Computational and Data Sciences

Within the archaeological record for Bronze Age Chinese culture, there continues to be a gap in our understanding of the sudden rise of the Erlitou State from the previous late Longshan chiefdoms. In order to examine this period, I develop and use an agent based model (ABM) to explore possible sociopolitically relevant hypotheses for the gap between the demise of the late Longshan cultures and rise of the first state level society in East Asia. I test land use strategy making and collective action in response to drought and flooding scenarios, the two plausible environmental hazards at that time. The model results show cases of emergent behavior where an increase in social complexity could have been experienced if a catastrophic event occurred while the population was sufficiently prepared for a different catastrophe, suggesting a plausible lead for future research into determining the life of the time period.

This abstract discusses a 2017 Winter Simulation Conference presentation on an examination of the Hollywood labor system as a network using an agent based model (ABM) that creates a co-actor network within a movie labor market based on preferential attachment and compares the findings with 50 co-production ego networks during the 2015 movie cycle. Using ABM, the tested hypothesis is that slight individual preference for racial and ethnic similarity within one's own network at the microlevel sufficiently explains the phenomena of Hollywood racial minority underrepresentation at the macrolevel. Using regression analysis of the real-world co-actor networks the tested hypothesis is that minority status affects one's position within the network of successful actors. In both cases, the hypotheses are insufficient to explain the phenomena and this paper proposes further exploration into causes of opportunity loss in accessing the labor market merit further study.

Based on examination of top grossing films it is clear there exists racial underrepresentation in Hollywood movies. I propose a socioeconomic approach to examine the Hollywood labor system as a network using an agent based model (ABM) within a notional movie labor market based on preferential attachment and compare the findings with 50 co-production ego networks during the 2015 movie cycle. Using an ABM, I test the hypothesis that slight individual preference for racial and ethnic similarity within one's own network at the microlevel sufficiently explains the phenomena of Hollywood underrepresentation at the macrolevel. Using regression analysis of the real world 2015 co-actor networks I test the hypothesis that race/ethnic minority status affects ones position within the network of successful actors. In both cases, the hypotheses are not sufficient to explain the phenomena and I propose further exploration into causes of opportunity loss in accessing the labor market.

Human resource management (HRM) draws on the field of organizational theory (OT) to identify, quantify, and manage people-based phenomena that impact organizational operations and outcomes. OT research has long used computational methods and agent-based modeling to understand complex adap-tive systems. Agent-based modeling methodologies within HRM, however, are still rare. Within the HRM and management science literature, Herzberg's et al. (1959) Two-Factor Theory (TFT) is a framework that has been tested and used for decades. Its ability to capture the interaction between a work force's motivation and their environment's hygiene lends itself well to agent-based modeling as a method of study. Here, we present the development of the Human Resources Management-Parameter Experimentation Tool (HRM-PET) as the first explicit ABM instanti-ation of TFT, filling the gap between the study of HRM and computational OT tools like agent-based modeling.

Within the archaeological record for Bronze Age Chinese culture, there continues to be a gap in our understanding of the sudden rise of the Erlitou State from the previous late Longshan chiefdoms. In order to examine this period, I develop and use an agent based model (ABM) to explore possible sociopolitically relevant hypotheses for the gap between the demise of the late Longshan cultures and rise of the first state level society in East Asia. I test land use strategy making and collective action in response to drought and flooding scenarios, the two plausible environmental hazards at that time. The model results show cases of emergent behavior where an increase in social complexity could have been experienced if a catastrophic event occurred while the population was sufficiently prepared for a different catastrophe, suggesting a plausible lead for future research into determining the life of the time period.

Within the archaeological record for Bronze Age Chinese culture, there continues to be a gap in our understanding of the sudden rise of the Erlitou State from the previous late Longshan chiefdoms. In order to examine this period, I develop and use an agent based model (ABM) to explore possible sociopolitically relevant hypotheses for the gap between the demise of the late Longshan cultures and rise of the first state level society in East Asia. I test land use strategy making and collective action in response to drought and flooding scenarios, the two plausible environmental hazards at that time. The model results show cases of emergent behavior where an increase in social complexity could have been experienced if a catastrophic event occurred while the population was sufficiently prepared for a different catastrophe, suggesting a plausible lead for future research into determining the life of the time period.

Computational social science methods and specifically agent-based modeling have increasingly been used within applied social science fields. A significant contributor to this trend has been the availability of fine-grained data about individual and social behavior. While data availability may aid this process, the true power of computational social science arises when data and theory are combined. Theories derived from different social science traditions vary in their development, testing methods, and interpretation of data. The applications of computational methods have largely excluded explicit consideration of what is gained or lost in the translation of theory derived from epistemic traditions that differ from that in computational social science. This dissertation addresses this gap in three ways. First, it defines a framework that may be used in the process of applying inductively derived, qualitatively developed theories. Second, it applies this framework by exploring management science theory. Third, it presents an agent-based model informed by a management science theory and validates it based on human resource management data. This application is presented as a replicable example of both epistemological translation and the computational methods applied.

Based on examination of top grossing films it is clear there exists racial underrepresentation in Hollywood movies. I propose a socioeconomic approach to examine the Hollywood labor system as a network using an agent based model (ABM) within a notional movie labor market based on preferential attachment and compare the findings with 50 co-production ego networks during the 2015 movie cycle. Using an ABM, I test the hypothesis that slight individual preference for racial and ethnic similarity within one's own network at the microlevel sufficiently explains the phenomena of Hollywood underrepresentation at the macrolevel. Using regression analysis of the real world 2015 co-actor networks I test the hypothesis that race/ethnic minority status affects ones position within the network of successful actors. In both cases, the hypotheses are not sufficient to explain the phenomena and I propose further exploration into causes of opportunity loss in accessing the labor market.

