GULLIVER wrote last week about American Airlines handing indignant flyers a notable victory. The carrier rescinded a plan to take away an inch of legroom from economy-class seats on new planes, following a public outcry. Such concessions are rare. Airlines generally worry about how customers vote with their wallets not how they grumble with their words. Hence, they cut comforts to offer the low fares that people demand.
Anyone hoping that American Airlines’ climbdown might signal a reversal of that trend should think again. Earlier this year, United Airlines introduced a new class of fare, “basic economy”. Such tickets, which strip out those few remaining comforts that economy passengers enjoy, have been derided as “last class”. But, like it or not, cost-conscious passengers are showing their approval.
The airline expanded the programme to all domestic markets last month. Andrew Levy, United’s CFO, said last week that about 30-40% percent of economy-class passengers have chosen basic-economy fares since they were introduced. These fares tend to be $15 to $20 cheaper for a one-way flight than regular economy, but they come with significant drawbacks. Flyers cannot take a carry-on bag on board (just a personal item), select their seats in advance, or be eligible for certain upgrades. And the fares are not even lower than they were before. As the airline explained to Gulliver earlier this year, basic economy tickets cost the same as standard economy ones used to. It is the latter that have been made more expensive.
So there is good reason not to love last class. Yet flyers are choosing it in their hordes. It is an inspired move by United and the other big American carriers, many of which have adopted similar programmes. Passengers who fly basic economy are often confused or surprised by the restrictions and end up paying extra to carry on bags. Often those fees are larger than the difference between the fares for standard and basic economy. Those who fly standard economy are now paying more for the privilege.
The profits for United will be mighty. Delta, which was the first to introduce basic economy, earned an additional $20m from it in the first three months of 2016. And that was when it was available on only 8% of the airline’s routes.
For travellers, there is less to be excited about. On social media, some flyers have expressed confusion and frustration over the restrictions (although, to be fair to the airlines, they are hardly hidden). But one angry flyer might have inadvertently put it best when tweeting:
— Miss GoWhitely (@missgowhitely) June 3, 2017
The answer appears to be “many”.
To understand how advances in artificial intelligence are likely to change the workplace — and the work of managers — you need to know where AI delivers the most value.
Major technology companies such as Apple, Google, and Amazon are prominently featuring artificial intelligence (AI) in their product launches and acquiring AI-based startups. The flurry of interest in AI is triggering a variety of reactions — everything from excitement about how the capabilities will augment human labor to trepidation about how they will eliminate jobs. In our view, the best way to assess the impact of radical technological change is to ask a fundamental question: How does the technology reduce costs? Only then can we really figure out how things might change.
To appreciate how useful this framing can be, let’s review the rise of computer technology through the same lens. Moore’s law, the long-held view that the number of transistors on an integrated circuit doubles approximately every two years, dominated information technology until just a few years ago. What did the semiconductor revolution reduce the cost of? In a word: arithmetic.
This answer may seem surprising since computers have become so widespread. We use them to communicate, play games and music, design buildings, and even produce art. But deep down, computers are souped-up calculators. That they appear to do more is testament to the power of arithmetic. The link between computers and arithmetic was clear in the early days, when computers were primarily used for censuses and various military applications. Before semiconductors, “computers” were humans who were employed to do arithmetic problems. Digital computers made arithmetic inexpensive, which eventually resulted in thousands of new applications for everything from data storage to word processing to photography.
AI presents a similar opportunity: to make something that has been comparatively expensive abundant and cheap. The task that AI makes abundant and inexpensive is prediction — in other words, the ability to take information you have and generate information you didn’t previously have. In this article, we will demonstrate how improvement in AI is linked to advances in prediction. We will explore how AI can help us solve problems that were not previously prediction oriented, how the value of some human skills will rise while others fall, and what the implications are for managers. Our speculations are informed by how technological change has affected the cost of previous tasks, allowing us to anticipate how AI may affect what workers and managers do.
