Matching Items (3)
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- All Subjects: Game Theory
- All Subjects: MapReduce
- Creators: Chen, Yi
Description
One of the salient challenges of sustainability is the Tragedy of the Commons, where individuals acting independently and rationally deplete a common resource despite their understanding that it is not in the group's long term best interest to do so. Hardin presents this dilemma as nearly intractable and solvable only by drastic, government-mandated social reforms, while Ostrom's empirical work demonstrates that community-scale collaboration can circumvent tragedy without any elaborate outside intervention. Though more optimistic, Ostrom's work provides scant insight into larger-scale dilemmas such as climate change. Consequently, it remains unclear if the sustainable management of global resources is possible without significant government mediation. To investigate, we conducted two game theoretic experiments that challenged students in different countries to collaborate digitally and manage a hypothetical common resource. One experiment involved students attending Arizona State University and the Rochester Institute of Technology in the US and Mountains of the Moon University in Uganda, while the other included students at Arizona State and the Management Development Institute in India. In both experiments, students were randomly assigned to one of three production roles: Luxury, Intermediate, and Subsistence. Students then made individual decisions about how many units of goods they wished to produce up to a set maximum per production class. Luxury players gain the most profit (i.e. grade points) per unit produced, but they also emit the most externalities, or social costs, which directly subtract from the profit of everybody else in the game; Intermediate players produce a medium amount of profit and externalities per unit, and Subsistence players produce a low amount of profit and externalities per unit. Variables influencing and/or inhibiting collaboration were studied using pre- and post-game surveys. This research sought to answer three questions: 1) Are international groups capable of self-organizing in a way that promotes sustainable resource management?, 2) What are the key factors that inhibit or foster collective action among international groups?, and 3) How well do Hardin's theories and Ostrom's empirical models predict the observed behavior of students in the game? The results of gameplay suggest that international cooperation is possible, though likely sub-optimal. Statistical analysis of survey data revealed that heterogeneity and levels of trust significantly influenced game behavior. Specific traits of heterogeneity among students found to be significant were income, education, assigned production role, number of people in one's household, college class, college major, and military service. Additionally, it was found that Ostrom's collective action framework was a better predictor of game outcome than Hardin's theories. Overall, this research lends credence to the plausibility of international cooperation in tragedy of the commons scenarios such as climate change, though much work remains to be done.
ContributorsStanton, Albert Grayson (Author) / Clark, Susan Spierre (Thesis director) / Seager, Thomas (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2014-12
Description
Keyword search provides a simple and user-friendly mechanism for information search, and has become increasingly popular for accessing structured or semi-structured data. However, there are two open issues of keyword search on semi/structured data which are not well addressed by existing work yet.
First, while an increasing amount of investigation has been done in this important area, most existing work concentrates on efficiency instead of search quality and may fail to deliver high quality results from semantic perspectives. Majority of the existing work generates minimal sub-graph results that are oblivious to the entity and relationship semantics embedded in the data and in the user query. There are also studies that define results to be subtrees or subgraphs that contain all query keywords but are not necessarily ``minimal''. However, such result construction method suffers from the same problem of semantic mis-alignment between data and user query. In this work the semantics of how to {\em define} results that can capture users' search intention and then the generation of search intention aware results is studied.
Second, most existing research is incapable of handling large-scale structured data. However, as data volume has seen rapid growth in recent years, the problem of how to efficiently process keyword queries on large-scale structured data becomes important. MapReduce is widely acknowledged as an effective programming model to process big data. For keyword query processing on data graph, first graph algorithms which can efficiently return query results that are consistent with users' search intention are proposed. Then these algorithms are migrated to MapReduce to support big data. For keyword query processing on schema graph, it first transforms a keyword query into multiple SQL queries, then all generated SQL queries are run on the structured data. Therefore it is crucial to find the optimal way to execute a SQL query using MapReduce, which can minimize the processing time. In this work, a system called SOSQL is developed which generates the optimal query execution plan using MapReduce for a SQL query $Q$ with time complexity $O(n^2)$, where $n$ is the number of input tables of $Q$.
First, while an increasing amount of investigation has been done in this important area, most existing work concentrates on efficiency instead of search quality and may fail to deliver high quality results from semantic perspectives. Majority of the existing work generates minimal sub-graph results that are oblivious to the entity and relationship semantics embedded in the data and in the user query. There are also studies that define results to be subtrees or subgraphs that contain all query keywords but are not necessarily ``minimal''. However, such result construction method suffers from the same problem of semantic mis-alignment between data and user query. In this work the semantics of how to {\em define} results that can capture users' search intention and then the generation of search intention aware results is studied.
Second, most existing research is incapable of handling large-scale structured data. However, as data volume has seen rapid growth in recent years, the problem of how to efficiently process keyword queries on large-scale structured data becomes important. MapReduce is widely acknowledged as an effective programming model to process big data. For keyword query processing on data graph, first graph algorithms which can efficiently return query results that are consistent with users' search intention are proposed. Then these algorithms are migrated to MapReduce to support big data. For keyword query processing on schema graph, it first transforms a keyword query into multiple SQL queries, then all generated SQL queries are run on the structured data. Therefore it is crucial to find the optimal way to execute a SQL query using MapReduce, which can minimize the processing time. In this work, a system called SOSQL is developed which generates the optimal query execution plan using MapReduce for a SQL query $Q$ with time complexity $O(n^2)$, where $n$ is the number of input tables of $Q$.
ContributorsShan, Yi (Author) / Chen, Yi (Thesis advisor) / Bansal, Srividya (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2016
Description
Recent years, there has been many attempts with different approaches to the human-robot interaction (HRI) problems. In this paper, the multi-agent interaction is formulated as a differential game with incomplete information. To tackle this problem, the parameter estimation method is utilized to obtain the approximated solution in a real time basis. Previous studies in the parameter estimation made the assumption that the human parameters are known by the robot; but such may not be the case and there exists uncertainty in the modeling of the human rewards as well as human's modeling of the robot's rewards. The proposed method, empathetic estimation, is tested and compared with the ``non-empathetic'' estimation from the existing works. The case studies are conducted in an uncontrolled intersection with two agents attempting to pass efficiently. Results have shown that in the case of both agents having inconsistent belief of the other agent's parameters, the empathetic agent performs better at estimating the parameters and has higher reward values, which indicates the scenarios when empathy is essential: when agent's initial belief is mismatched from the true parameters/intent of the agents.
ContributorsChen, Yi (Author) / Ren, Yi (Thesis advisor) / Zhang, Wenlong (Committee member) / Yong, Sze Zheng (Committee member) / Arizona State University (Publisher)
Created2021