Articles tagged with: NASDAQ:TSLA
Elon Musk is a man in a hurry. The selection of Nevada as the site of the Gigafactory is supposed to be a key part of Tesla Motors Inc (NASDAQ:TSLA)’s plans to ramp up car production to half a million by 2020 and introduce the Model 3 at a consumer-friendly $30,000.
Are these plans feasible?
What Price Model 3?
Experts are now doubting Tesla’s ability to price the Model 3 at the $30,000 level. Read more [...]
Hot Alert: Tesla Motors Inc (NASDAQ:TSLA), RXi Pharmaceuticals Corporation (NASDAQ:RXII), Silgan Holdings Inc. (NASDAQ:SLGN), United Community Financial Corp. (NASDAQ:UCFC), Banc of California (NYSE:BANC)
On 29 AUG Tesla Motors Inc (NASDAQ:TSLA) said that it will team with China Unicom to build charging stations across China. Tesla Motors Inc (NASDAQ:TSLA)’s stock on 29 August traded at beginning with a price of $268.70 and when day-trade ended the stock finally moved up 2.21% to end at $269.70. Tesla Motors Inc (NASDAQ:TSLA)’s showed weekly performance of 5.03%.
On 14 AUG RXi Pharmaceuticals Corporation Read more [...]
Investors in Tesla Motors Inc (NASDAQ:TSLA) may be having a few anxious moments as the company comes face to face with a bit of negative news. A test car developing a few minor technical problems is not exactly earth-shattering news but anything to do with Tesla gets outsize attention. That’s the flip side of the high-profile the company and its founder Elon Musk enjoy.
When Tesla came out with Read more [...]
Whats Hot Today : Tesla Motors Inc (NASDAQ:TSLA), API (NASDAQ:ATNY), Heron Therapeutics (NASDAQ:HRTX), First South Bancorp Inc. (NASDAQ:FSBK), Investors Title Co. (NASDAQ:ITIC)
Elon Musk, Tesla Motors Inc (NASDAQ:TSLA)’s chief executive officer, has big hopes for China, a country that should be a natural for his electronic cars. The world’s largest auto market has notoriously awful smog problems, and the Chinese government has made the pollution fight one of its top priorities. Last month, China’s State Council agreed to exempt electric cars (as well as plug-in hybrids and Read more [...]
Investors Watch List : Tesla Motors (NASDAQ:TSLA), Nasdaq OMX Inc. (NASDAQ:NDAQ), eBay Inc. (NASDAQ:EBAY), xG Technology (NASDAQ:XGTI), ZOOM Technologies (NASDAQ:ZOOM)
Give Tesla Motors, Inc. (NASDAQ:TSLA) CEO Elon Musk credit, he’s playing a strong hand when it comes to picking the location of a highly coveted battery plant. After months of considering several sites in five states as possible locations for Tesla’s new $5 billion Gigafactory, the electric car maker has made it clear one city appears to be out in front of others for landing the battery plant. Read more [...]
Active Stocks: Tesla Motors Inc(NASDAQ:TSLA), Vodafone Group Plc (ADR) (NASDAQ:VOD), Verizon Communications Inc. (NYSE:VZ), Sprint Corporation (NYSE:S), AT&T Inc. (NYSE:T)
Shares of Tesla Motors (NASDAQ:TSLA) have received an average rating of “Buy” from the twenty-two ratings firms that are presently covering the company, Stock Ratings Network.com reports. Nine investment analysts have rated the stock with a hold recommendation, six have issued a buy recommendation and one has issued a strong buy recommendation on the company. The average 1-year price target among Read more [...]