Machine Learning and Prediction
The recent advances in AI come under the rubric of what’s known as “machine learning,” which involves programming computers to learn from example data or past experience. Consider, for example, what it takes to identify objects in a basket of groceries. If we could describe how an apple looks, then we could program a computer to recognize apples based on their color and shape. However, there are other objects that are apple-like in both color and shape. We could continue encoding our knowledge of apples in finer detail, but in the real world, the amount of complexity increases exponentially.
Environments with a high degree of complexity are where machine learning is most useful. In one type of training, the machine is shown a set of pictures with names attached. It is then shown millions of pictures that each contain named objects, only some of which are apples. As a result, the machine notices correlations — for example, apples are often red. Using correlates such as color, shape, texture, and, most important, context, the machine references information from past images of apples to predict whether an unidentified new image it’s viewing contains an apple.
When we talk about prediction, we usually mean anticipating what will happen in the future. For example, machine learning can be used to predict whether a bank customer will default on a loan. But we can also apply it to the present by, for instance, using symptoms to develop a medical diagnosis (in effect, predicting the presence of a disease). Using data this way is not new. The mathematical ideas behind machine learning are decades old. Many of the algorithms are even older. So what has changed?
Recent advances in computational speed, data storage, data retrieval, sensors, and algorithms have combined to dramatically reduce the cost of machine learning-based predictions. And the results can be seen in the speed of image recognition and language translation, which have gone from clunky to nearly perfect. All this progress has resulted in a dramatic decrease in the cost of prediction.
The Value of Prediction
So how will improvements in machine learning impact what happens in the workplace? How will they affect one’s ability to complete a task, which might be anything from driving a car to establishing the price for a new product? Once actions are taken, they generate outcomes. (See “The Anatomy of a Task.”) But actions don’t occur in a vacuum. Rather, they are shaped by underlying conditions. For example, a driver’s decision to turn right or left is influenced by predictions about what other drivers will do and what the best course of action may be in light of those predictions.
The Anatomy of a Task
Seen in this way, it’s useful to distinguish between the cost versus the value of prediction. As we have noted, advances in AI have reduced the cost of prediction. Just as important is what has happened to the value. Prediction becomes more valuable when data is more widely available and more accessible. The computer revolution has enabled huge increases in both the amount and variety of data. As data availability expands, prediction becomes increasingly possible in a wider variety of tasks.
Autonomous driving offers a good example. The technology required for a car to accelerate, turn, and brake without a driver is decades old. Engineers initially focused on directing the car with what computer scientists call “if then else” algorithms, such as “If an object is in front of the car, then brake.” But progress was slow; there were too many possibilities to codify everything. Then, in the early 2000s, several research groups pursued a useful insight: A vehicle could drive autonomously by predicting what a human driver would do in response to a set of inputs (inputs that, in the vehicle’s case, could come from camera images, information using the laser-based measurement method known as LIDAR, and mapping data). The recognition that autonomous driving was a prediction problem solvable with machine learning meant that autonomous vehicles could start to become a reality in the marketplace years earlier than had been anticipated.
Judgment is the ability to make considered decisions — to understand the impact different actions will have on outcomes in light of predictions. Tasks where the desired outcome can be easily described and there is limited need for human judgment are generally easier to automate. For other tasks, describing a precise outcome can be more difficult, particularly when the desired outcome resides in the minds of humans and cannot be translated into something a machine can understand.
This is not to say that our understanding of human judgment won’t improve and therefore become subject to automation. New modes of machine learning may find ways to examine the relationships between actions and outcomes, and then use the information to improve predictions. We saw an example of this in 2016, when AlphaGo, Google’s DeepMind artificial intelligence program, succeeded in beating one of the top players in the world in the game of Go. AlphaGo honed its capability by analyzing thousands of human-to-human Go games and playing against itself millions of times. It then incorporated the feedback on actions and outcomes to develop more accurate predictions and new strategies.
