Endogenous Institutions and the Dynamics of Corruption

While empirical studies which analyze large cross section country data find that corruption lowers investment and thereby economic growth, this result cannot be established for certain subsamples of countries. We argue that one reason for these mixed findings may be that a country's corruption and growth rates are tightly linked as variables of a dynamic process which can have several equilibria or have different sets of equilibria. In order to understand the circumstances in which a country converges towards a certain equilibrium, we model the individual decisions to invest and corrupt as an evolutionary game. In this model the quality of government institutions is an endogenous variable, depending on the corruption rate, the population income, and the type of institutions; the quality of institutions itself then determines the future incentives to corrupt. The comprehension of these feedback effects allows us to study the role of the type of institutions for the dynamics of corruption. We present the equilibria for different types of institutions and discuss the resulting dynamics. The results suggest that cross country studies may significantly underestimate the impact of corruption on growth for certain countries. Depending on how the quality of institutions depends on corruption and income, corruption can either lower growth, suppress it entirely, or be positively correlated with growth in some special situationsCorruption; Institutions; Feedback Effects; Evolutionary Game

Replicator Dynamics with Frequency Dependent Stage Games

We analyze evolutionary games with replicator dynamics that have frequency dependent stage games. In such an evolutionary game, the payoffs of a strategy at any point in time are functions of the strategy shares given by the players' strategy choices at that time. This framework is suited to model feedback effects between population variables and individual incentives, indirect network effects, and behavior under social norms. We show that the replicator dynamics with frequency dependent stage games is well behaved, i.e. has unique solutions and is simplex invariant for all initial strategy states. Moreover, we present an extension of Liapunov's Theorem that facilitates the analysis of evolutionary equilibria for frequency dependent evolutionary gamesReplicator Dynamics; Frequency Dependent; State Dependent; Evolutionary Games; Liapunov

Learning, voting and the information trap

We consider a median voter model with uncertainty about how the economy functions. The distribution of income is exogenously given and the provision of a public good is financed through a proportional tax. Voters and politicians do not know the true production function for the public good, but by using Bayes rule they can learn from experience. We show that the economy may converge to an inefficient policy where no further inference is possible so that the economy is stuck in an information trap.Learning; voting and the information trap

Navigating the Kaleidoscope of Object(ive)s: A User-Experience Approach to Cultural-Historical Activity Theory

Activity Theory, specifically third-generation activity theory also known as Cultural-Historical Activity Theory or CHAT (Engeström, 2001, 2015; Leontiev, 1978, 1981; Vygotsky, 1978) has largely been used as a framework for studying different networks of activity, encountered by subjects who utilize tools or mediating artifacts in order to divide their labor within particular communities. This theoretical and empirical project analyzes a transnational user’s experiences performing their identity on Instagram by answering the research question: How does a user with transnational literacy experiences perform their identity and manage communities through the mediation of particular technologies on Instagram? Using mixed-methods from four data streams—1) semi-structured interviews, 2) rhetorical analysis of a participant’s personal Instagram data (including images, captions, account biographies, and stories), 3) recordings of a participant using think-aloud protocol, and 4) analytical memos of the participant’s Instagram activity—in this thesis project I aimed to accomplish three goals. First, to outline and historicize influential generations of Activity Theory. Second, to present a new approach to Cultural-Historical Activity Theory called the “User-Experience CHAT Model.” Third, to apply the new model to a case study. The results of the study suggest that users on social media sites may communicate with particular communities, but also past, present, and future versions of themselves. As users engage in activities across time, they encounter a field of interpretation informed by contexts, which influence their present experiences as they produce an object. Thus, users’ identities are constantly in a state of transformation and becoming as their object(ive)s in social media activities transform across time

Replicator Dynamics with Frequency Dependent Stage Games

We analyze evolutionary games with replicator dynamics that have frequency dependent stage games. In such an evolutionary game, the payoffs of a strategy at any point in time are functions of the strategy shares given by the players strategy choices at that time. This framework is suited to model feedback effects between population variables and individual incentives, indirect network effects, and behavior under social norms. We show that the replicator dynamics with frequency dependent stage games is well behaved, i.e. has unique solutions and is simplex invariant for all initial strategy states. Moreover, we present an extension of Liapunov’s Theorem that facilitates the analysis of evolutionary equilibria for frequency dependent evolutionary games