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Sometimes, I feel like Cameroon has broken me. Two years in this country have somehow taken away those sympathetic ways that once was a big part of my personality. I am often annoyed when people comment “aww… those poor people” on my pictures from this experience. I no longer “feel sorry” for Cameroonians or Africans in general. Don’t shed pity on them, they have more dignity than that. Be angry for them that so many of their governments on this continent are corrupt and that the people do not have the opportunities they deserve.
Feel angry, feel the injustice, but don’t feel sorry.
My first experience relating to international development was at a conference in Lawrence, Kansas where some speaker was talking about the UN Millennium Goals and later there was a pamphlet on an organization that has “child sponsorship” programs for people to financially support children in Africa. In return, you would receive letters and different correspondences. I wanted to sponsor a kid, but didn’t have the ability as a poor student. This was 4 years ago.
Over these past 4 years, or more so within the past two years, I’ve learned and realized the power of PR work of these aid organizations to portrait a situation that perhaps isn’t so grave, and turn them into a heart-wrenching picture that has you reaching for your VISA card. I know, because I did it myself to raise money for my library project.
Even still, I continue to contribute. I gave to the Haiti relief effort. I have long been a supporter of Kiva.org and I lend regularly to different projects around the world. Despite the fact, I learn that Kiva really doesn’t operate the way you think they do. Most of the pictures and stories you read or are lending to have likely already received funding. They do it for the PR and to reach and fulfill the human nature of needing a personal story. I understand. I am in no way disparaging the work of different NGOs, they are important in their own ways. I simply want to point out the difference in perception.
Recently, I received an update in my email inbox from Kiva’s field fellow reporting the progress of my loan here in Cameroon. Here is an excerpt from that message:
Having spent three months in the Northwest Region of Cameroon, I’m very pleased to share that your loans are making a big difference in an area that has so much promise and yet is in such dire need. Despite the strong agricultural potential of the Northwest Region, an industry that makes up nearly fifty percent of Cameroon’s overall economy, I have seen too much malnutrition, illness
anddeath. Easily the most difficult part of my time here in Cameroon has been that, within three short months, I have attended eight burials compared to one birth celebratio and one marriage. Poverty, illness and death are much too common parts of everyday life here in Bamenda, Cameroon, but there is hope.
I have been to Bamenda many times. It is the provincial capital of the Northwest Region – one of the anglophone regions in Cameroon, and one of the most developed. You wouldn’t get this idea from reading that paragraph, however. What this Kiva Fellow describes is the image of Cameroon that I might have had 20 months ago, first arriving to this country.
But now, when I think of Bamenda, I am amazed at all the things you can buy, and how developed the city is. Also, this fellow was likely here during the funéraille season, where many funerals and parties take place, some long after the deceased has passed. To contrast that, Cameroonians have SO many babies, they cannot possibly throw a party for every child that is born. It’s simply not in the custom.
Perception is relative. I am glad to have this experience and be able to perceive people’s life for what it is. I don’t feel bad for them, but instead, I try to see beauty in their way of life. In the Northwest and West region of Cameroon, food is plentiful, and people rarely starve. Illnesses, yes, but starvation is rare. No, they may not be able to afford the material goods that we can, but they also don’t spend thousands on therapy like a lot of us do, either. Relativity, my friends. The Western/capitalistic way, isn’t always the best.

domain: sociology


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The noise from the religious right can be deafening. And never has it been louder than in recent weeks, as fundamentalist leaders and their political allies have focused their ire on the federal judiciary.
At the center of the maelstrom is the question of the Senate filibuster, and whether Republicans can alter Senate rules to prevent Democrats from using unlimited debate to block seven Bush judicial nominees.
In an April 18 e-mail by the fundamentalist-leaning Focus on the Family, Albert Mohler, president of the Southern Baptist Theological Seminary, called the anti-filibuster crusade a battle against "black-robed tyranny." And Senate majority leader Bill Frist recently used his bully pulpit to fuel the erroneous suggestion by the religious right that the dispute is between the faithful and the faithless.
Amidst all this grandstanding, moderate voices - particularly moderate religious voices - have become more and more crucial. For Southern Baptists, factionalized since a massive split 14 years ago, it's a Tampa-trained lawyer-turned-minister named Brent Walker who's leading the effort to remind church leaders and others of the significance of the First Amendment.
"The goal is religious liberty for everyone by means of separation of church and state," says Walker, who heads a diverse non-profit coalition of Baptists across the U.S. "If you don't have separation between church and state, you don't have full religious freedom for everyone. As soon as government takes sides in matters of religion, someone's religious liberty gets denied."
It's an argument with particular relevance at the moment. And for moderates from a Southern Baptist background, the separation of church and state seems a vital part of their tradition. They have watched as fundamentalist forces within their denomination worked to erode that separation - and now they're speaking out as they see real signs of the same erosion happening in the country as a whole.
It's doubtful that many Floridians have heard of Brent Walker. A former attorney at Tampa law firm Carlton, Fields, he was raised in Tampa's Bayshore Baptist Church and played first base at Robinson High School. As executive director of the 69-year-old Baptist Joint Committee on Public Affairs, Walker is at the forefront of an effort to protect religious liberties in the U.S.On April 24, he helped organize a protest in Louisville, Ky., during the airing of "Justice Sunday" from the 2,000-person Highview Baptist Church. During that broadcast, Sen. Frist championed his plan to overhaul the 200-year-old Senate filibuster system.
The telecast, which was sponsored by the right-wing Family Research Council, made ominous references to "the filibuster against people of faith" and suggested that all religiously minded voters were of uniform opinion, locked in step with Frist and the FRC. Walker begs to differ: "People can speak out, and God can motivate people, but it's presumptuous to say 'God is on my side.'"
In a recent sermon, Walker talked about the issue this way:
"We need to stop trying to convince each other we've got God in our hip pockets. God is not a Republican or a Democrat, nor even an American for that matter. God's precinct is the universe. To paraphrase Abraham Lincoln's famous pronouncement about the Civil War, the question is not whether God is on our side, but whether we are on God's side. God is not aligned with any political party, but is able to work within and though all political movements and nations to accomplish his purposes."
A promising attorney slated for partnership when he decided to leave Carlton, Fields to study for the ministry, Walker entered the Southern Baptist Theological Seminary in Louisville in 1986. His longtime friend, Tampa insurance agent Richard Heider, remembers the morning, during a breakfast at the Village Inn on Dale Mabry, when Walker explained his life-changing plans.
"He just felt called," says Heider, who had grown close to Walker at Bayshore Baptist, and remembers being devastated by the news. Walker's wife and their two children had become so intertwined with Heider's family that their absence seemed unimaginable. "You don't have that many close friends in your life," says Heider.
Since that time, the families have stayed close and visit regularly, Heider says. But Walker's life has changed. After graduating from seminary, he worked for a short time as a minister. But in 1989, he decided to juggle both faith and law and take a job as associate general counsel with the Baptist Joint Committee.
Now, as executive director, Walker spends most of his time filing amicus briefs, or friend of the court papers, for upcoming cases at the U.S. Supreme Court that deal with the First Amendment. He also lectures at churches, colleges and conventions and issues weekly news releases on BJC's website (www.bjcpa.org).
As the head of the BJC, Walker and his group take no position on the proposed filibuster changes or the judicial nominees. The organization becomes concerned with those issues only when there's a perceived violation of church-state separation.

