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A THEORY OF COMPLEX ADAPTIVE INQUIRING ORGANIZATIONS: APPLICATION TO CONTINUOUS ASSURANCE OF CORPORATE FINANCIAL INFORMATION
Title
A THEORY OF COMPLEX ADAPTIVE INQUIRING ORGANIZATIONS: APPLICATION TO CONTINUOUS ASSURANCE OF CORPORATE FINANCIAL INFORMATION.
Creator
Kuhn, John, Cheney, Paul, University of Central Florida
Abstract / Description
Drawing upon the theories of complexity and complex adaptive systems and the Singerian Inquiring System from C. West Churchman's seminal work The Design of Inquiring Systems the dissertation herein develops a systems design theory for continuous auditing systems. The dissertation consists of discussion of the two foundational theories, development of the Theory of Complex Adaptive Inquiring Organizations (CAIO) and associated design principles for a continuous auditing system supporting a...
Show moreDrawing upon the theories of complexity and complex adaptive systems and the Singerian Inquiring System from C. West Churchman's seminal work The Design of Inquiring Systems the dissertation herein develops a systems design theory for continuous auditing systems. The dissertation consists of discussion of the two foundational theories, development of the Theory of Complex Adaptive Inquiring Organizations (CAIO) and associated design principles for a continuous auditing system supporting a CAIO, and instantiation of the CAIO theory. The instantiation consists of an agent-based model depicting the marketplace for Frontier Airlines that generates an anticipated market share used as an integral component in a mock auditor going concern opinion for the airline. As a whole, the dissertation addresses the lack of an underlying system design theory and comprehensive view needed to build upon and advance the continuous assurance movement and addresses the question of how continuous auditing systems should be designed to produce knowledge – knowledge that benefits auditors, clients, and society as a whole.
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Date Issued
2009
Identifier
CFE0002848, ucf:48052
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002848
ALLIANCES IN GRAPHS: PARAMETERIZED ALGORITHMS ANDON PARTITIONING SERIES-PARALLEL GRAPHS
Title
ALLIANCES IN GRAPHS: PARAMETERIZED ALGORITHMS ANDON PARTITIONING SERIES-PARALLEL GRAPHS.
Creator
Enciso, Rosa, Dutton, Ronald, University of Central Florida
Abstract / Description
Alliances are used to denote agreements between members of a group with similar interests. Alliances can occur between nations, biological sequences, business cartels, and other entities. The notion of alliances in graphs was first introduced by Kristiansen, Hedetniemi, and Hedetniemi in . A defensive alliance in a graph G=(V,E) is a non empty set S⊆V S where, for all x ∈S, |N∩S|≥|N-S|. Consequently, every vertex that is a member of a defensive alliance has at least as...
Show moreAlliances are used to denote agreements between members of a group with similar interests. Alliances can occur between nations, biological sequences, business cartels, and other entities. The notion of alliances in graphs was first introduced by Kristiansen, Hedetniemi, and Hedetniemi in . A defensive alliance in a graph G=(V,E) is a non empty set S⊆V S where, for all x ∈S, |N∩S|≥|N-S|. Consequently, every vertex that is a member of a defensive alliance has at least as many vertices defending it as there are vertices attacking it. Alliances can be used to model a variety of applications such as classification problems, communities in the web distributed protocols, etc . In , Gerber and Kobler introduced the problem of partitioning a graph into strong defensive alliances for the first time as the "Satisfactory Graph Partitioning (SGP)" problem. In his dissertation , Shafique used the problem of partitioning a graph into alliances to model problems in data clustering. Decision problems for several types of alliances and alliance partitions have been shown to be NP-complete. However, because of their applicability, it is of interest to study methods to overcome the complexity of these problems. In this thesis, we will present a variety of algorithms for finding alliances in different families of graphs with a running time that is polynomial in terms of the size of the input, and allowing exponential running time as a function of a chosen parameter. This study is guided by the theory of parameterized complexity introduced by Rod Downey and Michael Fellows in . In addition to parameterized algorithms for alliance related problems, we study the partition of series-parallel graphs into alliances. The class of series-parallel graphs is a special class in graph theory since many problems known to be NP-complete on general graphs have been shown to have polynomial time algorithms on series-parallel graphs. For example, the problem of finding a minimum defensive alliance has been shown to have a linear time algorithm when restricted to series-parallel graphs . Series-parallel graphs have also been to focus of study in a wide range of applications including CMOS layout and scheduling problems [ML86, Oud97]. Our motivation is driven by clustering properties that can be modeled with alliances. We observe that partitioning series-parallel graphs into alliances of roughly the same size can be used to partition task graphs to minimize the communication between processors and balance the workload of each processor. We present a characterization of series-parallel graphs that allow a partition into defensive alliances and a subclass of series-parallel graphs with a satisfactory partitions.
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Date Issued
2009
Identifier
CFE0002956, ucf:47945
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002956
The Diffusion and Performance of the Accountable Care Organization Model
Title
The Diffusion and Performance of the Accountable Care Organization Model.
