Solo book club

A counterpoint to most books in the (putative) genre, Burkeman aims to completely upend our perspective on personal time management. There is much more here on the fact and consequences of our mortality than on time management per se---the idea being that truly accepting the former fact will naturally lead to better "performance" in the latter domain. The upshot it this: Our expectations about personal productivity and the potential benefits of popular time management strategies have been vastly overinflated by the personal productivity industry and the currently fashionable idea of extreme busy-ness. We cannot possibly meet our currently too-high ambitions, so the best antidote is to lower our expectations to realistic levels and embrace the finite possibilities we have in the limited time available to us. Ten tips in the Appendix are the closest cousin in the book to the rules found in other time management books, and they are useful: adopt a "fixed volume" approach to productivity; serialize, serialize, serialize; decide in advance what to fail at; focus on what you've already completed, not just on what's left to complete; consolidate your caring; embrace boring and single-purpose technology; seek out novelty in the mundane; be a "researcher" in relationships; cultivate instantaneous generosity; practice doing nothing.

A first-person account of the origins and early results from a global survey experiment on the moral dilemmas posed by self-driving cars. The excitement and anxiety of the research process described by Bonnefon will be familiar to most scholars---if not the high stakes of publishing lightning-rod work in journals the likes of Science and Nature. The research strategy is beautifully simple and effective: ask as many people as possible (approaching 100 million responses at the time of writing!) whether they believe self-driving cars should be programmed to swerve away from a pregnant woman crossing the street if that would mean colliding with a young child. And many variations on this theme with different numbers, ages, and social positions of the hypothetical traffic victims. The results surely have much to teach us about our moral intuitions, how they vary within and across cultures, and the implications of these psychological regularities for the planned or unplanned adoption of self-driving cars as the technology improves in the coming years or decades.

Two professors of philosophy each provide a half-book length exposition of their strongest arguments against (Hunt) and in favor of (DeGrazia) more extensive gun control policies than currently prevail in the United States. This book was published in 2016, so well before the recent string of mass shootings (as I write this in June 2022) but well after earlier high-profile mass shootings (including Columbine, Sandy Hook, Virginia Tech, and Aurora, among others) that periodically bring this issue to the forefront of public conversation. Hunt argues for a right to self-defense that should include firearms as the best means of exercising that right, with collateral support based on the consequentialist claim that a higher prevalence of gun ownership will have a sufficiently strong deterrent effect on criminals that "more guns means less crime" (citing especially the empirical work of John Lott). DeGrazia argues that rights often conflict and so generally must be circumscribed in some way that balances the competing rights. In particular, a right to self-defense using firearms can conflict with a right to a safe environment. DeGrazia also mounts a consequentialist argument for gun control, which includes a selective discussion of empirical research that suggests, roughly speaking, "more guns means more suicides and more crime." My own interests center on the competing empirical claims. How strong is the deterrent effect of guns on crime? Does carrying a gun increase aggressive behaviors? Does a higher prevalence of gun ownership increase the overall rate of suicides, and if so by how much? Etc. These and related empirical issues are not absent from "Debating Gun Control," but they play supporting roles to the primarily ethics-based arguments for and against.

A robust, extended, and level-headed argument in support of moderate to serious gun control in the United States. The author fairly presents and thoroughly discusses the main arguments against gun control before explaining their limitations and offering rebuttals. One important argument against that the author passes over too quickly for my tastes is the "slippery slope" argument, which contends that accepting a little bit more gun control would inevitably lead to excessive gun control. That's a small minus. On the plus side is most everything else in this book. I especially appreciated the author's defense of armchair reasoning in applied ethics and policy evaluations more broadly. Typically the phrase "armchair reasoning" is used as a pejorative, as in "armchair reasoning is no substitute for empirical research." On the contrary, armchair reasoning may be the best we can do when the empirical research is thin or otherwise inconclusive, and in any case armchair reasoning may be indispensable for interpreting whatever empirical evidence is available. LaFollette combines armchair reasoning with a high-level survey of important empirical studies on guns and gun control to good effect in this volume. As such, this book provides substantive insights on one of the most contentious public policy topics of our times, and it serves as a model of applied ethics for others to follow.

