How to boil down a pile of diverse research papers into one cohesive picture–Mohammad S. Jalali

MIT Sloan Research Scientist Mohammad Jalali

From The Conversation 

From social to natural and applied sciences, overall scientific output has been growing worldwide – it doubles every nine years.

Traditionally, researchers solve a problem by conducting new experiments. With the ever-growing body of scientific literature, though, it is becoming more common to make a discovery based on the vast number of already-published journal articles. Researchers synthesize the findings from previous studies to develop a more complete understanding of a phenomenon. Making sense of this explosion of studies is critical for scientists not only to build on previous work but also to push research fields forward.

My colleagues Hazhir Rahmandad and Kamran Paynabar and I have developed a new, more robust way to pull together all the prior research on a particular topic. In a five-year joint project between MIT and Georgia Tech, we worked to create a new technique for research aggregation. Our recently published paper in PLOS ONE introduces a flexible method that helps synthesize findings from prior studies, even potentially those with diverse methods and diverging results. We call it generalized model aggregation, or GMA.

Pulling it all together

Narrative reviews of the literature have long been a key component of scientific publications. The need for more comprehensive approaches has led to the emergence of two other very useful methods: systematic review and meta-analysis.

In a systematic review, an author finds and critiques all prior studies around a similar research question. The idea is to bring a reader up to speed on the current state of affairs around a particular research topic.

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4.0 Lab: The future of food, finance, health, ed, & management–Otto Scharmer

MIT Sloan Sr. Lecturer Otto Scharmer

MIT Sloan Senior Lecturer Otto Scharmer

From Huffington Post

Last week, Amazon acquired Whole Foods in a move that has many wondering what this means for the direction of the economy. In my view, Amazon’s acquisition of Whole Foods does to organics what Uber did to the sharing economy: it takes something that was born out of a different economic logic (a grocery store dedicated to healthy food) and then molds and morphs it to fit into an economic operating system that is firmly based in the old paradigm—i.e. in a paradigm that aims for world domination rather than serving a goal of shared prosperity and well-being for all. 

In this post, inspired by a number of gatherings with change makers across sectors in China, Europe, and the Americas during the past few weeks, I outline a framework for understanding how the current limits of capitalism we are bumping up against in sectors such as food, finance, health, education and business are all related to the same outdated economic logic or “operating system” (OS). We need a new economic operating system, one that reinvents how we work together as neighbors, as businesses, as cities and as larger systems. Below I describe briefly the evolution of these five sectors from OS 1.0 to where we are today, which in most cases is OS 2.0 or 3.0.

The pressing challenges of our time, i.e. the challenge of losing our environment (ecological divide), our societal whole (social divide), and our humanity (spiritual divide) calls for reinventing our systems of food, health, education, finance and management towards 4.0. This essay lays out the rationale for OS 4.0 and a possible way to get us there through an Asian-American-European initiative called 4.0 Lab.

Five Sectors, One Problem

As the labels of the new economy have gone mainstream (green, organic, sharing economies) the underlying economic reality stays the same. That is to say, the immense buying power of giants like Amazon squeeze the supply chain, workers, farmers, and the planet through the same patterns of exploitation and structural violence that gave rise to the movement for a new economy in the first place.

On one level you could describe the problem by saying that companies like Amazon and Uber misperceive the new economy as just another app that runs on their old corporate operating system (i.e. world domination through economies of scale). In reality, though, the new economy is not just another app—it’s a radical upgrade of their entire operating system. The difference between the old and the new paradigms can be summarized in three words: ego vs. eco. Ego-system awareness means “me first”, while eco-system awareness means an awareness that focuses on the well-being of all.

There is a profound systemic barrier that exists in all major sectors today. It’s not only the mainstream players like Amazon and Uber that are stuck in their current economic operating systems; many of the innovators who once broke through that model are now also stuck. The global food system is still profoundly destructive. The health system is still sick. The educational system is unable to learn. The global financial system is heading full throttle into the next crash—as if 2008 never happened. Foundations and philanthropists still place their assets in the old economy, thereby harming people and planet, in order to use some of the profits to fund projects that alleviate symptoms but don’t deal with root causes. The innovators in all these spaces are stuck in the niches that first gave them space to develop something new. But now these niches are increasingly crowded, and mainstream players adopt the new labels and sound bites while often perpetuating the old models.

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Just the facts: Information access can shrink political divide – Evan Apfelbaum & Erik Duhaime

MIT Sloan Asst. Prof. Evan Apfelbaum

MIT Sloan Ph.D. student Erik Duhaime

From The Hill

Political polarization in the U.S. is at its highest level in decades. This isn’t surprising, especially in the wake of the recent presidential election.

It’s hard to go on social media, much less cable news these days and not see reports that support one political side and vilify the other. Is there any hope for bringing the country closer together? We think so.

In a recent study, we found that the way information is presented can influence political polarization. When it is presented in a way that engages people in an objective analysis of the information at hand, political polarization can decrease. Yet when the same information provokes people to think about their relevant political preferences, people remain polarized.

In other words, people might moderate their views when they have more information on how a contentious policy works, but not if they’re busy thinking about what they want or why they want it.

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Stephen Curry, the Golden State Warriors, and the power of analytics at work — Ben Shields

MIT Sloan Lecturer Ben Shields

MIT Sloan Lecturer Ben Shields

From MIT Sloan Management Review

Whether or not their 2016 season ends with a second consecutive NBA championship, the Golden State Warriors are making Silicon Valley proud. They broke the record for regular season wins with 73. They are headlined by Stephen Curry, the dynamic and eminently likeable two-time MVP. They have established themselves among the league’s elite franchises.

Like the “unicorns” along Highway 101, the Warriors have done it all with a deep organizational commitment to data-driven decision making – both on the court and as a business. The three-pointers Steph and running mate Klay Thompson hoist seemingly without abandon are actually grounded in troves of evidence supporting the shot’s relative value. Meanwhile, the business side of the organization is leveraging fan data to more effectively drive ticket, sponsorship, and merchandise revenue.

The Warriors are not the only team pioneering the analytics revolution in sports. Organizations across an increasing number of sports and levels (professional, college, and high school) are capitalizing on data to gain a competitive edge. Indeed, few industries have implemented data-driven decision making as successfully as sports.

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The rise of data-driven decision making is real but uneven — Kristina McElheran and Erik Brynjolfsson

Kristina McElheran, MIT Initiative on the Digital Economy Visiting Scholar

Kristina McElheran, MIT Initiative on the Digital Economy Visiting Scholar

 

 Professor of Information Technology, Director, The MIT Initiative on the Digital Economy


Professor of Information Technology,
Director, The MIT Initiative on the Digital Economy

From Harvard Business Review

Growing opportunities to collect and leverage digital information have led many managers to change how they make decisions – relying less on intuition and more on data. As Jim Barksdale, the former CEO of Netscape quipped, “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” Following pathbreakers such as Caesar’s CEO Gary Loveman – who attributes his firm’s success to the use of databases and cutting-edge analytical tools – managers at many levels are now consuming data and analytical output in unprecedented ways.

This should come as no surprise. At their most fundamental level, all organizations can be thought of as “information processors” that rely on the technologies of hierarchy, specialization, and human perception to collect, disseminate, and act on insights. Therefore, it’s only natural that technologies delivering faster, cheaper, more accurate information create opportunities to re-invent the managerial machinery.

At the same time, large corporations are not always nimble creatures. How quickly are managers actually making the investments and process changes required to embrace decision-making practices rooted in objective data? And should all firms jump on this latest managerial bandwagon?

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