Author Sunstein's stated objective is to suggest that without a massive reduction in its current functions, government can be far more effective, less confusing, and more helpful if it opts for greater simplicity whenever possible. Sunstein also believes we need to go beyond sterile, tired debates about 'more' or 'less' government and instead focus on empirical evidence as to what really works, while paying attention to costs and benefits.
Fewer rules and common sense are often better. However, companies often don't want ambiguity, even if it invited the use of common sense. One reason - to avoid legal risks; another - sometimes they want clarity as to what to do. The author's favorite tool is 'nudges.' Examples include providing clear information on healthy diets, a disclosure requirement that retirement plans must provide clear data on projected monthly retirement income for various options, a default rule that automatically enrolls people in a health care plan, graphic warnings on cigarette packs, reminders to a consumer that he/she is about to exceed their allotment of monthly minutes, an effort to inform consumers as to how their energy use compares to neighbors. Threats of punishment for those not using seat belts, increased cigarette taxes, cap and trade systems, taxes imposed for failing to purchase health insurance - these are examples of regulations, not nudges. Sunstein believes government needs to use both nudges and regulations.
A considerable amount of the book deals with psychological reactions to information - a topic better addressed by experts in the field, not Sunstein - an attorney.
Another topic covered by 'Simpler' - that of using data to help form regulations and guide enforcement. Sunstein's coverage of that in the current 'Foreign Affairs' issue is actually better organized and easier to follow than the same material in this book. There, his article ('The Rise of Big Data') reports using great volumes of data requires collecting/using large amounts of data instead of small amounts/samples, accepting 'messiness' (occasional errors), and accepting correlations as useful information. This reduces the need for complex statistical reasoning, and simplifies decisions on what data to collect. Large amounts of messy data trumps small amounts of cleaner data. Sunstein uses New York City as an example - it is using big data to improve services and lower costs. For example, it gets 25,000 complaints/year on overcrowded buildings, but has only 200 inspectors. After creating a database of all 900,000 buildings and augmenting this with data from 19 other agencies (tax liens, utility usage anomalies, service cuts, missed payments, ambulance visits, crime rates, rodents, etc.) and comparing this to records to building fires over the last five years, it was able to severity-rank the types of buildings, along with the year built, that were the best fire predictors. Also found the permits for exterior brick work correlated with a reduced risk of serious fire. None of the factors found caused fires, but the correlation information was valuable. They went from issuing vacate orders in 13% of visits to 70%.
The central question is 'How can the government avoid reliance on guesses and ideology?' Billy Beane, Oakland Athletics general manager, transformed professional baseball by substituting data for dogma, intuition, and anecdotes. Why not do likewise with government regulation, suggests Sunstein about his sphere of government involvement? This wouldn't necessarily be easy - regulations might involve both costs and benefits difficult to quantify and monetize. However, this doesn't require assigning monetary value to lives - simply assessing how much we should pay to avoid statistical risks. Sometimes the market can provide evidence - eg. risk premiums paid for various occupations.