Examples of machine learning are beginning to appear more in everyday contexts. For instance, x.ai, a New York City-based artificial intelligence startup, provides a virtual personal assistant for scheduling appointments over email and managing calendars. To train the virtual assistants, development team members had the virtual assistants study the email interactions between people as they schedule meetings with one another so that the technology could learn to anticipate the human responses and see the choices humans make. Although this training didn’t produce a formal catalog of outcomes, the idea is to help virtual assistants mimic human judgment so that over time, the feedback can turn some aspects of judgment into prediction problems.
By breaking down tasks into their constituent components, we can begin to see ways AI will affect the workplace. Although the discussion about AI is usually framed in terms of machines versus humans, we see it more in terms of understanding the level of judgment necessary to pursue actions. In cases where whole decisions can be clearly defined with an algorithm (for example, image recognition and autonomous driving), we expect to see computers replace humans. This will take longer in areas where judgment can’t be easily described, although as the cost of prediction falls, the number of such tasks will decline.
Employing Prediction Machines
Major advances in prediction may facilitate the automation of entire tasks. This will require machines that can both generate reliable predictions and rely on those predictions to determine what to do next. For example, for many business-related language translation tasks, the role of human judgment will become limited as prediction-driven translation improves (though judgment might still be important when translations are part of complex negotiations). However, in other contexts, cheaper and more readily available predictions could lead to increased value for human-led judgment tasks. For instance, Google’s Inbox by Gmail can process incoming email messages and propose several short responses, but it asks the human judge which automated response is the most appropriate. Selecting from a list of choices is faster than typing a reply, enabling the user to respond to more emails in less time.
Medicine is an area where AI will likely play a larger role — but humans will still have an important role, too. Although artificial intelligence can improve diagnosis, which is likely to lead to more effective treatments and better patient care, treatment and care will still rely on human judgment. Different patients have different needs, which humans are better able to respond to than machines. There are many situations where machines may never be able to weigh the relevant pros and cons of doing things one way as opposed to another way in a manner that is acceptable to humans.
The Managerial Challenge
As artificial intelligence technology improves, predictions by machines will increasingly take the place of predictions by humans. As this scenario unfolds, what roles will humans play that emphasize their strengths in judgment while recognizing their limitations in prediction? Preparing for such a future requires considering three interrelated insights:
1. Prediction is not the same as automation. Prediction is an input in automation, but successful automation requires a variety of other activities. Tasks are made up of data, prediction, judgment, and action. Machine learning involves just one component: prediction. Automation also requires that machines be involved with data collection, judgment, and action. For example, autonomous driving involves vision (data); scenarios — given sensory inputs, what action would a human take? (prediction); assessment of consequences (judgment); and acceleration, braking, and steering (action). Medical care can involve information about the patient’s condition (data); diagnostics (prediction); treatment choices (judgment); bedside manner (judgment and action); and physical intervention (action). Prediction is the aspect of automation in which the technology is currently improving especially rapidly, although sensor technology (data) and robotics (action) are also advancing quickly.
2. The most valuable workforce skills involve judgment. In many work activities, prediction has been the bottleneck to automation. In some activities, such as driving, this bottleneck has meant that human workers have remained involved in prediction tasks. Going forward, such human involvement is all but certain to diminish. Instead, employers will want workers to augment the value of prediction; the future’s most valuable skills will be those that are complementary to prediction — in other words, those related to judgment. Consider this analogy: The demand for golf balls rises if the price of golf clubs falls, because golf clubs and golf balls are what economists call complementary goods. Similarly, judgment skills are complementary to prediction and will be in greater demand if the price of prediction falls due to advances in AI. For now, we can only speculate on which aspects of judgment are apt to be most vital: ethical judgment, emotional intelligence, artistic taste, the ability to define tasks well, or some other forms of judgment. However, it seems likely that organizations will have continuing demand for people who can make responsible decisions (requiring ethical judgment), engage customers and employees (requiring emotional intelligence), and identify new opportunities (requiring creativity).
