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- Member of: ASU Electronic Theses and Dissertations
Description
The coastal fishing community of Barrington, Southwest Nova Scotia (SWNS), has depended on the resilience of ocean ecosystems and resource-based economic activities for centuries. But while many coastal fisheries have developed unique ways to govern their resources, global environmental and economic change presents new challenges. In this study, I examine the multi-species fishery of Barrington. My objective was to understand what makes the fishery and its governance system robust to economic and ecological change, what makes fishing households vulnerable, and how household vulnerability and system level robustness interact. I addressed these these questions by focusing on action arenas, their contexts, interactions and outcomes. I used a combination of case comparisons, ethnography, surveys, quantitative and qualitative analysis to understand what influences action arenas in Barrington, Southwest Nova Scotia (SWNS). I found that robustness of the fishery at the system level depended on the strength of feedback between the operational level, where resource users interact with the resource, and the collective-choice level, where agents develop rules to influence fishing behavior. Weak feedback in Barrington has precipitated governance mismatches. At the household level, accounts from harvesters, buyers and experts suggested that decision-making arenas lacked procedural justice. Households preferred individual strategies to acquire access to and exploit fisheries resources. But the transferability of quota and licenses has created divisions between haves and have-nots. Those who have lost their traditional access to other species, such as cod, halibut, and haddock, have become highly dependent on lobster. Based on regressions and multi-criteria decision analysis, I found that new entrants in the lobster fishery needed to maintain high effort and catches to service their debts. But harvesters who did not enter the race for higher catches were most sensitive to low demand and low prices for lobster. This study demonstrates the importance of combining multiple methods and theoretical approaches to avoid tunnel vision in fisheries policy.
ContributorsBarnett, Allain J. D (Author) / Anderies, John M (Thesis advisor) / Abbott, Joshua K (Committee member) / Bolin, Bob (Committee member) / Eakin, Hallie (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this thesis we deal with the problem of temporal logic robustness estimation. We present a dynamic programming algorithm for the robust estimation problem of Metric Temporal Logic (MTL) formulas regarding a finite trace of time stated sequence. This algorithm not only tests if the MTL specification is satisfied by the given input which is a finite system trajectory, but also quantifies to what extend does the sequence satisfies or violates the MTL specification. The implementation of the algorithm is the DP-TALIRO toolbox for MATLAB. Currently it is used as the temporal logic robust computing engine of S-TALIRO which is a tool for MATLAB searching for trajectories of minimal robustness in Simulink/ Stateflow. DP-TALIRO is expected to have near linear running time and constant memory requirement depending on the structure of the MTL formula. DP-TALIRO toolbox also integrates new features not supported in its ancestor FW-TALIRO such as parameter replacement, most related iteration and most related predicate. A derivative of DP-TALIRO which is DP-T-TALIRO is also addressed in this thesis which applies dynamic programming algorithm for time robustness computation. We test the running time of DP-TALIRO and compare it with FW-TALIRO. Finally, we present an application where DP-TALIRO is used as the robustness computation core of S-TALIRO for a parameter estimation problem.
ContributorsYang, Hengyi (Author) / Fainekos, Georgios (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sustainability depends in part on our capacity to resolve dilemmas of the commons in Coupled Infrastructure Systems (CIS). Thus, we need to know more about how to incentivize individuals to take collective action to manage shared resources. Moreover, given that we will experience new and more extreme weather events due to climate change, we need to learn how to increase the robustness of CIS to those shocks. This dissertation studies irrigation systems to contribute to the development of an empirically based theory of commons governance for robust systems. I first studied the eight institutional design principles (DPs) for long enduring systems of shared resources that the Nobel Prize winner Elinor Ostrom proposed in 1990. I performed a critical literature review of 64 studies that looked at the institutional configuration of CIS, and based on my findings I propose some modifications of their definitions and application in research and policy making. I then studied how the revisited design principles, when analyzed conjointly with biophysical and ethnographic characteristics of CISs, perform to avoid over-appropriation, poverty and critical conflicts among users of an irrigation system. After carrying out a meta-analysis of 28 cases around the world, I found that particular combinations of those variables related to population size, countries corruption, the condition of water storage, monitoring of users behavior, and involving users in the decision making process for the commons governance, were sufficient to obtain the desired outcomes. The two last studies were based on the Peruvian Piura Basin, a CIS that has been exposed to environmental shocks for decades. I used secondary and primary data to carry out a longitudinal study using as guidance the robustness framework, and different hypothesis from prominent collapse theories to draw potential explanations. I then developed a dynamic model that shows how at the current situation it is more effective to invest in rules enforcement than in the improvement of the physical infrastructure (e.g. reservoir). Finally, I explored different strategies to increase the robustness of the system, through enabling collective action in the Basin.
