Work again today.Ran my bottom off yesterday, no structured exercise but lots of running around. Food was ok.
Scale not moving great distrances but in the right direction.
Just gonna keep on going.
Hang in there, you're going in the right direction -- we can, and we WILL, do this!!!


text:
5 Greatest achievements of Jimmie Johnson's NASCAR career
The third driver to win 7 cup championships. Tied with Dale Earnhardt and Richard Petty for most championships.
NASCAR Driver With the Most Consecutive Titles wins, 5.
The first driver to be named Associated Press Male Athlete of the Year
A cup record 15 consecutive playoff appearances.
Leading all-time winner at Dover (11), Charlotte oval (8), Texas (7), Auto Club (6), Las Vegas (4), and Kansas (3; tie).
For the Latest NASCAR news and updates
Clich here

redpajama_set_name: RedPajamaCommonCrawl


text:
Graphene concrete | Photo source Pixabay
Graphene makes concrete stronger and greener
By incorporating graphene into concrete, scientists have created a stronger and more sustainable water-resistant material.
As the world calls for more eco-friendly construction, innovation has been enabling this cause. We have seen the development of a more environmentally-friendly version of concrete, Finite. This material uses desert sand to replace the traditional concrete binder, reducing its carbon footprint. Researchers have also found a way to improve concrete's performance. By infusing concrete with fibres from recycled tyres, scientists were able to create a stronger version of concrete and prevent it from cracking. And now, scientists at Exeter University have managed to find a way to integrate graphene into concrete, resulting in a greener and sturdier concrete.
The new concrete was created using nano-engineering technology. Scientists suspended microscopically-thin shards of graphene in water. They then use this water to mix the concrete, resulting in a composite material. According to the researchers, the final product is "more than twice as strong and four times more water-resistant than existing concretes". This makes the material more suitable than traditional concrete for construction, thanks to its strength and water-resistant properties. The process of making the graphene concrete is also much more efficient than making traditional concrete, requiring roughly fifty percent less material. This saves energy and reduces carbon emissions significantly.
Email: M.F.Craciun@exeter.ac.uk
Website: www.exeter.ac.uk
Contact: M.F.Craciun@exeter.ac.uk
The process of manufacturing concrete is incredibly polluting – releasing around 2.8 billion tonnes of carbon dioxide each year, between 4 and 8 percent of the total yearly global output. Concrete manufacture also accounts for up to 10 percent of the world's industrial water use. It is vitally important that greener forms of concrete are found that are also affordable. This work by researchers at Exeter may be an important step.
Drones spray biomaterials to improve construction projects
Stronger concrete made using waste vegetables
Engineers create sustainable Portland cement


text:
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Scottsboro Boys Stamp | History Detectives
THE DETECTIVE: Gwen Wright.
THE PLACE: Scottsboro, Alabama.
THE CASE: What is the connection between an inconspicuous black and white stamp purchased at an outdoor market and a landmark civil rights case? "Save the Scottsboro Boys" is printed on the stamp, above nine black faces behind prison bars and two arms prying the bars apart. One arm bears the tattoo "ILD." On the bottom of the stamp is printed "one cent." The Scottsboro Boys were falsely accused and convicted of raping two white girls in 1931 on a train near Scottsboro, Alabama. It took several appeals, two cases before the United States Supreme Court, and nearly two decades before all nine finally walked free. History Detectives delves into civil rights history and consults with a stamp expert to discover how a tiny penny stamp could make a difference in the young men's courageous defense effort.
Prairie Churches | Hope and Prayer
Learn about the Viking Lutheran Church in Maddock, North Dakota. Dedicated in 1909 it was at that time the largest Norwegian Evangelical Lutheran Church between the Twin Cities and Seattle.
Prairie Churches | A Hammer and a Nail, A Shovel and a Pail
Learn about Father Phillip Ruhe, self-taught architect, who was responsible for the building of over 40 churches, including the elaborate Ukrainian Catholic Church of the Immaculate Conception at Cooks Creek, Manitoba, built over 22 years by volunteer hand labor.
Looking for Lincoln | All Things Lincoln
In this video segment, from the PBS documentary Looking for Lincoln, Professor Henry Louis Gates, Jr. visits the Lincoln Museum to see the iconic "stove pipe" hat firsthand. He then travels to Beverly Hills for a tour of the world's largest private collection of Lincoln-related artifacts.
Looking for Lincoln | Abraham Lincoln, Attorney at Law
In this video segment, from the PBS documentay Looking for Lincoln, Professor Henry Louis Gates, Jr. and historian Doris Kearns Goodwin examine Lincoln's years as a "prairie" lawyer on the Illinois circuit, and discuss how they honed and polished Lincoln's confidence, sense of fairness, and social skills.
Welcome (中国欢迎您) from the First Lady
Join First Lady Michelle Obama as she discusses her upcoming trip to China and invites students to follow her journey.
War on Cocoa Beach | Central Florida Roadtrip
Many folks are not aware of the fact that in World War II, the war was not ALL being fought across the Atlantic. There were dangers right here in America, off the coast of Cocoa Beach.
Miriam McClendon
Miriam McClendon was 14 years old when she became involved in the Civil Rights Movement in Birmingham, Alabama. In this video segment, McClendon describes how she left school to participate in the Children's Crusade of 1963 and was then arrested and jailed for several days.
Audrey Hendricks
In 1963, at the age of nine, Audrey Hendricks left school and joined more than 2,000 students in a Birmingham demonstration that came to beknown as the Children's Crusade. In this interview, Hendricks recalls her participation and arrest.
Segregated Schooling in South Carolina
In 1950, a group of black parents in Clarendon County, South Carolina filed a lawsuit to equalize education for their children. Encouraged by the NAACP and a local minister, the Reverend Joseph Armstrong De Laine, the case became part of the landmark 1954 Brown v. Board of Education ruling. In this video segment, the Reverend De Laine's children, Joseph De Laine Jr. and Ophelia De Laine Gona, recall conditions in their segregated school.
This interview with civil rights activist Rosa Parks describes her role in the Montgomery Bus Boycott. On December 1, 1955, Parks refused to give up her seat to a white man on a bus in Montgomery, Alabama. Her refusal sparked a massive bus boycott that lasted 381 days, ending on December 21, 1956, after the United States Supreme Court ruled that racial segregation on city buses was unconstitutional.
A Class Divided 1: The Daring Lesson
When the Reverend Martin Luther King Jr. was murdered in April 1968, Jane Elliott taught her third-grade class a daring lesson in discrimination. The third time she taught the lesson, cameras were present.In this video segment from FRONTLINE: "A Class Divided,"Elliott divides her class into two groups — those with blue eyes and those with brown eyes — and discriminates against those with brown eyes.
A Class Divided 2: Day Two
When the Reverend Martin Luther King Jr. was murdered in April 1968,Jane Elliott taught her third-grade class a daring lesson in discrimination. The third time she taught the lesson, cameras were present.In this video segment from FRONTLINE: "A Class Divided,"Elliott changes the rules, and discriminates against students with blue eyes.
Mendez v. Westminster: Desegregating California's Schools
In 1946, eight years before the landmark Supreme Court decision in Brown v. Board of Education, Mexican Americans in Orange County, California won a class action lawsuit to dismantle the segregated school system that existed there. In this video segment, Sylvia Mendez recalls the conditions that triggered the lawsuit and her parents' involvement in the case.
Breaking the Gender Barrier in Little League, 1974
Ten-year-old Janine Cinseruli, her mother, her brother, and some neighborhood boys answer questions about whether girls should be allowed to play Little League baseball in this 1974 archival news footage from the WCVB Collection, Northeast Historic Film. Janine says most boys don't care if girls play baseball as long as they're good at it. The boys interviewed say they agree but express concerns about physical contact with female players. Janine's mother, who helped Janine file a complaint that ultimately led to a court case, says her daughter has been practicing with boys for years and should be allowed to play.
This video is primary source footage and is presented as originally recorded.


Your garage door isn't going to last forever. Just like everything else, your garage door has a fixed life span. Adverse weather conditions, improper use and accidents can drastically impair a door's functionality. Maintenance checks and repairs, on the other hand, can keep your door healthy and working properly. United Garage Doors provides a professional garage door maintenance, repair and installation service to residents of Mesa, AZ 85213. We have worked with numerous local residential and business owners in the past – we know what we're doing and you can trust us to provide you with an exceptional service at an affordable price.


The Interswitch Group has announced a new offering for the acquisition of its Interswitch SKIDATA parking system. The new offering is a package that allows for flexible payment plans and budget-friendly implementation options, with a guarantee of after-sale support and maintenance.
Interswitch is partnering with SKIDATA (its technical partner) to provide parking solutions that eases access for vehicles and people. The solution focuses on products for car parks, shopping malls, estates, parks, and event facilities. These range from barriers and columns, to automated payment machines, cash desks and validation systems to access readers, marketing and customer loyalty solutions and tickets – as well as the SKIDATA tools for reporting, control, and monitoring.
Group Head, Corporate Solutions at Interswitch, Olawale Akanbi said: "We are very excited about the new Interswitch SKIDATA parking solutions offer. Considering the current economic realities, customers are increasingly becoming price sensitive, and we understand this.
Akanbi, explained that the Interswitch SKIDATA parking solution holds huge benefits for professionals such as property developers, facilities managers, mechanical and electrical companies, IT solution contractors, car parking companies, project management companies, etc.
The benefits include: blocking off revenue leakages and increasing profits, Intelligent and secure access management systems, Data and report management with reliable insights, Optimization of operational processes which increases efficiency and turnover, and Bespoke ticketing models.
The SKIDATA solution has been successfully implemented in over 10,000 locations across over 100 countries. Locally, the solution has also been implemented at the MMA2 multilevel car park.
Be the first to comment on "Interswitch Announces New Offerings"


Hitting 50 is definitely a turning point in our lives. We are half a century old. And though we are told that 50 is the new 40, that can sometimes feel like cold comfort.
Just as we are preparing to wind down, the world at large is determined that we crank it up. Something like a rebirth is expected of us, one that requires the energy of a hyperactive 12 year old.
Greek philosopher Heraclitus said that "the only constant [in life] is change." But does it have to be so intensive at this stage in the game, requiring us to be prepared for all eventualities?
Sometimes, however, there is no going back. During our 50s, many of us will be faced with empty-nest syndrome when our children go off to study. And for expatriates that could mean to colleges and universities back home. Suddenly, we encounter a new reality that leads us to question where we belong. Should we follow our children back to the country of our birth or adjust ourselves to the ensuing long-distance relationship from a country that might consequently feel less our own?
But losing our children is only one of the many changes bombarding us during our 50s. Look at our bodies! How did that happen? When did those facial hairs get to be so thorny and the love handles morph into saddlebags? For 20 years, there hasn't been time for more than the odd glance in the mirror between ferrying the kids around, helping with homework and buying them new socks and pants. But now there are endless hours that can be dedicated to studying the change.
Look at our bodies! How did that happen? When did those facial hairs get to be so thorny and the love handles morph into saddlebags?
In a global culture that idolizes image, these changes threaten to relegate us to the realms of the invisible, with repercussions in workplaces that always appear to be crammed with 20- and 30-year-olds prepared to work 12-hour days for part-time salaries. And here we are, the dinosaurs in the corner, expecting outdated sums of money while not understanding anything about "memes" or the finer points of SEO.
What happened to the people of our age in the workplace anyway? Did they all get laid off? Or did they simply grow tired of the McJob culture and downsize, shifting lock, stock and barrel to the sticks where the pace of change is slow, and do-it-yourself tasks are altogether more manageable.