Creator
Lin, Yi-ling, Wan, Thomas, Malvey, Donna, Liu, Albert Xinliang, Steen, Julie, University of Central Florida
Abstract / Description
Background: Unity in pursuit of the Triple Aim: better health, better care, and lower per capita cost, can be achieved through a well-designed health care delivery system. The accountable care organizations (ACOs) model is considered a key component of health care delivery system improvement because the model fosters better coordination of care through clinical integration and financial accountability. Within the six Centers for Medicaid (&) Medicare Services (CMS) ACO programs, the Medicare...
Show moreBackground: Unity in pursuit of the Triple Aim: better health, better care, and lower per capita cost, can be achieved through a well-designed health care delivery system. The accountable care organizations (ACOs) model is considered a key component of health care delivery system improvement because the model fosters better coordination of care through clinical integration and financial accountability. Within the six Centers for Medicaid (&) Medicare Services (CMS) ACO programs, the Medicare Shared Savings Program (MSSP) ACO has the largest size with a total of 432 ACOs formed; the service subjects of the MSSP ACO are the fee-for-service beneficiaries. Recently, academicians and researchers have been attracted to exploring ACOs' formation and performance. However, most of the early ACO research types are either descriptive or case study. Also, early researchers had limited access to ACO data sets, so they could utilize only regional and demographic factors to identify the predictors of ACO formation.Purpose: An integrative theoretical framework, Rogers' diffusion of innovation theory and Duncan's POET model, was used to examine ACO formation and performance. The first purpose of this study was to determine the relative influences of contextual variables and ACO characteristic variables on how early an ACO model was adopted. The second purpose was to examine how executives' perceptions of ACO performance and the ACO first-year performance are influenced by the contextual variables, ACO characteristic variables, and timing of the adoption of an ACO model. Methods: A cross-sectional design was formulated to gather data from a survey supplemented by secondary data with the analysis unit at the organization level. Study participants in the ACO survey included 2012, 2013, 2014, and 2015 ACO cohorts. Logistic regression was performed to examine the effects of POET and Rogers' five core characteristics in the early adoption of an ACO model (dichotomous). Additionally, multiple linear regression analysis was used to examine the effects of POET and the timing of adoption of an ACO model in the perceptions of ACO performance. ACO first-year performance dataset consisted only of ACO cohorts from 2012 through 2014. Finally, confirmatory factor analysis and structural equation modeling were conducted to examine the measurement model of the ACO first-year performance and a full latent variable model, respectively. Major Findings: A survey of ACO executives/managers between October 2015 and February 2016 was conducted. The 447 MSSP ACOs in my mailing list yielded a response rate of 13.65 % (n=61). Of the 61 MSSP ACOs, 42 (52.5%) were late adopters whose contractual agreement with CMS started in 2014 or 2015, and 36 (59.0%) were with hospital-based composition. Among ACOs that participated in my survey, their current degree of IT adoption in functionalities (62.27 vs. 52.50 points), usage levels (65.19 vs. 49.49 points), and integration levels (62.24 vs. 53.37 points) were better than their initial years. The multiple logistic regression presented that MSSP ACOs were more likely to be early adopters of a CMS if their service areas had high unemployment rates (OR=2.23; 95% CI: 1.13 - 4.39). In the multiple linear regression analysis, the executives in the early ACOs perceived their organizations as more effective than the late adopters, with 12.65 points higher in an aggregate of eight ACO quality domains (p = .005). Three hundred and seventeen MSSP ACOs, with contractual agreements with CMS before 2015, had retained their year-one performance records (the actual ACO performance with eight quality domains). The variability in the actual ACO performance was explained by the predictor variables of the study with an R-square of 15%. The actual ACO performance was likely to be improved if ACOs had more Medicare assigned beneficiaries or had the hospital-based composition. On the other hand, if ACOs' service areas were located in areas of high poverty concentration, a high unemployment rate, or a lower competitive index, their ACO performance was relatively lower than their counterparts. Implications: The findings suggest that managers should consider strategies to increase economies of scale in size and to have hospital involvement in their ACOs in order to increase effective management. Inadequate capital for information technology improvements is the biggest barrier inhibiting healthcare providers' willingness to join an ACO. Regardless of rural or urban areas, financial support is still important for those potential ACO participants who are planning to invest in necessary infrastructure. ACOs that involved hospitals also showed better performance than those ACOs without hospital involvement. This information may help health policy makers to define core principles of the best ACO model in the future. Conclusions: This study makes a unique contribution using a theoretically integrative framework with Rogers' diffusion of innovation theory coupled with Duncan's POET model to examine ACO formation and ACO performance. In the early ACO adopters, three-fifths of the ACOs had hospital involvement; and the levels of their current IT degree in functionalities, usage levels, and integration levels are higher than the late ACO adopters. This study demonstrates that contextual variables, such as unemployment rates at ACO service areas, relatively influence how early an ACO model was adopted. Executives in the early ACOs had higher perceptions of overall organizational effectiveness as compared with the late adopters. The first-year performance of 2012, 2013, and 2014 ACO cohorts is positively influenced by the size of assigned Medicare beneficiaries and hospital-based ACO and is negatively influenced by the poverty rate, unemployment rate, and market competition scores (Herfindah-Hirschman Index).
