Browsing by Subject "Cooperation"
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Item Competition and collaboration issues in technology development and deployment(2007) Erzurumlu, Sadik Sinan; Gilbert, Stephen M.In today's marketplace firms have to become specialized in specific technological aspects in product development due to intensifying competition. Further, the increasing complexity of offerings make firms become more dependent on other value-chain contributors such as providers of complementary and component technologies. Therefore, in addition to the inherent market of appeal of product, a successful introduction may depend on the firm's interactions with suppliers and even "competitors". These interactions with other firms in the marketplace present a unique set of challenges to firms. In this dissertation, we explore how a firm's approach to interacting with supply chain partners and/or competitors may depend upon how its product provides value to customers. In the first essay, we look into how a firm should design the interdependence between a durable good and a consumable such as a printer and a cartridge and utilize the benefits of an industry of generic consumable suppliers. In the second essay, we analyze the different approaches that firms adopt while commercializing their technologies to competitors in a networked environment (such as telecommunications). We identify the impact of the competitor's development capabilities on the trade-off between the increased competition and network benefits. In the third essay, we explore situations in which firms collaborate to develop a component innovation that they later market individually; they codevelop and jointly market; and they choose to individually develop and market. We consider how competitive strategies between development partners should consider the influence of supplier formation on the investment incentives of an OEM. In summary, this dissertation examines how the management of interactions with supply chain partners and competitors can play an important role in technology development and deployment. Our results highlight key trade-offs and provide insights for managers who are involved in developing and deploying new products.Item Cooperation and communication in multiagent deep reinforcement learning(2016-12) Hausknecht, Matthew John; Stone, Peter, 1971-; Ballard, Dana; Mooney, Ray; Miikkulainen, Risto; Singh, SatinderReinforcement learning is the area of machine learning concerned with learning which actions to execute in an unknown environment in order to maximize cumulative reward. As agents begin to perform tasks of genuine interest to humans, they will be faced with environments too complex for humans to predetermine the correct actions using hand-designed solutions. Instead, capable learning agents will be necessary to tackle complex real-world domains. However, traditional reinforcement learning algorithms have difficulty with domains featuring 1) high-dimensional continuous state spaces, for example pixels from a camera image, 2) high-dimensional parameterized-continuous action spaces, 3) partial observability, and 4) multiple independent learning agents. We hypothesize that deep neural networks hold the key to scaling reinforcement learning towards complex tasks. This thesis seeks to answer the following two-part question: 1) How can the power of Deep Neural Networks be leveraged to extend Reinforcement Learning to complex environments featuring partial observability, high-dimensional parameterized-continuous state and action spaces, and sparse rewards? 2) How can multiple Deep Reinforcement Learning agents learn to cooperate in a multiagent setting? To address the first part of this question, this thesis explores the idea of using recurrent neural networks to combat partial observability experienced by agents in the domain of Atari 2600 video games. Next, we design a deep reinforcement learning agent capable of discovering effective policies for the parameterized-continuous action space found in the Half Field Offense simulated soccer domain. To address the second part of this question, this thesis investigates architectures and algorithms suited for cooperative multiagent learning. We demonstrate that sharing parameters and memories between deep reinforcement learning agents fosters policy similarity, which can result in cooperative behavior. Additionally, we hypothesize that communication can further aid cooperation, and we present the Grounded Semantic Network (GSN), which learns a communication protocol grounded in the observation space and reward function of the task. In general, we find that the GSN is effective on domains featuring partial observability and asymmetric information. All in all, this thesis demonstrates that reinforcement learning combined with deep neural network function approximation can produce algorithms capable of discovering effective policies for domains with partial observability, parameterized-continuous actions spaces, and sparse rewards. Additionally, we demonstrate that single agent deep reinforcement learning algorithms can be naturally extended towards cooperative multiagent tasks featuring learned communication. These results represent a non-trivial step towards extending agent-based AI towards complex environments.Item Cooperation and competition: a study of disarmament negotiations and the national interest(Texas Tech University, 1964-05) Mullen, Martha GillilandNot availableItem Distributed Linear Combination Estimators for Localization Based on Received Signal Strength(2013-12-05) Chen, Wei-YuLocating the position of a radio frequency device is indispensable in many wireless applications. The most famous method is the Global Positioning System (GPS), which uses trilateration with satellites, is generally unavailable for indoor devices and expensive for large networks. Therefore, this dissertation aims to develop and discuss accurate, fast, low-cost, energy-efficient, and robust localization algorithms especially based on the received signal strength (RSS). This dissertation proposes a distributed and iterative estimator by linearly combining location estimates from maximum likelihood based range estimates. In non- cooperative cases where unknown-location (blindfolded) devices only utilize the in- formation from known-location devices (anchors), each combining weight is proportional to the reciprocal of the estimated distance squared between the blindfolded node and an anchor. The numerical simulations demonstrate that the proposed LC estimator has similar error behaviors to the maximum likelihood estimator (MLE) and fewer computations under various topologies and