Judgment-related skills will be increasingly valuable in a variety of settings. For example, if prediction leads to cheaper, faster, and earlier diagnosis of diseases, nursing skills related to physical intervention and emotional comfort may become more important. Similarly, as AI becomes better at predicting shopping behavior, skilled human greeters at stores may help differentiate retailers from their competitors. And as AI becomes better at anticipating crimes, private security guards who combine ethical judgment with policing skills may be in greater demand. The part of a task that requires human judgment may change over time, as AI learns to predict human judgment in a particular context. Thus, the judgment aspect of a task will be a moving target, requiring humans to adapt to new situations where judgment is required.
3. Managing may require a new set of talents and expertise. Today, many managerial tasks are predictive. Hiring and promoting decisions, for example, are predicated on prediction: Which job applicant is most likely to succeed in a particular role? As machines become better at prediction, managers’ prediction skills will become less valuable while their judgment skills (which include the ability to mentor, provide emotional support, and maintain ethical standards) become more valuable.
Increasingly, the role of the manager will involve determining how best to apply artificial intelligence, by asking questions such as: What are the opportunities for prediction? What should be predicted? How should the AI agent learn in order to improve predictions over time? Managing in this context will require judgment both in identifying and applying the most useful predictions, and in being able to weigh the relative costs of different types of errors. Sometimes there will be well-acknowledged objectives (for example, identifying people from their faces). Other times, the objective will be less clear and therefore require judgment to specify the desired outcome. In such cases, managers’ judgment will become a particularly valuable complement to prediction technology.
At the dawn of the 21st century, the most common prediction problems in business were classic statistical questions such as inventory management and demand forecasting. However, over the last 10 years, researchers have learned that image recognition, driving, and translation may also be framed as prediction problems. As the range of tasks that are recast as prediction problems continues to grow, we believe the scope of new applications will be extraordinary. The key challenges for executives will be (1) shifting the training of employees from a focus on prediction-related skills to judgment-related ones; (2) assessing the rate and direction of the adoption of AI technologies in order to properly time the shifting of workforce training (not too early, yet not too late); and (3) developing management processes that build the most effective teams of judgment-focused humans and prediction-focused AI agents.
Monkeys can recognize faces thanks to a suite of neurons that identify particular facial features. Credit: Solvin Zanki/naturepl.com
People can pick a familiar face out of a crowd without thinking too much about it. But how the brain actually does this has eluded researchers for years. Now, a study shows that rhesus macaque monkeys rely on the coordination of a group of hundreds of neurons that pay attention to certain sets of physical features to recognize a face
The findings, published on 1 June in Cell, clarify an issue that has been the subject of multiple theories but no satisfying explanations (L. Chang and D. Y. Tsao Cell http://dx.doi.org./10.1016/j.cell.2017.05.011; 2017).
“The real cartoon view has been that individual cells are dedicated to respond to individual people,” says David Leopold, a neuroscientist at the US National Institute of Mental Health in Bethesda, Maryland. But other theories suggested that groups of neurons worked in concert to recognize a face.
The latest results show that each neuron associated with facial recognition, called a face cell, pays attention to specific ranked combinations of facial features. “We have cracked the code,” says study co-author Doris Tsao, a systems neuroscientist at the California Institute of Technology (Caltech) in Pasadena.
A leap forward
To start, Tsao and Le Chang, a neuroscientist also at Caltech, studied the brains of two rhesus macaque monkeys (Macaca mulatta) to determine the location of the animals’ face cells. They showed the monkeys images of human faces or other objects, including bodies, fruit and random patterns. They then used functional magnetic resonance imaging to see which brain regions lit up when the animals saw a face.
The team focused on those hotspots to see what the face cells were doing. Tsao and Chang used a set of 2,000 human faces with varying characteristics, such as the distance between the eyes or the shape of the hairline, for the monkeys to view. The neuroscientists then implanted electrodes into the macaques’ brains to compare the responses of individual neurons to the facial differences.
Tsao and Chang recorded responses from a total of 205 neurons between the two monkeys. Each neuron responded to a specific combination of some of the facial parameters.
