What countries and companies can do when trade and cybersecurity overlap – Stuart Madnick, Simon Johnson, Keman Huang

MIT Sloan Professor Stuart Madnick

MIT Sloan Professor Stuart Madnick

MIT Sloan Professor Simon Johnson

Postdoctoral Associate, Keman Huang

From Harvard Business Review 

Cybersecurity as a key issue for trade policy is a relatively new development. In the last few years there have been a number of news reports about various governments’ incorporating spyware, malware, or similar programs into computer-based products that are exported around the world. The governments typically have worked with private companies in their countries to do it. In the internet-of-things era, almost all products can be connected to the internet, and most of them can also be used for spying and other malicious activities. Furthermore, since data is considered a critical asset, services, from international banking to payment systems to consumer websites, are part of this too.

In late 2016 and 2017, for example, the voice-activated My Friend Cayla doll made headlines for its technology, which could be used to collect information on children or anyone in the room. In 2017 Germany banned the doll, alleging that it contained a surveillance device that violated the country’s privacy regulations. Another famous example is the 2010 Stuxnet attack on the Natanz nuclear enrichment facility in Iran. It was accomplished by planting malware, including Stuxnet, into industrial control systems that were shipped to Iran, resulting in the destruction of many centrifuges.

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Why the next two years are critical for the Paris climate deal’s survival – Henry D. Jacoby

MIT Sloan Professor Emeritus, Henry D. Jacoby

From The Conversation

A mounting sense of urgency will greet negotiators as they arrive at this year’s United Nations Climate Change Conference.  In Poland. In 2015, after 20 years of trying and failing to reach a global accord on climate-changing emissions, 195 nations hammered out a deal, the Paris Agreement, that all of them could accept.

Three years on, it’s becoming increasingly clear that national decisions about climate action, which country negotiators will convey in Poland and over the next two years, will determine whether the breakthrough Paris pact succeeds both on a political and emissions reduction front.

As scholars at the MIT Joint Program on the Science and Policy of Global Change, we have closely followed the global climate change agreements and studied their implications. Based on our analysis of nations’ commitments to cut emissions, getting the world on track to achieve the agreement’s signature goal – to keep global warming below two degrees Celsius – will require far more ambitious climate action than what countries have pledged so far. This action must begin sooner rather than later to avoid the worst consequences of climate change, from severe droughts to extreme flooding.

Climate change won’t wait for humanity’s response

Climate change is the type of problem where delay is especially costly. It is not like other pollution such as dirty urban air or a putrid stream. For these, people might clean up a polluted area this year, but if they put off the task, there will probably still be the same opportunity to get it done the following year.

Not so with greenhouse gases, which hang around for decades to centuries. So if societies delay revising our current practices – burning fossil fuels, chopping down forests, planting more polluting crops like rice, as well as raising cows – the total amount in the atmosphere will grow. The goals for limiting global warming will get steadily more difficult to achieve.

In addition, as the nations’ representatives gather in Poland to pursue their effort to gain control of this process, a crucial decision point is rapidly approaching.

Brutal timing

In the Paris Agreement, each nation is to make a pledge (what the agreement calls a Nationally Determined Contribution) to achieve a level of emissions control by a target date. For most countries this is the year 2030. They will also submit to a review of whether they did what they said they would do every two years.

Our analysis shows that, fortunately, meeting these Paris pledges will halt emissions growth at least to 2030, though without additional action growth will resume thereafter. This voluntary system of pledge and review represents real progress on this difficult issue. Experts in international affairs argue that it is perhaps the best deal possible for a 195-nation agreement.

Read the full post from The Conversation.

Henry D. Jacoby is the William F. Pounds Professor of Management, Emeritus in the M.I.T. Sloan School of Management and former Co-Director of the M.I.T. Joint Program on the Science and Policy of Global Change. 

How to spot a successful team – Tara Swart

MIT Sloan Senior Lecturer Tara Swart

From Forbes

In this post, I’d like to return again to my Organizational Plasticity Index (OPI), introduced in my post “Why Business Is Like The Brain.” We’ve already expanded on one aspect of the model, and here, I’d like to explore another, synaptic connection, which equates to the systemic organization of the relationships and communication channels within a business.  The OPI model that compares businesses to the brain, using key aspects of brain function as a metaphor to help make sense of the healthy, or dysfunctional, running of a business. The model is useful because it helps me work with clients to identify the unseen “pathways” within their business that go beyond chain of command diagrams, workflow models, and mission statements. The OPI rating helps me to measure the long-term resilience of the businesses I work with.