domain: news


- Dec 15th – 16th, 201812/15/2018 9:00 AM12/16/2018 3:00 PMWarren-Niles Gun ShowMore Information: https://gunshowtrader.com/gun-shows/warren-niles-gun-show/Trumbull Plaza 2485 Parkman Rd Warren, OH 44485MM/DD/YYYY
- Saturday: 9:00am - 5:00pm
- Sunday: 9:00am - 3:00pm
- General: $7.00
The Warren-Niles Gun Show will be held on Dec 15th-16th, 2018 in Warren, OH. This Warren gun show is held at Trumbull Plaza and hosted by Ohio Shows. All federal, state and local firearm ordinances and laws must be obeyed.
No Loaded firearms, no alcoholic beverages, no black powder. Loaded firearms are not permitted on the gun show premises. All guns must be secured from theft by ties through the trigger guard o placed in showcases. Exhibitors and patrons must comply with all local, state and federal laws.


3D Printing is recognized as “additive manufacturing” and it is seen to be very interesting to people. The process involves image of a 3D object (known as model) located in a computer and making real physical object.
Different sorts of materials can be used for its production, like ABS, PLA, plastic, biological materials etc. Print head puts different layers of the printing material layer by layer which results in real object. Often used software is 3D CAD software ranging from amateur level as Google Scetch Up to professional as AutoCAD or Solidworks etc. Users also use readymade models online and download then for free as at thingiverse.com. 3D Printing can be found in different environments as design agencies, schools, prototyping agencies or at home settings.
Professional 3D printers are used for over twenty years in commercial settings, costing several hundred dollars but in past few years it’s making giant steps forward. Technological improvements leaded to printers costing several hundred dollars and more.
More details on 3D printing principles
Different types of 3D printers exist today and many of them use different technology and materials but all of them have one basic role to fulfill and that is to make/print objects. Almost all models put very thin layers of material on top of each other.
First step in this process is to design 3D image of an object with usage of CAD (Computer Aided Design) software. This model is rendered and sliced into many layers (few hundred to thousands, depending of object size). Layers may vary from 0.01 microns to 0.04 microns.
One 3D printing model uses UV light to cure layers of material, cheaper models involve hot print head that melts ABS or PLA plastic and some models use light and oxygen as in Carbon3D explained by chief executive Prof Joseph DeSimone.


UNION PARISH, La. (KTVE/KARD) — Tri-Water system, Inc has issued a Boil Advisory.
The issue affects customers served by Tri-Water System, Inc from Highway 348 at Conway Baptist Church East, Burch Road, Meridian Church Road, Tinch Road, and Zion Watts Road.
The advisory is in place until further notice, and officials ask that you Boil water for one (1) full minute in a clean container. The one minute starts after the water has been brought to a rolling boil.
Once this advisory has been rescinded, we will keep you updated on air and online.


