3 simple habits that can protect your brain from cognitive decline – Tara Swart

MIT Sloan Senior Lecturer, Tara Swart

From Fast Company

You might think that the impact of aging on the brain is something you can’t do much about. After all, isn’t it an inevitability?

To an extent, as we may not be able to rewind the clock and change our levels of higher education or intelligence (both factors that delay the onset of symptoms of aging). But adopting specific lifestyle behaviors–whether you’re in your thirties or late forties–can have a tangible effect on how well you age. Even in your fifties and beyond, activities like learning a new language or musical instrument, taking part in aerobic exercise, and developing meaningful social relationships can do wonders for your brain. There’s no question that when we compromise on looking after ourselves, our aging minds pick up the tab.

THE AGING PROCESS AND COGNITIVE DECLINE

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The American populist reckoning – Simon Johnson

MIT Sloan Professor Simon Johnson

From Project Syndicate

Populism is an approach to government that relies on lavish promises that ultimately cannot be met. The most prominent historical cases since 1945 were, for a long while, mostly found in Latin America. There are always apologists who claim that a new source of economic miracle has been discovered. But the ending is always the same: some form of crisis and disaster. Populism today is again in the ascendancy, but now one of the most virulent forms is in the United States – and with the credibility of the central bank very much on the line.

Argentina under Juan Perón (1946-1955 and 1973-1974) and his successors is often held out as the canonical example of populist misrule. Each iteration of populism has its special features, but the general pattern is this: unsustainable wage increases, an overvalued exchange rate, and massive foreign borrowing (enabled by local recklessness and foreign short-sightedness). Critics are persecuted, experts disparaged, and ridicule piled onto anyone with any kind of reasonable concern. Central banks and other independent governmental bodies, such as courts, are always subverted through personnel changes and other pressures.

Then the reckoning comes, with some combination of inflation, significant exchange-rate devaluation, and a deep recession (or worse). All too often, the cycle then starts again with another round of promises that cannot possibly be met. The central bank’s credibility, once dismantled, does not easily return.

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Free Elon Musk! – Bill Fischer

Bill Fischer, Visiting Professor, Operations Management

From Forbes

No, it hasn’t gotten to this just yet, but we shouldn’t even be having this conversation.

The SEC’s decision to sue Tesla CEO Elon Musk, with the intention of barring him from serving as an executive or director not only of Tesla, but of any corporation under the jurisdiction of the SEC, was the height of folly. Do any of us, including the SEC commissioners involved, really believe that our society would have been better off with Elon Musk on the sidelines? Do they really think that anyone else could do the job of representing Tesla, or the future, better than Elon Musk?

Let’s be clear, there are a lot of smart people in Silicon Valley. But, most of them are not named “Elon Musk.” What we discovered over the weekend was that that name was worth at least $6 billion in value, and possibly a lot more, based on the fall in market capitalization on the day following the announcement of the SEC action. It’s hard to imagine very many other people whose suspension from work life would bring such a hit on the very next day. Yet, I suspect that very few of us are actually surprised. In our minds, Tesla is Musk, and without Musk, what is Tesla?

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Large corporate buyers may be the hidden culprit behind stagnant wages – Nathan Wilmers

MIT Sloan Assistant Professor Nathan Wilmers

From LSE USAPP

Slow wage growth since the Great Recession has been puzzling. As the economic recovery has clocked eight years of growth, unemployment has dropped, but real median wages have barely increased. Commentators have looked for explanations in everything from the rise of artificial intelligence to the scarring effects of the decade-old economic crisis. However, slow US wage growth has a longer history. Relative to the rapid growth marking the post-World War II period, median real wages have grown little since the 1970s (except for the economic boom of the late 1990s).

A growing body of research points to the decline in worker bargaining power as a core explanation. The long membership decline of labor unions has made it harder for workers to demand higher pay. In some local labor markets, increased market concentration has left few employers able to dictate terms to workers. The real federal minimum wage has slipped by around 30 percent from its peak in the late 1960s.

In recent research, I found another, more subtle reason why worker bargaining power has declined. When workers bargain over wages with employers, it is not just their clout vis-a-vis their immediate employer that matters. A combination of rising outsourcing and consolidation of large buyers has left more and more workers employed at companies dependent on a few outside buyers for sales revenue. These large buyers can effectively pressure suppliers to reduce wages: If buyers demand price and cost-cutting, often suppliers pass these pressures along to their workers.

These buyers do not see or meet their suppliers’ workers. This social distance means large buyers can ignore the fairness norms and social pressure that would otherwise raise workers’ pay. For example, when companies outsource janitorial or security workers, these outsourced workers face slower wage growth.

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Bias and belief in meritocracy in AI and engineering – Susan Silbey, Brian Rubineau, Erin Cech, Carroll Seron

Susan Silbey, Leon and Anne Goldberg Professor of Humanities, Professor of Behavioral and Policy Science, MIT Sloan School of Management

From LSE Business Review 

As artificial intelligence (AI) and machine learning techniques increasingly leave engineering laboratories to be deployed as decision-making tools in Human Resources (HR) and related contexts, recognition of and concerns about the potential biases of these tools grows. These tools first learn and then uncritically and mechanically reproduce existing inequalities. Recent research shows that this uncritical reproduction is not a new problem. The same has been happening among human decision-makers, particularly those in the engineering profession. In AI and engineering, the consequences are insidious, but both cases also point toward similar solutions.

Bias in AI

One common form of AI works by training computer algorithms on data sets with hundreds of thousands of cases, events, or persons, with millions of discrete bits of information. Using known outcomes or decisions (what is called the training set) and the range of available variables, AI learns how to use these variables to predict outcomes important to an organisation or any particular inquirer. Once trained by this subset of the data, AI can be used to make decisions for cases where the outcome is not yet known but the input variables are available.

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