ContributorsRubinos, Cathy (Author) / Anderies, John M (Thesis advisor) / Abbott, Joshua K (Committee member) / Janssen, Marcus A (Committee member) / Arizona State University (Publisher)
Created2017
Description
Diffusion processes in networks can be used to model many real-world processes, such as the propagation of a rumor on social networks and cascading failures on power networks. Analysis of diffusion processes in networks can help us answer important questions such as the role and the importance of each node in the network for spreading the diffusion and how to top or contain a cascading failure in the network. This dissertation consists of three parts.
In the first part, we study the problem of locating multiple diffusion sources in networks under the Susceptible-Infected-Recovered (SIR) model. Given a complete snapshot of the network, we developed a sample-path-based algorithm, named clustering and localization, and proved that for regular trees, the estimators produced by the proposed algorithm are within a constant distance from the real sources with a high probability. Then, we considered the case in which only a partial snapshot is observed and proposed a new algorithm, named Optimal-Jordan-Cover (OJC). The algorithm first extracts a subgraph using a candidate selection algorithm that selects source candidates based on the number of observed infected nodes in their neighborhoods. Then, in the extracted subgraph, OJC finds a set of nodes that "cover" all observed infected nodes with the minimum radius. The set of nodes is called the Jordan cover, and is regarded as the set of diffusion sources. We proved that OJC can locate all sources with probability one asymptotically with partial observations in the Erdos-Renyi (ER) random graph. Multiple experiments on different networks were done, which show our algorithms outperform others.
In the second part, we tackle the problem of reconstructing the diffusion history from partial observations. We formulated the diffusion history reconstruction problem as a maximum a posteriori (MAP) problem and proved the problem is NP hard. Then we proposed a step-by- step reconstruction algorithm, which can always produce a diffusion history that is consistent with the partial observations. Our experimental results based on synthetic and real networks show that the algorithm significantly outperforms some existing methods.
In the third part, we consider the problem of improving the robustness of an interdependent network by rewiring a small number of links during a cascading attack. We formulated the problem as a Markov decision process (MDP) problem. While the problem is NP-hard, we developed an effective and efficient algorithm, RealWire, to robustify the network and to mitigate the damage during the attack. Extensive experimental results show that our algorithm outperforms other algorithms on most of the robustness metrics.
In the first part, we study the problem of locating multiple diffusion sources in networks under the Susceptible-Infected-Recovered (SIR) model. Given a complete snapshot of the network, we developed a sample-path-based algorithm, named clustering and localization, and proved that for regular trees, the estimators produced by the proposed algorithm are within a constant distance from the real sources with a high probability. Then, we considered the case in which only a partial snapshot is observed and proposed a new algorithm, named Optimal-Jordan-Cover (OJC). The algorithm first extracts a subgraph using a candidate selection algorithm that selects source candidates based on the number of observed infected nodes in their neighborhoods. Then, in the extracted subgraph, OJC finds a set of nodes that "cover" all observed infected nodes with the minimum radius. The set of nodes is called the Jordan cover, and is regarded as the set of diffusion sources. We proved that OJC can locate all sources with probability one asymptotically with partial observations in the Erdos-Renyi (ER) random graph. Multiple experiments on different networks were done, which show our algorithms outperform others.
In the second part, we tackle the problem of reconstructing the diffusion history from partial observations. We formulated the diffusion history reconstruction problem as a maximum a posteriori (MAP) problem and proved the problem is NP hard. Then we proposed a step-by- step reconstruction algorithm, which can always produce a diffusion history that is consistent with the partial observations. Our experimental results based on synthetic and real networks show that the algorithm significantly outperforms some existing methods.
In the third part, we consider the problem of improving the robustness of an interdependent network by rewiring a small number of links during a cascading attack. We formulated the problem as a Markov decision process (MDP) problem. While the problem is NP-hard, we developed an effective and efficient algorithm, RealWire, to robustify the network and to mitigate the damage during the attack. Extensive experimental results show that our algorithm outperforms other algorithms on most of the robustness metrics.
ContributorsChen, Zhen (Author) / Ying, Lei (Thesis advisor) / Tong, Hanghang (Thesis advisor) / Zhang, Junshan (Committee member) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2018
Description
Anderies (2015); Anderies et al. (2016), informed by Ostrom (2005), aim to employ robust
feedback control models of social-ecological systems (SESs), to inform policy and the
design of institutions guiding resilient resource use. Cote and Nightingale (2012) note that
the main assumptions of resilience research downplay culture and social power. Addressing
the epistemic gap between positivism and interpretation (Rosenberg 2016), this dissertation
argues that power and culture indeed are of primary interest in SES research.
