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08 декабря, 17:48

Leverage the Price Momentum of These 4 Hot Stocks

This screen will help you to get into momentum stocks that are storming ahead. Highlighted stocks include JBT, TTMI and FNSR.

08 декабря, 17:30

Zacks.com featured highlights: Lancaster Colony, Sucampo Pharmaceuticals, Citrix Systems, VMware and Sanchez Energy

Zacks.com featured highlights: Lancaster Colony, Sucampo Pharmaceuticals, Citrix Systems, VMware and Sanchez Energy

08 декабря, 17:30

Zacks.com featured highlights: POSCO, TTM Technologies, PennyMac Financial Services, Amkor Technology and Cosan

Zacks.com featured highlights: POSCO, TTM Technologies, PennyMac Financial Services, Amkor Technology and Cosan

08 декабря, 17:04

5 Toxic Stocks to Discard or Play Short Right Now

Higher price of the toxic stocks can be ascribed to either an irrational exuberance associated with them or some serious fundamental drawbacks.

08 декабря, 16:25

H&R Block (HRB) Incurs Narrower Loss in Q2, Revenues Up

H&R Block Inc. (HRB) reported second-quarter fiscal 2017 (ended Oct 31, 2016).

08 декабря, 14:06

5 Picks to Excel on Solid Relative Price Strength

We ran a screen to select the top 5 stocks with solid relative price strength you want to have.

08 декабря, 13:55

5 Reasonable Breakout Stocks for Exceptional Returns

If properly implemented, such a strategy could deliver impressive returns.