Explains the 5 "vital signs" for successful private or public ventures (avoid false positives, pilot test on representative samples, scale ingredients not people, leverage positive spillovers and mitigate negative ones, and pay attention to opportunity costs) and the 4 "secrets" to scaling up and sustaining a venture for maximum impact (get the incentives right, think on the margin, quit before it's too late, and promote a positive workplace culture). Behind-the-scenes stories from List's tenures at both Uber and Lyft provide entertainment value as well as insights, including the observation that individualistic and competitive workplace cultures can function well for small start-ups but often become dysfunctional as the workforce becomes larger and more diverse. I wonder if that characterization will "scale" beyond Uber and if so how broadly. I also wonder about scale effects more generally and how to recognize the optimal time to quit growing in many other domains. What is the optimal size of this or that private or public venture? What is the optimal size of my undergraduate and graduate classes? What is the optimal size of government? And so on. Specific answers to these questions are not to be found in the book, but perhaps the 5 "vital signs" and 4 "secrets" can help us think about each and find connections among them.

Third edition of the essential guide for economists who write---that is, all economists. The original article that evolved into this book was my first impetus to fret about my writing, and I return to this book every year or so as a way to re-center myself. (Shame is a great motivator: fretting about my writing is the first step towards improving it.) This is not the only but it is usually the first book on academic writing that I recommend to others, and I'll be surprised if that changes anytime soon. So... "Get the little book."

A very slim volume (just under 150 pages) that is a better teaser for game theory than it has any right to be. Many fun and some surprisingly generative examples, especially good for tourists like myself. The presentation of "The Braess paradox" was new and frankly startling to me---that one alone is worth the price of admission. (I found a few anecdotes from the author's own experience awkwardly personal. Just pass over those.)

As a college instructor of environmental and natural resource economics, I naturally would like to see students take not only introductory economics courses but also more intermediate and higher level courses in the field. One reason is obvious and self-serving: more students in my own classes. Another reason is more general and is the motivation for this book: a little economics can be a dangerous thing. Quiggin opens with a quote from Samuelson that nails the concern, "When someone preaches 'Economics in One Lesson,' I advise: Go back for the second lesson." Early lessons in economics typically focus on why markets can work so well. There are important and subtle concepts here, and this is a natural place to start on a journey that will eventually lead to later lessons explaining when and why markets can fail to work well. The problem comes when students cut the journey short and go out into the world having learned only the early lessons and not the later ones. This book provides a useful corrective to this problem, perhaps sufficient for interested non-economists, and at least a large down-payment for economics students in the midst of their early lessons. The overarching message is that both Lesson One and Lesson Two are important for most pressing economic (and environmental) problems, but trying to apply Lesson One alone when Lesson Two is also relevant can lead us badly astray. Readers might not agree with all of the author's judgments about the relative importance of the two lessons in each instance they are applied in the book, but such disagreements already put us in the realm of productive discourse about the details.

This is a short book about very big ideas: expected utility, Bayesian decision theory, game theory, utilitarianism, egalitarianism, and what the processes of biological and cultural evolution imply about how they all fit together. Harsanyi vs Rawls vs Parfit etc... heavy stuff. The compact and conversational writing style often works well to help make abstract concepts more understandable to non-experts like myself, but sometimes the exposition is too compact to convey the full meaning (to me, anyway). Here is one telling passage: "Our big brains ... evolved to allow us to live amicably together in small societies without anyone ordering us around. Perhaps we can similarly use these social skills to live together more amicably in large societies." Much of the book (first 3/4) is devoted to explaining, with the aid of concepts from game theory, how the fairness norms that allow us to live amicably in small societies evolved into what they are today. My current understanding of the upshot (offered in the last 1/4 of the book) is that real-world policy reforms should be designed within the constraints of fairness norms that currently hold within a society, otherwise we should not be surprised when our reform proposals fail to earn the support of those they are meant to govern. That is far too pithy to capture the full weight of the ideas in this book, so I suspect this one will require multiple readings and sustained reflection, ideally in conversation with other interested readers.