Freedom, Legality, and the Rule of Law

There are numerous interactions between the rule of law and the concept of freedom. We can see this by looking at Fuller’s eight principles of legality, the positive and negative theories of liberty, coercive and empowering laws, and the formal and substantive rules of law. Adherence to the rules of formal legality promotes freedom by creating stability and predictability in the law, on which the people can then rely to plan their behaviors around the law—this is freedom under the law. Coercive laws can actually promote negative liberty by pulling people out of a Hobbesian state of nature, and then thereafter can be seen to decrease negative liberty by restricting the behaviors that a person can perform without receiving a sanction. Empowering laws promote negative freedom by creating new legal abilities, which the people can perform. The law can enhance positive freedom when it prohibits negative behaviors and promotes positive behaviors. Finally, the content of the law can be used to either promote or suppress individual freedom

Space Object Identification Using Feature Space Trajectory Neural Networks

The Feature Space Trajectory Neural Network (FSTNN) is a simple yet powerful pattern recognition tool developed by Neiberg and Casasent for use in an Automatic Target Recognition System. Since the FSTNN was developed, it has been used on various problems including speaker identification and space object identification. However, in these types of problems, the test set represents time series data rather than an independent set of points. Since the distance metric of the standard FSTNN treats each test point independently without regard to its position in the sequence, the FSTNN can yield less than optimal results in these problems. Two methods for incorporating sequence information into the FSTNN algorithm are presented. These methods, Dynamic Time Warping (DTW) and Uniform Time Warping (UTW), are described and compared to the standard FSTNN performance on the space object identification problem. Both reduce error induced by improper synchronization of the test and training sequences and make the FSTNN more generally applicable to a wide variety of pattern recognition problems. They incorporate sequencing information by synchronizing the test and training trajectories. DTW accomplishes this \u27on-the-fly\u27 as the sequence progresses while UTW uniformly compensates for temporal differences across the trajectories. These algorithms improve the maximum probability of false alarm (PFA) of the standard FSTNN by an average of 10.18% and 27.69%, respectively, although UTW is less consistent in its results. A metric for determining the saliency of the features in an FSTNN is also presented and demonstrated

On the Development of a Resident Monitoring System:Usability, Privacy and Security Aspects

Worldwide, the elderly have suffered disproportionately from the effects of the COVID-19 pandemic, both in terms of their prognosis once contracted the disease and in terms of the preventative measures required for this demographic, who are at much higher risk than the rest of the population. In the “new normal”, the well-being of older adults (residing either in their own homes or in care homes) will be ideally monitored remotely. These measures would preserve the independence of individuals without compromising on their safety. In this paper we discuss aspects of the design and implementation of a resident monitoring system (RMS) with particular emphasis on overcoming the barriers for adoption among these populations, by addressing the aspects of usability, privacy and security at the core of the development of such a system. We discuss the current challenges of this research and future work on the RMS

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection

Perspectives in Microvascular Fluid Handling: Does the Distribution of Coagulation Factors in Human Myocardium Comply with Plasma Extravasation in Venular Coronary Segments?

Background: Heterogeneity of vascular permeability has been suggested for the coronary system. Whereas arteriolar and capillary segments are tight, plasma proteins pass readily into the interstitial space at venular sites. Fittingly, lymphatic fluid is able to coagulate. However, heart tissue contains high concentrations of tissue factor, presumably enabling bleeding to be stopped immediately in this vital organ. The distribution of pro- and anti-coagulatively active factors in human heart tissue has now been determined in relation to the types of microvessels. Methods and Results: Samples of healthy explanted hearts and dilated cardiomyopathic hearts were immunohistochemically stained. Albumin was found throughout the interstitial space. Tissue factor was packed tightly around arterioles and capillaries, whereas the tissue surrounding venules and small veins was practically free of this starter of coagulation. Thrombomodulin was present at the luminal surface of all vessel segments and especially at venular endothelial cell junctions. Its product, the anticoagulant protein C, appeared only at discrete extravascular sites, mainly next to capillaries. These distribution patterns were basically identical in the healthy and diseased hearts, suggesting a general principle. Conclusions: Venular extravasation of plasma proteins probably would not bring prothrombin into intimate contact with tissue factor, avoiding interstitial coagulation in the absence of injury. Generation of activated protein C via thrombomodulin is favored in the vicinity of venular gaps, should thrombin occur inside coronary vessels. This regionalization of distribution supports the proposed physiological heterogeneity of the vascular barrier and complies with the passage of plasma proteins into the lymphatic system of the heart. Copyright (C) 2010 S. Karger AG, Base