noisy wireless environments. If the parameters for the RSS model are unknown, they are estimated by the least square and/or maximum likelihood methods. The accuracy difference of the linear combination estimators by estimated and perfect parameters is acceptable and decreasing as more anchors are deployed. In cooperative localization, a blindfolded node uses information from not only anchors but also other blindfolded nodes. The combining weight is now proportional to the reciprocal of the estimated distance squared and the transmitter?s positioning error. After being mainly compared with the distributed maximum likelihood estimator by coordinate descent method and the distributed weighted-multidimensional scaling (dwMDS) method, the LC estimator performs well in accuracy, computation time, and the use of wireless transmissions under various topologies, connectivities, and noisy environments. Moreover, the estimation error is clipped by upper and lower bounds. The drawback is that the convergence is not guaranteed, although non-convergent cases rarely happen. For the connectivity issue, placing more nodes with smaller transmitting ranges results in fewer connected nodes and less power consumption. However, to improve localization of an existing system, the relative costs of node and consumed power must be considered to determine the lowest cost system. Finally, the density of blindfolded nodes is two to three times to the density of anchors to achieve the same accuracy.Item Effects of communication mode and polling on cooperation in a commons dilemma(Texas A&M University, 2004-11-15) Watrous, Kristen MichelleThis study examined the effects of communication mode, both face-to-face (FTF) and computer-mediated communication (CMC), and polling on cooperation in a commons dilemma. Sixty-seven six-person groups used FISH, a computer program that uses a fishing metaphor to simulate a commons dilemma. Next, groups had a 10-minute discussion period, either FTF or via CMC, in which they devised a strategy for the second FISH session. Groups were randomly assigned to one of four conditions: FTF, no-poll CMC, end-poll CMC, and two-poll CMC. The polls allowed members to determine others' intended behavior, thus enhancing perceived consensus. Finally, groups used the FISH program again. Results indicted that experimental condition influenced consensus, with end-poll CMC groups reaching consensus most often, followed by FTF, two-poll CMC, and no-poll CMC groups. However, groups did not differ across experimental condition on resource pool sustainability or group profit. FTF groups were more satisfied with group performance than no-poll CMC groups and two-poll CMC and FTF groups had similar levels of satisfaction. The strategy the group decided to implement in the second FISH session had a significant effect on group profit but not resource pool sustainability. Thus, the harvest strategy implemented by the group may have been a stronger predictor of performance than experimental condition.Item Genes, judgments, and evolution : the social and political consequences of distributional and differential conflict(2012-05) Meyer, John Michael; Pedahzur, Ami; McDonald, PatrickThe following argument offers a sharper micro-foundational lens for studying human political and social behavior by demonstrating how political science might better incorporate the theory of evolution into its behavioral models, and by showing that differential conflict occasionally prevails over the materialist conflicts depicted in much of the modern social science literature. I take evolutionary psychology's understanding of manifest behavior as a point of departure, and then analyze the manifest behavior in terms of judgments, which are binary measurements at a particular point of reference; in other words, a given manifest behavior either did or did not occur at a particular point in time. I then show that judgments can 1) transmit from one individual to the next, 2) vary according to predictable adaptive processes, and 3) are either extinguished or flourish dependent upon the process of natural selection; judgments, therefore, meet the three requirements of evolutionary theory. Judgments, rather than genes, better describe the process of human political and social evolution, which becomes especially clear when one assesses the consequences of what I term "differential" outcomes in judgments.Item The Tao of coopetition in organizations: culture and categorization of competitive behaviors in teams and working relationships(2009-05) Keller, Josh Wheatly; Huber, George P.; Loewenstein, JeffreyThis dissertation provides a cultural-cognitive perspective on the relationship between cooperation and competition within organizations. Instead of explicitly defining the relationship between cooperation and competition, I examine lay beliefs about the relationship and the impact of these beliefs on perceptions and behavior. This dissertation consists of two studies. In the first study, I examine the role of peoples’ categorization of competitive behaviors as cooperative or non-cooperative in teams. I assess the influence of dialectical reasoning, a culturally-shaped reasoning style, on the categorization of competitive behaviors and the reaction to competitive behaviors within teams. I test my predictions with a laboratory experiment with participants in the US and China. The analyses from this study reveal cultural differences in perceptual and behavioral reactions to competitive behaviors, with differences partially attributed to reasoning style and categorization. In the second study, I examine the role of people’s categorization of competitive behaviors as cooperative or non-cooperative in working relationships. I assess the influence of culture and categorization on people’s ego-centric network of working relationships. I test my predictions with a survey of working professionals in the US and China. The analyses from this study demonstrate that people who categorize certain competitive behaviors as cooperative are more likely to be more cooperative with people they are more competitive with instead of having exclusively cooperative or competitive relationships. The analyses also reveal national cultural differences in people’s networks of working relationships that are partially attributable to categorization of competitive behaviors. By empirically connecting culture and reasoning style to cooperative and competitive behavior in teams and working relationships, this research enhances our understanding of fundamental aspects of organizations, suggesting a new approach to examining the influence of societal factors in behavior within organizations.