08 декабря, 13:05

A Guide to Solving Social Problems with Machine Learning

It’s Sunday night. You’re the deputy mayor of a big city. You sit down to watch a movie and ask Netflix for help. (“Will I like Birdemic? Ishtar? Zoolander 2?”) The Netflix recommendation algorithm predicts what movie you’d like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don’t believe it’s possible to forecast which families will wind up on the streets. You’d love to move your city’s use of predictive analytics into the 21st century, or at least into the 20th century. But how? You just hired a pair of 24-year-old computer programmers to run your data science team. They’re great with data. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like? You’re also not reassured by the vendors the city interacts with. They’re always trying to up-sell you the very latest predictive tool. Decisions about how these tools are used seem too important for you to outsource, but raise a host of new issues that are difficult to understand. Insight Center The Next Analytics Age Sponsored by SAS Harnessing the power of machine learning and other technologies. This mix of enthusiasm and trepidation over the potential social impact of machine learning is not unique to local government or even to government: non-profits and social entrepreneurs share it as well.  The enthusiasm is well-placed. For the right type of problem, there are enormous gains to be made from using these tools. But so is the trepidation: as with all new “products,” there is potential for misuse. How can we maximize the benefits while minimizing the harm? In applying these tools the last few years, we have focused on exactly this question. We have learned that some of the most important challenges fall within the cracks between the discipline that builds algorithms (computer science) and the disciplines that typically work on solving policy problems (such as economics and statistics). As a result, few of these key challenges are even on anyone’s radar screen. The good news is that many of these challenges, once recognized, are fairly straightforward to solve. We have distilled what we have learned into a “buyer’s guide.” It is aimed at anyone who wants to use data science to create social good, but is unsure how to proceed. How machine learning can improve public policy First things first: There is always a new “new thing.” Especially in the social sector. Are these machine learning tools really worth paying attention to? Yes. That’s what we’ve concluded from our own proof-of-concept project, applying machine learning to a dataset of over one million bond court cases (in joint work with Himabindu Lakkaraju and Jure Leskovec of Stanford University). Shortly after arrest, a judge has to decide: will the defendant await their legal fate at home? Or must they wait in jail? This is no small question. A typical jail stay is between two and three months. In making this life-changing decision, by law, the judge has to make a prediction: if released, will the defendant return for their court appearance, or will they skip court? And will they potentially commit further crimes? We find that there is considerable room to improve on judges’ predictions.  Our estimates show that if we made pre-trial release decisions using our algorithm’s predictions of risk instead of relying on judge intuition, we could reduce crimes committed by released defendants by up to 25% without having to jail any additional people. Or, without increasing the crime rate at all, we could jail up to 42% fewer people. With 12 million people arrested every year in the U.S., this type of tool could let us reduce jail populations by up to several hundred thousand people.  And this sort of intervention is relatively cheap. Compared to investing millions (or billions) of dollars into more social programs or police, the cost of statistically analyzing administrative datasets that already exist is next-to-nothing. Plus, unlike many other proposals to improve society, machine learning tools are easily scaled. By now, policymakers are used to hearing claims like this in sales pitches, and they should appropriately raise some skepticism. One reason it’s hard to be a good buyer of machine learning solutions is that there are so many overstated claims. It’s not that people are intentionally misstating the results from their algorithms. In fact, applying a known machine learning algorithm to a dataset is often the most straightforward part of these projects. The part that’s much more difficult, and the reason we struggled with our own bail project for several years, is accurately evaluating the potential impact of any new algorithm on policy outcomes. We hope the rest of this article, which draws on our own experience applying machine learning to policy problems, will help you better evaluate these sales pitches and make you a critical buyer as well. Look for policy problems that hinge on prediction Our bail experience suggests that thoughtful application of machine learning to policy can create very large gains. But sometimes these tools are sold like snake oil, as if they can solve every problem. Machine learning excels at predicting things. It can inform decisions that hinge on a prediction, and where the thing to be predicted is clear and measurable. For Netflix, the decision is what movie to watch. Netflix mines data on large numbers of users to try to figure out which people have prior viewing histories that are similar to yours, and then it recommends to you movies that these people have liked. For our application to pre-trial bail decisions, the algorithm tries to find past defendants who are like the one currently in court, and then uses the crime rates of these similar defendants as the basis for its prediction. If a decision is being made that already depends on a prediction, why not help inform this decision with more accurate predictions? The law already requires bond court judges to make pre-trial release decisions based on their predictions of defendant risk. Decades of behavioral economics and social psychology teach us that people will have trouble making accurate predictions about this risk – because it requires things we’re not always good at, like thinking probabilistically, making attributions, and drawing inferences. The algorithm makes the same predictions judges are already making, but better. But many social-sector decisions do not hinge on a prediction. Sometimes we are asking whether some new policy or program works – that is, questions that hinge on understanding the causal effect of something on the world. The way to answer those questions is not through machine learning prediction methods. We instead need tools for causation, like randomized experiments. In addition, just because something is predictable, that doesn’t mean we are comfortable having our decision depend on that prediction. For example we might reasonably be uncomfortable denying welfare to someone who was eligible at the time they applied just because we predict they have a high likelihood to fail to abide by the program’s job-search requirements or fail a drug test in the future. Make sure you’re comfortable with the outcome you’re predicting Algorithms are most helpful when applied to problems where there is not only a large history of past cases to learn from but also a clear outcome that can be measured, since measuring the outcome concretely is a necessary prerequisite to predicting. But a prediction algorithm, on its own, will focus relentlessly on predicting the outcome you provide as accurately as possible at the expense of everything else. This creates a danger: if you care about other outcomes too, they will be ignored. So even if the algorithm does well on the outcome you told it to focus on, it may do worse on the other outcomes you care about but didn’t tell it to predict. This concern came up repeatedly in our own work on bail decisions. We trained our algorithms to predict the overall crime rate for the defendents eligible for bail. Such an algorithm treats every crime as equal. But what if judges (not unreasonably) put disproportionate weight on whether a defendant engages in a very serious violent crime like murder, rape, or robbery? It might look like the algorithm’s predictions leads to “better outcomes” when we look at overall rates of crime. But the algorithm’s release rule might actually be doing worse than the judges with respect to serious violent crimes specifically. The possibility of this happening doesn’t mean algorithms can’t still be useful. In bail, it turns out that different forms of crime are correlated enough so that an algorithm trained on just one type of crime winds up out-predicting judges on almost every measure of criminality we could construct, including violent crime. The point is that the outcome you select for your algorithm will define it. So you need to think carefully about what that outcome is and what else it might be leaving out. Check for bias Another serious example of this principle is the role of race in algorithms. There is the possibility that any new system for making predictions and decisions might exacerbate racial disparities, especially in policy domains like criminal justice. Caution is merited: the underlying data used to train an algorithm may be biased, reflecting a history of discrimination. And data scientists may sometimes inadvertently report misleading performance measures for their algorithms. We should take seriously the concern about whether algorithms might perpetuate disadvantage, no matter what the other benefits. Ultimately, though, this is an empirical question. In our bail project, we found that the algorithm can actually reduce race disparities in the jail population. In other words, we can reduce crime, jail populations and racial bias – all at the same time – with the help of algorithms. This is not some lucky happenstance. An appropriate first benchmark for evaluating the effect of using algorithms is the existing system – the predictions and decisions already being made by humans. In the case of bail, we know from decades of research that those human predictions can be biased. Algorithms have a form of neutrality that the human mind struggles to obtain, at least within their narrow area of focus. It is entirely possible—as we saw—for algorithms to serve as a force for equity. We ought to pair our caution with hope. The lesson here is that if the ultimate outcome you care about is hard to measure, or involves a hard-to-define combination of outcomes, then the problem is probably not a good fit for machine learning. Consider a problem that looks like bail: Sentencing. Like bail, sentencing of people who have been found guilty depends partly on recidivism risk. But sentencing also depends on things like society’s sense of retribution, mercy, and redemption, which cannot be directly measured. We intentionally focused our work on bail rather than sentencing  because it represents a point in the criminal justice system where the law explicitly asks narrowly for a prediction. Even if there is a measurable single outcome, you’ll want to think about the other important factors that aren’t encapsulated in that outcome – like we did with race in the case of bail – and work with your data scientists to create a plan to test your algorithm for potential bias along those dimensions. Verify your algorithm in an experiment on data it hasn’t seen Once we have selected the right outcome, a final potential pitfall stems from how we measure success. For machine learning to be useful for policy, it must accurately predict “out-of-sample.” That means it should be trained on one set of data, then tested on a dataset it hasn’t seen before. So when you give data to a vendor to build a tool, withhold a subset of it. Then when the vendor comes back with a finished algorithm, you can perform an independent test using your “hold out” sample. An even more fundamental problem is that current approaches in the field typically focus on performance measures that, for many applications, are inherently flawed. Current practice is to report how well one’s algorithm predicts only among those cases where we can observe the outcome. In the bail application this means our algorithm can only use data on those defendants who were released by the judges, because we only have a label providing the correct answer to whether the defendant commits a crime or not for defendants judges chose to release. What about defendants that judges chose not to release? The available data cannot tell us whether they would have reoffended or not. This makes it hard to evaluate whether any new machine learning tool can actually improve outcomes relative to the existing decision-making system — in this case, judges. If some new machine learning-based release rule wants to release someone the judges jailed, we can’t observe their “label”, so how do we know what would happen if we actually released them? This is not merely a problem of academic interest. Imagine that judges have access to information about defendants that the algorithm does not, such as whether family members show up at court to support them. To take a simplified, extreme example, suppose the judge is particularly accurate in using this extra information and can apply it to perfectly predict whether young defendants re-offend or not. Therefore the judges release only those young people who are at zero risk for re-offending. The algorithm only gets to see the data for those young people who got released – the ones who never re-offend. Such an algorithm would essentially conclude that the judge is making a serious mistake in jailing so many youthful defendants (since none of the ones in its dataset go on to commit crimes). The algorithm would recommend that we release far more youthful defendants. The algorithm would be wrong. It could inadvertently make the world worse off as a result. In short, the fact that an algorithm predicts well on the part of the test data where we can observe labels doesn’t necessarily mean it will make good predictions in the real world. The best way to solve this problem is to do a randomized controlled trial of the sort that is common in medicine. Then we could directly compare whether bail decisions made using machine learning lead to better outcomes than those made on comparable cases using the current system of judicial decision-making. But even before we reach that stage, we need to make sure the tool is promising enough to ethically justify testing it in the field. In our bail case, much of the effort went into finding a “natural experiment” to evaluate the tool. Our natural experiment built on two insights. First, within jurisdictional boundaries, it’s essentially random which judges hear which cases. Second, judges are quite different in how lenient they are. This lets us measure how good judges are at selecting additional defendants to jail. How much crime reduction does a judge with a 70% release rate produce compared to a judge with an 80% release rate? We can also use these data to ask how good an algorithm would be at selecting additional defendants to jail. If we took the caseload of an 80% release rate judge and used our algorithm to pick an additional 10% of defendants to jail, would we be able to achieve a lower crime rate than what the 70% release rate judge gets? That “human versus machine” comparison doesn’t get tripped up by missing labels for defendants the judges jailed but the algorithm wants to release, because we are only asking the algorithm to recommend additional detentions (not releases).  It’s a comparison that relies only on labels we already have in the data, and it confirms that the algorithm’s predictions do indeed lead to better outcomes than those of the judges. It can be misguided, and sometimes outright harmful, to adopt and scale up new predictive tools when they’ve only been evaluated on cases from historical data with labels, rather than evaluated based on their effect on the key policy decision of interest. Smart users might go so far as to refuse to use any prediction tool that does not take this evaluation challenge more seriously. Remember there’s still a lot we don’t know While machine learning is now widely used in commercial applications, using these tools to solve policy problems is relatively new. There is still a great deal that we don’t yet know but will need to figure out moving forward. Perhaps the most important example of this is how to combine human judgment and algorithmic judgment to make the best possible policy decisions. In the domain of policy, it is hard to imagine moving to a world in which the algorithms actually make the decisions; we expect that they will instead be used as decision aids. For algorithms to add value, we need people to actually use them; that is, to pay attention to them in at least some cases. It is often claimed that in order for people to be willing to use an algorithm, they need to be able to really understand how it works. Maybe. But how many of us know how our cars work, or our iPhones, or pace-makers? How many of us would trade performance for understandability in our own lives by, say, giving up our current automobile with its mystifying internal combustion engine for Fred Flintstone’s car? The flip side is that policymakers need to know when they should override the algorithm. For people to know when to override, they need to understand their comparative advantage over the algorithm – and vice versa. The algorithm can look at millions of cases from the past and tell us what happens, on average. But often it’s only the human who can see the extenuating circumstance in a given case, since it may be based on factors not captured in the data on which the algorithm was trained. As with any new task, people will be bad at this in the beginning. While they should get better over time, there would be great social value in understanding more about how to accelerate this learning curve. Pair caution with hope A time traveler going back to the dawn of the 20th century would arrive with dire warnings. One invention was about to do a great deal of harm. It would become one of the biggest causes of death—and for some age groups the biggest cause of death. It would exacerbate inequalities, because those who could afford it would be able to access more jobs and live more comfortably. It would change the face of the planet we live on, affecting the physical landscape, polluting the environment and contributing to climate change. The time traveler does not want these warnings to create a hasty panic that completely prevents the development of automobile transportation. Instead, she wants these warnings to help people skip ahead a few steps and follow a safer path: to focus on inventions that make cars less dangerous, to build cities that allow for easy public transport, and to focus on low emissions vehicles. A time traveler from the future talking to us today may arrive with similar warnings about machine learning and encourage a similar approach. She might encourage the spread of machine learning to help solve the most challenging social problems in order to improve the lives of many. She would also remind us to be mindful, and to wear our seatbelts.