“They have developed a model that goes from a picture on a computer screen to the responses of neurons way the heck down in the visual cortex,” says Greg Horwitz, a visual neurophysiologist at the University of Washington in Seattle. “This takes a huge step forward,” he says, because the model maps out how each cell responds to all possible combinations of facial features, instead of just one.
Tsao and Chang wondered whether, within the specific combination of characteristics that a face cell recognized, each neuron was better tuned to particular features than to others. They tested this idea by trying to recreate the faces the monkeys were shown, on the basis of each neuron’s response to its cast of characteristics. Based on the strength of those signals, the neuroscientists could recreate the real faces almost perfectly.
When the monkeys saw faces that varied according to features that a neuron didn’t care about, the individual face cell’s response remained unchanged.
In other words, “the neuron is not a face detector, it’s a face analyser”, says Leopold. The brain “is able to realize that there are key dimensions that allow one to say that this is Person A and this is Person B.”
Human brains probably use this code to recognize or imagine specific faces, says Tsao. But scientists are still unsure about how everything is linked together.
One message is clear for neuroscientists. “If their inclination is to think: ‘We know how faces are recognized because there are a small number of face cells that sing loud when the right face is seen,’ I think that notion should gradually go away, because it’s not right,” says Leopold. “This study presents a more realistic alternative to how the brain actually goes and analyses individuals.”
Source: nature.com, 01 June 2017
One of the speakers at this week’s IAFIE Conference in Charles Town – Hosted by AMU
Panelists at the International Association for Intelligence Education (IAFIE) conference, held this week in Charles Town, West Virginia, agreed on Monday that there is a growing need for more collegiate cybersecurity programs and training.
“The growth of cybercrime is creating an entirely new industry,” said keynote speaker Garry W.G. Clement, former National Director for the Royal Canadian Mounted Police’s Proceeds of Crime Program. “Cybercrimes will cost the United States about $108 billion by 2020,” Clement added.
According to Clement, higher education “is more important than ever before.” That figure rises to $200 billion if you include the 10 leading world economies.
New Curricula and Courses Needed to Address the Lack of Cybersecurity Skill Sets
Clement called for the development of new curricula and courses that can address the lack of skill sets among new law enforcement recruits. “We need people who can detect fraud because there are too many threats and too few professionals.”
Understanding fraud, such as Ponzi schemes, tax evasion, money laundering, cyber and accounting principles, are some of the skill sets Clement said should be part of new higher education programs.
Clement predicted a shortage of two million cybersecurity professionals by 2019, a figure that will continue to grow unless there is greater cooperation among law enforcement agencies and higher education. In terms of combatting financial crimes and money laundering programs, “We’ve missed the boat. We haven’t achieved a thing.”
The Nation’s Safety Requires Closure of the Digital Divide
Dr. Kevin Harris, Program Director of Information Systems Security at American Public University System (APUS), said the safety of the nation requires that we close the digital divide between affluent regions with advanced technology and less affluent areas where the latest technology is often not available or taught in school. He spoke on a cyber issues and threats panel.
He noted that it is important to teach students as early as middle or high school about technology as a career in order to put this career path on students’ radar. Harris said 80 percent of cybercrime could be addressed through better training. He called for greater collaboration among academics, corporations and government agencies.
Law enforcement agencies especially feel the need for more students trained in cybersecurity. Dr. Chuck Russo, Program Director of Criminal Justice Security and Global Studies at APUS, recalled that when he joined law enforcement, “there was no expectation of digital literacy.” Recruits needed only a high school diploma or General Educational Development (GED) certificate.
Today, however, with law enforcement so dependent on technology, recruits must have at least a college degree. Russo acknowledged that it is difficult to keep current on new technology.
Nevertheless, “we don’t have enough people to train,” he said. Russo called for local agencies to partner with institutions of higher education to create programs especially designed for law enforcement.