This paper proposes an examination of the Hollywood labor system as a network using an agent based model (ABM) that creates a co-actor network within a movie labor market based on preferential attachment and compares the findings with 50 co-production ego networks during the 2015 movie cycle. Using ABM, the tested hypothesis is that slight individual preference for racial and ethnic similarity within one's own network at the microlevel sufficiently explains the phenomena of Hollywood racial minority underrepresentation at the macrolevel. Using regression analysis of the real-world co-actor networks the tested hypothesis is that minority status affects one's position within the network of successful actors. In both cases, the hypotheses are insufficient to explain the phenomena and this paper proposes further exploration into causes of opportunity loss in accessing the labor market merit further study.

Within the archaeological record for Bronze Age Chinese culture, there continues to be a gap in our understanding of the sudden rise of the Erlitou State from the previous late Longshan chiefdoms. In order to examine this period, I develop and use an agent based model (ABM) to explore possible sociopolitically relevant hypotheses for the gap between the demise of the late Longshan cultures and rise of the first state level society in East Asia. I test land use strategy making and collective action in response to drought and flooding scenarios, the two plausible environmental hazards at that time. The model results show cases of emergent behavior where an increase in social complexity could have been experienced if a catastrophic event occurred while the population was sufficiently prepared for a different catastrophe, suggesting a plausible lead for future research into determining the life of the time period.

Data Farming is a methodology and capability that makes use of high performance computing to run models many times. This capability gives modelers and their clients the enhanced ability to discover trends and outlier in results, do sensitivity studies, verify and validate over extended ranges of input parameters, and consider modeling and analyzing non-linear phenomena with characteristics that can not be precisely defined. As high performance computing, in the form of distributed computing capabilities and commodity node systems becomes more pervasive and cost effective, Data Farming can become more available to modelers. In this paper the authors summarize Data Farming and the processes and data architecture of Data Farming systems that make high performance computing readily available to modelers.

Data Farming, network applications and approaches to integrate network analysis and processes to the data farming paradigm are presented as approaches to address complex system questions. Data Farming is a quantified approach that examines questions in large possibility spaces using modeling and simulation. It evaluates whole landscapes of outcomes to draw insights from outcome distributions and outliers. Social network analysis and graph theory are widely used techniques for the evaluation of social systems. Incorporation of these techniques into the data farming process provides analysts examining complex systems with a powerful new suite of tools for more fully exploring and understanding the effect of interactions in complex systems. The integration of network analysis with data farming techniques provides modelers with the capability to gain insight into the effect of network attributes, whether the network is explicitly defined or emergent, on the breadth of the model outcome space and the effect of model inputs on the resultant network statistics.

This event was the 27th in a series of workshops where we have focused on gaining insights into our what-if? questions through data farming. This event also contained the third meeting of the NATO Modeling and Simulation Task Group "Developing Actionable Data Farming Decision Support for NATO." This Task Group has been designated MSG-124 and is applying the work from the recently completed MSG-088 Task Group that documented the data farming process. International What-if? Workshop (IWW) 27 was held from January 26 through 31, 2014. Five teams worked in two locations in Finland, Helsinki and Tarttila. Four of the teams worked at the primary location at the Finnish National Defense University in Helsinki (pictured below) and one at the Päivölä Institute in Tarttila (pictured on page 11). They all had the goal during the week to use data farming methods to explore questions in their teams. These teams were Cyber Defence, Operational Defence Planning, Humanitarian Assistance Modeling, Hierarchic use of Data Farming, and Testing the MSG-088 Data Farming Process with the Sandis Software. I would like to express our thanks to the team leaders, the plenary speakers, and all of the participants in IWW 27! And, of course, a large thank you to the Finnish Defence University for hosting the workshop and all of the Finns who made the workshop possible, especially Niina Nissinen and Jukka Korhonen. This issue, our fifteenth, of the Scythe contains a summary of each team effort. And I would like to offer special thanks to Ted Meyer for putting together this publication. Looking ahead, you are invited to the 28th in the series of workshops where we will focus on gaining insights into our questions through data farming to be held in October 2014 near Washington, DC as shown on the inside back cover of this publication. Also an invitation and more details regarding IWW 28 can be found at www.datafarming.org. We hope to see you there!

International Data Farming Community Overview The International Data Farming Community is a consortium of researchers interested in the study of Data Farming, its methodologies, applications, tools, and evolution. The primary venue for the Community is the biannual Workshops, where researchers participate in team-oriented model development, experimental design, and analysis using high performance computing resources... that is, Data Farming.

Data farming uses simulation modeling, high performance computing, experimental design, and analysis to examine questions of interest with large possibility spaces. This methodology allows for the examination of whole landscapes of potential outcomes and ...

Most of the current software reliability analysis approaches are geared to traditional desktop software systems, which are relatively stable and static throughout their execution. In this paper, we present a framework targeted at mobile computing domain that addresses the uncertainties associated with the reliability analysis in this setting. Moreover, the framework's architecture-centric reliability estimates are leveraged to improve the runtime reliability of the system through dynamic architectural reconfiguration.

Most of the current software reliability analysis approaches are geared to traditional desktop software systems, which are relatively stable and static throughout their execution. In this paper, we present a framework targeted at mobile computing domain that addresses the uncertainties associated with the reliability analysis in this setting. Moreover, the framework's architecture-centric reliability estimates are leveraged to improve the runtime reliability of the system through dynamic architectural reconfiguration.