Nudge: Improving Decisions about Health, Wealth and Happiness , and: Thinking, Fast and Slow , and: Information and Exclusion (review)
Bibliographic record
Abstract
Reviewed by: Nudge: Improving Decisions about Health, Wealth and Happiness, and: Thinking, Fast and Slow, and: Information and Exclusion Megan Lloyd Richardson Nudge: Improving Decisions about Health, Wealth and Happiness Richard H Thaler and Cass R Sunstein New Haven, CT: Yale University Press, 2008. Pp. 293. Thinking, Fast and Slow Daniel Kahneman Allen Lane, 2011. Pp. 499. Information and Exclusion Lior Jacob Strahilevitz New Haven, CT: Yale University Press, 2011. Pp. 255. Anyone interested in information should also be interested in how it is used. Therefore, the fact that several important books on decision making have emerged in recent years is a matter worth noting. One of these books, Richard Thaler and Cass Sunstein’s Nudge, is already a popular classic on behavioural economics. Daniel Kahneman’s serious and scholarly treatment of behavioural psychology in Thinking Fast and Slow and Lior Strahilevitz’s more upbeat Information and Exclusion have appeared more recently. While superficially these books deal with different questions – the first two delving into human psychology, the last more concerned with techniques of exclusion and their reliance on available information – their arguments are not unconnected, which is why it is useful to deal with them together. Indeed, the central thesis of Information and Exclusion that the law should respect the new information possibilities offered by the Internet has to be qualified by the behavioural insights of Nudge and Thinking Fast and Slow. And, in general, these insights challenge many of the assumptions made by those of us engaged in exploring the interactions of law with human behaviour. Of course, Sunstein is used to challenging familiar assumptions. In his earlier work, The Cost of Rights (Norton, 1999), Sunstein, together with Stephen Holmes, challenged the favourite conservative assumption that property and contractual rights promote the libertarian values of limited governance and individual freedom. As these authors pointed out, even rights over property and with respect to contracts serve to constrain as well as foster human freedom and require state resources to monitor and enforce. So they are not simply low-cost alternatives to governance but alternative forms of governance which come with their own costs as well as benefits. Now, in Nudge, Thaler and Sunstein go further in challenging the conservative economist’s assumption that normally individuals exercising the kinds of choices required by property and contract rights – as well as many of the other choices they may be required to make in life – will behave ‘rationally’ when it comes to making simple decisions about ends and means. Rather, these authors point out, there is plenty of evidence to show that human decisions are influenced by all [End Page 453] kinds of heuristics and biases, and this means that individuals cannot simply be left to exercise their property rights and enter into, abide by, and enforce their contracts. Some further levels of state involvement in decision making may be beneficial. The difficulty is how to effect that involvement without seriously undermining individual freedom. The solution that Thaler and Sunstein suggest for policy makers who want to tread a fine line between paternalism and libertarianism is to consider choice architecture as a science devoted to maximizing individual choice and well-being. Given that anchors and frames effectively set the parameters of decisions, default choices (they say) should be set to nudge decision makers toward decisions that policy makers think will best serve the decision makers’ interests, while still allowing individuals the freedom to deviate. Overconfidence and loss aversion that distort assessments of risk can be overcome by cooling-off periods and fuller explanations of risks and benefits, without precluding the decision completely. And social norms can be appealed to as a way of persuading individuals to decide in ways that serve their enlightened self-interest, while allowing a choice to those who prefer not to ‘follow the herd.’ In such ways, Thaler and Sunstein argue, individuals can be encouraged to make decisions which policy makers think are best for them without their decision-making process being taken over completely. Many of these persuasive techniques are familiar ones and, as those who observe them also know, it is easy to slip from encouraging decision makers to serve their interests to manipulating them to...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".