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Girl Gang Multi Heart | Tee | Adult
How fun is this brand new exclusive design. The colors, the distressing, and the font make the perfect combo for you and your girl gang. This tee is perfect to dress up or down! Tie it in a knot or wear it as a regular tee, and you're sure to be comfy for your trip.
This new design prints beautifully, with no cracking or peeling! No transfers or vinyl here -- we print straight to your tee, so it's sure to last, with proper care and washing.
We use the highest quality blanks available. This tee is 100% cotton and so soft. It has the perfect amount of stretch, to keep you comfy and cute. The sizing is unisex and true to size. with a little longer length and slender body than traditional boxy tees. If in doubt about sizing, we suggest sizing up.

domain: design


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UPDATED 8/5/13, 12:44 p.m.: The judge has ordered that 50 Cent, who pled not guilty to the domestic violence charges, to turn in all of his guns and says that he may not come within 100 yards of Daphne Joy, reportedly the Mother of his second child, according to TMZ.
Watch part of 50 Cent’s arraignment here:
50 Cent was also reportedly served a summons for appearing in court over child custody, which also reportedly relates to this domestic violence case.
Watch 50 Cent being served with legal papers here:
LOS ANGELES — Rapper and actor 50 Cent faces arraignment on charges that he attacked his ex-girlfriend and trashed her condo.
The hearing for the singer, whose real name is Curtis Jackson, was scheduled for Monday.
If convicted, the “In Da Club” singer faces up to five years in jail and $46,000 in fines.
Watch news coverage of 50 Cent denying the domestic violence allegations here:
The woman told police that during a June 23 argument, Jackson began destroying property at her Toluca Lake condo before she locked herself in a bedroom. She says Jackson kicked open the bedroom door and kicked her, causing injury.
Police estimated about $7,100 in damage to the home.
The woman told police she had been in a three-year relationship and has a baby with Jackson, who has referenced drug dealing and violence in many songs.

domain: celebrity


Little Details About the Gun M1 From WoodTrick
The weapon looks almost like a real gun, but it's safe to use and will give you the same drive as shooting a real firearm. The first thing you have to do is assemble the gun itself. The detailed instructions and pictures of each assembly kit will help you with that. As a result, you can assemble your gun in just half an hour. Interacting with the model will be fun and safe as we make all the parts from sanded plywood that is safe for the environment and your hands. The speed and weight of the bullets will not be big enough to hurt if you accidentally hit a person.
Gun M1 is a great and unusual gift for all shooting fans and those who would like to try shooting for the first time. In addition, our puzzles develop the brain and fine motor skills of the hands!


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Introduction to Mastering Pingu Fishing
The allure of Pingu fishing resides not just in the thrill of the catch but in the intricate dance with nature it entails. This delightful pursuit demands a confluence of strategy and skill, elevating it to an art form eagerly sought by anglists around the world.
Diving Deep into Pingu Behavior
To truly excel in Pingu fishing, a thorough grasp of their behavioral nuances and natural habitats is indispensable. The areas where they congregate are often ripe with nutrition and provide protection from predators, creating the ideal spots for this fishing method.
Critical Equipment Selection
A flourishing expedition rests on the selection of proper equipment—a resilient rod, a robust reel, premium line, and baits and lures that are irresistible to Pingus. Durability and suitability to withstand the vigor of a Pingu’s resistance are key.
Optimal Bait and Lure Choices
The crux of attracting Pingus lies in the discerning choice of bait and lures. While live offerings such as worms can be effective, artificial lures replicating Pingu prey can be equally compelling, especially when tailored to seasonal dietary patterns.
Top Pingu Fishing Methods
The techniques adopted by anglers range in style and efficacy. Whether preferring fly fishing’s finesse or the meticulousness of casting, each method requires dedicated practice and knowing exactly when to apply it.
Expertise in Hook Setting
Mastering the instantaneous decision to set the hook is paramount. Anglers pursue perfection in this skill, fine-tuning their reflexes to align with the Pingu’s movements precisely.
The Thrill of the Capture
The true test of an angler’s prowess is in playing and landing the Pingu. The deft handling of line tension, strategic rod movements, and the finesse of reeling in without losing the catch define the climax of Pingu fishing.
Sustaining Pingu Populations
Conservation-minded approaches are critical in Pingu fishing, advocating for practices like catch and release to ensure the longevity of both sport and species.
Innovative Strategies for Veterans
For the seasoned angler, advanced strategies involving water current analysis, high-tech equipment, and meteorological acumen become part of an arsenal to outwit the elusive Pingu.
Unveiling Prime Fishing Localities
Finding prime Pingu fishing locales is pivotal. Communities of anglers often exchange insights on these hotspots while mindful of preserving the scarcity of their bounty.
Gastronomic Delights Post-Catch
The gratification from a successful outing extends into the kitchen, with myriad culinary methods to savor your Pingu harvest.
Pingu Fishing Events
Competitive spirits can indulge in tournaments that foster skill demonstration, camaraderie, and a celebration of the sport, enriching participants with more than just accolades.
Conclusion: The Essence of Pingu Fishing
The journey to Pingu fishing mastery intertwines science, aptitude, and artistry, offering a path filled with continual learning, perseverance, and reverence for the dynamic aquatic ecosystem.
- 7 Reasons Why the Magic of Pingu in the City Captivates Audiences Worldwide
- 10 Unseen Aspects of Pingu and Pinga’s Unforgettable Adventures
- 5 Tips for a Pingu-Themed Christmas Celebration
- 7 Fascinating Aspects of the World of Pingu: A Comprehensive Dive into the Penguin’s Universe
- 5 Impactful Highlights from Pingu’s Animated Adventures