08 декабря, 11:06

Shark sightings are higher in a shark tank

What do shark sightings; startup failure and data analytics have in common? Well, if your name is Mike or Peter your chances of encountering a shark are much higher than if your name is Jonas. So, do sharks have a preference for those named Mike or Peter or are there simply fewer surfers named Jonas? Biased sampling and jumping to conclusions can fool even the smartest among us. There is a big difference between the correlation of events moving in concert and events that merely influence each other. As an example, there is a strong correlation between the number of nesting storks in Denmark and the number of newborn babies. However, one cannot conclude that these storks bring the babies from Egypt from this one fact. Even when events are shown to influence each other, how can one really know which event influenced the other and if the events are statistically significant. Even events such as higher temperatures and soft drink consumption can be hard to prove. Ventures, especially new entrepreneurial ventures and startups, rely on new big reliable insights to create breakthrough innovative offerings. However, all too often they are fooled by randomness and burn precious cash before they can realize their error and pivot, if they ever realize it at all. Business Analytics of Small and Big Data is an artful science, requiring logic as well as intuition. This is especially true when it comes to creating insights that can inspire breakthrough innovation and not just simple A/B split testing, such as, should the site have a blue or green background. Below is a seven-step Design Science Research approach to systematically developing rigorous insights that work. 1) Generating Big Ideas from fundamental meanings The founders or their development team meet in multiple sessions over a short period of time (weeks) to generate Whys for the ventures by exploring a proven taxonomy of meanings. 2) Qualitative expert interviews Using an open-ended interview guide, experts are consulted in the domains from which the startup is considering synthesizing knowledge, to create a breakthrough innovative new venture. 3) Brainstorming concepts business concepts Applying standard brainstorming, invite experts to generate criteria for success, criteria for concepts and create a new venture concept addressing Why, How and What. The experts then select the most promising concepts with which to proceed. 4) Qualitative crowdsourcing, using a range of diverse social networks. The new venture concepts are presented in a wide range of forums to gather feedback and, more importantly, to uncover biases, such as Pro-Innovation Bias, Curse of Knowledge Bias and Stereotyping. 5) Co-creating a hypothesis and plan for quantitative and qualitative data gathering Critical assumptions behind the new venture are listed and approaches devised for testing. 6) Data collection Quantitative data collection, supplemented with qualitative control questions/data to make sure research team and/or interviewee are clear on what is being measured and understand the use of the measuring scale(s). 7) Data analysis Standard statistics are applied, such as correlations and comparing means/averages. Unexpected results require extra attention, since they can reveal poor procedures, true insights or simply be random occurrences and outliers. Since the Greeks found no value in testing their models, for two thousand years, we all believed that light and heavy objects fell at different speeds. Now we cannot wait that long! Now, however, established and new ventures in particular, can achieve immense competitive advantages by the application of scientific methods, such as Design Science Research, to discover and validate their big ideas. They can address the Why - How - What of their venture without spending another two thousand years before discovering their best road forward. -- This feed and its contents are the property of The Huffington Post, and use is subject to our terms. It may be used for personal consumption, but may not be distributed on a website.

07 декабря, 19:00

The Holocene hangover: it is time for humanity to make fundamental changes

Fredrik Albritton Jonsson examines Amitav Ghosh’s take on climate change and considers if the basic aims of economic development must be completely redefined to acknowledge Earth’s finite resourcesBy Fredrik Albritton Jonsson for Public Books, part of the Guardian Books NetworkAs a child growing up in the early 1980s, I often daydreamed of space exploration and interstellar frontiers. The leap into outer space seemed tantalizingly close. In the science fiction stories I read, the chronology of the future was also the potential biography of adulthood. One story projected a settlement on Mars in 1995; another depicted the grim labor of asteroid mining a decade later; a third imagined an encounter with alien artifacts in the Alpha Centauri system after 2020. The common thread in these stories, easily intuited even by an 11-year-old, was the lesson that the Earth was not our home.Now the science fiction dream of leaving the planet behind appears to be coming true. One of the most striking effects of climate change — often remarked upon by writers — is its power to unsettle our basic understanding of the modern world. Our planet is changing into a strange and unstable new environment, in a process seemingly outside technological control. The fossil fuels that once promised mastery over nature have turned out to be tools of destruction, disturbing the basic biogeochemical processes that make our world habitable. Even the recent past is no longer what we thought it was. Scientists are telling us that the whole territory of modern history, from the end of World War II to the present, forms the threshold to a new geological epoch. Continue reading...

07 декабря, 18:01

5 Stocks to Buy for Remarkable Earnings Growth in 2017

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07 декабря, 17:30

Zacks.com featured highlights: Stamps.com, Gibraltar Industries, FutureFuel and Cambrex

Zacks.com featured highlights: Stamps.com, Gibraltar Industries, FutureFuel and Cambrex

07 декабря, 17:30

Zacks.com featured highlights: Movado Group, Align Technology, Arch Capital Group, Gibraltar Industries and Rogers

Zacks.com featured highlights: Movado Group, Align Technology, Arch Capital Group, Gibraltar Industries and Rogers

07 декабря, 16:42

5 Bargain Stocks with Strikingly Low EV/EBITDA Ratios

We have screened bargain stocks based on EV/EBITDA ratio that offers a clearer picture of a company's valuation and earnings potential.

