249 research outputs found
Lucky or clever? From expectations to responsibility judgments
How do people hold others responsible for the consequences of their actions? We propose a computational model that attributes responsibility as a function of what the observed action reveals about the person, and the causal role that the person's action played in bringing about the outcome. The model first infers what type of person someone is from having observed their action. It then compares a prior expectation of how a person would behave with a posterior expectation after having observed the person's action. The model predicts that a person is blamed for negative outcomes to the extent that the posterior expectation is lower than the prior, and credited for positive outcomes if the posterior is greater than the prior. We model the causal role of a person's action by using a counterfactual model that considers how close the action was to having been pivotal for the outcome. The model captures participants' responsibility judgments to a high degree of quantitative accuracy across three experiments that cover a range of different situations. It also solves an existing puzzle in the literature on the relationship between action expectations and responsibility judgments. Whether an unexpected action yields more or less credit depends on whether the action was diagnostic for good or bad future performance
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The trajectory of counterfactual simulation in development
Young children often struggle to answer the question âwhat would have happened?â particularly in cases where the adult-like âcorrectâ answer has the same outcome as the event that actually occurred. Previous work has assumed that children fail because they cannot engage in accurate counterfactual simulations. Children have trouble considering what to change and what to keep fixed when comparing counterfactual alternatives to reality. However, most developmental studies on counterfactual reasoning have relied on binary yes/no responses to counterfactual questions about complex narratives and so have only been able to document when these failures occur but not why and how. Here, we investigate counterfactual reasoning in a domain in which specific counterfactual possibilities are very concrete: simple collision interactions. In Experiment 1, we show that 5- to 10-year-old children (recruited from schools and museums in Connecticut) succeed in making predictions but struggle to answer binary counterfactual questions. In Experiment 2, we use a multiple-choice method to allow children to select a specific counterfactual possibility. We find evidence that 4- to 6-year-old children (recruited online from across the United States) do conduct counterfactual simulations, but the counterfactual possibilities younger children consider differ from adult-like reasoning in systematic ways. Experiment 3 provides further evidence that young children engage in simulation rather than using a simpler visual matching strategy. Together, these experiments show that the developmental changes in counterfactual reasoning are not simply a matter of whether children engage in counterfactual simulation but also how they do so. (PsycInfo Database Record (c) 2021 APA, all rights reserved
The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning
Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of oneâs current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical accountâthe Search for Invariance (SI) hypothesisâwhich suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and interventionânot to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy
Learning in the European Union: Theoretical Lenses and Meta-Theory
notes: This paper is based on research carried out with the support of the European Research Council grant on Analysis of Learning in Regulatory Governance, ALREG http://centres.exeter.ac.uk/ceg/research/ALREG/index.php. The authors wish to express their gratitude to the other authors in this special edition and in particular its editor, Nikos Zaharaidis and X anonymous referees.publication-status: AcceptedThe European Union may well be a learning organization, yet there is still confusion about the nature of learning, its causal structure and the normative implications. In this article we select four perspectives that address complexity, governance, the agency-structure nexus, and how learning occurs or may be blocked by institutional features. They are transactional theory, purposeful opportunism, experimental governance, and the joint decision trap. We use the four cases to investigate how history and disciplinary traditions inform theory; the core causal arguments about learning; the normative implications of the analysis; the types of learning that are theoretically predicted; the meta-theoretical aspects and the lessons for better theories of the policy process and political scientists more generally
A PCR-based screening program to assess the prevalence of Taylorella equigenitalis in breeding stallions in South Africa
The first outbreak of Contagious Equine Metritis (CEM) due to Taylorella equigenitalis in
South Africa was reported to the OIE in May 2011 subsequent to importation of a stallion,
the index case. Two additional positive stallions were identified on an initial trace-back. The
outbreak-response prompted determination of the national prevalence and distribution of
CEM. A nation-wide PCR-based screening of all breeding stallions motivated by a previous
outbreak report [1] was implemented via a mandatory CEM-negative clearance certificate
prior to use for natural breeding or semen collection. Compliance from breeders was
facilitated by developing a web-based system providing an easily-accessed, rapid and costeffective
sampling, testing and reporting process on www.cemsa.co.za. A submission form,
information, a breed-indexed list of stallions achieving CEM-clearance and a method for
obtaining and submitting two sets of swabs (with an interval > 7d) from the external
genitalia were accessible on the website. A duplex PCR was chosen as the assay method due
to potential for submission of samples with minimal restrictions on transit time and
temperature criteria and rapid, high throughput, cost-effi-ciency and reported sensitivity
*1,2+. A clearance certificate was issued via the website after negative results from both sets
of samples.http://www.journals.elsevier.com/journal-of-equine-veterinary-sciencehb2016Equine Research Centr
Systematizing Policy Learning: From Monolith to Dimensions
notes: The authors wish to express their gratitude to the Norwegian Political Science Association Annual Conference, 6 January 2010, University of Agder, Kristiansand, participants of the âEstablishing Causality in Policy Learningâ panel at the American Political Science Association (APSA) annual meeting,2â5 September 2010,Washington DC, and the European Consortium of Political Research (ECPR) Joint Sessions, St Gallen, 12â17 April 2011, workshop 2. Dunlop and Radaelli gratefully acknowledge the support of the European Research Council, grant on Analysis of Learning in Regulatory Governance, ALREG, http://centres.exeter.ac.uk/ceg/research/ALREG/index.php.publication-status: AcceptedThe definitive version is available at www.blackwell-synergy.com and also from DOI: 10.1111/j.1467-9248.2012.00982.xThe field of policy learning is characterised by concept stretching and lack of systematic findings. To systematize them, we combine the classic Sartorian approach to classification with the more recent insights on explanatory typologies. At the outset, we classify per genus et differentiam â distinguishing between the genus and the different species within it. By drawing on the technique of explanatory typologies to introduce a basic model of policy learning, we identify four major genera in the literature. We then generate variation within each cell by using rigorous concepts drawn from adult education research. Specifically, we conceptualize learning as control over the contents and goals of knowledge. By looking at learning through the lenses of knowledge utilization, we show that the basic model can be expanded to reveal sixteen different species. These types are all conceptually possible, but are not all empirically established in the literature. Up until now the scope conditions and connections among types have not been clarified. Our reconstruction of the field sheds light on mechanisms and relations associated with alternatives operationalizations of learning and the role of actors in the process of knowledge construction and utilization. By providing a comprehensive typology, we mitigate concept stretching problems and aim to lay the foundations for the systematic comparison across and within cases of policy learning.European Research Council, grant no 230267 on Analysis of Learning in Regulatory Governance, ALREG
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