domain: hobby


An homage to the 96 Christian Delaware Native Americans killed by Pennsylvania militia men on March 8, 1782. The massacre, often known as the Moravian massacre occurred at the Moravian missionary village of Gnadenhutten in Ohio. A century later, Theodore Roosevelt called the massacre "a stain on frontier character that the lapse of time cannot wash away"
Our Tee's are pre-shrunk 100% premium cotton (6oz (203g) and designed for maximum durability, comfort and quality. With a seamless rib knit collar reinforced with a cover seamed front neck and taped neck and shoulder seams, this shirt is carefully constructed for those who are active and require clothing that lasts.
[ View Size Guide For Fitting Help ]


Next-Gen 13-Inch MacBook Pro With Scissor Keyboard To Arrive In First Half Of 2020
Now that Apple has finally released the heavily rumored and highly anticipated 16-inch MacBook Pro with a redesigned scissor keyboard, attentions are turning to the 13-inch model. Now a new report by DigiTimes has that machine arriving in the first half of 2020.
While everyone was keen to see the larger screen that the 16-inch MacBook Pro would offer, the scissor keyboard was perhaps the most important change. Ditching the troublesome butterfly keyboard is a big deal and the same move for the popular 13-inch MacBook Pro can’t come soon enough.
The new DigiTimes report says that the notebook will arrive in early 2020 although hopes of the screen size increasing to 14-inches might be dashed. DigiTimes says the same 13.3-inch screen will be used but importantly the new scissor keyboard will be employed. And that might just be enough to make this new notebook a winner.
However, it’s important to temper any expectations or excitement with the fact that DigiTimes is notoriously unreliable when it comes to getting these kinds of claims right. Supply chain sources have been wrong in the past, although analyst Ming-Chi Kuo has previously said that Apple will move its entire notebook line to scissor keyboards at some point next year.
Apple will presumably want to have the old keyboard out of the market as quickly as possible, especially given the costs it must be incurring in terms of the keyboards it has to replace free of charge.


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Larry Gaaga – African Princess (feat. Spotless) Mp3 download
Highly Rated Nigerian Music Executive Larry Gaaga just dropped this song titled African Princess featuring Nigerian Fast-rising singer Spotless and this song was released as the 3rd song off Larry Gaaga’s 2020 Album tagged Nneka (The Pretty Serpent) Album.
Larry Ndianefo decided to feature Nigerian Singer and Producer from Ebonyi State who goes by the name Augustine Uche.
Download Larry Gaaga Baby ft. Zoro mp3 and make sure to share this amazing song with your friends and loved ones.

domain: entertainment


Jessica Harp Releases New Album This Week Jessic Harp, formerly one half of the duo The Wreckers with Michelle Branch, releases her most recent solo album, A Woman Needs. The album is three years in the making, and marks an exciting milestone in Harp’s career. “It’s a bittersweet moment, knowing that I am moving forward with new career plans. But I also feel very happy that you all-can now have the record that I have been and will always be proud of.” -Jessica Harp It features eleven tracks, including the previously released popular single, “Boy Like Me“. Released March 16th, 2011, A Woman Needs is currently available on iTunes. Text COUNTRY to 22828 to be eligible for exclusive text subscriber giveaways from CountryMusicNewsBlog.com!
Jessica Harp News
Jessica Harp may be best known as half of the country duo The Wreckers, but this summer we’ll get to see more of this rockin country newcomer. Jessica Harp is set to release her debut solo album “A Woman Needs” with the help of Warner Nashville this summer. Jessica is already turning heads with tracks like “Boy Like Me” which is currently available as a free download on iTunes. Take a peek at her video below and tell me what your predictions are for this album and artist? Country Music Photo Gallery


A bill to amend agritourism legislation introduced in 2022 has been submitted to NSW Parliament.
Member for Kiama Gareth Ward introduced the Environmental Planning and Assessment Legislation Amendment (Agritourism) Bill 2023 this week.
The bill responds to council and community concerns about planning changes that allow, among other things, property owners to establish a function centre on an agricultural property without a development application.
The bill that introduced follows a consultation period, community group sessions, meetings with councils and hundreds of supportive submissions.
"Agritourism should celebrate farming; it should complement agricultural practices, but it shouldn't impact the quiet enjoyment of property enjoyed by neighbouring property owners," Mr Ward told the House.
"Changes by the former government have allowed for a free-for-all and risk everything, from the fragmentation of agricultural land to biosecurity.
"My bill will seek to correct the current imbalance and ensure that genuine agritourism is supported but these regulations aren't used as a back-door for inappropriate development of agricultural land."
The NSW Department of Planning and Environment released an amended policy in October 2022.
The policy, which came into effect on December 1, 2022, amended the terms for farm gate and experience activities and farm stay accommodation and created new planning pathways to allow these activities to be either fast-tracked for complying developments or with no planning approval for those exempt.
"A function centre is not agritourism," Mr Ward said.
"Not only is this a ridiculous proposition, but current regulations also mean that everyone who owns agricultural land can set up a function centre without a development application (DA).
"This provision could literally turn every farmer into a restaurateur without a single approval from your local council or notification to your neighbours.
"If people want to establish a function centre on their land, it is appropriate that such development be approved by way of local DA."


The London borough of Westminster is rolling out a new digital card which residents will be able to use to access a range of local services. It is understood to be one of the first multi-service digital cards to be implemented by a UK local authority.
The card, which works in conjunction with the new MyWestminster portal, can be downloaded to both Apple Wallet and Google Pay. It will enable residents to access services run by Westminster City Council without needing to remember a physical card or repeatedly prove eligibility.
Initially, the digital card will provide access across Westminster’s library services. It will also be compatible with the free-to-use City Save scheme, which offers residents discounts on select local services and attractions, including galleries, food and drink, and leisure.
Councillor Paul Swaddle, Cabinet Member for Finance and Smart City at Westminster City Council, commented: “The new MyWestminster portal is a key example of how we plan to make Westminster a world-leading smart city with people at its heart and marks a key first step in our digital journey.
“The portal makes accessing services simpler and more intuitive, and our digital card will offer a technology-driven, environmentally-friendly experience sitting alongside other cards in your Apple or Google Pay wallet.
“London is the fintech capital of the world and so it’s absolutely right that residents in the city centre are able to harness technology to navigate Westminster with ease. We look forward to opening up access to a range of services, from local swimming pools to gyms – watch this space.”
Residents will be invited to submit feedback on the card, including which services they would like to see added next and ideas for improvements.
Workshops will also be offered, in partnership with library teams, for those residents who are less confident with laptops or mobile phones.
Smart city strategy
Aruj Haider, Westminster’s Chief Digital and Innovation Officer, commented: “For us, a truly smart city puts the needs of its residents at the heart of how services are designed – the launch of the MyWestminster portal and card puts this strategy into action. As we add services to our digital card, we hope our city will become even more accessible for the benefit of all.”
US cities including New Orleans and Los Angeles have also launched multifunctional city card programmes – with a focus on financial inclusion.
Image: | Dreamstime.com
The post Westminster Council introduces multi-service digital card appeared first on capitaltribunenews.com.


We stayed at a small and far removed local inn on a mountain slope, called I Cod El Alte Rural Hotel, in retrospect it would have been wiser to stay closer to a main road.
DAY 1| We traveled to the North West of the island in the direction of Code De Los Vinos, where the phenomenal tree known as the Dragon Tree grows. From there we continued in the NW direction along the whole coast line where there are numerous walking trails, it is possible to retrace your steps. WE reached Punta de Teno through the forest of Buena Vista and Garachico.
DAY 2| We toured the whole day in the Park National del Teide, where the well known highest all year round snow capped volcano is situated. The road going up twists and turns through Canary fir trees. At the altitude of 2, 500 meters the area became suddenly flat and volcanic, with lava rocks with huge fantastic formations and natural shapes of beautiful proportions. We took a number of walks in the area (Los Roques de Garcia) and climbed to the tip of the mountain at a level of 3,500 meters where it was bitterly cold.
DAY 3| We did not miss a visit to the Botanical Gardens in Puerta de la Crus and rode along the coast in the direction of El Sauzel till Las Agus.
DAY 4 | We drove to the TENO reserve through MASACA on a twisting road, along the road we could see Euphorbia and some species of Aeonium and a most astounding and beautiful waterfall in the city of ADEJO.
DAY 5 | This day we travelled in the direction of the fishing village Benijo, on winding and twisting roads when we finally reached a pastoral fishing village with delicious eateries along the beach.
It is to be noted that all the roads are winding and very narrow, and if traveling in normal sedans one needs to be very careful. Every 200 meters there are spots arranged with scenic views providing excellent scenic sights.


The ability of individuals to form teams and collaborate is crucial to their performance in many environments. The success of humans as a species hinges on our capability to cooperate at scale Henrich (2015). Similarly, cooperation between learning agents is necessary to achieve high performance in many environments Lowe et al. (2017); Baker et al. (2019); Vinyals et al. (2019); Jaderberg et al. (2019) and is a fundamental problem in artificial intelligence Stone and Veloso (2000); Dafoe et al. (2021). Individual agents are often not incentivized by the joint reward achieved by a team but rather by their share of the spoils. Hence, teams are only likely to be formed when the overall gains obtained by the team are appropriately distributed between its members. However, understanding how collective outcomes arise from subsets of locally interacting parts, or measuring the impact of individuals on the team’s performance, remain open problems.
Direct applications exist in multiple domains. One example is identifying the most influential features that drive a model to make a certain prediction Datta et al. (2016); Lundberg and Lee (2017); Bachrach et al. (2012); Sundararajan et al. (2017); Lorraine et al. (2021b, a); one of the cornerstones of explainable AI Arrieta et al. (2020); Metz et al. (2021). Another example is sharing the costs of data acquisition or a joint venture in a fair way between participants Balkanski et al. (2017); Agarwal et al. (2019), or sharing gains between cooperating agents Gately (1974); Sim et al. (2020). In many legislative bodies individual participants have different weights, and passing a decision requires support from a set of participants holding the majority of the weight; different states in the US electoral college have different numbers of electors, and different countries in the EU Council of Ministers vary in their voting weight. Here, would like to quantify the true political power held by each participant, or allocate a common budget between them Mann and Shapley (1962); Bilbao et al. (2002).
Cooperative game theory can provide strong theoretical foundations underpinning such applications. The field provides solution concepts that measure the relative impact of individuals on team performance, or the individual rewards agents are entitled to. Power indices such as the Shapley value Shapley (1953) or Banzhaf index Banzhaf III (1964) attempt to divide the joint reward in a way that is fair, and have recently been used to compute feature importance Lundberg and Lee (2017). In contrast, other solutions such as the Core Gillies (1953) attempt to offer a stable allocation of payoffs, where individual agents are incentivised to continue working with their team, rather than attempting to break away from their team in favor of working with other agents. Despite their theoretical appeal, these solution concepts are difficult to apply in practice due to computational constraints. Computing them is typically a hard problem, even in restricted environments Elkind et al. (2007); Chalkiadakis et al. (2011); Deng and Papadimitriou (1994).
We construct models that predict fair or stable payoff allocations among team members, combining solution concepts from cooperative game theory with the predictive power of neural networks. These neural “payoff machines” take in a representation of the performance or reward achievable by different subsets of agents, and output a suggested payoff vector allocating the total reward between the agents. By training the neural networks based on different cooperative solution concepts, the model can be tuned to aim for a fair distribution of the payoffs (the Shapley value or Banzhaf index) or to minimize the incentives of agents to abandon the team (the Least-CoreGillies (1953); Maschler et al. (1979); Deng and Papadimitriou (1994)). Figure 1 depicts the two well-studied classes of games on which we evaluate our approach: weighted voting games Mann and Shapley (1962); Bilbao et al. (2002); Chalkiadakis et al. (2011) and feature importance games in explainable AI Datta et al. (2016); Lundberg and Lee (2017).
Weighted voting games (WVGs) are arguably the most well-studied class of cooperative games. Each agent is endowed with a weight and a team achieves its goal if the sum of the weights of the team members exceeds a certain threshold (quota). We train the neural networks by generating large sets of such games and computing their respective game theoretic solutions. Our empirical evaluation shows that the predictions for the various solutions (the Shapley value, Banzhaf index and Least-Core) accurately reflect the true game theoretic solutions on previously unobserved games. Furthermore, the resulting model can generalize even to games that are very far from the training distribution or with more players than the games in the training set.
Feature importance games
are a model for quantifying the relative influence of features on the outcome of a machine learning modelDatta et al. (2016); Lundberg and Lee (2017). Solving these games for the Shapley value (or other game theoretic measure) provides a way to reverse-engineer the key factors that drove a model to reach a specific decision. This approach is model-agnostic, thus can be applied to make any “black-box” model more interpretable Arrieta et al. (2020). One drawback of this approach is the computational complexity of calculating the Shapley value, making such analysis slow even when using approximation algorithms. Our approach provides a way to significantly speedup Explainable AI analyses, particularly for datasets with a large number of instances.
We provide a brief overview of cooperative game theory (examined in details in various books Osborne and Rubinstein (1994); Chalkiadakis et al. (2011)) and discuss how solution concepts in cooperative game theory have been applied in Explainable AI Datta et al. (2016); Lundberg and Lee (2017); Lundberg et al. ; Das and Rad (2020); Yan and Procaccia (2021).
2.1 Cooperative Game Theory
A (transferable utility) cooperative game consists of a set
of agents, or players, and a characteristic functionwhich maps each team of players, or coalition , to a real number. This number indicates the joint reward the players obtained as a team. Games where (binary range) are simple games.
Weighted voting games (WVGs) are a restricted class of simple cooperative games Chalkiadakis et al. (2011), where each agent has a weight and a team of agents wins if the sum of the weights of its participants exceeds a quota . Formally, a WVG is defined as the triple with weights and quota (threshold) where for any we have if and otherwise . If we say is a losing coalition, and if we say it is a winning coalitions. WVGs have been thoroughly investigated as a model of voting bodies, such the US Electoral College or the EU Council of Ministers Mann and Shapley (1962); Bilbao et al. (2002).
