In our work at BehaviourWorks Australia (BWA) we are frequently asked ‘What does the research say about getting audience Y to do behaviour X?’. When our partners need an urgent answer we often provide it using a Rapid Review. In this article I explain Rapid Reviews, why you should do them, and a process that you can follow to conduct one. What is a Rapid Review? Rapid Reviews are “a form of knowledge synthesis in which components of the systematic review process are simplified or omitted to produce information in a timely manner” [1]. Indeed, with sufficient resources (e.g., multiple staff working simultaneously) you can do a Rapid Review in less than a day. The outputs of these reviews are, of course, brief and descriptive, but they can be very useful where rapid evidence is needed, for example, in addressing COVID-19. Rapid Reviews can therefore provide detailed research within reduced timeframes and also meet most academic requirements by being standardised and reproducible. They are often, but not always, publishable in peer-reviewed academic journals.

Workplace behavior change interventions, or workplace nudges, are strategies used to encourage people to act in their own self-interest. These interventions can be made possible with the help of digital technology, such as mobile applications or email, as well as choice architecture design in the physical environments of the workplace, such as posters, objects or furniture arrangement. To this end, we are going to focus on walking, napping, and eating. First, we will examine general workplace wellness programs - what other researchers have tried, how employees reacted to the programs, and their impact. Then, we will go into further detail about interventions related to our three focus areas.

This is the website for a PhD-level mini-course in behavioral public economics developed by Hunt Allcott and Dmitry Taubinsky. Through the lens of neoclassical economics, the role of government is to provide public goods, correct externalities, provide information, and address other market failures. In practice, however, some public policies are motivated by the concern that people do not act in their own best interest. For example, many countries ban drugs, tax cigarettes, alcohol, and sugary drinks, or subsidize retirement savings and energy-efficient appliances, all largely on the grounds that consumers would be better off consuming more or less than they do. Standard approaches to policy analysis rely on revealed preference assumptions to measure an agent’s welfare. Under these assumptions, the direct effect of any policy that changes choices is to reduce consumer welfare. However, empirical evidence from behavioral economics in a variety of domains suggests that people sometimes do make systematic mistakes. The field of behavioral public economics extends the theoretical and empirical tools of public economics to incorporate the possibility of consumer mistakes into questions about policy evaluation and design. This is a PhD-level mini-course in behavioral public economics. In this course, we’ll consider questions like the following: How can we do welfare analysis if choice does not necessarily identify utility? How do we empirically measure consumer biases? How do we set socially optimal policies in settings when consumers may not act in their own best interest? Nudges change behavior at low cost. Does that mean they are a good idea? What are the costs and benefits of tax complexity?

transmedia course syllabus

Version 2: https://www.youtube.com/watch?v=SYk29tnxASs Every field in science uses the same, old, wall-of-text poster design. If we can improve the knowledge transfer efficiency of that design even by a little bit, it could have massive ripple effects on all of science. Also, poster sessions tend to suck, so here's my pitch to make them more efficient AND more fun with a new approach to designing scientific posters/academic posters that is both more usable, and easier to create!

Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11,12,13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure–property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

Special Issue on Paternalism - open access

Lance Weiler's syllabus for his Columbia University class