Should network structure matter in agent-based finance?

We derive microscopic foundations for a well-known probabilistic herding model in the agent-based finance literature. Lo and behold, the model is quite robust with respect to behavioral heterogeneity, yet structural heterogeneity, in the sense of an underlying network structure that describes the very feasibility of agent interaction, has a crucial and non-trivial impact on the macroscopic properties of the model

Understanding the importance of side information in graph matching problem

Graph matching algorithms rely on the availability of seed vertex pairs as side information to deanonymize users across networks. Although such algorithms work well in practice, there are other types of side information available which are potentially useful to an attacker. In this thesis, we consider the problem of matching two correlated graphs when an attacker has access to side information either in the form of community labels or an imperfect initial matching. First, we propose a naive graph matching algorithm by introducing the community degree vectors which harness the information from community labels in an e cient manner. Next, we analyze the basic percolation algorithm for graphs with community structure. Finally, we propose a novel percolation algorithm with two thresholds which uses an imperfect matching as input to match correlated graphs. We also analyze these algorithms and provide theoretical guarantees for matching graphs generated using the Stochastic Block Model. We evaluate the proposed algorithms on synthetic as well as real world datasets using various experiments. The experimental results demonstrate the importance of communities as side information especially when the number of seeds is small and the networks are weakly correlated. These results motivate the study of other types of potential side information available to the attacker. Such studies could assist in devising mechanisms to counter the effects of side information in network deanonymization

Data and Design: Advancing Theory for Complex Adaptive Systems

Complex adaptive systems exhibit certain types of behaviour that are difficult to predict or understand using reductionist approaches, such as linearization or assuming conditions of optimality. This research focuses on the complex adaptive systems associated with public health. These are noted for being driven by many latent forces, shaped centrally by human behaviour. Dynamic simulation techniques, including agent-based models (ABMs) and system dynamics (SD) models, have been used to study the behaviour of complex adaptive systems, including in public health. While much has been learned, such work is still hampered by important limitations. Models of complex systems themselves can be quite complex, increasing the difficulty in explaining unexpected model behaviour, whether that behaviour comes from model code errors or is due to new learning. Model complexity also leads to model designs that are hard to adapt to growing knowledge about the subject area, further reducing model-generated insights. In the current literature of dynamic simulations of human public health behaviour, few focus on capturing explicit psychological theories of human behaviour. Given that human behaviour, especially health and risk behaviour, is so central to understanding of processes in public health, this work explores several methods to improve the utility and flexibility of dynamic models in public health. This work is undertaken in three projects. The first uses a machine learning algorithm, the particle filter, to augment a simple ABM in the presence of continuous disease prevalence data from the modelled system. It is shown that, while using the particle filter improves the accuracy of the ABM, when compared with previous work using SD with a particle filter, the ABM has some limitations, which are discussed. The second presents a model design pattern that focuses on scalability and modularity to improve the development time, testability, and flexibility of a dynamic simulation for tobacco smoking. This method also supports a general pattern of constructing hybrid models --- those that contain elements of multiple methods, such as agent-based or system dynamics. This method is demonstrated with a stylized example of tobacco smoking in a human population. The final line of work implements this modular design pattern, with differing mechanisms of addiction dynamics, within a rich behavioural model of tobacco purchasing and consumption. It integrates the results from a discrete choice experiment, which is a widely used economic method for study human preferences. It compares and contrasts four independent addiction modules under different population assumptions. A number of important insights are discussed: no single module was universally more accurate across all human subpopulations, demonstrating the benefit of exploring a diversity of approaches; increasing the number of parameters does not necessarily improve a module's predictions, since the overall least accurate module had the second highest number of parameters; and slight changes in module structure can lead to drastic improvements, implying the need to be able to iteratively learn from model behaviour

The structure and dynamics of multilayer networks

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.Comment: In Press, Accepted Manuscript, Physics Reports 201

A Multidisciplinary Study Of Antecedents To Voluntary Knowledge Contribution Within Online Forums

One challenge faced by online forums is the provision of a sustainable supply of contributions of knowledge (Wasco et al., 2009). Previous studies have identified online trust and perceived critical mass as antecedents of online knowledge contributions. However, the dynamic aspects of antecedents are little investigated. Moreover, how the dynamics together impact on members’ willingness to contribute knowledge is an open question to be further investigated. To examine the dynamic antecedents of online knowledge continuance, this thesis seeks to develop a holistic approach through three studies. Drawing on a decomposed theory of planned behaviour (Taylor and Todd, 1995), study one identifies dynamic antecedents of intentional online contribution behaviours. Covariance-based structural equation modelling analysis of 910 responses obtained shows that perceived critical mass and trust in online forums that mediates trust in members are the highlighted antecedents in the context of online forums. The development of trust in online forums is investigated through a time series approach in study two. Findings using webnographic and machine learning analysis show that the cognitive dimension of institutional trust is essential in initial trust building. Study three uses network analysis techniques to explore the role of critical mass members. Results indicate that only 5% of critical mass members can sustain online forums. However, critical mass members compete for their connections, inferring the importance of brand building in the beginning of online forums development. A summary of findings from the three studies suggests that the structure assurance of online forums can mediate the effects of interactions between members to a coalition of membership over time. The study provides further knowledge on the voluntary contribution within online forums by taking a dynamic approach, while previous studies in this field are predominantly cross-sectional and un-prophetic

Building on the Symptom Network: An Examination of Symptom Networks, Expanded Networks, and Racial Network Comparisons to Understand the Relationship between COVID-19-Related Stressors and Postpartum Psychopathology

Background: Throughout the COVID-19 pandemic, women carried, birthed, and cared for infants in a drastically changed world. For perinatal women, the sudden increase in stressors compounded an already vulnerable time where they are at an elevated risk of developing symptoms of psychopathology. Moreover, the pandemic exacerbated pre-existing racial health disparities and disproportionately impacted Black, Indigenous, and People of Color (BIPOC)— particularly perinatal BIPOC women, due to the intersection of their race and perinatal status. This study investigated the relationships between COVID-19-related stressors and postpartum psychopathology using network analysis. Network analysis is used as an alternative technique for investigating the activation and maintenance of psychopathology and is increasingly used to examine the influence of external variables (e.g., stressors) on network dynamics. The relationship between psychological symptoms and stressors is typically examined in a unilinear manner—that is, stress causes psychopathology or vice versa. By using network analysis, we were able to investigate the bidirectional relationship between COVID-19-related stressors and postpartum psychopathology to reveal new insights into the individual stressor-symptom interactions that may underlie the emergence of psychological disorders for the perinatal population during the pandemic. Methods: Participants (N=630) were recruited via social media and listservs and completed an online Qualtrics survey. Data quality measures were used to identify repeated, incomplete, and potentially fraudulent responses, which were removed prior to data analysis. Goldbricker, inter-item correlations, and variance inflation factor analyses were used to address topological overlap and identify statistically unique items to be included in the networks. A comorbidity symptom network was estimated to investigate the relationship between postpartum depression and anxiety symptoms in all participants. Bridge symptoms between the two conditions were identified using bridge analysis and clique percolation analysis. Next, an expanded model was estimated to investigate the relationship between postpartum symptoms and COVID-19-related stressors. Node-wise predictability and moderation analyses were used to investigate the effects of adding external variables (i.e., positive experiences, maternal functioning domains, and predictors of psychopathology) to the expanded model. Finally, moderated networks were estimated to investigate differences in the structure of the comorbidity network and the expanded network for mothers from different racial and ethnic groups. Results: Fear-based symptoms were central in both the comorbidity and expanded networks and bridged postpartum anxiety and depression symptoms in the comorbidity network. The Depressed Mood and two Home Stress domains were central in the expanded network. Additional bridge symptoms in the comorbidity network included feeling overwhelmed, concentration difficulties, and feeling disliked by others, and in the expanded network included the Postpartum Stress, Emotional Stress, and Difficulty Adjusting domains. Moderation analyses revealed that the more mothers felt competent and the less challenging they perceived their infant’s temperament, the weaker the node connections were in their expanded networks. Furthermore, mothers with a history of prenatal depression, prenatal anxiety, or baby blues had denser expanded networks (i.e., stronger and more unique edges) compared to mothers with no history of these conditions. Contrary to expectations, moderation analyses revealed that: 1) social support and engaging in positive experiences during the pandemic strengthened connections between stressors and symptoms; 2) middle-income mothers had denser networks compared to low- and high-income mothers. Finally, racial network comparisons revealed that Black mothers' comorbidity and expanded networks were denser compared to all other racial groups. Conclusions: Our findings highlight the influence of major contextual changes, such as the COVID-19 pandemic, on network dynamics—that is, previously established peripheral network nodes (e.g., fear) may shift to the center during large-scale events. Therefore, researchers cannot assume that previously identified central nodes will remain as the main drivers of psychopathology irrespective of changes in context, as this may lead to a misdirection of prevention and intervention efforts. Further, our findings underscore that people with multiple intersecting vulnerabilities may be disproportionately impacted by these major events