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Date Issued
2016
Identifier
CFE0006347, ucf:51576
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006347
A Fitness Function Elimination Theory for Blackbox Optimization and Problem Class Learning
Title
A Fitness Function Elimination Theory for Blackbox Optimization and Problem Class Learning.
Creator
Anil, Gautham, Wu, Annie, Wiegand, Rudolf, Stanley, Kenneth, Clarke, Thomas, Jansen, Thomas, University of Central Florida
Abstract / Description
The modern view of optimization is that optimization algorithms are not designed in a vacuum, but can make use of information regarding the broad class of objective functions from which a problem instance is drawn. Using this knowledge, we want to design optimization algorithms that execute quickly (efficiency), solve the objective function with minimal samples (performance), and are applicable over a wide range of problems (abstraction). However, we present a new theory for blackbox...
Show moreThe modern view of optimization is that optimization algorithms are not designed in a vacuum, but can make use of information regarding the broad class of objective functions from which a problem instance is drawn. Using this knowledge, we want to design optimization algorithms that execute quickly (efficiency), solve the objective function with minimal samples (performance), and are applicable over a wide range of problems (abstraction). However, we present a new theory for blackbox optimization from which, we conclude that of these three desired characteristics, only two can be maximized by any algorithm.We put forward an alternate view of optimization where we use knowledge about the problem class and samples from the problem instance to identify which problem instances from the class are being solved. From this Elimination of Fitness Functions approach, an idealized optimization algorithm that minimizes sample counts over any problem class, given complete knowledge about the class, is designed. This theory allows us to learn more about the difficulty of various problems, and we are able to use it to develop problem complexity bounds.We present general methods to model this algorithm over a particular problem class and gain efficiency at the cost of specifically targeting that class. This is demonstrated over the Generalized Leading-Ones problem and a generalization called LO**, and efficient algorithms with optimal performance are derived and analyzed. We also tighten existing bounds for LO***. Additionally, we present a probabilistic framework based on our Elimination of Fitness Functions approach that clarifies how one can ideally learn about the problem class we face from the objective functions. This problem learning increases the performance of an optimization algorithm at the cost of abstraction.In the context of this theory, we re-examine the blackbox framework as an algorithm design framework and suggest several improvements to existing methods, including incorporating problem learning, not being restricted to blackbox framework and building parametrized algorithms. We feel that this theory and our recommendations will help a practitioner make substantially better use of all that is available in typical practical optimization algorithm design scenarios.
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Date Issued
2012
Identifier
CFE0004511, ucf:49268
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004511
Learning Dynamic Network Models for Complex Social Systems
Title
Learning Dynamic Network Models for Complex Social Systems.
Creator
Hajibagheri, Alireza, Sukthankar, Gita, Turgut, Damla, Chatterjee, Mainak, Lakkaraju, Kiran, University of Central Florida
Abstract / Description
Human societies are inherently complex and highly dynamic, resulting in rapidly changing social networks, containing multiple types of dyadic interactions. Analyzing these time-varying multiplex networks with approaches developed for static, single layer networks often produces poor results. To address this problem, our approach is to explicitly learn the dynamics of these complex networks. This dissertation focuses on five problems: 1) learning link formation rates; 2) predicting changes in...
Show moreHuman societies are inherently complex and highly dynamic, resulting in rapidly changing social networks, containing multiple types of dyadic interactions. Analyzing these time-varying multiplex networks with approaches developed for static, single layer networks often produces poor results. To address this problem, our approach is to explicitly learn the dynamics of these complex networks. This dissertation focuses on five problems: 1) learning link formation rates; 2) predicting changes in community membership; 3) using time series to predict changes in network structure; 4) modeling coevolution patterns across network layers and 5) extracting information from negative layers of a multiplex network.To study these problems, we created a rich dataset extracted from observing social interactions in the massively multiplayer online game Travian. Most online social media platforms are optimized to support a limited range of social interactions, primarily focusing on communication and information sharing. In contrast, relations in massively-multiplayer online games (MMOGs) are often formed during the course of gameplay and evolve as the game progresses. To analyze the players' behavior, we constructed multiplex networks with link types for raid, communication, and trading.The contributions of this dissertation include 1) extensive experiments on the dynamics of networks formed from diverse social processes; 2) new game theoretic models for community detection in dynamic networks; 3) supervised and unsupervised methods for link prediction in multiplex coevolving networks for both positive and negative links. We demonstrate that our holistic approach for modeling network dynamics in coevolving, multiplex networks outperforms factored methods that separately consider temporal and cross-layer patterns.
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Date Issued
2017
Identifier
CFE0006598, ucf:51306
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006598