Last week I posted the first batch of noteworthy titles that appeared in The Capital Spectator’s weekly Book Bits column in 2018. Here’s the second half of the year-end recap of books that deserve another look.
By Chris Clearfield and Andras Tilcsik
Excerpt via Big Think
We can’t turn back the clock and return to a simpler world. Airlines shouldn’t switch back to paper tickets and traders shouldn’t abandon computers. Instead, we need to figure out how to manage these new systems. Fortunately, an emerging body of research reveals how we can overcome these challenges.
The first step is to recognize that the world has changed. But that’s a surprisingly hard thing to do, even in an era where businesses seem to celebrate new technologies like blockchain and AI. When we interviewed the former CEO of Knight Capital years after the firm’s technological meltdown, he said, “We weren’t a technology company—we were a broker that used technology.” Thinking of technology as a support function, rather than the core of a company, has worked for years. But it doesn’t anymore.
By Barry Eichengreen
Review via Foreign Affairs
In this survey of two centuries of populist movements and political revolts in Western democracies, Eichengreen argues that from the Luddites in early-nineteenth-century England to the upheavals of the interwar period, economic insecurity, labor dislocations, and rising inequality fueled backlash politics. Yet not all periods of economic hardship generate populist revolts, and not all populist revolts succeed. Eichengreen shows that populism tends to thrive most when economic insecurity exposes the divergent interests of the people and the elites. Societies become particularly ripe for populist backlashes in the wake of financial crises that lead to bailouts for plutocrats.
Also available as a free pdf
By Ray Dalio
Review via MarketWatch
Let’s make one thing perfectly clear: Ray Dalio sees parallels between the U.S. today and during the unstable 1930s, but the billionaire hedge-fund manager does not expect the next financial crisis to rival the one that knee-capped Americans in 2008 and nearly triggered another Great Depression.
That said, the founder of Bridgewater Associates, the world’s largest hedge-fund firm, is concerned that when the downturn hits — likely within the next couple of years, by his reckoning — investors, companies, politicians and policy makers will be blindsided, or worse. Capitalism and democracy, our system of government, will be under heavier fire than it is already.
By Gary B. Gorton and Ellis W. Tallman
Summary via publisher (University of Chicago Press)
If you’ve got some money in the bank, chances are you’ve never seriously worried about not being able to withdraw it. But there was a time in the United States, an era that ended just over a hundred years ago, in which bank customers had to pay close attention to whether the banking system would remain solvent, knowing they might have to rush to retrieve their savings before the bank collapsed. During the National Banking Era (1863–1913), before the establishment of the Federal Reserve, widespread banking panics were indeed rather common. Yet these pre-Fed banking panics, as Gary B. Gorton and Ellis W. Tallman show, bear striking similarities to our recent financial crisis. In both cases, something happened to make depositors—whether individual customers or corporate investors—“act differently” and find reason to question the value of their bank debt.
By Scott E. Page
Essay by author via Harvard Business Review
Without models, making sense of data is hard. Data helps describe reality, albeit imperfectly. On its own, though, data can’t recommend one decision over another. If you notice that your best-performing teams are also your most diverse, that may be interesting. But to turn that data point into insight, you need to plug it into some model of the world — for instance, you may hypothesize that having a greater variety of perspectives on a team leads to better decision-making. Your hypothesis represents a model of the world.
Though single models can perform well, ensembles of models work even better. That is why the best thinkers, the most accurate predictors, and the most effective design teams use ensembles of models. They are what I call, many-model thinkers.