The growth of the Internet of Things (IoT) has created “an intelligence nightmare,” Daniel Benjamin, Vice President and Dean of STEM at APUS, told the panel. In the world of IoT, 30 billion sensors will be connected by 2020.
Benjamin said IoT cybersecurity is the number one problem. Smartphones pose high security risks from data leakage and disclosure, discarded cell phones, phishing and other hacking attacks.
Source: David E. Hubler, Contributor, In Homeland Security, May 26, 2017
President Michel Temer Photo: Evaristo SA/AFP/Getty Images
An end to Brazil’s plunge into political instability is not yet on the horizon, which means a sustained economic recovery may not be either. A new obstruction of justice scandal has engulfed Brazilian President Michel Temer and his close allies in Congress at a time when the Brazilian economy was just beginning to climb out of two years of deep recession.
On May 18, Brazilian Supreme Court judge Edson Fachin authorized an investigation into allegations that Temer sought to bribe a former lawmaker who has been threatening to implicate the president in various corruption scandals. The accusations stem from reports a day earlier, which claimed that the president had been caught on tape authorizing the chairman of Brazil’s JBS meat-packing company, Joesley Batista, to pay hush money to former lower house speaker Eduardo Cunha, who has been imprisoned for corruption since November 2016 and has tacitly threatened to help prosecutors build a case against Temer in exchange for a plea bargain.
The audio quality of the recording, which was released on May 18, is poor, and Temer does not appear to explicitly direct Batista to buy Cunha’s silence. But there were clearer parts in which Batista discussed other ways to interfere in the corruption probe with Temer. Though the president is defiantly shunning calls for his resignation, widespread suspicion of Temer’s wrongdoing, combined with the potential for a lengthy probe into the matter, is likely to weaken him. Culture Minister Roberto Freire has already resigned, and other Temer allies will have cause to leave the ruling coalition as well. Meanwhile, street protests against Temer are expected to grow, and at a minimum Temer will lose congressional backing for important economic reforms needed to sustain the country’s recovery.
The Brazilian economy has shown more promise in 2017 than it has in years. Earlier this week, the Central Bank of Brazil’s economic activity index — a leading indicator released ahead of official gross domestic product figures — reported 1.12 percent growth in the first quarter. The news provided some relief for Temer, since it suggests that the economy posted quarterly growth for the first time in two years.
The recession, which began in 2015, has been Brazil’s deepest since the 1930s — a period of decline worse even than the era of hyperinflation in the 1980s and early 1990s. In both 2015 and 2016, the economy contracted more than 3 percent, while unemployment soared from around 5 percent to 13 percent. Inflation surpassed 10 percent in 2015, compelling the Central Bank to begin a monetary tightening cycle that saw interest rates rise to over 14 percent. The same year, Brazil’s currency depreciated more than 48 percent, while public debt jumped more than 21 percent.
At the time there was little cause for optimism. The intensity of the slump, combined with former President Dilma Rousseff’s inability to pass legislation to tame public spending, fueled concerns that Brazil was facing another interminable stretch of double-digit inflation. With the currency continuing to plummet, the government would have difficulties staving off its debts, forcing it to print money at faster rates. Politics only made matters worse. A corruption probe into state-owned oil giant Petroleo Brasileiro became one of the most far-reaching scandals in Brazilian history, implicating all of the country’s traditional political parties as well as its largest construction companies. Last August, it ousted Rousseff herself.
Temer’s Unfulfilled Promise
Temer inherited this storm as acting president in May 2016, and he assumed full responsibility for the crisis following the Senate’s conviction of Rousseff. So far, Temer’s biggest strength has been his substantial support in Congress. From the start, he has prioritized building the strong ruling coalition needed to pass unpopular but overdue economic reforms meant to check public spending and tame inflation and interest rates. Last year, he pushed through a constitutional amendment that will limit public spending growth for the next 20 years, with backing from more than two-thirds of the country’s lawmakers. His administration also eased requirements that Petrobras be the primary operator on every pre-salt oil project, making the Brazilian oil sector more attractive to foreign companies.