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~10 min 15 sec
Breaking: BitMEX Is Facing Civil and Criminal Charges as CFTC and DoJ Take Action
Oct 01, 2020 | Peter Wind
Key highlights:
US market regulator CFTC and the US Department of Justice have both announced separate actions against BitMEX
BitMEX CTO Samuel Reed was arrested in Massachussets
BitMEX is one of the most popular platforms for trading cryptocurrency derivatives with high leverage
The CFTC files civil enforcement action against BitMEX
Major cryptocurrency derivatives exchange BitMEX has come under fire from US market regulator CFTC, as well as the US Department of Justice.
The Commodity Futures Trading Commission (CFTC) has filed a civil enforcement action against BitMEX and a number of prominent individuals associated with the company, such as Arthur Hayes, Ben Delo and Samuel Reed. The regulator has charged BitMEX with »operating an unregistered trading platform and violating multiple CFTC regulations, including failing to implement required anti-money laundering procedures«.
While BitMEX's official stance is that it does not accept customers from the United States, the CFTC is alleging that the platform was »conducting significant aspects of its business from the U.S. and accepting orders and funds from U.S. customers«.
CFTC chairman Heath Tarbert commented on his agency's action:
»Digital assets hold great promise for our derivatives markets and for our economy. For the United States to be a global leader in this space, it is imperative that we root out illegal activity like that alleged in this case. New and innovative financial products can flourish only if there is market integrity.«
Department of Justice announces criminal charges
While CFTC's civil action against BitMEX is already cause for concern, it doesn't get much better for the popular leveraged trading platform - the U.S. Attorney's Office for the Southern District of New York has announced criminal charges against Arthur Hayes, Ben Delo, Samuel Reed and Gregory Dwyer. Samuel Reed, who is the CTO of BitMEX, was arrested in Massachussets earlier today. The other three, who are most likely not in the United States, »remain at large«.
The four are being charged with »violating the Bank Secrecy Act and conspiring to violate the Bank Secrecy Act«. Each of the two charges could result in up to five years in prison.
The Bank Secrecy Act requires US financial institutions to implement measures to combat money laundering and report suspicious activity that could be connected to money laundering, tax evasion, or other illegal activity.
BitMEX was already feeling the pressure to make its platform more compliant with KYC/AML laws even before today's announcements. In August, BitMEX announced that all newly-created accounts will have to go through an identity verification process, while all existing accounts would have until February 2021 to complete KYC if they wanted to continue using BitMEX.
The news has had a noticeable impact on the cryptocurrency market as a whole, with prices of crypto assets dropping practically across the board. Bitcoin fell from $10,850 to $10,500 in the space of about an hour, and the rest of the market showed similar price action.
This is not too surprising to see, as BitMEX is a major player in the cryptocurrency market – the platform handled $1.67 billion worth of volume in the last 24 hours. In addition, the platform holds a very substantial amount of . According to popular cryptocurrency analyst Nic Carter, BitMEX holds approximately $2 billion worth of BTC in total across user deposits and the platform's insurance fund.
Bitmex has ~$2b worth of Bitcoin in their vaults (around 193k BTC) ? pic.twitter.com/rVKGeT31ZV
— nic carter (@nic__carter) October 1, 2020
24H7D1M1Y
Total Market Cap
$ 2.09T (2.10%)
Gainers (79%)
Losers (21%)
$ 42,907
$ 3,216.79
$ 464.38
Never miss a story...


The first part of this two-part series discussed our first simply day-trading rule: only initiate a long position when the current bar rises above the previous bar. Keep in mind that this is a trading trigger – it is independent of our bias as to whether the stock is expected to go up or down. This is a focused and narrow time-frame rule to be used on a 5 minute chart. But what is our second basic entry rule when shorting a stock? It is the exact opposite of our previous rule.
Basic Short Entry Rule #2: Only enter a short position when the bar you want to trade goes lower than the previous bar. Look at the previous two charts to see all the wonderful entries you could have had by following this one simple rule. There are 6 to 7 profitable entries on each chart even if this was the only rule you used to enter trades. Of course, you need to know when to get out of a position – or when to exit - and that will be discussed in a later article.
The chart below is Ford and each candle represent 5 minutes of trading. For this exercise we do not yet have a long or short bias. It can trade in either direction. As soon as the bar goes above the high of the previous bar – pretend that you bought or entered into a long position. If the bar dips below the lowest point of the previous candle – imagine you enter a short position. You are concentrating on two bars at a time when deciding to use this trigger or not. In this example you will only hold for the duration of that one bar (or you exit at the top of the solid part or the white bar or the bottom of the candle body of the black bar – but not the wicks since these are not closing prices).
How many trades could you have broke-even or made money on by following that rule? You should have about 12 or so trades where you could have made money and about three or four where you did not. This one simple rule puts the statistical odds in your favor of being profitable. For instance, imagine you were trading Ford with 1,000 shares and your goal was to take two pennies profit with a stop-loss at two pennies. You should have grossed a profit of $140 (figures may vary somewhat).
Again, don't walk up to a stock and blindly trade the moment prices go above or below the previous bar. Consider this to be a 'junior trader' trigger but we still do not know whether we are biased long or short and we do not have an actual setup (or trading pattern).
Earnings can be a particularly exciting time to trade as prices become volatile and quick moving. Avis Budget Group reported earnings and it wasn't well received. The chart below uses candles of 2 minutes each. Of course the most profitable move would be to short the stock during its morning drop, but for this example we will assume that we had a bias to buy. In general you should follow the trend, but the key here is to illustrate how these two rules will add a layer of protection even if you make the mistake of buying a falling stock (which should be avoided until you are proficient at trend-trading).
The chart below is merely a closeup of the first 14 bars of the above Avis chart. Notice how the stock falls and keeps bouncing lower. Let's focus on the early part of the morning with our mistaken buying bias.
When is the first opportunity you would have to buy? Prices are as high as $13.54 and over the next 10 minutes they drop down to a low of $12.37. If you were trading 100 shares this would be a loss of $117. The first opportunity you have to buy is with the first white bar as it goes above the high of the previous bar. You would buy at $12.56 and the bar went as high as $12.87 or $31 profit on 100 shares.
As a new trader, you should only buy when a new high is formed and only short when a new low is formed when comparing it to the previous bar. This is true regardless of what time frame you are trading. Never try to guess 'how low is low enough' and buy when prices are falling making new lows. Looking back, the majority of bad entries came when I felt prices had fallen far enough coupled the compounding stupidity of averaging down – which is buying more and more shares while prices dropped. The adage is true that 'the market can stay irrational longer than you can stay liquid'. 'Never try to catch a falling knife'.
*Tip: The only time you should ignore this rule is if you show up late to the party. If one bar spikes from $10 to $11 you should not wait for the following bar to go above $11 to buy. The trade got away from you and the risk is now too high. It is better to avoid this two-bar rule when the first bar is extremely long.
This completes the two-part article series on simple trading triggers. To become a well-rounded trader you should first have a bias as to whether the stock is expected to rise or fall, and have exit strategies. You can read more on this in other articles at this site.


My first experience was very good. The staff are very nice and the nurses and doctors are really good, they make you feel comfortable and answer any concerns you have. When having the procedure they walk you through everything and have a nurse to give you support. I also had a polyp that was remove at the same time as the procedure. They also make sure that you are feeling well before discharging you. The office is clean also the bathroom that's the first thing I notice, when you walk in how clean they maintain the clinic. The appointment are fast and they also work on weekends which is perfect. I was self paid patient but they accept Medicaid and most health insurance . I really recommend this clinic so far no complaints.
Being young and pregnant is terrifying, especially when you're farther into it than expected. The staff at Parkmed were amazingly caring and kind, worked fast and efficiently, made recovery as comfortable and less stressful as they could, and made me feel extremely safe through the entire process. From the women at the desks to the doctors and nurses every staff member I encountered was unbelievably kind and made you feel that you were more than just a patient and genuinely cared for. I would recommend it here to anyone and everyone who asks because I'm confident in their amazing service.
I don't have any suggestions... As I sit in the waiting room I thought I would take the opportunity to thank you. My coming here was nothing I want to experience again however I was so thankful for the care I was given. The doctor and nurses all made me feel as comfortable as can be and made a very hard time in my life a little easier. I would recommend your facility without a doubt.
Great people, very professional and competent but also easy and warm interactions. I felt I could trust advice, care, and intentions. They operate consistently with their mission, and I have much respect for their goal to provide affordable care and fee transparency. Would make Parkmed and it's staff my primary care if they provided that service. Recommend this place highly for women's reproductive health care. Thank you!!
I had my appointment for 9:45 am but because of traffic jam I got there 10 something. They did all the blood work and sonogram really fast. The staff is very passionate and warm, specially nurse Marie and another young one I don't remember her name. I went on general anesthesia and even the anesthesiologist was very friendly. The whole procedure lasted like 10 min and I didn't feel nothing since I was under general anesthesia. Afterward I didn't feel no pain at all, not even cramps. Although this is a situation really hard to go through, Parkmed made it easier.
Great facility, clean and very welcoming and friendly nurse and doctor.
Looking for a GYN can be stressful. Finding one where the entire staff is kind and down to earth is rare! Out of all of the sweet staff, Marie was my favorite. She wears her big heart on her sleeve and makes you feel right at home. The care is professional and compassionate. I can see why people have been coming here for years. We moved and now have to travel to our children's pediatrician. But he's so awesome, I wouldn't dare leave his practice. That's how I feel about Parkmed!
Everyone was super helpful + friendly. Surgeon + nurses made experience excellent for an unpleasant circumstance. Definetly will reccomend.
Best place, its a dream come true.
Parkmed is wonderful. Very professional and attentive. Will definately recommend to friends.
Nurses were very helpful and nuturing. I appreciate that. It felt good after going through such a challenging time.
The staff is exceptional. I feel very protected here.
Excelent staff and service is high end.
Wonderful staff. I was at first annoyed of the wait but I see now why. Everyone took their time and was courteous and sweet. I felt no judgement whatever. Thank you to everyone for being so great!
Great people. Everyone I have met from the moment I stepped in the office was great!!
Thank you guys for being so gentle, this was difficult for me and everyone made me feel comfortable and at peace with my decision.