Human use of symbols is seen as an evolved semiotic capacity. First, representation is
argued to arise as matter achieves semiotic closure (Pattee 1969; Rocha 2001) at the onset
of natural selection. Guided by models by Kauffman (1993), the evolution of a symbolic
code in genes is examined, and thereon the origin of representations other than genetic
in evolutionary transitions (Maynard Smith and Szathmáry 1995; Beach 2003). Human
symbolic interaction is proposed as one that can support its own evolutionary dynamics.
The model offered for wider dynamics in society are “flywheels,” mutually reinforcing
networks of relations. They arise as interactions in a domain of social activity intensify, e.g.
due to interplay of infrastructures, mediating built, social, and ecological affordances (An-
deries et al. 2016). Flywheels manifest as entities facilitated by the simplified interactions
(e.g. organizations) and as cycles maintaining the infrastructures (e.g. supply chains). They
manifest internal specialization as well as distributed intention, and so can favor certain
groups’ interests, and reinforce cultural blind spots to social exclusion (Mills 2007).
The perspective is applied to research of resilience in SESs, considering flywheels a
semiotic extension of feedback control. Closer attention to representations of potentially
excluded groups is justified on epistemic in addition to ethical grounds, as patterns in cul-
tural text and social relations reflect the functioning of wider social processes. Participatory
methods are suggested to aid in building capacity for institutional learning.
feedback control models of social-ecological systems (SESs), to inform policy and the
design of institutions guiding resilient resource use. Cote and Nightingale (2012) note that
the main assumptions of resilience research downplay culture and social power. Addressing
the epistemic gap between positivism and interpretation (Rosenberg 2016), this dissertation
argues that power and culture indeed are of primary interest in SES research.
Human use of symbols is seen as an evolved semiotic capacity. First, representation is
argued to arise as matter achieves semiotic closure (Pattee 1969; Rocha 2001) at the onset
of natural selection. Guided by models by Kauffman (1993), the evolution of a symbolic
code in genes is examined, and thereon the origin of representations other than genetic
in evolutionary transitions (Maynard Smith and Szathmáry 1995; Beach 2003). Human
symbolic interaction is proposed as one that can support its own evolutionary dynamics.
The model offered for wider dynamics in society are “flywheels,” mutually reinforcing
networks of relations. They arise as interactions in a domain of social activity intensify, e.g.
due to interplay of infrastructures, mediating built, social, and ecological affordances (An-
deries et al. 2016). Flywheels manifest as entities facilitated by the simplified interactions
(e.g. organizations) and as cycles maintaining the infrastructures (e.g. supply chains). They
manifest internal specialization as well as distributed intention, and so can favor certain
groups’ interests, and reinforce cultural blind spots to social exclusion (Mills 2007).
The perspective is applied to research of resilience in SESs, considering flywheels a
semiotic extension of feedback control. Closer attention to representations of potentially
excluded groups is justified on epistemic in addition to ethical grounds, as patterns in cul-
tural text and social relations reflect the functioning of wider social processes. Participatory
methods are suggested to aid in building capacity for institutional learning.
ContributorsBožičević, Miran (Author) / Anderies, John M (Thesis advisor) / Bolin, Robert (Committee member) / BurnSilver, Shauna (Committee member) / Arizona State University (Publisher)
Created2017
Description
For this thesis a Monte Carlo simulation was conducted to investigate the robustness of three latent interaction modeling approaches (constrained product indicator, generalized appended product indicator (GAPI), and latent moderated structural equations (LMS)) under high degrees of nonnormality of the exogenous indicators, which have not been investigated in previous literature. Results showed that the constrained product indicator and LMS approaches yielded biased estimates of the interaction effect when the exogenous indicators were highly nonnormal. When the violation of nonnormality was not severe (symmetric with excess kurtosis < 1), the LMS approach with ML estimation yielded the most precise latent interaction effect estimates. The LMS approach with ML estimation also had the highest statistical power among the three approaches, given that the actual Type-I error rates of the Wald and likelihood ratio test of interaction effect were acceptable. In highly nonnormal conditions, only the GAPI approach with ML estimation yielded unbiased latent interaction effect estimates, with an acceptable actual Type-I error rate of both the Wald test and likelihood ratio test of interaction effect. No support for the use of the Satorra-Bentler or Yuan-Bentler ML corrections was found across all three methods.
