Causal System Scenario Tool for Near Miss Accident Analysis

Sociotechnical systems (STS) approach to the design of complex systems has been researched extensively. It stresses the fact that system design is never complete; it is an open-ended iterative process wherein designing never stops. With increasing technological advancement and diverse social issues emerging, the new dynamics, system approach has to be followed ingenuously. This Causal System Scenario Tool is applied in this study to a near-miss accident analysis for Indian railways. In particular, we look at the incidence of signal passing at danger (SPAD) by the train pilot due to various sociotechnical and organizational factors. We modify the system scenarios tool by proposing an additional causal system scenario tool. The contribution of this paper is threefold. Firstly, the STS analysis of SPAD is a novel approach. Secondly, STS has been applied in the context of developed economies but we use the Indian scenario, that is, in emerging economies. Thirdly, it contributes to the methodology by supplementing STS analysis with the causal system scenario tool, making it more robust. The first step maps the ‘as is situation’ and utilizes affinity diagram for plotting the issues on the framework. This is followed by replicating the process for the ‘to be situation’ and causal modelling for balancing the system is used, validating this tool. This approach can provide insights to the practitioners and policy makers in designing appropriate interventions to enhance safety of train travel.

Transfer of property based on property law rules and contract law rules under forthcoming Kosovo draft-civil code

In most civil law jurisdictions, the contract is the most used derivative title for the transfer of ownership (movable and immovable property). Very often, the law of property and law of contract are seen as distinct and one can envisage their role from different legal perspectives. This is closely connected with the type of transfer system based on whether the (Austrian) causal system, (German) abstract system or (French) consensual system is applicable. Kosovo is in the process of civil law codification and the Kosovo Draft-Civil Code which has followed the application of the causal system of transfer of property and such an above mentioned interaction of these two branches of civil law is mandatory, and only with a common survey can the contractual transfer of property be illustrated. The aim of this paper is to focus solely on the influence of contract law rules in connection with the acquisition of ownership over movable and immovable property determined by Kosovo Draft-Civil Code.

Changing many things at once sometimes makes for a good experiment, and children know that

Changing one variable at a time while controlling others is a key aspect of scientific experimentation and is a central component of STEM curricula. However, children struggle to learn and implement this strategy. Why do children's intuitions about how best to intervene on a causal system conflict with accepted scientific practices? Interestingly, mathematical analyses have shown that controlling variables is not always the most efficient learning strategy, and that its effectiveness depends crucially on the "causal sparsity" of the problem, i.e. how many variables are likely to impact the outcome. We show that children as young as seven are sensitive to the causal sparsity of an unfamiliar causal system and use this information to tailor their testing strategies. Our findings suggest that the education literature, claiming that school children are unable to learn and master the control variables strategy, may have undersold their causal learning skills. Our analyses also help to clarify under what conditions controlling variables is actually a worthwhile approach to scientific inquiry, a fact that might come as a surprise to even professional scientists.

CONCEPTUAL PICTURE OF THE WORLD: THE TYPES OF RELATIONS THAT ARISE IN THE INTERACTION OF OBJECTS

The presence of a connection between the concept and the image of the word gives the opportunity to say that the word reflects (induces) the concept in the human consciousness. The relations that arise between a word and its image in the human consciousness are causal, and the word and its image in the human consciousness form, thus, the causal system. Being elements of the linguistic unit (tokens), the image of the word and the notion form a functional system, because they are interconnected functionally. In the language property (attribute) is one of the main objective characteristics that promotes the identification of the word as a carrier of object characteristics. The fact that in nature exists as a "subject" (functional system of qualities), the language is represented as a system of relations between the attribute and the name, name and predicate, which reflects the structures formed in our consciousness between the concepts of "subject", "quality", "action". Types of interpersonal relations, presented in the language, express the structure of relations between concepts in the picture of the world, which is formed as a result of reflection of real objective relations.

Bereavement Outcomes as Causal Systems: A Network Analysis of the Co-Occurrence of Complicated Grief and Posttraumatic Growth

Bereavement can lead to negative outcomes such as complicated grief (CG), but some mourners with symptoms of CG often experience positive sequelae of loss such as posttraumatic growth (PTG) as well. We propose that grief and growth co-occur and change one another because they alternately reinforce and weaken each other at the level of their respective constituent elements. We investigated the structure of a network of CG and PTG elements to elucidate how grief and growth can co-occur within a potentially causal system in bereaved young adults. Challenges to control and identity disturbance ranked as the most highly central symptoms in the CG network; the discovery of a new life path and greater personal strength were similarly central elements of PTG. Finally, the degree of disruption and change in mourners’ worldviews emerged as the element that most strongly bridged the two domains, suggesting a pivotal connection between grief and growth.

Successful Structure Learning from Observational Data

Previous work suggests that humans find it difficult to learn the structure of causal systems given observational data alone. We identify two conditions that enable successful structure learning from observational data: people succeed if the underlying causal system is deterministic, and if each pattern of observations has a single root cause. In four experiments, we show that either condition alone is sufficient to enable high levels of performance, but that performance is poor if neither condition applies. A fifth experiment suggests that neither determinism nor root sparsity takes priority over the other. Our data are broadly consistent with a Bayesian model that embodies a preference for structures that make the observed data not only possible but probable.

Testing one or multiple: How beliefs about sparsity affect causal experimentation

What is the best way of discovering the underlying structure of a causal system composed of multiple variables? One prominent idea is that learners should manipulate each candidate variable in isolation to avoid confounds (sometimes known as the “Control of Variables” or CV strategy). We demonstrate that CV is not always the most efficient method for learning. Using an optimal actor model which aims to minimize the average number of tests, we show that when a causal system is sparse (i.e., when the outcome of interest has few or even just one actual cause among the candidate variables) it is more efficient to test multiple variables at once. Across a series of behavioral experiments, we then show that people are sensitive to causal sparsity and adapt their strategies accordingly. When interacting with a non-sparse causal system (high proportion of actual causes among candidate variables), they use a CV strategy, changing one variable at a time. When interacting with a sparse causal system they are more likely to test multiple variables at once. However, we also find that people sometimes use a CV strategy even when a system is sparse.