text:
Home > Faculty Publications > 3878
From Social Movement to Moral Market: How the Circuit Riders Sparked an IT Revolution and Created a Technology Market
Curtis Child, Brigham Young University - ProvoFollow
book review, Paul-Brian McInerney, social movement
It feels odd, when I can simultaneously stream a European soccer game to my laptop while reading news headlines from around the world and answering emails, and at a time when many of our students know nothing other than the ubiquity of digital connectedness, to think of the turn of the century as a time when an entire sector of the economy was technologically unsophisticated—especially when the turn in question is from the twentieth to the twenty-first. Yes, before the year 2000 the nonprofit sector was surprisingly low-tech, and perhaps more surprising only because of our collective forgetfulness regarding how quickly things have changed, practitioners in the sector were not all that concerned about their embryonic state of affairs. After all, they had the work of social and environmental justice to pursue, and their sup- porting foundations could be forgiven for not feeling inclined to support administrative costs such as those associated with connecting to a newish tech- nology called the World Wide Web.
Original Publication Citation
Curtis Child, From Social Movement to Moral Market: How the Circuit Riders Sparked an IT Revolution and Created a Technology Market, Social Forces, Volume 95, Issue 4, June 2017, Page e16.
Child, Curtis, "From Social Movement to Moral Market: How the Circuit Riders Sparked an IT Revolution and Created a Technology Market" (2016). Faculty Publications. 3878.
https://scholarsarchive.byu.edu/facpub/3878
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http://lib.byu.edu/about/copyright/


The Great Falls High and C.M. Russell High volleyball teams are heading to Billings road for their final two regular season games this weekend with playoff positioning in mind.
The Bison (2-9, 5-20) face Billings West on Friday while the Rustlers (4-5, 9-11) take on Billings Skyview, with both games starting at 7 p.m. The teams swap opponents on Saturday at 1 p.m.
GFH is sitting in sixth in the Eastern AA, one game behind West and two behind CMR. A win over West would likely lock the Bison as the fifth seed.
Outside hitter Brianna Perkins said the Bison have a good mindset heading into the weekend.
GFH head coach Katherine Sunwall said her team is looking to seize the opportunity of moving up in the standings to close out the regular season.
The Bison are coming off a pair of losses over the weekend, losing both matches in straight sets to Billings Senior, the No. 1 team in Class AA, and second-ranked Bozeman.
Sunwall said facing such stiff competition always sharpens a team's skills.
GFH defeated West in a tight, five-set match on Sept. 18, rallying after losing the first two frames.
Sunwall said the Bison know it could be a very close game once again against West.
GFH lost in straight sets in the previous match with Skyview, but only lost the first two games by two points.
Sunwall said while the Bison have playoff seeding in mind, they're mainly focusing on themselves.
CMR holds the No. 4 slot in the East and a win over West on Saturday would clinch a home playoff game for the Rustlers.
Senior defensive specialist Kierstin Evenson said CMR knows the importance of their final two games.
Rustler mentor Theresa Besich said her team has been steadily improving all season.
CMR is coming off a pair of losses to Senior and Bozeman but Besich said there were some promising things that came out of the matchups.
The Rustlers lost to Skyview in straight sets in the first matchup of the season.
Besich said the Falcons are a squad that doesn't give up too easily.
The Rustlers lost in four sets to West earlier in the season. Besich said the Bears are a very technically sound team.
Evenson said the Rustlers are playing together much better than the first time they faced West and Skyview.


text:
\section{Introduction}

In domains such as management and education, organizational
practices often seem to come and go in puzzling ways.  They are
introduced with fanfare, but then they diffuse with little evidence
that they work well. While they are sometimes discarded at later
times, they are so with little conclusive evidence about their
performance. Consider, for example, Quality Circles. Possibly as
many as 90 percent of the Fortune 500 companies had adopted QCs by
1985, but by 1987 ``more than 80\% of the \emph{Fortune} 500
companies that adopted QCs in the 1980s had abandoned them by 1987''
\cite[pp.~256]{abrahamson:96}. Yet one is hard pressed to find hard
evidence of their impact, even after the fact.

Fads are so common in American education that one observer reports
that ``School leaders in a whimsical mood sometimes play a parlor
game called `Spot That Jargon,' in which the goal is to name as many
past educational fads as possible.  The list is usually
impressive.'' \cite{lashway:98, maddux:04}

This paper examines the diffusion of such innovations or ideas.  We
call them \emph{soft technologies}, not because of their physical
properties but because evidence for or against them is equivocal,
inconclusive, or even nonexistent.  We contend that the choices made
by adopters are quasi-rational: they reflect both an attempt to
assess the imperfect data surrounding such innovations as well as a
reliance on social cues, i.e., what peers have done.  We argue that
these two elements are linked by what could be called Festinger's
Hypothesis: the more equivocal the evidence, the more people rely on
social cues \cite[pp.~118]{festinger:54}.

In this paper we present a model that considers soft technologies as
those for which (1) the objective evidence is weak and (2) people
rely heavily on the prior choices of people in similar roles. We
then show that the dynamics of the model leads to outcomes that
appear to be deterministic in spite of being governed by a
stochastic process. In other words, when the objective evidence for
the adoption of a soft-technology is weak, any sample path of this
process quickly settles down to a fraction of adopters that is not
predetermined by the initial conditions: ex ante, every outcome is
just as (un)likely as every other. In the case when the objective
evidence is strong, the process settles down to a value that is
determined by the quality of the evidence. In both cases the
proportion of adopters of the technology never settles into either
zero or one.

\subsection{Related Work}

In the most highly developed mathematical models of fads---economic
theories of ``herding''---decision makers also use social cues but
do so in perfectly rational ways, via Bayesian
updating.\footnote{There is now a large literature on informational
cascades triggered by fully rational agents: see the annotated
bibliography of Bikhchandani, Hirshleifer and Welch, available on
the Web \cite{bikhchandani:96}. For seminal papers in this line of
work see \cite{banerjee:92, bikhchandani:92, welch:92}.}  Though we
agree that social cues matter we think that the premise of
Bayesianism exaggerates the rationality of agents facing the
difficult decision of whether or not to adopt a soft technology. In
particular, there is little evidence for the claim that people are
perfect Bayesians. A leading experimental economist summarizes the
evidence as follows:
\begin{quote}
Much research in cognitive psychology suggests that the way in which
people form judgments of probability departs systematically from the
laws of statistics and from Bayesian updating. (This should not be
surprising, because there is no reason to think that evolution of
brain processes like memory, language, perception, categorization,
and reasoning would have adapted us to use a rule that Bayes only
``discovered'' a couple of hundred years ago.)  Some research points
toward systematic departures, or ``biases'', which spring from a
small number of ``heuristics'', like anchoring, availability, and
representativeness \cite[pp.~171]{camerer:98}.
\end{quote}

Thus, the \emph{theoretical} value of herding models---the
intriguing demonstration that what appears to be conformity behavior
in the aggregate is consistent with perfectly rational action of
individuals---should not be confused with \emph{empirical}
confirmation of its micro-postulates.  As a purely theoretical point
it is interesting to recognize that perfect information-processing
by individual agents is, under certain circumstances, consistent
with conformity-like behavior. But we suspect that to the extent
that such models receive empirical support, the support will be
``weak'' in the sense that the data on conformity or herding will
also be consistent with a wide variety of other
sensible-though-suboptimal forms of individual
information-processing. Again, Camerer's assessment of Bayesianism
is pertinent:
\begin{quote}
As a descriptive theory, Bayesian updating is weakly grounded in the
sense that there is little direct evidence for Bayesian updating
which is not also consistent with much simpler theories. Most of the
evidence in favor of Bayesian updating boils down to the fact that
if new information favors hypothesis A over B, then the judged
probability of A, relative to B, rises when the information is
incorporated.  This kind of monotonicity is consistent with Bayesian
updating but also with a very wide class of non-Bayesian rules (such
as anchoring on a prior and adjusting probabilities up or down in
light of the information) \cite[pp.~171]{camerer:98}.
\end{quote}

In this paper we propose a model that is consistent with all of
Camerer's observations and so is an alternative to canonical herding
models. Thus our agents exhibit normatively desirable and
empirically plausible monotonicity properties: in particular, the
more the social cues favor innovation A over B, the more likely it
is that an agent will select A, ceteris paribus.  Yet the reasoning
that underlies such choices is adaptively rational rather than fully
rational. Moreover, unlike many adaptive models of fads, the present
model generates analytical solutions, not just computational
ones.\footnote{Many---perhaps most---adaptive models of fads are
what has come to be called ``agent-based models'' and it is
virtually a defining feature of such models that they be
computational.  (For a survey of agent-based models, including
several applied to fads, see \cite{macy:02}.)}

\section{The Model}

A world in which people can make mistakes (e.g., adopt an inferior
method of instruction, partly because many other school districts
have already done so) and where they are influenced by the possibly
erroneous, possibly correct choices of similar decision makers is
inherently probabilistic.  Hence our model is built around a
probabilistic choice process.

We assume that two alternatives, A and B, diffuse through a
population of decision makers.\footnote{We shall often interpret A
and B as competing innovations, but the model allows for different
interpretations.  For example, one of the options could be the
population's status quo alternative while the other is an
innovation.  We will return to this specific interpretation in
Section \ref{sec:interpretations}.} In every period one decision
maker makes up his mind about whether to adopt A or B; this choice
is final. (In this sense the formulation is like most ``contagion''
models.) The diffusion continues until everyone in the population
has selected either A or B.  To get the process going, we assume
that initially ($t=0$) at least one person has made a choice, i.e.,
at least one person already champions either A or B.  However, we
allow for the possibility that either option may have multiple
initial champions. (One could regard these early champions as the
inventors of the two alternatives.) A useful benchmark case to keep
in mind is a fair start, in which A and B are backed by the same
number of initial champions. The numbers of A- and B-champions at
$t=0$ will be denoted by $m_0$ and $n_0$.

The heart of the model is how agents decide on which option to
adopt. As noted earlier, we assume that there are two components to
the adoption decision.  The first is based on individual judgment;
the second, on social influence.  Regarding the first component, we
assume that a person isolated from social influence would choose the
objectively superior option (labeled A in our model) with
probability $p$. Thus $p$ reflects the quality of the evidence about
the relative merits of A versus B, plus whatever a priori bias
(possibly due to a folk theory) exists.  If A's superiority is
obvious then $p$ will be close to 1; if the two alternatives are
nearly interchangeable or if evaluation technologies are primitive
then $p$ will be close to $1/2$.  If there is an a priori,
theory-driven bias against A, then $p$ could be less than $1/2$. In
general $p$ is in $(0,1)$.

We assume that the impact of social influence is linearly increasing
in the proportion of the ``converted'' who have adopted in a
particular adoption.  Thus, if $M_t$ denotes the number of people
who by period $t$ have chosen A and $N_t$ denotes the number who
have selected B, then the social pressure to choose A is simply
$M_t/(M_t+N_t)$. The social pressure to choose B is, of course,
$N_t/(M_t+N_t)$.\footnote{This is consistent with a simple search
process: if the decision maker looks for social cues (i.e., the
choices that the already-converted have made), then with probability
$M_t/(M_t+N_t)$ the first convert she bumps into is an A-adherent.
And so with that probability she is persuaded to adopt A
(conditional on her choosing via social imitation).}  (We will use $m_0$ and
$n_0$ to denote the initial number of adherents to A and B, respectively.)

Since we are trying to construct a simple benchmark model that has a
clean structure, we assume that an agent's choice is simply a
weighted average of the above components (individual judgement and
social influence). Thus
\begin{equation}
\label{eq:dyn-A}P[\text{agent in period $t+1$ chooses }A] = \alpha
\cdot p + (1-\alpha) \frac{M_t}{M_t+N_t}
\end{equation}
where $0 \le \alpha \le 1$.  Similarly, the probability that the
chooser in period $t+1$ selects B is
\begin{equation}
P[\text{agent in period $t+1$ chooses }B] = \alpha \cdot (1-p) +
(1-\alpha) \frac{N_t}{M_t+N_t}.
\end{equation}

Festinger's hypothesis---that people are more open to social
influence when evidence is equivocal---amounts here to assuming that
$\alpha$ and $p$ are positively correlated if $p>1/2$ and negatively
correlated if $p<1/2$. We believe that this is a sensible
proposition but we do not require it for our analytical results.  We
do use it in most of our simulations, however.  Moreover,
Festinger's hypothesis informs our understanding of what we consider
soft technologies: in the context of Eq.~(\ref{eq:dyn-A}), a soft
technology is one with a $p$ in the vicinity of $1/2$ and a low
$\alpha$.

To get a feel for how this process works it is useful to consider
first the two extreme cases: i.e., when $\alpha=1$ and $\alpha=0$.
The former is simply an independent trials process with a
probability of ``heads'' of $p$. This process and its properties are
well-understood. The case of $\alpha=0$ (pure social influence) is
the standard Polya's urn process \cite{polya:23}. Given that we are
particularly interested in soft technologies, which here are
represented by low values of $\alpha$, we will pause for a moment to
recapitulate its features.

\section{Pure social influence ($\alpha=0$)}

Suppose 100 people have made up their minds, with 70 having chosen A
and 30 having chosen B.  Then in the current period agent 101 has a
70\% chance of selecting A.  Consequently the \emph{expected}
proportion of the population who cleave to A is $.7 (\frac{71}{101})
+ .3 (\frac{70}{101})=\frac 1{101} (.7\cdot 71 + .3\cdot 70)=\frac
1{101} (70.7)=.70$; i.e., the expected proportion exactly equals the
current proportion.  It is easy to show that this \emph{martingale
property} holds in general: on average the pure social influence
process stays exactly where it currently is. That is,
\begin{equation}
\label{eq:martingale}E\left[F_{t+k} \Big|\, F_t= \frac{M_t}{M_t+N_t}
\right] = \frac{M_t}{M_t+N_t} = F_t
\end{equation}
for all $k>0$. Hence the pure social influence process is strongly
path dependent: in expectation it tends to stay wherever it
is---i.e., wherever it has arrived via the particular sample path it
has been traveling.

\begin{prop}
\label{prop:polya}If $m_0=n_0=1$ (and $\alpha=0$), then $P[M_t=1]=
P[M_t=2] = \cdots = P[M_t=t+1] = 1/(t+1)$, for all $t>0$.
\end{prop}
\begin{proof}
This is a classical result \cite{polya:23}.
\end{proof}

Thus every feasible outcome is equally likely at every date: given a
fair start, the pure process of linear social pressure is completely
`blind'. Therefore the process is just as likely to wind up
generating a heterogeneous diffusion, with half the population
championing A and the other half B, as it is to wind up with a
sharply skewed outcome, with nearly everyone backing A.\footnote{It
is worth mentioning that Proposition \ref{prop:polya} depends upon
the initial seed being $m_0=n_0=1$. If it is a fair start but there
are more two initial champions then the resulting distributions are
not uniform, though they are symmetric around $1/2$. Nevertheless,
the martingale conditional expectations property \emph{does}
continue to hold, for any values of $m_0$ and $n_0$. So in this
sense the strong path dependence property is insensitive to the
initial conditions.}

\begin{figure}
\begin{center}
\includegraphics[scale=.7]{polya.eps}
\caption{\label{fig:polya}A simulation of the pure Polya's urn
process for 1000 rounds. The initial condition is one A and one B.
As can be seen from the figure, the three sample paths converge to
different limits.}
\end{center}
\end{figure}

It is instructive to look at a typical sample path; see
Fig.~\ref{fig:polya}. Note that initially the relative frequencies
of A- versus B-adherents swing wildly: with only a small number of
initial converts, early adopters have a lot of influence. But once
hundreds of people have taken sides subsequent adopters have little
impact on relative frequencies, and so the process settles down.
\emph{Hence it may appear to agents inside the process that the
diffusion is moving toward some predetermined equilibrium.} Part of
this impression is correct: as $t \to \infty$, any sample path of
this process will settle down near some long-run proportion of
A-adherents and B-adherents.  But we know from Proposition
\ref{prop:polya} that this asymptotic state was not at all
predetermined: ex ante, every outcome is just as (un)likely as every
other.

What does the pure Polya process tell us about the complex process,
in which objective evidence does play a role? Recall that by
Festinger's hypothesis, soft technologies have low $\alpha$'s.
Moreover, an agent's adoption probability, as represented by
Eq.~(\ref{eq:dyn-A}), is continuous in $\alpha$. Hence for $\alpha$
``close'' to zero the diffusion will approximate a pure social
influence process: it will be ``nearly'' blind, ex ante.

In such circumstances agents are influenced by a mixture of
conformity and individual judgment about the evidence.  Yet they may
not have good access to the fine structure of their own choice
processes \cite{wegner:98}.  For one thing, they may not recognize
how much of their choice is influenced by social cues. And even if
they do, they may rationalize that part of it, along the lines of
herding models: the behavior of peers conveys information, and so it
is rational to be socially influenced.  And of course that may be
so. But if $\alpha$ is sufficiently low there is a good chance (less
than half but still appreciable) that at date $t$ a majority of the
converts will back the weaker option; hence the next adopter could
be led astray.

Further, we suspect that people involved in a diffusion of a soft
technology do not have good intuitions for the stochastic properties
of the process.  In particular, we suspect that the fact that with a
low $\alpha$ a soft technology could, given a large population of
adopters, wind up at many different outcomes with nearly equal ex
ante probabilities is underappreciated.  Life unfolds as a sample
path, and the ``settling down'' feature of the particular sample
path one inhabits (as in Fig.~1) will be much more salient than
theoretical ex ante probabilities---if the latter are recognized at
all.

\section{Properties of the Complex Process: $\alpha \in (0,1)$}

\subsection{The mean of the process}

We now directly investigate properties of the complex process.
First let us examine how it behaves over time.

\begin{prop}
\label{prop:EF_monotone} Assume $\alpha>0$. $EF_t\to p$ monotonously
as $t\to \infty$.
\end{prop}

In fact, instead of expectations, we can establish a strong
convergence result:

\begin{prop}
\label{prop:strong_conv}$F_t\to p$ a.s. as $t\to\infty$.
\end{prop}

\begin{figure}
\begin{center}
\begin{minipage}{10cm}
\centering
\includegraphics[scale=.7]{cp1.eps}\\
\small{$\alpha=0.9$}
\end{minipage}

\bigskip

\begin{minipage}{10cm}
\centering
\includegraphics[scale=.7]{cp2.eps}\\
\small{$\alpha=0.1$}
\end{minipage}
\caption{Simulations of the complex process after $1000$ rounds. In
both cases ($\alpha=0.9$ and $0.1$) we choose $p=0.7$ and initial
condition one A and one B. In each figure three sample paths are
shown. As can be seen, the convergence rate for small $\alpha$ is
much slower than the rate for large $\alpha$.}
\end{center}
\end{figure}

Thus the process exhibits a constant drift or bias toward $p$, the
value of individual judgment. This reason behind this is because the
martingale property is broken. In fact, instead of
Eq.~(\ref{eq:martingale}), it can be shown (see Appendix) that for
the complex process $\alpha\in (0,1)$,
\begin{equation}
\label{eq:mean_dynamics}E[F_{t+1}|F_t] = F_t +
\frac{\alpha(p-F_t)}{t+s+1}.
\end{equation}

Note that the process could be mostly one of social
construction---most of the weight is on imitation---yet improvement
will tend to occur anyway, if $\frac{m_0}{m_0 + n_0}< p$. Henceforth
we denote $\frac{m_0}{m_0 + n_0}$ by $f_0$.

Next let us consider how $F_t$ is affected by variations in the
model's two basic parameters, $\alpha$ and $p$.  The latter's effect
is both obvious and unconditional.  Clearly, the average fraction of
correct choices, $EF_t$, is increasing in $p$.  But more can be
said.

\begin{prop}
\label{prop:high_p}A higher value of $p$ yields a distribution of
$F_t$ that stochastically dominates a distribution of $F_t$ produced
by a lower value of $p$, for all $t>0$.
\end{prop}

This immediately implies that $EF_t$ is increasing in
$p$.\footnote{However, the converse does not hold: an increase in
the mean fraction of correct choices does not imply that one
distribution stochastically dominates the other.  So Proposition
\ref{prop:high_p} is stronger than just a comparison of means.}

It is worth emphasizing that this strong effect obtains even if the
process is mostly one of social construction (low $\alpha$'s).  Thus
the presence of social construction does \emph{not} utterly prevent
``rational engineering'' (e.g., concerning the technology of
evaluation) from having benign effects.  (Of course, in a sense this
is built into the model, but hopefully it is built-in in a plausible
way.)

Further, Festinger's hypothesis implies that if $p > 1/2$, then as
the evaluation technology improves, decision makers will rely more
on the evidence and less on social cues: the rise in $p$ will be
followed by an increase in $\alpha$.  This \emph{indirect} impact of
changes of $p$ might be just as important as its direct effect on
$EF_t$.  This naturally raises the question, how do changes in
$\alpha$ affect the process?

\begin{prop}
\label{prop:high_alpha}\mbox{}
\begin{itemize}
\item[(i)] If $f_0<p$, then $EF_t$ is increasing in $\alpha$, for all
$t>0$.
\item[(ii)] If $f_0>p$, then $EF_t$ is decreasing in $\alpha$, for all
$t>0$.
\end{itemize}
\end{prop}

Thus, absent a lucky start ($f_0>p$), less reliance on social cues
improves matters.  Consequently, if Festinger's hypothesis is
correct, improvements in evaluation technology have \emph{two}
benign effects on diffusions that don't enjoy lucky starts: the
obvious direct one (Proposition \ref{prop:high_p}) and a less
obvious indirect one (Proposition \ref{prop:high_alpha}), via
increases in $\alpha$.

\subsection{The unpredictability of the process}

If we compare the two pure processes, it is evident that, given a
standard start ($m_0=n_0=1$), the pure social process is less
predictable (as measured by the variance of $F_t$).  When the
diffusion is purely a matter of social construction, every feasible
outcome is equally likely; this is intuitively very random, and it
translates into one with a high variance.  In contrast, even the
variant of the pure individualistic process with maximal variance
($p=1/2$), extreme outcomes---nearly everyone choosing A or B, for
example---are less likely than other outcomes.  So the variance must
be less than that produced by pure social imitation.

Since the complex process is, at the level of individual choice, a
weighted average of the two pure processes, it is intuitively
reasonable that the variance of the complex process would be
increasing in $1-\alpha$, the weight on the higher variance
component, given a fair start. This intuition is correct, but since
it conditions on a particular starting point it is incomplete. The
next result shows that more weight on the social component can
\emph{reduce} the variance of the complex process, at least for
awhile.

\begin{prop}
\label{prop:alpha} $\partial(\var F_t)/\partial \alpha <0$ for all
$t>0$ iff either
\begin{itemize}
\item[(i)] $f_0<p$ and $p_0=(1-\alpha)f_0+\alpha p>1/2$, or
\item[(ii)] $f_0>p$ and $p_0=(1-\alpha)f_0+\alpha p<1/2$.
\end{itemize}
\end{prop}

Thus for parameters in this range, increases in the weight on
evidence both improve community-wide accuracy (on average) and
reduce the variance of outcomes.

While it makes sense that the more socially constructed a diffusion
is the more variable are its outcomes, it is important to emphasize
that this conclusion does \emph{not} always hold, as is indicated by
the ``only if'' part of Proposition \ref{prop:alpha}.   Here is why.
Suppose initially A-adherents are few and far between; for
simplicity, let $f_0=0$.  Then if diffusion were based only on
imitation ($\alpha=0$), everyone would adopt the wrong option---and
the process, although built completely on social cues, would exhibit
no variability whatsoever.  In such a case small increases in
$\alpha$ would \emph{increase} the variance of the outcomes, at
least in the early goings. Of course this increase in variance is
thoroughly benign: without it, the diffusion would be stuck in a
highly predictable but consistently inferior sample path.

\begin{prop}
\mbox{}
\begin{itemize}
\item[(i)] $\partial(\var F_t)/\partial p<0$ for all $t>0$ if $p\ge
1/2$ and $p_0>1/2$;
\item[(ii)] $\partial(\var F_t)/\partial p>0$ for all $t>0$ if $p\le
1/2$ and $p_0<1/2$.
\end{itemize}
\end{prop}

Regarding part (ii), note that increases in $p$ increase the
variance in a socially desirable way.  However, people who like
predictability could resent the ``rationalizing'' effect of
improvements in program information and evaluation technology,
because in these circumstances increasing $p$ makes things
``messier'' and less predictable.

As a matter of interpretation, one might say that a $p\le 1/2$ and
an $p_0<1/2$ involves a ``soft'' technology with a vengeance: there
is some kind of bias against the better technology, maybe because it
is not as trendy, and there is also a bias in terms of the initial
social proclivities ($p_0<1/2$). In this case, improving evaluation
technology also creates a ``messier'' and more confusing process ex
ante, in that such improvements increase the variance of the
outcomes.

Indeed, even if $p>1/2$ (i.e., the ``standard'' case), we know that
in period 1 the variance of $F_1$ is increasing in $p$ if $p_0<1/2$.
(And probably this will hold for some finite number of periods after
period 1 too.)  This is a very natural case of a soft technology:
there is a social bias against the better technology (bad luck,
essentially, or maybe glamour is on the side of the weaker
technology), in that $f_0<1/2$, and things are noisy enough so that
$p$, though above $1/2$, is still fairly low. Hence if Festinger's
hypothesis were to kick in, so that $\alpha$ were pretty low too,
then $p_0<1/2$, and the variance of $F_1$ would be increasing in
$p$.

\subsection{The effects of initial conditions}

There are two types of initial conditions: the \emph{size} of the
initial seed ($m_0 + n_0$) and the \emph{proportion}
($f_0=\frac{m_0}{m_0+n_0}$). Each has effects independently of the
other (i.e., holding the other fixed), so we will examine them
separately and in a ceteris paribus manner.

\subsubsection{Varying the size of the initial seed}

\begin{prop}
Suppose $F_t'$ is a bigger process than $F_t$ in that $m_0'=km_0$
and $n_0'=kn_0$, where $k$ is an integer larger than one. In all
other respects the two processes are identical.
\begin{itemize}
\item[(i)] $EF_t'<EF_t$ for all $t>0$ iff $f_0<p$.
\item[(ii)] $EF_t'>EF_t$ for all $t>0$ iff $f_0>p$.
\end{itemize}
\end{prop}

Thus a bigger initial seed acts as an inertial anchor, slowing down
the movement of $E[F_t]$ to its attractor $p$.

\subsubsection{Varying the initial proportions}

It is obvious that, all else equal, increasing the initial bias
toward A (i.e., increasing $f_0$) boosts $EF_t$ at every date.
Suppose, e.g., we start out with $m_0+1$ A-converts, instead of with
just $m_0$.  This increases the social conformity pressure for the
agent in period 1 to adopt A.  That in turn increases $F_1$, the
expected fraction of the converts who adhere to A at the end of
period 1, which in turn provides more social cues to the decision
maker in period 2 to adopt A and so on.

But a higher $f_0$ has an even stronger effect---stronger even than
ordinary first-order stochastic dominance.  To see what this sense
is, consider the following definition.

\begin{definition}
Suppose the support of $F_t$ and $F'_t$ can be divided into three
mutually disjoint subsets: a non-empty set of high states $\{
f_{h_1,t}, f_{h_2,t}, \dots, f_{h_a,t} \}$, a non-empty set of low
states $\{ f_{l_1,t}, f_{l_2,t}, \dots, f_{l_b,t} \}$, and a
possibly empty set of intermediate states $\{ f_{m_1,t}, f_{m_2,t},
\dots, f_{m_c,t}\}$. Suppose the three subsets are connected in the
sense that any low state is less than any intermediate state, and
any intermediate state is less than any high state. We say that
$F'_t$ is stochastically bigger than $F_t$ in a strong sense if
$P[F'_t = f_{h,t}] > P[F_t = f_{h,t}]$ for any high state $h$,
$P[F'_t = f_{m,t}] = P[F_t = f_{m,t}]$ for any intermediate state
$m$, and $P[F'_t = f_{l,t}] < P[F_t = f_{l,t}]$ for any low state
$l$.
\end{definition}

Thus a distribution of $F_t$ that is stochastically bigger in a
strong sense puts strictly more weight on high fractions of converts
who have chosen correctly, and strictly less weight on low
fractions. (Clearly this implies first-order stochastic dominance,
but the converse need not hold, so this is in fact a ``strong
sense'' of stochastic dominance.)

\begin{prop}
All else equal, the distribution of $F_t$ is stochastically
increasing, in a strong sense, in $f_0$, for all $t>0$.
\end{prop}

Naturally, smaller $f_0$'s produce distributions of $F_t$ that are
stochastically smaller, in a strong sense.  Suppose, therefore, that
A is an innovation that is superior to B.  If B is not an
innovation---it is in fact the status quo---and the community is
quite traditional (everyone initially uses B, though people are
willing to consider an innovation), then effectively $f_0$ equals
zero.  This is the toughest possible starting point for an
objectively superior innovation.

The effect of varying initial bias on the variance of the process is
perhaps less intuitive.

\begin{prop}
$\partial (\var F_t)/\partial f_0 <0$ for all $t>0$ if $p_0>1/2$ and
$p>1/2$.
\end{prop}

\subsection{The convergence rate}

If we wish to use our complex process to explain diffusion of
innovations in the real world, only studying the limit behavior is
not enough. If the characteristic time needed to reach the asymptote
state is too long to be observed, then the asymptote state cannot be
very meaningful in the practical sense. A rough estimation of the
convergence rate of the complex process can be achieved from a
mean-field approximation. Neglecting the noise term, the stochastic
process $F_t$ can be approximated by Eq.~(\ref{eq:mean_dynamics}):
\begin{equation}
F_{t+1}=F_t + \frac{\alpha(p-F_t)}{t+s+1}.
\end{equation}
Further approximating $F_t$ by a continuous process $F(t)$, we can write
\begin{equation}
\frac{dF(t)}{dt} = \alpha \frac{p-F(t)}{t+s+1} \sim \alpha \frac{p-F(t)}t.
\end{equation}
Solving for $F(t)$, we find
\begin{equation}
|F(t)-p| \sim t^{-\alpha}.
\end{equation}
If we define a characteristic convergence time $T$ to be the time it takes
 $F(t)$ to converge to a vicinity within $\epsilon$ from $p$, then we have
\begin{equation}
T \sim \left(\frac 1\epsilon\right)^{\frac 1\alpha}.
\end{equation}
Thus as $\alpha\to 0$, the characteristic convergence time diverges
exponentially in $1/\alpha$. Indeed, when $\alpha=0$ (Polya's urn)
the process \emph{never} converges to $p$. (To be precise, the Polya
process can converge to any $p\in [0,1]$, but the probability that
it converges to any particular $p$ is zero.)

The mean-field estimation is in principle only for the mean of $F_t$, of course. For a fine estimation of the variance of $F_t$, we need a central limit theorem.

\begin{prop}
\mbox{}
\begin{itemize}
\item[(i)] If $1/2<\alpha\le 1$, then as $t\to \infty$, $\sqrt t(F_t-p)$
converges in distribution to a normal distribution with mean zero
and variance $p(1-p)/(2\alpha-1)$. In particular, $\var F_t =
O(1/t)$.
\item[(ii)] If $0< \alpha \le 1/2$, then $\var F_t \to 0$
slower than $O(1/t)$.
\end{itemize}
\end{prop}

\subsection{Implications regarding herding and massive conformity}

Assume, as in cascade models with rational agents, that the
``initial seed'' is exactly one person who chooses A with
probability $p$ and $B$ with probability $1-p$, and everyone else
follows sequentially.

\begin{definition}
We say that there is ``herding'' if after some period $T$ everyone
makes the same choice.\footnote{This is the term some scholars
(e.g., \cite{banerjee:92}) use.  Others (e.g.,
\cite{bikhchandani:96}) call this an informational cascade.}
\end{definition}

\begin{fact}
Herding occurs in our model (with positive probability) iff one of
the following condition holds:
\begin{itemize}
\item[(a)] $\alpha>0$ and $p=1$; then we get herding on option A.
\item[(b)] $\alpha>0$ and $p=0$; then we get herding on option B.
\item[(c)] $\alpha=0$; then we get herding on option A with
probability $p$ and herding on option B with the complementary
probability $1-p$.
\end{itemize}
\end{fact}
\begin{proof}
\emph{Sufficiency.} All three parts are trivial, by induction.

\medskip\noindent
\emph{Necessity.} Suppose none of (a)--(c) hold. Then we know that
$\alpha>0$ and $p\in (0,1)$. By Prop.~\ref{prop:strong_conv},
$F_t\to p$ almost surely. Hence both A and B are chosen infinitely
often with probability one.
\end{proof}

\begin{fact}
Under the assumptions of our model, if heterogeneous behavior ever
occurs then herding is impossible (with probability one).
\end{fact}
\begin{proof}
By Fact 1, if heterogeneous behavior has emerged by some date $t>0$
then the extreme conditions (a)--(c) in Fact 1 cannot hold. Hence we
know that $\alpha>0$ and $p\in (0,1)$. The rest is the same as the
necessity part of Fact 1.
\end{proof}

Conformity could be overwhelming---\emph{nearly} everyone in a
community winds up making the same choice---without being complete.
One of the surprising results of the rational cascade models is that
massive-conformity-in-the-making can be very fragile.  As
Bikhchandani, Hirshleifer and Welch put it, ``A little bit of public
information (or an unusual signal) can overturn a long-standing
\emph{informational cascade}.  That is, even though a million people
may have chosen one action, seemingly little information can induce
the next million people to choose the opposite action.  Fragility is
an integral component of the \emph{informational cascades} theory!''
(\cite{bikhchandani:96} original emphasis).

But this fragility is intimately linked to the agents' complete
rationality and deep understanding of informational cascades.  As
Bikchandani et al.~remark, in standard rational models ``everyone
knows that there is very little information in a cascade
\cite{bikhchandani:96}. That ``everyone'' pertains, of course, to
the model's rational agents, not to real decision makers:  the
evidence is that the latter do \emph{not} realize that there is very
little information in a cascade.\footnote{This is the pattern that
Kubler and Weiszacker found in their experiments on cascades
\cite{kubler:04}. As they put it, ``players do not consider what
their predecessors thought about their respective predecessors.
Thus, they do not understand that some of the decisions they observe
have been herding decisions, not based on any private information
(pp.~438).}  The agents in our model are imperfectly rational and
lack a deep understanding of cascades. Hence, diffusion processes in
our model are \emph{not} fragile (in the above sense). This is
easily established by re-inspecting Eq.~(\ref{eq:dyn-A}): the
probability that an agent makes the correct decision is continuous
in $p$, so a little bit of new public information---represented as a
sudden positive shock to $p$---will only increase the probability of
choosing correctly by a little bit. (For an illustration of this,
see Fig.~\ref{fig:change_p}.)
\begin{figure}
\centering
\includegraphics[scale=.7]{change_p.eps}
\caption{\label{fig:change_p}A simulation of the complex process
with $\alpha=0.5$ and initial seed one A and one B. $p$ is set to
$0.2$ for the first 500 rounds and is updated to $0.3$ for another
500 rounds. The vertical line indicates the change point. As can be
seen, the sample path is not significantly affected by the new level
of $p$.}
\end{figure}

\section{\label{sec:interpretations}Alternative Interpretations}

It is worth noting that one can use the model to represent
situations in which there is a status quo option that everyone
already uses.  Then the diffusion is simply this: in every period
one agent has an opportunity to either take up the innovation (say,
A) or keep the status quo (say, B).  Further, one could allow for
the innovation to be objectively \emph{inferior} to the status quo.

Under this interpretation of a novel technology competing with a
status quo, it is reasonable to suppose that $f_0$ equals zero. This
is, from the point of view of an innovation, the toughest possible
starting point.

If it's a soft innovation then presumably both $p$ and $\alpha$ are
relatively low, so it is probably the case that $\alpha p +
(1-\alpha) f_0$ is less than one-half.  Hence by Proposition
\ref{prop:alpha}, decreasing the reliance on social cues (i.e., a
smaller $1-\alpha$) would \emph{increase} the variance of outcomes.

\section{Extensions of the Model}

\subsection{Endogenizing Festinger's Hypothesis}

If Festinger's hypothesis is correct, then the weight on social
cues, should depend on how difficult the choice is.  That is,
$\alpha$ should be a function of $p$.  We can stipulate a priori
several properties that this function should have.  First,
$1-\alpha$ should reach its maximum when $p=1/2$: it is in this
circumstance that an agent is maximally uncertain about the relative
merits of A versus B and so, following Festinger, s/he would be
maximally reliant on social cues.  Second, as a benchmark the
function should be symmetric around one half: $f(.5-k) = f(.5+k)$,
for $.5 \le k \le 1$.

A simple function with both properties is $1-\alpha = 4p(1-p)$. Then
the probability that the chooser in period $t$ would pick A would
equal $4p(1-p)\cdot F_t + 1-4p(1-p)\cdot p$.

An interesting feature of this choice equation is that it creates
the possibility that the probability of a correct choice is
\emph{decreasing} in $p$.  This seems bizarre but it is explicable:
it arises because $\alpha$ is a function of $p$.  Note that
$4p(1-p)$, the weight on social cues, is increasing when $p$ is less
than one half, since in this range increases in individual-level
accuracy make the choice problem more confusing or troubling.  Thus,
as we suggested earlier, increases in $p$ have two effects: a direct
effect on individualistic choice (which is always benign, as shown
by Proposition \ref{prop:high_p}) and an indirect one on the weights
(which is not always benign).  For certain parametric values the
indirect effect is sufficiently large and negative so as to swamp
the positive direct effect.\footnote{To see this concretely, note
that the derivative of $4p(1-p)\cdot f_0 + 1-4p(1-p)\cdot p$ with
respect to $p$ equals $4(1-2p)\cdot f_0 + (1-8p + 12p^2)$.  Then
suppose for example that $f_0 = 0$ and $p=.25$.  With these
parametric values the derivative equals $-.25$, so small increases
in $p$ make the agent in period one more likely to err.  However, if
$p$ is sufficiently big---specifically, if it exceeds $\max(.5,
f_0)$---then increases in $p$ do decrease the chance of mistakes.
(When $p>.5$ then a higher $p$ decreases the decision maker's
confusion, thus raising the weight on individualistic judgment,
which is benign since $p>f_0$.)}

Thus endogenizing Festinger's hypothesis will generate interesting
hypotheses.

\subsection{Diffusion in Communities with Internal Structure}

\subsubsection{Status Hierarchies}

There is considerable empirical support for the hypothesis that the
higher the decision maker's status, the more impact his/her adoption
choice has on the unconverted (e.g., \cite[pp.~275]{strang:98}). It
would be easy to incorporate this empirical regularity in a stark
way in the current model: only high status agents are imitated; the
adoption choices of low status agents are ignored.

An intriguing issue concerns the relative competence of high status
and low status agents.  In diffusions that tap into a relatively
strong scientific or technical base (pharmaceuticals, computer
hardware, etc.), one would expect higher status agents to be more
likely to make the right choice (higher $p$'s).  However, there may
be circumstances in which status is purely a subjective phenomenon,
lacking any objective correlate.  (People inside the community may
\emph{believe} that higher status is correlated with more expertise
but this would be an illusion.)

\subsubsection{Clustered Interaction}

Most diffusions occurs in communities that exhibit biased
interactions.  In general, executives in the same industry are more
likely to interact with each other than with executives in a
different industry.  School superintendents in the same state are
more likely to encounter each other than are superintendents in
different states. (At the limit---if these subcommunities were
completely sealed off from each other---then our model applies as it
is to each subcommunity.)\footnote{For a study of the influence of
social structure on opinion formation, see e.g.~\cite{wu:05,
sood:05}.}

\subsection{Leakage of Information}

Here, $p$ would be a function of time.  Typically we would expect
that $p$ would increase over time, as information about the
technology (by the already-converted or by third parties) leaks out.
As already noted, the  baseline diffusion process (stationary $p$)
is robust: small positive shocks to $p$ will on average have small
effects on the fraction of the population that adopts option A.

\subsection{Waves of Innovations}

Today it seems that the business world never rests.  No sooner has
one innovation passed from the scene---or at least from public
attention---than another one appears.  Becauase there are good
reasons for this---it is not accidental \cite{abrahamson:96}---one
can expect this pattern to continue indefinitely.

Although waves of innovation will obviously produce some new
patterns, we suspect that some of the present paper's results will
continue to hold.  In particular, we conjecture that if the waves
are composed of soft technologies, with $p$ around $1/2$ and strong
propensities to imitate, then the process will continue to be
Polya-like in two senses.  First, objectively uncertainty will be
very great: many adoption patterns will be possible ex ante. Second,
if the waves do not come too often then sample paths of diffusion
will settle down. Hence, subjectively it will feel as if the process
is moving toward a predetermined equilibrium---an illusion.

\section{Conclusion}

Because they diffuse with little objective information about their
effects, soft technologies pose challenges for decision makers.
Psychologists have long argued that when faced with such choice
problems, people use a reasonable though imperfect imitation
heuristic.  We have presented a mathematical model of diffusion that
combines this heuristic with agents' efforts to make
factually-grounded decisions and we established both analytically
and computationally that such processes exhibit clear stochastic
properties. We then showed that the dynamics of the model leads to
outcomes that appear to be deterministic in spite of being governed
by a stochastic process. In other words, when the objective evidence
for the adoption of a soft-technology is weak, any sample path of
this process quickly settles down to a fraction of adopters that is
not predetermined by the initial conditions: ex ante, every outcome
is just as (un)likely as every other.  When the objective evidence
is strong, the process settles down to a value that is determined by
the joint effect of the quality of the evidence and the agents'
competence. In neither case does the proportion of adopters settle
into either zero or one: pure herding does not occur except in
parametrically extreme situations.

Further, unlike informational cascades generated by fully rational
actors, the  process of the present model is robust:  diffusions
that have for a long time tilted massively toward one option cannot
be suddenly derailed by small infusions of new public information.
The fragility of cascades generated by fully Bayesian agents is, we
believe, an artifact of unrealistic assumptions of
hyper-rationality.  Diffusions may be \emph{initially} volatile, as
they are in the present model, but we believe that these processes
stabilize once the weight of public opinion has been brought to
bear.\footnote{Of course, the introduction of a new innovation can,
by restarting the process, destabilize it.   But that is not what is
producing fragility in the full-rationality cascades: these are not
robust against small shocks associated with the \emph{pre-existing}
options.  Moreover, as indicated earlier, we believe that the
present model can be extended to accommodate waves of innovation.}

\pagebreak

\bibliographystyle{alpha}

redpajama_set_name: RedPajamaArXiv


text:
Data Science: An Introduction
Wikibook
Welcome to Data Science
01: A History of Data Science
02: A Mash-up of Disciplines
03: Definitions of Data
04: The Impact of Data Science
Thinking about the World
05: Thinking Like a Visual Artist
06: Thinking Like a Data Engineer (Oct 13, 2012)
07: Thinking Like a Hacker
08: Thinking Like a Programmer
09: Thinking Like a Scientist
10: Thinking Like a Mathematician
11: Thinking Like a Statistician
12: Thinking Like a Domain Expert
Analyzing and Visualizing, Part One
13: Single Variable Analysis
14: Single Variable Tables and Plots
Setting up the Problem
15: Theory-Based Inquiry
16: Theoretical vs Measured Variables
17: Hypothesis Testing
Collecting, Ingesting, Transforming Data
18: Collecting vs Finding Data
19: Data Dictionaries and Schemas
20: Data Preparation and Metadata
Analyzing and Visualizing, Part Two
21: Two Variable Analysis
22: Two Variable Tables
23: Two Variable Plots
Emergent Answers to Free Form Problems
24: Non-Theory-Based Inquiry
25: Exploratory Analysis
Analyzing and Visualizing, Part Three
26: Multiple Variable Analysis
27: Multiple Variable Tables
28: Multiple Variable Plots
Presenting Results
29: Statistical Significance vs Substantive Significance
30: Telling the Story
31: Writing Good Data-Rich Paragraphs
32: Creating Good Data-Rich Slides
02. Working in Groups
03. 250 R Commands
04. Research Design
05. Data Dictionary
PrefaceEdit
This book is a very basic introduction to data science. It is designed for the advanced high school student or average college freshman with a high school-level understanding of math, science, word processing and spreadsheets. No understanding of computer science is assumed. The main emphasis of this book is to help students think about the world in data science terms. While some elementary data science skills will be taught, the point is not skill development, but rather critical thinking and problem solving development. These are skills that can be successfully applied to all phases of life, not just data science.
Data science--as a profession and as an academic discipline unto itself—is new, having been born in the first decade of the 21st century. It is a child born of the mature parental disciplines of scientific methods, data and software engineering, statistics, and visualization. This book is not intended to do justice to any of those disciplines by themselves, but to bring them together in a productive synthesis. As such, the student will be introduced to the parent disciplines and then given exercises that will fuse the parental disciplines into data science. In addition, "hacking" in the original positive sense of the term, is also a contributing parent to the data science child, even though "hacking" is not taught as an academic discipline.
Obviously, a mature data scientist will be proficient in each of the parent disciplines, studying them individually and combining them to solve serious data problems. This text book is but just a first tentative step in that direction.
Data science, as practiced today, arises out of the "big data/cloud computing" world and complexity science. This means data science is an advanced discipline, requiring proficiency in parallel processing, map-reduce computing, petabyte-sized noSQL databases, machine learning, advanced statistics and complexity science. In this sense, "true" data science is more appropriately taught at the Master's and Doctorate level. We believe, however, that data science is as much about mindset as it is about the skillful use of tools. Thus we want to engage students early in their careers to start thinking holistically about data science. This textbook will not address the more advanced technologies and techniques of data science. It will, however, help students to start thinking like a data scientist.
In business and government today, data science is performed as teams. We want the students in this class have that experience. Thus, all the homework, assignments, and exercises are designed for teams of 2 to 6 students. We hope the students will have a chance to work with everyone else in the class over the course of the semester. Most data scientists do not get to choose who they work with, but must learn to work with whomever is assigned to their team.
We will do most of our data manipulation, computer programming, and statistical analysis in the open source R package. We know that intermediate or advanced students would use other tools such as MySQL, PHP, Python, Java, Hadoop, HBase, AllegroGraph, Mahout, MATLAB, SPSS, SAS, etc. For this introduction, however, we are keeping it simple and sticking to just a single general purpose computing environment.
Finally, we try to use terms and concepts which are already defined in the Wikipedia, Wiktionary, and Wikiversity. This way people can refer to the corresponding Wikipedia/Wiktionary page to get a deeper understanding of the concept.
StagesEdit
In the table of contents on the right side of the page, you will notice there is a little box of four squares. The box indicates the maturity of the chapter. For example,
Wikibook Development Stages
Sparse text
Developing text
Maturing text
Developed text
Comprehensive text
Note to InstructorsEdit
We have designed this text for a 16-week 3-credit class. That is, a class that has three classroom-hours of instruction for 16 weeks—for example, 48 1-hour class periods. There are 32 chapters, which allows for—averaged over the semester—one day a week for student project presentations, for reviews and help sessions, and for testing. We image that there will be more lecture periods toward the beginning of the semester and more presentation and review days toward the end of the semester. The book also assumes 1 to 2 hours of "homework" per class period, which includes readings, assignments, study, and projects. The book's philosophy is that as much will be learned about data science by doing team homework projects as will be learned during the lectures.
In the professional world, data science is a team sport. We designed the difficulty and scope of the homework project for teams. At this level (high school senior or first semester college freshman), it would be difficult for a single individual to complete these assignments alone. We also assume there is a place students can go to get help with the R programming language.
Note to ContributorsEdit
First, please register yourself with Wikibooks (and list yourself below), so that we know who our co-contributors are. Also, please abide by the Wikibooks Editing Guidelines, Manual of Style, and Policies and Guidelines. Thank you.
Secondly, we only need basic, clear, straightforward information in each chapter. We are not trying to be exhaustive or complete—the value of this book is in the simple synthesis across subjects. There are other venues in which to wax eloquent on the deepness and complexities of a particular subject. Please place yourself in a "beginner's mind" as you make contributions. Please also scope each chapter so that it can be taught in a one-hour class period. If the chapter requires more than an hour to teach, it is probably too detailed.
To the extent possible, please use terms and concepts in the way in which they are defined in the Wikipedia and Wiktionary. This way students can refer to the corresponding Wikipedia / Wiktionary page to get a deeper understanding of the concept.
Thirdly, this is a cross-disciplinary book. We want to help people apply data science to all fields. Therefore, we need a wide variety of simple examples and simple exercises.
Fourthly, please adhere to the simple structure of each chapter: Summary of Main Points, Discussion, More Reading, Exercises, and References. We want the More Reading section to link to on-line resources. The References section may contain off-line resources. To start a new page, you should use the wiki markup from this prototype page.
Fifthly, as with any Wikibook please feel free to make corrections, expand explanations, and make additions where necessary, even if it is not "your" chapter. Use the discussion page to explain changes that might be controversial.
Sixthly, some syntax rules:
Please bold key terms and phrases the student should learn.
Put the name of functions and code snippets using the 'code' tags: lm()
Use in-line links [[ ]] to the Wikipedia, Wiktionary, WikiCommons, Wikibooks, and other Wikimedia Foundation properties.
Use references (<ref> </ref>) to "external" sources—both on-line and off-line.
Use the citations templates to make citations : Template:Cite book, Template:Cite web, Template:Cite journal
When inserting R code into a page, please adhere to Google's R Style Guide.[1]
If you want to add an image or graph, you should load it into the Commons rather than uploading into Wikibooks.
If appropriate, add the tag {{Created with R}}) when you upload the graph.
If using a different package than R standard packages, put the name of the package in bold in parenthesis after each function : MCMCprobit() ('''MCMCpack''')
You can use the third chapter Definitions of Data as an example of how to craft a chapter.
Finally, thank you so much for volunteering to be part of our our team!
List of Co-AuthorsEdit
Calvin Andrus
Jon Cook
Suresh Sood
See the following Wikibooks for good follow-on texts to this introduction:
The Scientific Method - Scientific Method
Data Engineering - Relational Database Design, Data Structures, SQL
Software Engineering - The Science of Programming, R Programming
Mathematics - High School Mathematics Extensions
Statistical Analysis - Statistics, Statistical Analysis: An Introduction Using R, Data Mining Algorithms in R
Visualization -
Hacking -
↑ "R Style Guide". Google, Inc.. http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html. Retrieved 6 July 2012.
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Viewed with 347 years of hindsight, it was possibly history's most one-sided trade. In 1667, when spices were worth more than gold, England and Holland agreed to divvy up two islands over which both claimed sovereignty.
The Dutch took Run, a remote outpost of Indonesia's Spice Islands, where the aroma of nutmeg, cloves and cinnamon scents the breeze. The English got what would become Manhattan.
Today, while New York reigns as the world's financial capital, Run doesn't have a single bank branch.
"Even just an ATM would be nice," Burhan, 42, a batik-shirted schoolteacher, nutmeg farmer and guesthouse owner, says as he sits on his porch overlooking the volcano-studded Banda Sea, 1,550 miles and two time zones east of Jakarta.
Burhan, who like many Indonesians goes by one name, may not have long to wait. The country's biggest banks, among the most profitable on the planet, according to Bloomberg data, are emerging from their big-city strongholds to compete for customers in the farthest flung of the 17,500 islands that make up the world's fourth-most-populous nation. Some even plan to use boats that double as floating ATMs.
The banks' challenge: to maintain an average 24.3 percent return on equity that dwarfs that of other banks in the world's 20 biggest economies — including the 9.4 percent U.S. lenders deliver.
As Indonesians prepare to vote July 9 to elect the country's first new president in 10 years, investors are betting that, whatever the outcome, millions of newly prosperous Indonesians will embrace financial services as enthusiastically as they have democracy — from cities such as Jakarta to villages such as Run.
Enriched by a 13-year consumption- and resource-driven economic surge, Indonesians have accumulated assets faster than the residents of any other major Asia-Pacific country, according to Credit Suisse, which reported in October that average wealth per adult had jumped almost fivefold to $11,839 since 2000.
By 2030, Indonesia, the world's 17th-largest economy, with a gross domestic product of $878 billion, will have overtaken Germany and Britain to rank seventh, according to a McKinsey forecast.
Already, parts of Indonesia's capital resemble upmarket British or German neighborhoods. The McLaren dealership has sold 19 of the $750,000 British sports cars since November 2012.
As of May 20, the Jakarta Composite Index outperformed this year's 3.3 percent rise in the MSCI Emerging Markets Index to leap 21.4 percent in dollar terms, making it the world's sixth-best performer of 93 indexes Bloomberg tracks.
Indonesia is the world's biggest exporter of power-station coal, nickel, tin and palm oil and home to the world's biggest gold mine and recoverable copper reserve. As such, it hasn't been immune to tumbling commodities prices and a slowdown in its biggest export market, China.
In 2013, Indonesia's growth declined to 5.8 percent from 6.3 percent in 2012. Last year, a slowing economy, a rising current-account deficit and capital flight from developing nations by skittish foreign investors wiped 20 percent from the capitalization of the Jakarta Stock Exchange.
That's now changed. Beginning in May 2013, Agus Martowardojo, Indonesia's central bank governor, reacted swiftly to foreign capital flight by raising interest rates five times to 7.5 percent. The rupiah surged 6 percent this year as of May 20 — the biggest rise among 11 Asian currencies; and bonds jumped 6.9 percent.
The tactic also brought annualized inflation down to 7.25 percent in April from 8.22 percent in January and helped cut the current-account deficit to $4.1 billion in the first quarter of 2014 from $8.5 billion in the third quarter of 2013.
"Compared to Brazil and Russia, Indonesia is the best-run large commodity economy," says Ruchir Sharma, who oversees $25 billion, including shares in Indonesian companies, as head of emerging markets at Morgan Stanley Investment.
The question for investors now is how well the next government will run the economy. In October, President Susilo Bambang Yudhoyono must step down after his second five-year term. The favorite to replace him in July's election is Jakarta Governor Joko Widodo.
Whoever wins, Indonesians' need for financial services will grow. Only 50 million of the country's 250 million inhabitants have bank accounts, says Budi Gunadi Sadikin, chief of PT Bank Mandiri, the country's largest lender by assets. "The penetration is very, very low," he says.
Sadikin says he's so determined to tap that new market that he's opening 300 branches a year. Mandiri's major rival, PT Bank Rakyat Indonesia, the country's most profitable bank, has plans to open 570 branches focusing on microfinance loans to entrepreneurs and small businesses, Director Sofyan Basir says.
In the Indonesian archipelago, taking banking to the people means taking banks to the water. Rakyat is acquiring boats with ATMs to act as floating branches.
Rakyat and Mandiri also send cash via public water transport, watched over by armed guards. To service customers in Run, Rakyat has to send money from Ambon City — the provincial capital of Maluku, as the Spice Islands are known — to a bank on the island of Banda Neira. That's an eight-hour trip by rusty ferry.
From Banda Neira, it takes another two hours to reach the 2,000 inhabitants of Run by traveling in a smaller boat through treacherous currents and reefs.
The banks' high returns are possible because they're charging borrowers as much as 13 percent interest while paying depositors 7 percent or less for the funds.
By expanding into riskier microlending, Mandiri and Rakyat are able to charge even higher interest rates of 30 percent a year, according to Boston Consulting.
"For three to five years, the profitability numbers of Indonesian banks will continue to look very good compared with their global peers," Sharma says.
In Maluku, the spice trade is undergoing a revival. In the 17th century, these islands were the world's most valuable pieces of real estate — especially after doctors judged that nutmeg, which grew nowhere else, could cure bubonic plague.
In the 19th century, the islands lost their monopoly after the British transplanted nutmeg trees to their own colonies in Asia, Africa and the Americas.
Now, entrepreneurs from abroad are joining international aid workers to rebuild the Spice Islands nutmeg trade.
Dean Cycon, founder of Dean's Beans Organic Coffee Co., visited Run last year and bought nutmeg to flavor his coffee. Next year, he says, he'll return to collect a shipload — and perhaps open an account.
The full version of this Bloomberg Markets article appears in the magazine's June issue.