07 декабря, 14:32

Why Mayors Should Run From Walmart

Sometimes it pays to look a gift horse in the mouth. Even a cheap gift horse. In October of 2007, a group called Good Jobs First released a report titled Rolling Back Property Tax Payments, which charged that Walmart methodically plotted to lower its property taxes by challenging the assessments of its stores and distribution centers. The group said Walmart "drains vitally needed funds from communities by regularly challenging the valuation put on its properties by public officials. When the company succeeds in one of these challenges, it diminishes the funds available to pay for education, police and fire protection, and other essential services provided by local governments." Based on a national sample of Walmart stores and distribution centers as of the beginning of 2005, Walmart had filed assessment challenges at more than one-third of its facilities around the country. At many facilities there were appeals over multiple years. Good Jobs First estimated that Walmart had filed more than 2,100 property tax challenges nationwide. "These systematic property tax challenges are part of a larger pattern of state and local tax avoidance by Wal-Mart," GJF noted. "They are consistent with the company's reported use of a real estate investment trust gimmick to dodge income taxes in many states." The Good Jobs First report found that the Walmart had won a total of about $30 million from those appeals over a decade. Although Walmart's campaign to rollback its property tax payments has been blunted in some states, the company has won big tax cuts in certain individual communities. In 2004, for example, Walmart asked that the assessment of its distribution center in Tomah, Wisconsin be lowered from $43.6 million to $23 million. The city settled the matter by agreeing to drop the assessment to $31.4 million and refunding the retailer more than $300,000 for each of three years -- a total of $949,000 in lost revenues to the city. This week, a report by Fox 6 News in Milwaukee, Wisconsin concluded that "local municipalities are losing millions each year, and thousands of dollars are being spent on legal fees" due to property tax abatements filed by big box stores like Walmart, Target, Nordstrom and Lowe's. The Mayor of Wauwatosa, Wisconsin, Kathleen Ehley, went so far as to say that big box stores were bad business for local cities and towns. "It would make me very hesitant to support a big box coming in," Mayor Ehley admitted. Her community is facing no less than 12 big box property tax appeals. Wautatosa has spent $1.1 million in legal fees to fight these appeals over the last 4 years. Wauwatosa has been sued by Target, Lowe's and Firestone, using a scam called "the dark store theory." Big box stores are pressuring local assessors to value their property the same as a closed store. "Now all of a sudden,' one assessor told Fox 6, "just for property tax purposes, we have to consider using sales of vacant or abandoned locations as evidence of value for good-thriving locations. If municipalities begin lowering values because of this dark store strategy, there will be a shift in taxes." Lawyers for the big box stores argue that operating stores should be assessed like similar-sized stores that have closed down. In Pleasant Prairie, Wisconsin, Target built a store a decade ago for $16 million, but now the retailer wants the village to value it at $6.5 million, roughly half of what the assessors think its worth. The typical residential taxpayer in Pleasant Prairie would see their taxes rise by $900 if Target wins its case. It is absurd for big box stores to steal from local communities by saying a store that is performing well should be taxed at the same market value as an empty store. When a Walmart shuts down and goes on the market, it will sell for a lot less. Sometimes they have to give the store away and take a tax write-off. Sometimes towns have to tear the store down at their expense. But a live store is worth much more than a dead one. "Intuitively it doesn't make any sense," an official with the League of Wisconsin Municipalities told Fox 6. In locations where Walmart or other big boxes rent their space, the rent charged to an open store is much higher than a dead store. The landlord charges the store a flat base rent, plus a rent tied to the level of sales at the location. Higher sales, higher rent. In cases where the big box owns the building, the value of their property should include a market factor based on sales. When the store is closed, only then should their tax bill be lowered due to reduced sales output. The city of Wauwatosa is under siege. Local officials are draining property tax revenues defending their assessments. Companies like Walmart sucker local communities into providing a candy store of incentives -- like infrastructure grants, tax incremental financing, and sales tax rebates -- and then turn around and appeal their property tax assessments. Most Mayors in America think they've won a gift horse when Walmart rolls into town. But inside the gift horse is a tax abatement. "People do need to be aware of this," Mayor Kathleen Ehley warns. Al Norman is the founder of Sprawl-Busters. He has been helping communities fight big box sprawl since 1993. His latest book is Occupy Walmart. -- This feed and its contents are the property of The Huffington Post, and use is subject to our terms. It may be used for personal consumption, but may not be distributed on a website.

07 декабря, 14:32

Why Mayors Should Run From Walmart

Sometimes it pays to look a gift horse in the mouth. Even a cheap gift horse. In October of 2007, a group called Good Jobs First released a report titled Rolling Back Property Tax Payments, which charged that Walmart methodically plotted to lower its property taxes by challenging the assessments of its stores and distribution centers. The group said Walmart "drains vitally needed funds from communities by regularly challenging the valuation put on its properties by public officials. When the company succeeds in one of these challenges, it diminishes the funds available to pay for education, police and fire protection, and other essential services provided by local governments." Based on a national sample of Walmart stores and distribution centers as of the beginning of 2005, Walmart had filed assessment challenges at more than one-third of its facilities around the country. At many facilities there were appeals over multiple years. Good Jobs First estimated that Walmart had filed more than 2,100 property tax challenges nationwide. "These systematic property tax challenges are part of a larger pattern of state and local tax avoidance by Wal-Mart," GJF noted. "They are consistent with the company's reported use of a real estate investment trust gimmick to dodge income taxes in many states." The Good Jobs First report found that the Walmart had won a total of about $30 million from those appeals over a decade. Although Walmart's campaign to rollback its property tax payments has been blunted in some states, the company has won big tax cuts in certain individual communities. In 2004, for example, Walmart asked that the assessment of its distribution center in Tomah, Wisconsin be lowered from $43.6 million to $23 million. The city settled the matter by agreeing to drop the assessment to $31.4 million and refunding the retailer more than $300,000 for each of three years -- a total of $949,000 in lost revenues to the city. This week, a report by Fox 6 News in Milwaukee, Wisconsin concluded that "local municipalities are losing millions each year, and thousands of dollars are being spent on legal fees" due to property tax abatements filed by big box stores like Walmart, Target, Nordstrom and Lowe's. The Mayor of Wauwatosa, Wisconsin, Kathleen Ehley, went so far as to say that big box stores were bad business for local cities and towns. "It would make me very hesitant to support a big box coming in," Mayor Ehley admitted. Her community is facing no less than 12 big box property tax appeals. Wautatosa has spent $1.1 million in legal fees to fight these appeals over the last 4 years. Wauwatosa has been sued by Target, Lowe's and Firestone, using a scam called "the dark store theory." Big box stores are pressuring local assessors to value their property the same as a closed store. "Now all of a sudden,' one assessor told Fox 6, "just for property tax purposes, we have to consider using sales of vacant or abandoned locations as evidence of value for good-thriving locations. If municipalities begin lowering values because of this dark store strategy, there will be a shift in taxes." Lawyers for the big box stores argue that operating stores should be assessed like similar-sized stores that have closed down. In Pleasant Prairie, Wisconsin, Target built a store a decade ago for $16 million, but now the retailer wants the village to value it at $6.5 million, roughly half of what the assessors think its worth. The typical residential taxpayer in Pleasant Prairie would see their taxes rise by $900 if Target wins its case. It is absurd for big box stores to steal from local communities by saying a store that is performing well should be taxed at the same market value as an empty store. When a Walmart shuts down and goes on the market, it will sell for a lot less. Sometimes they have to give the store away and take a tax write-off. Sometimes towns have to tear the store down at their expense. But a live store is worth much more than a dead one. "Intuitively it doesn't make any sense," an official with the League of Wisconsin Municipalities told Fox 6. In locations where Walmart or other big boxes rent their space, the rent charged to an open store is much higher than a dead store. The landlord charges the store a flat base rent, plus a rent tied to the level of sales at the location. Higher sales, higher rent. In cases where the big box owns the building, the value of their property should include a market factor based on sales. When the store is closed, only then should their tax bill be lowered due to reduced sales output. The city of Wauwatosa is under siege. Local officials are draining property tax revenues defending their assessments. Companies like Walmart sucker local communities into providing a candy store of incentives -- like infrastructure grants, tax incremental financing, and sales tax rebates -- and then turn around and appeal their property tax assessments. Most Mayors in America think they've won a gift horse when Walmart rolls into town. But inside the gift horse is a tax abatement. "People do need to be aware of this," Mayor Kathleen Ehley warns. Al Norman is the founder of Sprawl-Busters. He has been helping communities fight big box sprawl since 1993. His latest book is Occupy Walmart. -- This feed and its contents are the property of The Huffington Post, and use is subject to our terms. It may be used for personal consumption, but may not be distributed on a website.

07 декабря, 00:30

To Bot or Not to Bot. Here Come the Chatbots

Donna Peeples, CCO, Pypestream How intelligent automation can improve the customer experience for brands The bot economy has arrived. These days, chatbots are on the tip of everyone’s tongue and at our fingertips. Easier to build and distribute than mobile apps, bots are invading the mobile messaging platforms of choice for consumers today. While it’s still early, thousands of bots are now available. Consider the fact that Facebook Messenger had zero bots in February of 2016 and by November of this year had over 34,000. Today bots allow consumers to do everything from call an Uber, book a flight or make a restaurant reservation, to review an e-commerce order or ask for the latest news or weather forecast. While many bots are more annoying than helpful, 2017 represents the turning point where we’ll see more companies leverage bots for customer service and to aid consumers in making buying decisions. That could mean fewer Google searches for consumers in the future, allowing them to get the information or help they need directly from brands in a more conversational and engaging way. Chatbots offer brands a chance to be where consumers are: messaging. While smartphone owners only use a handful of apps, messaging apps are the platform of choice for consumers with more than 2.5 billion global users this year. And this trend is set to continue, with messaging apps now outpacing social media networks in growth. Without a doubt, mobile messaging is a channel brands absolutely must embrace. And chatbots, if done the right way, offer businesses an opportunity to create a better real-time experience for customers. That said, not all examples of chatbots we’re seeing right now are good ones.  In the case of Microsoft’s Tay earlier this year, we saw how disastrous an open-ended AI bot system can be. Tay showed us what can go wrong when there are no guardrails in place to prevent comments outside the scope of what would be helpful to a customer. As we head into 2017, one of the biggest misconception about chatbots is they can answer anything and everything. The belief that automating conversations in an open-ended way will in itself add value for customers. The reality is the most effective bots are purpose-built to solve very specific problems for customers–making common customer service requests and commerce easier, while ensuring customer privacy. In other words, less is more. The focus of any bot should be intelligent automation of existing business processes delivered in a conversational way. And it’s critical to keep the customer experience in mind. Delivering a great experience through intelligent automation At the end of the day, chatbots should improve customer service, save customers time or help them with their buying decisions - such as customizing a product order or helping with a specific request. The experience and use case has to make sense and add value to the conversations customers are already having. How will a bot relate to customers? What specific problems will it solve? How will it improve existing processes for customer service, communication and commerce? The ideal approach is to analyze customer communication and transactional processes, then identify areas where automation is both easy and effective. An example of this is the range of frequently asked questions that require a repeated and often scripted response from a live agent. Instead of having the customer go through the process of speaking with an agent, a chatbot can easily handle this conversation and transform an otherwise annoying experience. Solving for these low-hanging-fruit issues first with bots allows brands to learn how to effectively automate their business, and over time they can increase the complexity. But keeping it simple is key, initially. We’re only just starting to see the ways in which chatbots can improve customer relationships. Any new technology needs to be implemented strategically and mastered over time, in gradual increments. Trying to do too much, too soon, often results in poor customer experiences. Our approach at Pypestream reflects this philosophy. When we deploy bots for businesses we assess specific conversations and look for the repeatable interactions and apply business rules that a chatbot can handle with ease. From there, we grow and expand the chatbot’s capability using both business and behavioral data. Eventually, the chatbot can handle the majority of conversations allowing for lightning fast interactions and happy customers. Customer service: the sweet spot for bots Customer service is a natural for chatbots. Most often we see about 80-90% of customer service inquiries are for the same issues and require the same responses. These repetitive interactions are easily automated and streamlined with chatbots. The desired result is a reduction in operational costs for businesses while improving the speed and efficiency of customer service. In addition, chatbots can be triggered to proactively address real-time issues avoiding the costs of inbound calls. For example, alerts to a cable outage with instructions on how to reset the modem; where is my insurance claim in process and when can I expect my payment or storm notifications with safety instructions around down power lines and updates on when power will be restored. Given how fresh chatbot technology is right now, the best outcomes are those that combine bots with humans. This is particularly true for customer service interactions. It’s difficult to predict or plan for every potential customer inquiry. Therefore, live agents are still needed to field the questions and inquiries that fall outside of a chatbot’s parameters - the more complex, higher touch interactions. Overall though, for customers, the ideal experiences with businesses are intuitive and easy. The less friction, the better. That’s the central idea for the use of bots: convenience. When customers send a message to businesses to resolve problems, schedule appointments and make secure payments, the customer service experience is streamlined, frictionless and, well, easy. Expect chatbots to continue to grow in popularity Mobile messaging is steadily becoming the most popular means of communicating, as indicated by the staggering number of people on WhatsApp, Facebook Messenger and other p2p applications. Chatbots offer a way for businesses to enter the messaging era and join the conversation. New platforms will emerge to support issues of privacy and security that are so essential to customer communication. But ultimately, as investment in the technology increases, we can expect to see more companies ditching traditional communication models for messaging. If done the right way, conversational technology and bots have the potential to make a dramatic and positive impact on the customer experience, but only if brands take the right approach through intelligent automaton. -- This feed and its contents are the property of The Huffington Post, and use is subject to our terms. It may be used for personal consumption, but may not be distributed on a website.

07 декабря, 00:24

6 Reasons 'Pokemon Go' Might be Disappearing for Good

Augmented reality game Pokémon Go saw explosive growth in the weeks following its release. People swarmed onto the streets in search of their next catch, visiting Pokéstops in obscure locations to obtain items and battling others at Pokégyms. At its height in July, Pokémon Go had 45 million active users. However, a few months later and this number has decreased to 30 million. In such a short amount of time, it's difficult to understand how 15 million people could have chosen to abandon ship. Here are 6 reasons why Pokémon Go is continuing to decrease in popularity: 1) Excitement Waning For those of us who grew up during the early days of Pokémon, the dream to become a Pokémon Master and catch 'em all was widespread. Many of us spent our elementary school recesses playing with Pokémon cards, always in search of those special shiny cards. When the news arrived that we finally had our chance to do just that, worldwide hype snowballed into a collective frenzy. Before the app was even released in some areas of the world, people had found a way to download it and begin catching Pokémon. In the beginning, many of us thought Pokémon was here to stay. In fact, when surveyed, 68% of players anticipated continuing the game for more than a month. This initial excitement solicited a Pokémon bandwagon on which even people who don't do any regular gaming downloaded the app to see what it was all about. At first, I found myself out late at night excitedly searching for Pokémon. As time has progressed and my ability to catch anything other than seemingly innumerable Drowzees continued, I have simply lost interest. 2) The Same Old Pokémon Playing Pokémon Go can begin to get a bit tedious when you encounter the same old Rattata or Weedle over and over again. Obviously, as you level up you begin to see different Pokémon and even rarer ones. However, it takes a significant amount of time to arrive at level 15 or higher. Even when you do get to level 15, you are still greeted by easy-to-catch Pokémon and using your Pokémon balls on them can seem like a waste. The rarest Pokémon of all may not even be on your continent. Users who have managed to catch them all have had to travel overseas to find the most elusive Pokémon. With this in mind and the abundance of lower grade Pokémon, it can be difficult to motivate one's self to search for the seemingly unfindable. 3) Summer's Over For those living in the global North, Pokémon Go was released in the middle of summer, a time in which hunting for Pokémon is more accessible as the weather permitted one to search day or night. As winter gets closer and closer, the opportunities to find your next catch become limited. In the summertime, people gathered excitedly en masse in public spaces to catch Pokémon together. As the days grow shorter and the weather colder, there is less of a likelihood that people will attend Pokémon meetups or continue scouring the streets at all hours of the day. 4) Technical Difficulties Despite the significant decrease in active users, Pokémon Go still retains a large following. With so many people using the app, the servers for Pokémon Go have been constantly in flux and subject to technical difficulties. There have been many times when I have opened the app only to see a message describing that the server was overloaded and I would have to wait. This has been frustrating as I have only been able to play at certain times and to consistently open up the app and see this message has proven anti-climatic. Throughout playing Pokémon Go, it has been difficult to identify whether or not the tracking system has actually worked. My tracker has always seemed to show rarer Pokémon lurking in the vicinity but they never show up. Instead, I have been greeted by a plethora of Zubats. Intuitively, the tracking system seemed to say that if I walked further, I would find more diverse Pokémon. As this never seemed to be the case, I simply ignored the tracking system altogether. 5) Item Costs On several occasions, I have found a rare Pokémon only to run out of Pokéballs. Not wanting to let it get away, I have quickly attempted to buy Pokéballs before the Pokémon disappeared forever. It isn't always possible to visit Pokéstop after Pokéstop to stock up on Pokéballs. Therefore, purchasing Pokéballs can often be a lot more accessible. However, having to continuously pay to stock up is a bit of a buzzkill. Not everyone is able to afford the in-app game purchases of Pokémon Go and it's frustrating to know that someone who can can essentially game the system by stocking up on essential items. At some point, you begin to feel that if you cannot afford to keep paying, you may need to simply take a break from Pokémon Go. 6) Safety There has been significant media coverage of people getting finding themselves in precarious situations due to Pokémon Go distracting them from their surroundings. People have been caught playing the game while driving. Distracted driving can result in bodily injury or death of not only the person playing but also passengers in their vehicle, other road users and pedestrians. In general, it is important when you are driving or doing another activity to focus on whatever you are doing to avoid being injured. When a game becomes too addicting, people may sacrifice their safety in exchange for catching a nearby Pokémon. The crackdown on distracted driving is a contributing factor to the decline of the game's popularity. With these six reasons in mind, it is easier to understand why the total amount of active users on Pokémon Go continue to decline. People may stop playing as much or altogether due to one or several of the reasons described above. Whatever your reasons, it is undeniable that the initial hype and warm weather of July are no longer here to keep us outside hunting for a rare catch. -- This feed and its contents are the property of The Huffington Post, and use is subject to our terms. It may be used for personal consumption, but may not be distributed on a website.

