Evidentialist Foundationalist Argumentation in Multi-Agent Systems

This dissertation focuses on the explicit grounding of reasoning in evidence directly sensed from the physical world. Based on evidence from human problem solving and successes, this is a straightforward basis for reasoning: to solve problems in the physical world, the information required for solving them must also come from the physical world. What is less straightforward is how to structure the path from evidence to conclusions. Many approaches have been applied to evidence-based reasoning, including probabilistic graphical models and Dempster-Shafer theory. However, with some exceptions, these traditional approaches are often employed to establish confidence in a single binary conclusion, like whether or not there is a blizzard, rather than developing complex groups of scalar conclusions, like where a blizzard's center is, what area it covers, how strong it is, and what components it has. To form conclusions of the latter kind, we employ and further develop the approach of Computational Argumentation. Specifically, this dissertation develops a novel approach to evidence-based argumentation called Evidentialist Foundationalist Argumentation (EFA). The method is a formal instantiation of the well-established Argumentation Service Platform with Integrated Components (ASPIC) framework. There are two primary approaches to Computational Argumentation. One approach is structured argumentation where arguments are structured with premises, inference rules, conclusions, and arguments based on the conclusions of other arguments, creating a tree-like structure. The other approach is abstract argumentation where arguments interact at a higher level through an attack relation. ASPIC unifies the two approaches. EFA instantiates ASPIC specifically for the purpose of reasoning about physical evidence in the form of sensor data. By restricting ASPIC specifically to sensor data, special philosophical and computational advantages are gained. Specifically, all premises in the system (evidence) can be treated as firmly grounded axioms and all arguments' conclusions can be numerically calculated directly from their premises. EFA could be used as the basis for well-justified, transparent reasoning in many domains including engineering, law, business, medicine, politics, and education. To test its utility as a basis for Computational Argumentation, we apply EFA to a Multi-Agent System working in the problem domain of Sensor Webs on the specific problem of Decentralized Sensor Fusion. In the Multi-Agent Decentralized Sensor Fusion problem, groups of individual agents are assigned to sensor stations that are distributed across a geographical area, forming a Sensor Web. The goal of the system is to strategically share sensor readings between agents to increase the accuracy of each individual agent's model of the geophysical sensing situation. For example, if there is a severe storm, a goal may be for each agent to have an accurate model of the storm's heading, severity, and focal points of activity. Also, since the agents are controlling a Sensor Web, another goal is to use communication judiciously so as to use power efficiently. To meet these goals, we design a Multi-Agent System called Investigative Argumentation-based Negotiating Agents (IANA). Agents in IANA use EFA as the basis for establishing arguments to model geophysical situations. Upon gathering evidence in the form of sensor readings, the agents form evidence-based arguments using EFA. The agents systematically compare the conclusions of their arguments to other agents. If the agents sufficiently agree on the geophysical situation, they end communication. If they disagree, then they share the evidence for their conclusions, consuming communication resources with the goal of increasing accuracy. They execute this interaction using a Share on Disagreement (SoD) protocol. IANA is evaluated against two other Multi-Agent System approaches on the basis of accuracy and communication costs, using historical real-world weather data. The first approach is all-to-all communication, called the Complete Data Sharing (CDS) approach. In this system, agents share all observations, maximizing accuracy but at a high communication cost. The second approach is based on Kalman Filtering of conclusions and is called the Conclusion Negotiation Only (CNO) approach. In this system, agents do not share any observations, and instead try to infer the geophysical state based only on each other's conclusions. This approach saves communication costs but sacrifices accuracy. The results of these experiments have been statistically analyzed using omega-squared effect sizes produced by ANOVA with p-values < 0.05. The IANA system was found to outperform the CDS system for message cost with high effect sizes. The CDS system outperformed the IANA system for accuracy with only small effect sizes. The IANA system was found to outperform the CNO system for accuracy with mostly high and medium effect sizes. The CNO system outperformed the IANA system for message costs with only small effect sizes. Given these results, the IANA system is preferable for most of the testing scenarios for the problem solved in this dissertation

B Cells Regulate Neutrophilia during Mycobacterium tuberculosis Infection and BCG Vaccination by Modulating the Interleukin-17 Response

We have previously demonstrated that B cells can shape the immune response to Mycobacterium tuberculosis, including the level of neutrophil infiltration and granulomatous inflammation at the site of infection. The present study examined the mechanisms by which B cells regulate the host neutrophilic response upon exposure to mycobacteria and how neutrophilia may influence vaccine efficacy. To address these questions, a murine aerosol infection tuberculosis (TB) model and an intradermal (ID) ear BCG immunization mouse model, involving both the μMT strain and B cell-depleted C57BL/6 mice, were used. IL (interleukin)-17 neutralization and neutrophil depletion experiments using these systems provide evidence that B cells can regulate neutrophilia by modulating the IL-17 response during M. tuberculosis infection and BCG immunization. Exuberant neutrophilia at the site of immunization in B cell-deficient mice adversely affects dendritic cell (DC) migration to the draining lymph nodes and attenuates the development of the vaccine-induced Th1 response. The results suggest that B cells are required for the development of optimal protective anti-TB immunity upon BCG vaccination by regulating the IL-17/neutrophilic response. Administration of sera derived from M. tuberculosis-infected C57BL/6 wild-type mice reverses the lung neutrophilia phenotype in tuberculous μMT mice. Together, these observations provide insight into the mechanisms by which B cells and humoral immunity modulate vaccine-induced Th1 response and regulate neutrophila during M. tuberculosis infection and BCG immunization. © 2013 Kozakiewicz et al

Reducing Implicit Racial Preferences: II Intervention Effectiveness Across Time

Implicit preferences are malleable, but does that change last? We tested 9 interventions (8 real and 1 sham) to reduce implicit racial preferences over time. In 2 studies with a total of 6,321 participants, all 9 interventions immediately reduced implicit preferences. However, none were effective after a delay of several hours to several days. We also found that these interventions did not change explicit racial preferences and were not reliably moderated by motivations to respond without prejudice. Short-term malleability in implicit preferences does not necessarily lead to long-term change, raising new questions about the flexibility and stability of implicit preferences. (PsycINFO Database Recor

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Reducing implicit racial preferences: II. Intervention effectiveness across time.

Implicit preferences are malleable, but does that change last? We tested 9 interventions (8 real and 1 sham) to reduce implicit racial preferences over time. In 2 studies with a total of 6,321 participants, all 9 interventions immediately reduced implicit preferences. However, none were effective after a delay of several hours to several days. We also found that these interventions did not change explicit racial preferences and were not reliably moderated by motivations to respond without prejudice. Short-term malleability in implicit preferences does not necessarily lead to long-term change, raising new questions about the flexibility and stability of implicit preferences. (PsycINFO Database Recor