Neil McGlynn

Bio: Neil McGlynn is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 4348 citations.

Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Dual-Process Theories of Higher Cognition: Advancing the Debate

Abstract: Dual-process and dual-system theories in both cognitive and social psychology have been subjected to a number of recently published criticisms. However, they have been attacked as a category, incorrectly assuming there is a generic version that applies to all. We identify and respond to 5 main lines of argument made by such critics. We agree that some of these arguments have force against some of the theories in the literature but believe them to be overstated. We argue that the dual-processing distinction is supported by much recent evidence in cognitive science. Our preferred theoretical approach is one in which rapid autonomous processes (Type 1) are assumed to yield default responses unless intervened on by distinctive higher order reasoning processes (Type 2). What defines the difference is that Type 2 processing supports hypothetical thinking and load heavily on working memory.

Twenty years of ecosystem services: How far have we come and how far do we still need to go?

Abstract: It has been 20 years since two seminal publications about ecosystem services came out: an edited book by Gretchen Daily and an article in Nature by a group of ecologists and economists on the value of the world’s ecosystem services. Both of these have been very highly cited and kicked off an explosion of research, policy, and applications of the idea, including the establishment of this journal. This article traces the history leading up to these publications and the subsequent debates, research, institutions, policies, on-the-ground actions, and controversies they triggered. It also explores what we have learned during this period about the key issues: from definitions to classification to valuation, from integrated modelling to public participation and communication, and the evolution of institutions and governance innovation. Finally, it provides recommendations for the future. In particular, it points to the weakness of the mainstream economic approaches to valuation, growth, and development. It concludes that the substantial contributions of ecosystem services to the sustainable wellbeing of humans and the rest of nature should be at the core of the fundamental change needed in economic theory and practice if we are to achieve a societal transformation to a sustainable and desirable future.

The National Academies Press

Abstract: The National Academy of Sciences founded The National Academies Press (NAP) with the goal of publishing reports of all four national academies. Annually, NAP publishes more than 200 books from the fields of science, engineering and medicine and offers more than 4000 titles in PDF on its website (http://www.nap.edu/) free of charge.

Fundamental Disruption in Big Data Science and Biological Discovery

Abstract: A key contemporary trend emerging in big data science is the quantified self (QS)‐individuals engaged in the selftracking of any kind of biological, physical, behavioral, or environmental information as n =1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual’s continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity—barriers to widespread adoption and a critique regarding scientific soundness—but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality.