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THREE INVESTIGATIONS INTO THE DYNAMICS AND IMPLICATIONS OF IDENTITY-PROTECTIVE COGNITION FOR PUBLIC RESPONSES TO ENVIRONMENTAL PROBLEMS
In the case of responding to climate change and related environmental problems, opinions about the best course of action have become starkly polarized along ideological lines. The identity-protective cognition thesis posits that when individuals experience a sense of challenge to these identities, they are motivated to engage in cognitive shortcuts and other reasoning processes to protect these identities against threat. In this research, I discuss three investigations into identity-protective cognition in the context of responding to environmental problems, applying the broader identity-protective cognition framework to a diverse set of theoretical and practical questions. Chapter 2 highlights research exploring the effect of motivated reasoning on responses to natural disasters linked with climate change. Chapter 3 looks at how brand and environmental identities influence responses to corporate environmental scandals that are personally relevant and require individual-level action. Chapter 4 extends this research paradigm by exploring public responses to visual imagery used to depict climate change across three countries, while also examining how identity-protective processes shape these responses. In addition to the theoretical and practical contributions for environmental engagement, explicit emphasis is placed on the use of full Bayesian inference for quantitative environmental decision making research. Implications for theory, methodology, and practice are considered
Regularization and Bayesian Learning in Dynamical Systems: Past, Present and Future
Regularization and Bayesian methods for system identification have been
repopularized in the recent years, and proved to be competitive w.r.t.
classical parametric approaches. In this paper we shall make an attempt to
illustrate how the use of regularization in system identification has evolved
over the years, starting from the early contributions both in the Automatic
Control as well as Econometrics and Statistics literature. In particular we
shall discuss some fundamental issues such as compound estimation problems and
exchangeability which play and important role in regularization and Bayesian
approaches, as also illustrated in early publications in Statistics. The
historical and foundational issues will be given more emphasis (and space), at
the expense of the more recent developments which are only briefly discussed.
The main reason for such a choice is that, while the recent literature is
readily available, and surveys have already been published on the subject, in
the author's opinion a clear link with past work had not been completely
clarified.Comment: Plenary Presentation at the IFAC SYSID 2015. Submitted to Annual
Reviews in Contro
Bayesian Networks with Expert Elicitation as Applicable to Student Retention in Institutional Research
The application of Bayesian networks within the field of institutional research is explored through the development of a Bayesian network used to predict first- to second-year retention of undergraduates. A hybrid approach to model development is employed, in which formal elicitation of subject-matter expertise is combined with machine learning in designing model structure and specification of model parameters. Subject-matter experts include two academic advisors at a small, private liberal arts college in the southeast, and the data used in machine learning include six years of historical student-related information (i.e., demographic, admissions, academic, and financial) on 1,438 first-year students. Netica 5.12, a software package designed for constructing Bayesian networks, is used for building and validating the model. Evaluation of the resulting model’s predictive capabilities is examined, as well as analyses of sensitivity, internal validity, and model complexity. Additionally, the utility of using Bayesian networks within institutional research and higher education is discussed.
The importance of comprehensive evaluation is highlighted, due to the study’s inclusion of an unbalanced data set. Best practices and experiences with expert elicitation are also noted, including recommendations for use of formal elicitation frameworks and careful consideration of operating definitions. Academic preparation and financial need risk profile are identified as key variables related to retention, and the need for enhanced data collection surrounding such variables is also revealed. For example, the experts emphasize study skills as an important predictor of retention while noting the absence of collection of quantitative data related to measuring students’ study skills. Finally, the importance and value of the model development process is stressed, as stakeholders are required to articulate, define, discuss, and evaluate model components, assumptions, and results
When Celebrities Speak: A Nationwide Twitter Experiment Promoting Vaccination in Indonesia
Celebrity endorsements are often sought to influence public opinion. We ask
whether celebrity endorsement per se has an effect beyond the fact that their
statements are seen by many, and whether on net their statements actually lead
people to change their beliefs. To do so, we conducted a nationwide Twitter
experiment in Indonesia with 46 high-profile celebrities and organizations,
with a total of 7.8 million followers, who agreed to let us randomly tweet or
retweet content promoting immunization from their accounts. Our design exploits
the structure of what information is passed on along a retweet chain on Twitter
to parse reach versus endorsement effects. Endorsements matter: tweets that
users can identify as being originated by a celebrity are far more likely to be
liked or retweeted by users than similar tweets seen by the same users but
without the celebrities' imprimatur. By contrast, explicitly citing sources in
the tweets actually reduces diffusion. By randomizing which celebrities tweeted
when, we find suggestive evidence that overall exposure to the campaign may
influence beliefs about vaccination and knowledge of immunization-seeking
behavior by one's network. Taken together, the findings suggest an important
role for celebrity endorsement.Comment: 55 pages, 13 tables, 6 figure
Bayesian statistics and modelling
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. We discuss the importance of prior and posterior predictive checking, selecting a proper technique for sampling from a posterior distribution, variational inference and variable selection. Examples of successful applications of Bayesian analysis across various research fields are provided, including in social sciences, ecology, genetics, medicine and more. We propose strategies for reproducibility and reporting standards, outlining an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist. Finally, we outline the impact of Bayesian analysis on artificial intelligence, a major goal in the next decade
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