Robert Pozen, Senior Lecturer, MIT Sloan School of Management
From Wall Street Journal
President Trump tweeted on Friday that he had directed the Securities and Exchange Commission to study a suggestion from a business leader, later revealed as outgoing Pepsi CEO Indra Nooyi: “Stop quarterly reporting & go to a six month system.” The popular theory is that quarterly reporting discourages firms from making long-term investments.
But switching to semiannual reporting wouldn’t help. Find us CEOs with stockpiles of good, long-term projects that they are not pursuing—but that they would, if only they had three extra months to report earnings. Reporting every six months is nobody’s definition of “long term.” Besides, investors have waited patiently as Amazon, Netflix and many biotech firms have followed long-term strategies.
In 2007, financial reporting in the United Kingdom moved from semiannual to quarterly. Yet capital expenditures and research-and-development spending didn’t fall significantly over the next three to six years, according to a study from the CFA Institute Research Foundation. When the quarterly requirement was ended in 2014, investment by U.K. companies didn’t change.
Tauhid Zaman, Associate Professor, Operations Mangement
From Computerworld Colombia
¿ Creen que sería genial si un computador lograra identificar un criminal antes de cometer un crimen? Ese es el objetivo del Machine Learning, que está convirtiéndose en una herramienta popular en la prevención del crimen.
Por medio del análisis de datos como edad, género y las condenas anteriores, los computadores pueden predecir si es probable que alguien cometa un delito. Si usted es un juez y está decidiendo si conceder una fianza o enviar a alguien a la cárcel, esa información puede ser bastante útil. El problema es que el aprendizaje automático también puede ser extremadamente peligroso porque, si se confía por completo en él, puede mantener a una persona inocente tras las rejas.
En un estudio reciente, analizamos si el aprendizaje automático podría aplicarse para identificar terroristas en las redes sociales. Utilizando datos de varios cientos de miles de cuentas de extremistas en Twitter, desarrollamos un modelo de comportamiento para usuarios que podría predecir si las cuentas nuevas también estaban conectadas al ISIS. Si bien el modelo podría atrapar a muchos extremistas, también vimos cómo el aprendizaje automático es susceptible de dos errores comunes: primero, el algoritmo puede mostrar falsos positivos al identificar erróneamente a alguien como un terrorista. En segundo lugar, puede mostrar falsos negativos, al errar la identificación de verdaderos terroristas.
Nathan Wilmers, Assistant Professor, Work and Organizational Studies
From Washington Center for Equitable Growth
Stagnating wages among U.S. workers since the 1970s is well-documented. Also well-known is the outsized—and still growing—market impact of a small number of giant retailers such as Amazon.com Inc and Walmart Inc. What is less known is whether these two trends are linked.
In research I’ve been conducting—detailed in an article recently published in the American Sociological Review—I’ve found that increased pressure from large corporate buyers decreases wages among their suppliers’ workers. The growing influence of these buyers on workers’ wages is significant enough that it accounts for around 10 percent of wage stagnation since the 1970s. My findings show how shifts in market power have affected workers’ wage growth.
Relative to the postwar economic boom, U.S. workers’ pay growth has slowed by around one-half since the 1970s. During that same period, market restructuring has shifted many workers into workplaces heavily reliant on sales to outside corporate buyers. Large retailers such as Walmart and Amazon wield increasing power against manufacturing suppliers and warehousing and shipping contractors. When this happens, big corporate buyers are able to demand lower prices for the goods and services they are buying, and suppliers and contractors must sell at lower prices and try to cut costs. Likewise, companies increasingly outsource noncore functions, including food service, janitorial, and security jobs, a phenomenon known as the fissured workplace. The result is that more and more workers are employed by intermediate employers, which in turn rely on sales to outside corporate buyers.
Superminds: The Surprising Power of People and Computers Thinking Together
MIT Sloan’s Thomas Malone and Brian Moran, CEO of Brian Moran & Associates, dedicated to helping small business owners and entrepreneurs run better businesses, and previous Executive Director of Sales Development at the Wall Street Journal, will discuss Malone’s new book, Superminds: The Surprising Power of People and Computers Thinking Together, which shows how groups of people working together in superminds have been responsible for almost all human achievements in business, government, science, and beyond., during a Twitter chat on June 26th at 2 p.m. EDT.
Thomas W. Malone is the Patrick J. McGovern (1959) Professor of Management at the MIT Sloan School of Management and the founding director of the MIT Center for Collective Intelligence. At MIT, he is also a Professor of Information Technology and a Professor of Work and Organizational Studies. Previously, he was the founder and director of the MIT Center for Coordination Science and one of the two founding co-directors of the MIT Initiative on Inventing the Organizations of the 21st Century. Malone teaches classes on organizational design, information technology, and leadership, and his research focuses on how new organizations can be designed to take advantage of the possibilities provided by information technology.
Malone will discuss his work with host Brian Moran, the CEO of Brian Moran & Associates, dedicated to helping small business owners and entrepreneurs run better businesses. Previously, he was the Executive Director of Sales Development at the Wall Street Journal, and was President of Veracle Media and Moran Media Group, two companies that provided guidance to business owners to help them start, manage and grow their companies.
Join us on Twitter on June 26 at 2 p.m. ET, follow along using #MITSloanExperts, and potentially win a free copy of Superminds: The Surprising Power of People and Computers Thinking Together.
Thomas W. Malone Professor of Information Technology
Elon Musk’s sometimes antagonistic relationship with the press is no secret. But last week, the billionaire chief executive of SpaceX and Tesla exhibited a new level of hostility.
In a series of tweets, Musk referred to journalists as “holier-than-thou” hypocrites, said that news organizations had lost their credibility and the respect of the public, and blamed the media for the election of President Trump.
Then things got interesting. Musk proposed creating a “media credibility rating site” where the public would be able to “rate the core truth of any article & track the credibility score over time of each journalist, editor & publication.” He suggested calling this site “Pravda”—the Russian word for “truth” and also the name of the longtime Communist newspaper. He likened the rating platform to Yelp for journalism.
Despite the bluster, Musk may be on to something. At a time when public trust in the media is at an all-time low, a reputation system that allows citizens to gauge the reliability and accuracy of news they consume could be a step in the right direction. Read More