Sequential Processing of Observations in Human Decision-Making Systems

In this work, we consider a binary hypothesis testing problem involving a group of human decision-makers. Due to the nature of human behavior, each human decision-maker observes the phenomenon of interest sequentially up to a random length of time. The humans use a belief model to accumulate the log-likelihood ratios until they cease observing the phenomenon. The belief model is used to characterize the perception of the human decision-maker towards observations at different instants of time, i.e., some decision-makers may assign greater importance to observations that were observed earlier, rather than later and vice-versa. The global decision-maker is a machine that fuses human decisions using the Chair-Varshney rule with different weights for the human decisions, where the weights are determined by the number of observations that were used by the humans to arrive at their respective decisions

Augmented Human Machine Intelligence for Distributed Inference

With the advent of the internet of things (IoT) era and the extensive deployment of smart devices and wireless sensor networks (WSNs), interactions of humans and machine data are everywhere. In numerous applications, humans are essential parts in the decision making process, where they may either serve as information sources or act as the final decision makers. For various tasks including detection and classification of targets, detection of outliers, generation of surveillance patterns and interactions between entities, seamless integration of the human and the machine expertise is required where they simultaneously work within the same modeling environment to understand and solve problems. Efficient fusion of information from both human and sensor sources is expected to improve system performance and enhance situational awareness. Such human-machine inference networks seek to build an interactive human-machine symbiosis by merging the best of the human with the best of the machine and to achieve higher performance than either humans or machines by themselves. In this dissertation, we consider that people often have a number of biases and rely on heuristics when exposed to different kinds of uncertainties, e.g., limited information versus unreliable information. We develop novel theoretical frameworks for collaborative decision making in complex environments when the observers may include both humans and physics-based sensors. We address fundamental concerns such as uncertainties, cognitive biases in human decision making and derive human decision rules in binary decision making. We model the decision-making by generic humans working in complex networked environments that feature uncertainties, and develop new approaches and frameworks facilitating collaborative human decision making and cognitive multi-modal fusion. The first part of this dissertation exploits the behavioral economics concept Prospect Theory to study the behavior of human binary decision making under cognitive biases. Several decision making systems involving humans\u27 participation are discussed, and we show the impact of human cognitive biases on the decision making performance. We analyze how heterogeneity could affect the performance of collaborative human decision making in the presence of complex correlation relationships among the behavior of humans and design the human selection strategy at the population level. Next, we employ Prospect Theory to model the rationality of humans and accurately characterize their behaviors in answering binary questions. We design a weighted majority voting rule to solve classification problems via crowdsourcing while considering that the crowd may include some spammers. We also propose a novel sequential task ordering algorithm to improve system performance for classification in crowdsourcing composed of unreliable human workers. In the second part of the dissertation, we study the behavior of cognitive memory limited humans in binary decision making and develop efficient approaches to help memory constrained humans make better decisions. We show that the order in which information is presented to the humans impacts their decision making performance. Next, we consider the selfish behavior of humans and construct a unified incentive mechanism for IoT based inference systems while addressing the selfish concerns of the participants. We derive the optimal amount of energy that a selfish sensor involved in the signal detection task must spend in order to maximize a certain utility function, in the presence of buyers who value the result of signal detection carried out by the sensor. Finally, we design a human-machine collaboration framework that blends both machine observations and human expertise to solve binary hypothesis testing problems semi-autonomously. In networks featuring human-machine teaming/collaboration, it is critical to coordinate and synthesize the operations of the humans and machines (e.g., robots and physical sensors). Machine measurements affect human behaviors, actions, and decisions. Human behavior defines the optimal decision-making algorithm for human-machine networks. In today\u27s era of artificial intelligence, we not only aim to exploit augmented human-machine intelligence to ensure accurate decision making; but also expand intelligent systems so as to assist and improve such intelligence

Looking Through Whiteness: Objectivity, Racism, Method, and Responsibility

Does a white philosopher have anything of value to offer to the philosophy of race and racism? If this philosophical subfield must embrace subjective experience, why should we value the perspective of white philosophers whose racial identity is often occluded by racial normativity and who lack substantive experiences of being on the receiving end of racism? Further, if we should be committed to experience, in what sense can the philosophy of race and racism be “objective”? What should that word mean?Tackling this question first, “objective” should at least mean general, that the ideas of the literature can be coherently integrated. An objective take on racism brings together a plurality of perspectives. What’s wrong with just a plurality of satellite ideas? It implies a fragmented approach to ameliorating racism, where different specialists have different recommendations. How can racism, generally, be lessened? If major views of racism are unifiable, then we have a general method to ameliorate racism. This project might appear tone-deaf: a white philosopher unifying things by reducing ideas to some central notion. But this unity isn’t about reducing things but rather integrating them in a way that respects difference. Yet, there’s a reason we should be interested in the white perspective. Whites can speak about racism from a participatory perspective. If whites are knowledgeable, and believe themselves to have no implicit bias, they may suppose they’re “beyond” racism or no longer at risk for perpetuating it. I explore this idea in a psychologically realistic way via my notion of overlooking, where ameliorating racism from the white perspective is an ongoing project. I end by considering how racism is applicable to other philosophical ideas beyond its typical or circumscribed purview. Here, I re-frame responsibility, arguing that we needn’t be forced to choose between responsibility models divided into individual versus social camps. We ought to instead think of responsibility in terms of power, which provides a realistic lens by which persons and groups are held to account. In being more generally convincing, it might actually get folks to take responsibility where they might not otherwise—theory in service of praxis

09-04 "Sociology, Economics, and Gender: Can Knowledge of the Past Contribute to a Better Future?"

This essay explores the profoundly gendered nature of the split between the disciplines of economics and sociology which took place in the late 19th and early 20th centuries, emphasizing implications for the relatively new field of economic sociology. Drawing on historical documents and feminist studies of science, it investigates the gendered processes underlying the divergence of the disciplines in definition, method, and degree of engagement with social problems. Economic sociology has the potential to heal this disciplinary split, but only if the field is broadened, deepened, and made wiser and more self-reflective through the use of feminist analysis.

"Misery Business?": The Contribution of Corpus-Driven Critical Discourse Analysis to Understanding Gender-Variant Twitter Users' Experiences of Employment

This contribution is a corpus-based analysis of gender-variant discourse on Twitter, exploring users’ strategies for organizing their experience and understanding of employment. The data are two specialized corpora: (1) the biographies of each of 2,881 self-identifying gender-variant users; (2) c.4,000,000 tweets posted by those users. The corpora are analyzed using a sociocognitive approach to discourse analysis (Van Dijk, 2009, 2015, 2017). The biographies are used to determine the demographic make-up of the sample. An analysis of the corpus of users’ tweets will explore, and attempt to explain, the activated discourses around aspects of employment (i.e. representations of the self-as-employee, co-worker relationships, employers, and experiences in employment). In considering the contribution linguistics can make in understanding gender-variant people’s experiences of employment, the focus of this research is three-fold: (1) I consider the role of gender-variant users’ cognitive organization of employment experience in either perpetuating or challenging marginalization in the workplace; (2) I consider the validity and reliability of a corpus-driven analysis in comparison to the credibility and validity of previous studies on the employment experiences of gender-variant people; (3) I consider the logical and ethical implications of considering only the roles of employers, policymakers, and co-workers in remedying marginalization in the workplace