06 декабря, 19:11

Pulling The Lever For Doomsday In Trump's America

Or How Donald Trump Changed Everything (2016-2020) Cross-posted with TomDispatch.com I didn’t vote in the pivotal American election of 2016. Thirty-five years ago, in that unseasonably warm month of November, I was in Antarctica’s Allan Hills taking ice core samples with a hand augur. The pictures I have from that time show my team drilling deep into the blue ice, but what we were actually doing was digging a million years into the planetary past to gaze upon the panorama of climate change. The election was a bad soap opera playing out far beyond my field of vision. At the time, I lived in Washington, D.C. So my vote, I told myself for years afterward, wouldn’t have made any difference in that overwhelmingly Democratic city. And of course, I never had a doubt about the result, nor did my family and friends, nor did the pollsters, the media, and the entertainment industry, nor the members of the political and economic elite of both major parties. Ours was a confidence composed in equal parts of ignorance and arrogance. We underestimated the legitimate anger and despair of large sections of the country -- as well as the other darker motivations much discussed in the years since. “Remember, Rachel,” my ex-husband used to say, “Homo homini lupus: man is wolf to man.” I criticized him for slandering the poor wolf, but he was right. Beastliness has always lain just beneath the surface of our world. My ex-husband, the author Julian West, is a man who cared little about ice or nature. We couldn’t have been more ill-suited in that regard. He was always focused on politics. At that moment, he was less worried about Donald Trump winning the presidency than a far slicker populist coming along to galvanize the same anti-establishment constituency four years after a Trump defeat. In 2016, Julian was still a relatively conventional political scientist. The election would change all that, setting in motion the events that ultimately inspired his seminal bestseller, Splinterlands, which, as you no doubt remember, was published in 2020 and predicted -- with considerable accuracy -- the broke-down, shattered world all of us now live in. I used to think geologically, which transformed the grand sweep of human history into a mere sliver in the planet’s 4.6-billion-year timeline. The Earth had repeatedly warmed and cooled in a set of protracted mood swings that encompassed the epochs. Don’t imagine, though, that just because I thought in million-year intervals I was entirely above the fray. By examining those columns of ice we were extracting from Antarctica, I hoped to understand far more about our own era of global warming. What I’d learned by 2016 was not encouraging. In every previous cycle, the Earth had regulated itself. Then we humans came along and started fiddling with the global thermostat. The era of climate change that began in the nineteenth century with our concerted use of fossil fuels would prove unprecedented. Scientists began to speak of our 11,700-year epoch, the Holocene, as the Anthropocene, the first period in which the actions of a particular species, our very own anthropos, changed the planet. (I used to half-jokingly call our era the Anthro-obscene.) Already by 2016, we were experiencing “the hottest summer on record” year after dismal year. By then, we’d raised the global temperature by one degree, and that fall the Arctic was an astonishing 36 degrees warmer than normal. In Antarctica, where our 12-person team was using a Badger-Eclipse drill and hand augurs to collect samples, the ground seemed to be turning liquid beneath us as we worked. At that point, of course, the looming reality of global warming should have been obvious to everyone, not just scientists. But in that era of fake news and rampant conspiracy theories, climate change proved to be just one more “debatable” topic. In the past, at comparable moments, wisdom had eventually won out over wrongheadedness, whether the shape of the world or the position of Earth in the universe was in question. Alas, in the most important debate of them all, the one on which the very existence of human life on this planet depended, calmer heads did not prevail -- not in time anyway. As time itself began to telescope, many of us, in the United States in particular, simply closed our eyes and pretended that species death was not staring humanity (and many other species) in the face. Geologic time would, of course, go marching on, just not for us. The four-year term of Donald Trump proved such a disaster that a chastened nation, instead of christening public buildings after the disgraced president, bestowed his name on the devastating, climate-change-energized hurricane that struck the country’s East Coast in 2022. Like its namesake, Hurricane Donald began as a squall, only later to develop into the destructive force that ruined the national capital and caused billions of dollars of damage. Julian and I lost our home in Hurricane Donald. Having never liked Washington, I was, in the end, happy enough to leave the city to the floodwaters. I divorced my husband (no need to go into that story here), reverted to Rachel Leopold, the name I’d previously used only for my scientific publications, and retreated to Vermont.  There, in our community of Arcadia, I’ve cultivated my garden and watched the inexorable rise of the global thermometer ever since. The good news: our citrus crop was excellent this year. The bad news: a significant coastal chunk of what was once the habitable world is now underwater. How much of that is the responsibility of President Trump, how much his shortsighted predecessors' and his blinkered successors', I leave to scholars like my ex-husband to mull over. I can tell you only what I saw with my own eyes. I was pretty good with an augur back in the day, so let me drill down one last time through the crust of history. The Trump Years Since I take the long view, I know that time can march backward. Just ask the graptolites. Oh, sorry, actually you can’t. Graptolites were tiny sea creatures that once lived in colonies huddled at the bottom of oceans or floating like ribbons of seaweed on the water’s surface. For nearly 200 million years, they prospered in their aquatic world. They probably thought -- if they thought at all -- that such longevity guaranteed them eternal life on this planet. Then came the Carboniferous Period and a brief but severe ice age. Poof, the graptolites were gone, along with 86% of all other species. Before evolution culminated in its most glorious and destructive creation -- and you know just who I mean -- the planet experienced five mass extinctions. The most devastating came at the end of the Permian era, around 250 million years ago, when 96% of all species died out because a huge volcano exploding in present-day Siberia set off a chain reaction that raised the temperature of the seas radically. All of those long-gone creatures left behind no more than a few marks on stone or some petro-carbon pools beneath the Earth’s surface. The essential law of evolution is the survival of the fittest. Many species die out thanks to some spectacular event or other: an asteroid crashing into the Earth, say, or a massive volcanic eruption. But no wrathful god or malevolent alien force proved necessary for human beings: we were quite capable of being our own worst cataclysm. In an instant of geologic time, we heedlessly burned through our natural resources, while creating weapons of mass destruction that could do in the world hundreds of times over. And then, in 2016, roughly half the voting population of the United States walked into the polls and pulled the lever for doomsday. My ex-husband loved to regale me with comparable stories from history -- of empires that rose and fell, great civilizations that left behind not much more than the poor graptolites had. He believed, however, that the Enlightenment had fundamentally changed human consciousness, that history thereafter was slated to move forward, with only a few stutter steps, into a radiant future. The election of 2016 changed him and his thinking on such subjects irrevocably.   Definition of a pessimist: an optimist mugged by current events. I, too, didn’t quite realize how quickly a country could move backward, dragging the world with it. I watched helplessly as the Trump administration toppled one scientific enterprise after another, like a sullen child kicking over the sand castles of other kids. As soon as he took office, the new president green-lighted every dirty energy project within reach. Over the objections of environmentalists, scientists, and anyone with a modicum of common sense, his administration boosted a dying coal industry, lifted regulations on carbon emissions, opened up federal land to drilling and fracking, and okayed pipelines that pumped out yet more oil and gas to turn into carbon emissions and further heat the planet. It was the equivalent of a second Industrial Revolution in Saudi America, at the very moment when the planet could ill afford another fossil fuel spree. Worse yet was the new administration’s decidedly lukewarm attitude toward the Paris Accord on climate change. Even as the president revised his earlier contention that global warming was a Chinese hoax, the United States turned its back on its pledge to reduce greenhouse gas emissions in concert with the other industrialized powers. It also stopped all payments to other countries to help them reduce such emissions. In the space of months, years of patient negotiations unraveled. The Trump energy stimulus -- along with tax cuts for the wealthy, military budget increases, and a major, privatizing infrastructure program -- provided a short-term boost to the American economy. It was like giving an exhausted worker a hit of meth. Even then, it hardly took an Einstein to know that what goes up must inevitably come down. The new president’s “plan” threw the American economy into even more serious debt, and the initial spike in employment it caused -- the new jobs in mining, pumping, fracking, and building -- proved unsustainable, even as an already yawning gap between rich and poor continued to widen. The global economy responded by sliding into stagnation (and then worse), while the positive effects of the short-term stimulus in the United States soon evaporated. Perhaps if there had been more resistance to the Trump juggernaut, we wouldn’t find ourselves in the present situation. Most critics saw the new president as only a variation, however strange, on all-American themes. They acted as if the normal melody of politics was continuing to play. They ignored the growing cacophony in the country and the world.  