Other reforms, however, are still in limbo. This year, for instance, he’s guided contentious labor reforms through the lower house, but the measures have yet to pass the Senate. And in May, Temer was expected to send Congress a proposal to rein in the growing deficits of Brazil’s pension system by, among other things, increasing the retirement age.
The markets initially reacted well to Temer’s ability to move through unpopular reforms, as reflected in the improvement of some macroeconomic indicators. Brazil’s currency has appreciated around 20 percent over the past year, and inflation is expected to decline from over 7 percent last year to around 4 percent this year. This progress has allowed the Central Bank to lower interest rates from 14.25 percent to 11.25 percent, spurring economic activity.
But Temer’s presidency has never been on solid footing. The leader’s lack of charisma, his own corruption allegations, and the fact that he was never elected to office have plagued him from the start. And over the past year, his push for austerity measures has contributed to a steady decline in his approval ratings, which hit just 9 percent this month. Now under fire from new corruption allegations, Temer’s unpopularity is likely to erode his congressional support. At least eight impeachment requests have already been filed since the hush money scandal broke on May 18. (An impeachment process, however, could take more than six months to complete, and at this point the president of the lower house appears reluctant to move forward with it.)
Even without the scandal, popular dissatisfaction with the economy would present a major political challenge for Temer. Brazil’s nascent signs of economic recovery have been uneven, at best. The agricultural sector, for example, is expected to grow 3.6 percent this year, but the pharmaceuticals industry is expected to decline by 15.4 percent. Prolonged political uncertainty will only further undermine the country’s recovery — especially if it derails the push for much-needed economic reforms or cuts short Temer’s term altogether.
Brazil Beyond Temer
The Cunha affair isn’t the president’s only problem. Temer is also under scrutiny from the country’s Superior Electoral Court, which is hearing a case on allegations that the president accepted illegal donations on behalf of his vice president during the 2014 campaign season. His trial is supposed to resume June 6-8, and if just four of the court’s seven justices rule against him, it could lead to his downfall. Temer may be able to buy time by appealing the decision to the Supreme Court. In this scenario, the president of the lower house, Rodrigo Maia, would assume the presidency for 30 days until Congress votes on a successor. (Rumored candidates include Finance Minister Henrique Meirelles, former Supreme Court President and Defense Minister Nelson Jobim, and even former President Fernando Henrique Cardoso.) The new president would then hold the office until October 2018, when the country is supposed to have a presidential election. Nevertheless, this successor would be merely a caretaker and almost certainly struggle to push through contentious austerity measures. Attention would quickly move from reform to preparing for the 2018 vote.
It is possible that Congress will instead try to pass a constitutional amendment (requiring two-thirds support) to move up the election. Should it do so, several notable candidates will be gunning for Temer’s job, including former President Luiz Inacio Lula da Silva and Jair Bolsonaro, Marina Silva and Ciro Gomes. (A requirement that members of the executive or judiciary relinquish their posts six months prior to a presidential campaign would effectively remove all sitting mayors, governors, judges and prosecutors from the race, including popular Sao Paulo Mayor Joao Doria.) Early polls show da Silva leading the pack with support from around 25 percent of the voting public, followed by Bolsonaro at 21 percent and Marina Silva at 7 percent. The seemingly never-ending crisis of political scandal and economic woe in Brazil has fueled a surge in anti-establishment sentiment, creating an environment that favors candidates seen as outsiders such as Bolsonaro and Doria — both of whom have moved up in polls as support for traditional parties has waned.
Should the next president be an outsider from a small or medium-size party, he or she would face the tall order of forming the broad coalition needed to pass legislation in a multiparty system. So, regardless of how Temer’s fall plays out, congressional paralysis may remain a fact of life in Brazil. And amid persistent political uncertainty, the economic recovery that was supposed to be around the corner is likely to remain just that.
Source: Copyright ©2017 Stratfor Enterprises, LLC. All rights reserved. May 19, 2017 | 16:22 GMT