Robust interventions in network epidemiology

Which individual should we vaccinate to minimize the spread of a disease? Designing optimal interventions of this kind can be formalized as an optimization problem on networks, in which we have to select a budgeted number of dynamically important nodes to receive treatment that optimizes a dynamical outcome. Describing this optimization problem requires specifying the network, a model of the dynamics, and an objective for the outcome of the dynamics. In real-world contexts, these inputs are vulnerable to misspecification---the network and dynamics must be inferred from data, and the decision-maker must operationalize some (potentially abstract) goal into a mathematical objective function. Moreover, the tools to make reliable inferences---on the dynamical parameters, in particular---remain limited due to computational problems and issues of identifiability. Given these challenges, models thus remain more useful for building intuition than for designing actual interventions. This thesis seeks to elevate complex dynamical models from intuition-building tools to methods for the practical design of interventions. First, we circumvent the inference problem by searching for robust decisions that are insensitive to model misspecification.If these robust solutions work well across a broad range of structural and dynamic contexts, the issues associated with accurately specifying the problem inputs are largely moot. We explore the existence of these solutions across three facets of dynamic importance common in network epidemiology. Second, we introduce a method for analytically calculating the expected outcome of a spreading process under various interventions. Our method is based on message passing, a technique from statistical physics that has received attention in a variety of contexts, from epidemiology to statistical inference.We combine several facets of the message-passing literature for network epidemiology.Our method allows us to test general probabilistic, temporal intervention strategies (such as seeding or vaccination). Furthermore, the method works on arbitrary networks without requiring the network to be locally tree-like .This method has the potential to improve our ability to discriminate between possible intervention outcomes. Overall, our work builds intuition about the decision landscape of designing interventions in spreading dynamics. This work also suggests a way forward for probing the decision-making landscape of other intervention contexts. More broadly, we provide a framework for exploring the boundaries of designing robust interventions with complex systems modeling tools

Change-point Problem and Regression: An Annotated Bibliography

The problems of identifying changes at unknown times and of estimating the location of changes in stochastic processes are referred to as the change-point problem or, in the Eastern literature, as disorder . The change-point problem, first introduced in the quality control context, has since developed into a fundamental problem in the areas of statistical control theory, stationarity of a stochastic process, estimation of the current position of a time series, testing and estimation of change in the patterns of a regression model, and most recently in the comparison and matching of DNA sequences in microarray data analysis. Numerous methodological approaches have been implemented in examining change-point models. Maximum-likelihood estimation, Bayesian estimation, isotonic regression, piecewise regression, quasi-likelihood and non-parametric regression are among the methods which have been applied to resolving challenges in change-point problems. Grid-searching approaches have also been used to examine the change-point problem. Statistical analysis of change-point problems depends on the method of data collection. If the data collection is ongoing until some random time, then the appropriate statistical procedure is called sequential. If, however, a large finite set of data is collected with the purpose of determining if at least one change-point occurred, then this may be referred to as non-sequential. Not surprisingly, both the former and the latter have a rich literature with much of the earlier work focusing on sequential methods inspired by applications in quality control for industrial processes. In the regression literature, the change-point model is also referred to as two- or multiple-phase regression, switching regression, segmented regression, two-stage least squares (Shaban, 1980), or broken-line regression. The area of the change-point problem has been the subject of intensive research in the past half-century. The subject has evolved considerably and found applications in many different areas. It seems rather impossible to summarize all of the research carried out over the past 50 years on the change-point problem. We have therefore confined ourselves to those articles on change-point problems which pertain to regression. The important branch of sequential procedures in change-point problems has been left out entirely. We refer the readers to the seminal review papers by Lai (1995, 2001). The so called structural change models, which occupy a considerable portion of the research in the area of change-point, particularly among econometricians, have not been fully considered. We refer the reader to Perron (2005) for an updated review in this area. Articles on change-point in time series are considered only if the methodologies presented in the paper pertain to regression analysis