The “synaptic connections” within a business relate to the way relationships function: both linear and lateral; hierarchical and “official” and informal. If I were to map these out, they would appear more like constellations or complex webs of connections than hierarchical family trees. The more closely I have studied these connection-maps, the more I became convinced that they mimic the similar lattices and asymmetrical cross-hatching of connections that appear between neurons in the brain. The similarity is uncanny.

At the moment, this complexity is compounded by the incoming and fast-evolving impact of AI on teams, and the fact that managers must now evolve to manage teams that marry AI and human roles and expertise. This requires a sophisticated combination of computational thinking and a manager’s most human qualities: emotional intelligence, intuition and creativity.

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Why long-tenured CEOs fail? The case of Nissan – Egor Matveyev

Visiting Assistant Professor of Finance, Egor Matveyev

From Nikkei Business Online

When is it time to get a new CEO? This is the question that every board of directors asks – or at least has to ask – itself every year when evaluating performance of their CEOs. While we know that CEOs get fired for really bad performance, most of the time performance is not bad enough to justify such a drastic measure. On top of that, what are the guarantees that the new CEO will be any better? Therefore, the default choice is to stick with the current CEO.

If we could identify who is a good CEO and who is a bad one, then replacement decisions would be easier. But in practice, it is a very difficult task. In my recent work with co-authors, we tried to address this question. We use state-of-the-art methodology and a large sample of CEOs to identify who is good and who is bad. Of all possible predictors of CEO quality, three factors emerged to have the strongest predictive power. These factors are CEOs’ age, tenure, and founder status. In our study, we define young CEOs as those whose age is below 58 and old CEOs as those whose age is above 65. Short-tenured CEOs are those who have been in the office for less than 8 years, and long-tenured – above 18 years. (These cutoffs – 58 and 65 for age, and 8 and 18 for tenure – are tercile breakpoints for age and tenure in our sample.)

We find that young CEOs tend to be good on average. They account for more than 4% of the market value of their firms. This means that if they leave, firm value drops by 4% on average. On the other hand, old and long-tenured CEOs tend to be bad. They destroy more than 3% of firm value, which means when they leave, firm value rises by as much as 3%. Among founder-CEOs, the age and tenure effects are even stronger. Young founders account for almost 9% of firm value, while old and long-tenured founders destroy more than 5%. These differences are striking. Our study is the first one to document such strong and heterogeneous age and tenure effects.

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A 2019 forecast for data-driven business: from AI to ethics – Tom Davenport

Fellow, MIT Center for Digital Business, Tom Davenport

From Forbes

It should come as no surprise that 2018 continued to mark another year in the progression of data adoption in business.  Companies are pushing forward with efforts to become increasingly data-driven.  Firms are investing in transformation initiatives to establish a “data culture” within their organizations.  Early adopters are focused on data-driven business innovation.

As we look ahead to 2019, we reflect on a year of accomplishments and emerging areas of focus – from AI through Ethics (listed alphabetically)

  • AI/Machine Learning—AI continued to grow in popularity over the past year, becoming well-institutionalized within many large enterprises. We argued in a previous post, however, that too many companies employed AI pilots and prototypes, and not enough firms had implemented production deployments. As with analytics, the use of AI is increasingly being democratized through automated machine learning (AutoML). Several contributors to KD Nuggets’ review of AI and ML trends for 2019 suggested that AutoML would become more popular over the next year. It will make machine learning models easier to create for business analyst types, as well as dramatically increasing the productivity of data scientists—that is, if they can be persuaded to use it. We also predict that deep learning, which has been the fastest-growing and most popular AI technology over the past several years, will continue to advance in power and prevalence for several years. However, we also expect that deep learning will increasingly be augmented by other approaches to AI. NYU professor Gary Marcus has argued, and we agree, that artificial general intelligence—or even generally useful AI—will have to employ various techniques beyond deep learning in order to be successful.

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