ContributorsCham, Hei Ning (Author) / West, Stephen G. (Thesis advisor) / Aiken, Leona S. (Committee member) / Enders, Craig K. (Committee member) / Arizona State University (Publisher)
Created2010
Description
Artificial Intelligence (AI) systems have achieved outstanding performance and have been found to be better than humans at various tasks, such as sentiment analysis, and face recognition. However, the majority of these state-of-the-art AI systems use complex Deep Learning (DL) methods which present challenges for human experts to design and evaluate such models with respect to privacy, fairness, and robustness. Recent examination of DL models reveals that representations may include information that could lead to privacy violations, unfairness, and robustness issues. This results in AI systems that are potentially untrustworthy from a socio-technical standpoint. Trustworthiness in AI is defined by a set of model properties such as non-discriminatory bias, protection of users’ sensitive attributes, and lawful decision-making. The characteristics of trustworthy AI can be grouped into three categories: Reliability, Resiliency, and Responsibility. Past research has shown that the successful integration of an AI model depends on its trustworthiness. Thus it is crucial for organizations and researchers to build trustworthy AI systems to facilitate the seamless integration and adoption of intelligent technologies. The main issue with existing AI systems is that they are primarily trained to improve technical measures such as accuracy on a specific task but are not considerate of socio-technical measures. The aim of this dissertation is to propose methods for improving the trustworthiness of AI systems through representation learning. DL models’ representations contain information about a given input and can be used for tasks such as detecting fake news on social media or predicting the sentiment of a review. The findings of this dissertation significantly expand the scope of trustworthy AI research and establish a new paradigm for modifying data representations to balance between properties of trustworthy AI. Specifically, this research investigates multiple techniques such as reinforcement learning for understanding trustworthiness in users’ privacy, fairness, and robustness in classification tasks like cyberbullying detection and fake news detection. Since most social measures in trustworthy AI cannot be used to fine-tune or train an AI model directly, the main contribution of this dissertation lies in using reinforcement learning to alter an AI system’s behavior based on non-differentiable social measures.
ContributorsMosallanezhad, Ahmadreza (Author) / Liu, Huan (Thesis advisor) / Mancenido, Michelle (Thesis advisor) / Doupe, Adam (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2023
Description
This work considers the task of vision-and-language inference (VLI): predicting whether an inputthe sentence is true for given images or videos and starts with an investigation of model robustness to
a set of 13 linguistic transformations, categorized as Semantics-Preserving or Semantics-Inverting
based on whether they change the meaning of the sentence. It is observed that existing VLI models
degenerate to close-to-random performance when tested on these linguistic transformations which
include simple phenomena such as synonyms, antonyms, negation, swap-ping of subject and object,
paraphrasing, and the substitutions of pronouns, comparatives, and numbers.
This observation is utilized to design STAT(Semantics-Transformed Adversarial Training) { a
model-agnostic and task-agnostic min-max optimization algorithm, with an inner maximization
that utilizes semantic perturbations of in-put sentences to nd adversarial samples and an outer
maximization that updates model parameters. Extensive experiments on three benchmark datasets
(NLVR2, VIOLIN, VQA \Yes-No") not only demonstrate large gains in robustness to adversarial
input sentences but also show model-agnostic performance improvements. This works also presents
the suite of linguistic transformations as a robustness benchmark that may benet future research
in vision and language robustness.
ContributorsChaudhary, Abhishek (Author) / Yang, Yezhou Dr. (Thesis advisor) / Li, Baoxin Dr. (Committee member) / Baral, Chitta Dr. (Committee member) / Arizona State University (Publisher)
Created2021
Description
Autonomous Driving (AD) systems are being researched and developed actively in recent days to solve the task of controlling the vehicles safely without human intervention. One method to solve such task is through deep Reinforcement Learning (RL) approach. In deep RL, the main objective is to find an optimal control behavior, often called policy performed by an agent, which is AD system in this case. This policy is usually learned through Deep Neural Networks (DNNs) based on the observations that the agent perceives along with rewards feedback received from environment.However, recent studies demonstrated the vulnerability of such control policies learned through deep RL against adversarial attacks. This raises concerns about the application of such policies to risk-sensitive tasks like AD. Previous adversarial attacks assume that the threats can be broadly realized in two ways: First one is targeted attacks through manipu- lation of the agent’s complete observation in real time and the other is untargeted attacks through manipulation of objects in environment. The former assumes full access to the agent’s observations at almost all time, while the latter has no control over outcomes of attack. This research investigates the feasibility of targeted attacks through physical adver- sarial objects in the environment, a threat that combines the effectiveness and practicality.
Through simulations on one of the popular AD systems, it is demonstrated that a fixed optimal policy can be malfunctioned over time by an attacker e.g., performing an unintended self-parking, when an adversarial object is present. The proposed approach is formulated in such a way that the attacker can learn a dynamics of the environment and also utilizes common knowledge of agent’s dynamics to realize the attack. Further, several experiments are conducted to show the effectiveness of the proposed attack on different driving scenarios empirically. Lastly, this work also studies robustness of object location, and trade-off between the attack strength and attack length based on proposed evaluation metrics.
ContributorsBuddareddygari, Prasanth (Author) / Yang, Yezhou (Thesis advisor) / Ren, Yi (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2021
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021