They simply didn’t see the true nature of the threat. They didn’t understand how fracked we all were. Of course, we did finally stop fracking -- the pumping of high-pressure liquid under the ground to extract otherwise hard-to-get hydrocarbons -- once we fully understood more than two decades ago the devastating consequences it had for the environment and for us. But by then it was too late. Donald Trump had already fulfilled his promise to get at those hidden reserves of oil and gas. In doing so, he ensured that yet more rounds of carbon emissions would head into the atmosphere, unleashing a wave of destructive force that widened the existing cracks in American society. It’s no surprise that the world began to splinter. But I don’t want to cover the ground my ex-husband has already explored. I have my own story to tell. From Reconstruction to Deconstruction Here in this Vermont community where I’ve lived for the past quarter century, I’ve had a lot of time to read. I no longer take ice core samples. There isn’t much point (or much ice left either). Instead, we survive as best we can, while bracing for yet another tempo shift that will force us to measure our lives not in decades but in years, or even days. We have a good library here in Arcadia, assembled from the basements and attics of farmhouses in the area. No one reads books any more, so we had our pick. In addition to taking charge of the greenhouses in our community, I teach science in our school. In the evenings, when I have the time, I also read history. For all those years we were together, I listened to my husband’s take on the world of the past. Now I’ve developed my own interpretation. From my reading, I think I understand what happened to the United States in the aftermath of Hurricane Donald. I think I know now why the country cracked into so many pieces. At the time, I believed it was because of the political divisions of the day, the disagreements over immigration and guns and trade. I didn’t realize that all of these disputes stemmed from a much older conflict built into the very foundations of this country. Like most Americans, I assumed that our forefathers beat the British in the Revolutionary War and, in short order, created a new experiment in democracy. I’d forgotten -- or never even knew -- that a decentralized group of not-so-united states existed for six years between the end of that war and the Constitutional Convention of 1787. In those years, the 13 states that had agreed to the Articles of Confederation were quite interested in forming a more perfect union. They evidently liked their status and felt resistant to replacing an imperial overlord with a federal one. Only through a sleight of hand did the founding fathers conjure up an American federation. It was a brilliant piece of politics, but Washington, Hamilton, Madison, and the others never fully convinced those skeptical of federation. Indeed, the Constitution papered over the problem by forging compromises between the one government and the many states that would prove increasingly vexing over the ensuing decades. Ultimately, it was brought to a head by the Civil War, thanks to the perennial disagreement about whether new states admitted to the Union would be “slave” or “free.” It wasn’t so much the North as the federal government that emerged victorious from that war and then tried to impose a solution on the rebellious states, which balked at constitutional amendments enfranchising freed slaves as equal citizens and -- for the men at least -- members of the political community. The post-war Reconstruction project remained unfinished until, a century later, the civil rights movement successfully challenged the refusal of the southern states to abide fully by those amendments. Still, even that movement could not resolve the fundamental divide. In the 1990s and the first years of the new century, economic globalization took the top spot as the issue that split America into two parts -- an A team of the economically successful and a B team of the left behind. At first blush, the election of Donald Trump seemed to represent a victory, at long last, for Team B. Certainly, economics did drive enough voters in the Rust Belt to abandon their traditional allegiance to the Democratic Party to lift him to victory in the electoral college. As his administration got down to work, it became clear that economics only went so far in explaining his victory. Rather, it was again the old issue of whether the federal government had the mandate to implement policies for the entire nation. Those who supported Trump thought not. They didn’t want comprehensive national health care. They were not happy with the way the federal government permitted abortion and same-sex marriage and yet outlawed prayer in school and kept creationism out of the textbooks. They didn’t like the way the government taxed them, regulated them, and kept their cattle off public lands. They didn’t want the government resettling immigrants in their communities. They cared little for affirmative action, feminism, or transgender activism. And they were leery of any restrictions on their access to guns. Trump supporters were not against elites, at least not all elites. After all, they’d just elected a celebrity billionaire who promptly filled his administration with his equally wealthy friends and colleagues. No, they were against the elites they associated with the imposition of federal authority. America B didn’t want to secede territorially from the United States. Rather, it wanted to deconstruct federal power. As a result, the United States pushed the rewind button and, in some sense, went all the way back to 1781. The Trump administration began to undo the ties that bound the country together, and we very quickly became less than the sum of our parts. The so-called red states, unshackled from federal requirements, went their own way. Liberal East Coast and West Coast states, appalled by the hijacking of federal authority for the ultimate purpose of undermining federal authority, tried to hold onto constitutional values as they understood them. It didn’t take long -- in fact, the pundits regularly commented on the blinding speed of the process -- for the failure of the larger project of integration to become self-evident.  By 2022, the United States existed in name only (and an increasingly ironic one at that). The Age of Diminished Expectations Imagine that you are a 16-year-old girl, healthy and happy and looking forward to many decades of love and life. And then, one terrible day, you’re blindsided by a Stage Four cancer diagnosis. You had been measuring the future in decades. Suddenly, those decades disappear, leaving you with possibly only a few years to go. Your parents, once skeptical about vaccinating you as a child, now reject conventional cancer treatments. First they deny the diagnosis outright. Then they urge you to eat ground-up apricot pits, drink special teas, and go on a high-fat diet. Nothing works, and the years turn into months, and those months into days, as the world closes in. Yes, it’s a real tearjerker, but substitute “human race” for “16-year-old girl” and “climate change” for “cancer” and you’ll see how accurate it is.  At the time, though, many people just looked away and shrugged. By that pivotal year of 2016, the world had already received a poor diagnosis. The election of Donald Trump was our way, as a country, of first denying that there was even a problem, then refusing medical treatment, and finally embracing one quack remedy after another. In the aftermath of that election, I struggled with the contraction of time and space, as geologic time shifted into human time, as we all came to terms (or not) with the obvious planetary diagnosis. So, too, did the map of my world shrink. During the first part of my adult life, I imagined myself as part of an international community of scientists. Then I worked at a national level to save my country. Here in Vermont, I’ve ended up confined to quite a small plot of land: our intentional community of Arcadia, which we’ve walled off from an increasingly dangerous and hostile world. Soon enough, I’ll find myself in an even smaller space: an urn in the community’s mausoleum. We’re doing fine here in Arcadia. Climate change has turned northern Vermont into a farming paradise. No federal government interferes with our liberal community guidelines. We have enough guns to defend ourselves against outside aggressors. Everything that has killed the larger community beyond our walls has only made us stronger. Perhaps, like the monasteries of the Middle Ages, communities like ours will preserve knowledge until the distant day when we exit this era of ignorance and pain. Or perhaps, like the graptolites, we’ll fade away and evolution will produce another species without the flawed operating system that doomed us. The graptolites were mute. We humans can speak and write and film ourselves in glorious 3-D. These skills haven’t saved us, but our ability to document our times will perhaps save someone someday somewhere.  Everyone prefers a happy ending to a tearjerker. With these documents, these core samples of our era, perhaps we can still, somehow, save the future. John Feffer is the author of the new dystopian novel, Splinterlands (a Dispatch Books original with Haymarket Books), which Publishers Weekly hails as “a chilling, thoughtful, and intuitive warning.” He is the director of Foreign Policy In Focus at the Institute for Policy Studies and a TomDispatch regular. Follow TomDispatch on Twitter and join us on Facebook. Check out the newest Dispatch Book, Nick Turse’s Next Time They’ll Come to Count the Dead, and Tom Engelhardt's latest book, Shadow Government: Surveillance, Secret Wars, and a Global Security State in a Single-Superpower World. -- This feed and its contents are the property of The Huffington Post, and use is subject to our terms. It may be used for personal consumption, but may not be distributed on a website.

06 декабря, 18:05

Bet on 4 High-Flying Stocks With Increasing Cash Flows

Cash indicates a company's true financial health. It holds the key to its existence, development and success.