Introduction to basic scientific literacy, philosophy of science, and critical thinking. Covers definitions (or conceptions) of science, the role of experiments and non-experimental methods, the use of models, types of inference (deductive, inductive, and abductive), probability and statistics, and causal modeling. Eight chapters, each broken into three sections, 300 pages. Good text for an introductory course suitable for all undergraduates. From a personal perspective, I am interested in this genre of books on the methodology of science to help me understand how economics---and the particular version of it that I practice---fits into the larger scientific enterprise.

I like many of these Oxford Very Short Introductions for, well, very short introductions to topics outside of my main areas of interest. I picked this one up as a potential supplemental reading for a course that I teach on natural resource economics, which includes a section on the transition of our economies from non-renewable to renewable sources of energy. The coverage seems fairly broad but of course not very deep. Yet I think it will be sufficient to provide some reality checks on our classroom discussions of the potential speed and possible directions of this pending transition in the race against climate change.

Guided tour of logic and critical thinking, deductive and inductive inference, rational choice, classical and Bayesian statistics, statistical decision theory, game theory, and causal inference. Aspires to reach a broad audience, and I sure hope it succeeds. Great introduction for beginners in any one of the areas covered, and a nice refresher and supplement for more experienced practitioners to solidify the links among these topics as it is all to easy to lose sight of the wider landscape after extended focus on one's own small patch of territory. Final chapters on 'why it seems scarce' and 'why it matters' provide only partial answers but are welcome additions nonetheless.

Exceptional book for understanding the logic of quantitative analysis, from correlation to causal inference with a little bit of decision-science to top it off. Heavy on narrative, light on math, good use of graphs to bridge the gap between the two. I will recommend this highly to all of my econometrics students as an essential supplement to our more methods-oriented readings and exercises. Best update I have discovered so far to the intuition provided by Kennedy's classic A Guide to Econometrics for the new age of causal inference and program evaluation.

I sympathize with Quiggin's view that Hazlitt's "Economics in One Lesson" can be a good place to start learning about economics but should not be the place one ends their study of the field. Bourne's book was inspired by Hazlitt's "One Lesson," but I was pleased to discover that it is more in the spirit of Quiggin's "Two Lessons" than Hazlitt's "One."

This is a wide-ranging and wise yet down-to-earth guide for economics graduate students and young faculty. Highly suitable for a graduate course that I teach on research methods for economists, so I am looking forward to using it for the first time in class this year. Includes sage advice on selecting research topics and developing a research portfolio, writing research papers, making presentations and promoting your work, navigating the journal submission and review process, and being a productive graduate student. One of those books I wish I could have read sooner, but still happy to read now.

This book works on two very different levels. It can be read for fun---by folks who are at least not turned off by the occasional mathematical equation---or it could be used as the anchor of a university-level course on modeling in which students can get their hands as dirty as you please by writing code and conducting computational experiments to reproduce or extend any combination of the dozens of models presented in the 29 chapters of this book. You can find the author's own course based on this book online, but one day I'd love to teach a bespoke version of such a course to a handful of motivated undergrad or grad students. If only as a commitment device for myself to get hands-on practice with some of the less familiar models that are so nicely introduced in this book (and hopefully my students would get something out of it too).

Highly recommended scholarly introduction to the replication crisis and responsible research practices in economics and other social sciences. Suitable for advanced undergrads, grad students, and seasoned researchers new to the area. Topics covered include: the Mertonian ideal of ethical scientific conduct; common practices that deviate from the ideal; diagnostics for detecting and adjusting for publication-bias in a body of research including caliper tests, p-curves, and forms of meta-analysis; possible correctives including pre-analysis plans, registered reports, blind analysis, systematic sensitivity analysis of alternative specifications and data cleaning decisions; and more. One hopes that this portfolio of remedies, and perhaps others yet to be devised, can serve to (re-)invigorate the self-correcting nature of scientific inquiry and thereby help accelerate scientific progress and reverse the apparent recent erosion of public trust in science.

Several questions come to mind while reading the book: Generally, how much research effort should be devoted to the self-correction function? I would guess more than the prevailing status quo, but how much more? Specifically, what is the optimal allocation of effort between original research and replication studies? How does the answer to this and analogous questions depend upon the attributes of the field in question, e.g., prevalence of experimental vs observational studies, typical sample sizes, plausible range of true effect sizes, etc.? What can the theory of mechanism design teach us about how to modify researcher incentives to improve and maintain the integrity of social science research?