Child walking alone on the street with two dogs in the early morning hours Wednesday.
A mother was arrested yesterday after her one-year-old daughter was found walking alone on a highway. 24-year-old Lindsey Moody of Lonoke County, Arkansas was arrested for endangering the welfare of a minor after authorities found her daughter walking alone with two dogs along State Highway 319 around 7:00am Wednesday.
According to KARK, police found the home of the girl based upon information from the dog's collar. When they arrived, they reportedly found Moody asleep and unable to be woken. They allegedly spotted a glass smoking device and meth nearby.
Moody was arrested for possession of a controlled substance for the meth, possession of drug paraphernalia and endangering the welfare of a minor in the first degree. The child has been turned over to the Department of Human Services.
Moody has her first court appearance Friday and is being held at the Lonoke County Jail on $2,500 bond.


I'm a bit embarrassed by the lack of detail in this post, but I've found information difficult to uncover at best. The aerial above shows Fairfield Road, North Duke St., Roxboro Road, and Duke Homestead Road in 1959 - the mostly rural nature of the landscape is even more evident in a non-zoomed view of this, but clearly the large tract in the center featured another of the large houses lining this portion of Roxboro Road.
I haven't been able to determine when North Duke Mall was built - I can trace the deeds back to 1973, when a series of merged partnerships result in the transformation of what was entitled "North Duke Plaza" into North Duke Mall. But there I've hit a bit of a dead end. So the best I can say for now is that, sometime before that, it appears that this land was purchased, house demolished, and a mall built. That mall is now North Duke Mall.


text:
<?php

	/**
	*
	*/
	class editRestaurant extends PHPUnit_Framework_TestCase{

		public function editRestaurant(){

			$restaurant = new restaurant();
			$restaurant->setRestaurant($restaurant);
		}

		public function testComment(){
			$restaurant->textType = 'SeafoodRestaurant';
			$this->assertTrue('SeafoodRestaurant',restaurant("SeafoodRestaurant"));
		}

	}

?>

redpajama_set_name: RedPajamaGithub


Once they'd had a moment to take each other in we invited the wedding party over for some shots before heading to the church for the ceremony. Bre & Elliott opted for a traditional service with a sand blessing box filled by their friends and family as they enetered and then completed by them. It was a beautiful gesture once completed and shaken up.
After the ceremony the guests were treated to ice cream sundaes (it was a very hot day) while we completed family photos out in the sun. While the family went to cool off with sundaes I grabbed Bre & Elliott for some couples portraits before they headed back in and got their first dance on.
Set to Ed Sheeran's Perfect, the two danced away gazing deeply into each others eyes. Parent dances had everyones allergies acting up all of a sudden so it was good thing the dancefloor . opened to all shortyly after . Then the time came for Bre & Elliott to be sent off through an arch of their friends.


text:
Wellingborough Men's Shed gets funding boost
The Men's Shed has been given a funding boost.
Sam Wildman
A Wellingborough workshop which supports the health and wellbeing of men has been given a funding boost.
Teamwork Trust's 'Men's Shed' is part of a rapidly-growing UK-wide initiative to create more community spaces where men – who typically find it more difficult to build social connections than women – can come together to pursue practical interests such as woodwork and carpentry.
The charity, which supports adults with mental health needs, learning and physical disabilities, set up the workshop at its Oxford Street centre in May 2018 with funding from the Co-op's Local Community Fund.
They have now been given a further £1,200 from Central England Co-op to help pay for a variety of additional tools and equipment.
Vickie Bell, head of learning and development at Teamwork Trust, said: "We're delighted to have been awarded this grant, which will enable us to re-kit our Men's Shed.
"Those who have used our services tell us what a difference it's made to their self-confidence and mindset, and that is why having the most up to date equipment so that service-users can feel really accomplished with their projects is so important. This £1,200 means we can replace our tools and buy new ones."
Teamwork Trust was one of 36 good causes across 16 counties to share a payout of £44,000.
Central England Co-operative store manager Steven Towers said: "We are delighted that these great projects and good causes have been handed a grant for the fantastic work they do in the community.
"Community Dividend Fund grants like this showcase how by being a member and shopping at your local Central England Co-op store allows us as a society to continue to invest and fund vital projects in the area."
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