An event service supporting autonomic management of ubiquitous systems for e-health

An event system suitable for very simple devices corresponding to a body area network for monitoring patients is presented. Event systems can be used both for self-management of the components as well as indicating alarms relating to patient health state. Traditional event systems emphasise scalability and complex event dissemination for internet based systems, whereas we are considering ubiquitous systems with wireless communication and mobile nodes which may join or leave the system over time intervals of minutes. Issues such as persistent delivery are also important. We describe the design, prototype implementation, and performance characteristics of an event system architecture targeted at this application domain

Mutability and the Determinants of Conceptual Transformability

Features differ in their mutability. For example, a robin could still be a robin even if it lacked a red breast; but it would probably not count as one if it lacked bones. One hypothesis to explain this differential transformability is that having bones is more critical to a biological theory than having a red breast is. W e reject this hypothesis in favor of a theory of mutability based solely on local dependency links and expressed in the form of an iterative equation. W e hypothesize that features are immutable to the extent other features depend on them and offer supporting data

Walking the party line: The growing role of political ideology in shaping health behavior in the United States

Objective To assess the extent to which political ideology affects COVID-19 preventive behaviors and related beliefs and attitudes in the U.S. Methods Two surveys, one using a convenience sample and another using a nationally representative sample, were conducted in September and November 2020, respectively. Multiple regressions compared political ideology with identified COVID-19 risk factors and demographics as well as knowledge measures. Surveys were followed by a review of the emerging COVID-19 behavioral literature (completed in January 2021) to assess the frequency of ideological effects in publicly reported data. Results In the survey data, political ideology was a significant predictor for all dependent variables in both surveys, and the strongest predictor for most of them. Out of 141 estimates from 44 selected studies, political ideology was a significant predictor of responses in 112 (79%) and showed the largest effect on COVID-19-related measures in close to half of these estimates (44%). Conclusions This study reinforces previous research that found partisan differences in engaging in behaviors with long-term health consequences by showing that these ideologically-driven differences manifest in situations where the possibility of severe illness or death is immediate and the potential societal impact is significant. The substantial implications for public health research and practice are both methodological and conceptual

Raising argument strength using negative evidence: A constraint on models of induction

Both intuitively, and according to similarity-based theories of induction, relevant evidence raises argument strength when it is positive and lowers it when it is negative. In three experiments, we tested the hypothesis that argument strength can actually increase when negative evidence is introduced. Two kinds of argument were compared through forced choice or sequential evaluation: single positive arguments (e.g., “Shostakovich’s music causes alpha waves in the brain; therefore, Bach’s music causes alpha waves in the brain”) and double mixed arguments (e.g., “Shostakovich’s music causes alpha waves in the brain, X’s music DOES NOT; therefore, Bach’s music causes alpha waves in the brain”). Negative evidence in the second premise lowered credence when it applied to an item X from the same subcategory (e.g., Haydn) and raised it when it applied to a different subcategory (e.g., AC/DC). The results constitute a new constraint on models of induction

Causality and the semantics of provenance

Provenance, or information about the sources, derivation, custody or history of data, has been studied recently in a number of contexts, including databases, scientific workflows and the Semantic Web. Many provenance mechanisms have been developed, motivated by informal notions such as influence, dependence, explanation and causality. However, there has been little study of whether these mechanisms formally satisfy appropriate policies or even how to formalize relevant motivating concepts such as causality. We contend that mathematical models of these concepts are needed to justify and compare provenance techniques. In this paper we review a theory of causality based on structural models that has been developed in artificial intelligence, and describe work in progress on a causal semantics for provenance graphs.Comment: Workshop submissio

Label Entrenchment Heuristic in Political Communities

Hemmatian and Sloman (2018) showed that the degree to which a label is perceived to be entrenched in society impacts the judged quality of the categorical explanation that invokes it, irrespective of how informative the explanation actually is ("label entrenchment heuristic"). Across four experiments, we show that US partisans rate the informativeness of a circular categorical explanation as higher when the label it invokes is perceived to be entrenched in a particular political community than when the label is not entrenched in any community irrespective of whether the label belongs to the political in-group or the political out-group. Furthermore, we demonstrate that one's relationship to the community that entrenches the label mediates the effect: Democrats find the categorical explanations to be more persuasive when the label they invoke is entrenched in the Democratic political community than when it is entrenched in the Republican political community, and vice versa for Republicans

Causality in Solving Economic Problems

The role of causal beliefs in people’s decisions when faced with economic problems was investigated. Two experiments are reported that vary the causal structure in prisoner’s dilemma-like economic situations. We measured willingness to cooperate or defect and collected justifications and think-aloud protocols to examine the strategies that people used to perform the tasks. We found: (i) Individuals who assumed a direct causal influence of their own action upon their competitor’s action tended to be more cooperative in competitive situations. (ii) A variety of different strategies was used to perform these tasks. (iii) Strategies indicative of a direct causal influence led to more cooperation. (iv) Temporal cues were not enough for participants to infer a particular causal relation. It is concluded that people are sensitive to causal structure in these situations, a result consistent with a causal model theory of choice (Sloman & Hagmayer, 2006)

Knowledge overconfidence is associated with anti-consensus views on controversial scientific issues

Public attitudes that are in opposition to scientific consensus can be disastrous and include rejection of vaccines and opposition to climate change mitigation policies. Five studies examine the interrelationships between opposition to expert consensus on controversial scientific issues, how much people actually know about these issues, and how much they think they know. Across seven critical issues that enjoy substantial scientific consensus, as well as attitudes toward COVID-19 vaccines and mitigation measures like mask wearing and social distancing, results indicate that those with the highest levels of opposition have the lowest levels of objective knowledge but the highest levels of subjective knowledge. Implications for scientists, policymakers, and science communicators are discussed

A comparison of methods to elicit causal structure

We compare two methods to elicit graphs from people that represent the causal structure of common artifacts. One method asks participants to focus narrowly on local causal relations and is based on the “make-a-difference” view of causality, specifically on an interventional theory of causality and so we call it “Intervention.” It asks subjects to answer a series of counterfactual questions. The second method draws directly from the graphical aspect of Causal Bayesian Networks and allows people to consider causal structure at a more global level. It involves drawing causal graphs using an online interface called “Loopy.” This method does not depend on a definition of causal relatedness. We use signal detection theory to analyze the likelihoods of people generating correct and incorrect causal relations (hit rates and false alarm rates, respectively) using each method. The results show that the intervention method leads people to generate more accurate causal models