paradigm in which applications sense and adapt their behavior to changes in their operational environment, is key to developing dependable agent-based soft-ware systems for use in the often unpredictable settings of ad hoc net-works. However, designing an application agent which interacts with other agents to gather, maintain, and adapt to context can be a difficult undertaking in an open and continuously changing environment, even for a seasoned programmer. Our goal is to simplify the programming task by hiding the details of agent coordination from the programmer, allowing one to quickly and reliably produce a context-aware application agent for use in large-scale ad hoc networks. With this goal in mind, we introduce a novel abstraction called context-sensitive data structures (CSDS). The programmer interacts with the CSDS through a familiar programming interface, without direct knowledge of the context gathering and maintenance tasks that occur behind the scenes. In this paper, we define a model of context-sensitive data structures, and we identify key requirements and issues associated with building an infrastructure to support the development of context-sensitive data structures

Making friends on the fly : advances in ad hoc teamwork

textGiven the continuing improvements in design and manufacturing processes in addition to improvements in artificial intelligence, robots are being deployed in an increasing variety of environments for longer periods of time. As the number of robots grows, it is expected that they will encounter and interact with other robots. Additionally, the number of companies and research laboratories producing these robots is increasing, leading to the situation where these robots may not share a common communication or coordination protocol. While standards for coordination and communication may be created, we expect that any standards will lag behind the state-of-the-art protocols and robots will need to additionally reason intelligently about their teammates with limited information. This problem motivates the area of ad hoc teamwork in which an agent may potentially cooperate with a variety of teammates in order to achieve a shared goal. We argue that agents that effectively reason about ad hoc teamwork need to exhibit three capabilities: 1) robustness to teammate variety, 2) robustness to diverse tasks, and 3) fast adaptation. This thesis focuses on addressing all three of these challenges. In particular, this thesis introduces algorithms for quickly adapting to unknown teammates that enable agents to react to new teammates without extensive observations. The majority of existing multiagent algorithms focus on scenarios where all agents share coordination and communication protocols. While previous research on ad hoc teamwork considers some of these three challenges, this thesis introduces a new algorithm, PLASTIC, that is the first to address all three challenges in a single algorithm. PLASTIC adapts quickly to unknown teammates by reusing knowledge it learns about previous teammates and exploiting any expert knowledge available. Given this knowledge, PLASTIC selects which previous teammates are most similar to the current ones online and uses this information to adapt to their behaviors. This thesis introduces two instantiations of PLASTIC. The first is a model-based approach, PLASTIC-Model, that builds models of previous teammates' behaviors and plans online to determine the best course of action. The second uses a policy-based approach, PLASTIC-Policy, in which it learns policies for cooperating with past teammates and selects from among these policies online. Furthermore, we introduce a new transfer learning algorithm, TwoStageTransfer, that allows transferring knowledge from many past teammates while considering how similar each teammate is to the current ones. We theoretically analyze the computational tractability of PLASTIC-Model in a number of scenarios with unknown teammates. Additionally, we empirically evaluate PLASTIC in three domains that cover a spread of possible settings. Our evaluations show that PLASTIC can learn to communicate with unknown teammates using a limited set of messages, coordinate with externally-created teammates that do not reason about ad hoc teams, and act intelligently in domains with continuous states and actions. Furthermore, these evaluations show that TwoStageTransfer outperforms existing transfer learning algorithms and enables PLASTIC to adapt even better to new teammates. We also identify three dimensions that we argue best describe ad hoc teamwork scenarios. We hypothesize that these dimensions are useful for analyzing similarities among domains and determining which can be tackled by similar algorithms in addition to identifying avenues for future research. The work presented in this thesis represents an important step towards enabling agents to adapt to unknown teammates in the real world. PLASTIC significantly broadens the robustness of robots to their teammates and allows them to quickly adapt to new teammates by reusing previously learned knowledge.Computer Science