The characteristic function defines the joint value of a coalition, but it does not specify how the value should be distributed among the agents. Solution concepts attempt to determine an allocation of the utility achieved by the grand coalition of all the agents; an allocation is called an imputation if for any player we have and . 111In a WVG, the value of a coalition is bounded by 1, so if the grand coalition indeed has a value of then a solution would be a payoff vector where . This allocation is meant to achieve some desiderata, such as fairly reflecting the contributions of individual agents, or achieving stability in the sense that no subset of agents is incentivized to abandon the team and form a new team. We describe three prominent solution concepts that we use in our analysis.
Rational players may abandon the grand coalition of all the agents if they can increase their individual utility by doing so. The Core is defined as the set of all payoff vectors where no subset of agents can generate more utility, as measured by the characteristic function, than the total payoff they are currently awarded by the payoff vector. As such, the Core is viewed as the set of stable payoff allocations. Formally, the core Gillies (1953) is defined as the set of all imputations such that and that for any coalition .
The -Core and Least-Core
Maschler et al. (1979); Deng and Papadimitriou (1994). Some games have empty cores, meaning that no payoff allocations achieves full stability (i.e. for any imputation there exists at least one coalition such that ). In such cases, researchers have proposed minimizing the instability. A relaxation of the core is the -core, consisting of imputations where for any value coalition we have . Given an imputation the difference is called the excess of the coalition, and represents the total improvement in utility the members of can achieve by abandoning the grand coalition and working on their own. For an imputation in the -core, no agent subset can achieve an addition of in utility over the current total payoff offered to the team (i.e. no coalition has an excess of more than ). The minimal for which the -core is non-empty is called the Least-Core Value
(LCV). The Least-Core minimizes the incentive of any agent subset to abandon the grand coalition, and the LCV thus represents the degree of instability (excess) under the imputation that best minimizes this instability. We find the set of payoffs associated with the LVC through linear programming (full details in AppendixC).
We now discuss two power indices, payoff distributions reflecting the true influence a player has on the performance of the team, that fairly allocate the total gains of the teams among the agents in it.
The Shapley Value
Shapley (1953) measures the average marginal contribution of each player across all permutations of the players. The Shapley value is the unique solution concept that fulfills several natural fairness axioms Dubey (1975)
, and has thus found many applications from estimating feature importanceDatta et al. (2016); Lundberg and Lee (2017) to pruning neural networks Wang (2021); Frankle and Carbin (2018); Ghorbani and Zou (2020). Formally, we denote a permutation of the players by , where is a bijective mapping of to itself, and the set of all such permutations by . By we denote all players appearing before in the permutation . The Shapley value of player is defined as:
Intuitively, one can consider starting with an empty coalition and adding the players’ weights in the order of the permutation; the first player whose addition meets or exceeds the quota is considered the pivotal player in the permutation. The Shapley value then measures the proportion of permutations where a player is the pivotal player.
The Banzhaf index Banzhaf III (1964) is another method for distributing payoffs according to a players’ ability to change the outcome of the game, but it reflects slightly different fairness axioms Straffin jr (1988). The Banzhaf index of a player is defined as the marginal contribution of a player across all subsets not containing that player:
In practice, we first compute the set of winning coalitions and count, for each player, the number of times it is critical or pivotal, that is, .
2.2 Speeding Up Explainable AI In Large Datasets
An important goal of our work is to speedup Shapley/Core computations of many data instances. In machine learning, we train a model to learn a mapping from some set of input features to an outcome. This could we can train a model to predict the price of a house based on a number of features, such as the number of rooms, the year it was built, and so on (Figure 1). In such settings, it is desirable, but challenging, to explain the model outputs in terms of the input features. Explainable AI addresses this issue, and recent years showed several applications of game theoretic metrics for measuring feature importance in the machine learning community Sundararajan et al. (2017); Arrieta et al. (2020).
The fastest method to approximate Shapley values (also used in the SHAP package) is a Monte-Carlo approach Lundberg and Lee (2017). A number of other methods exist whose runtime and accuracy depend on the number of samples used, usually on the order of several thousands Leech (2002a); Datta et al. (2016); Maleki et al. (2013); Bachrach et al. (2010). In a model setting, the characteristic function takes the value of the trained model output for a given instance: . The Shapley value of a feature in a data instance , is the effect that feature has on the model outcome. Sampling based-methods compute the contributions with respect to a base value, which is the average model output across all instances: . Sampling based methods are not ideal for large datasets because they require a large number of re-evaluation samples per computation. We show how our approach can be employed to speedup Shapley or Core computations of many instances by training models to learn representations of feature attribution schemes.
Our approach uses machine learning to create game-theoretic estimators. We generate synthetic datasets of games to train our models, spending compute up-front to allow for instant solutions afterwards. Our first domain concerns weighted voting games (WVGs) as they provide a generic framework for studying cooperation in multi-agent settings.
Afterwards, we apply our approach to the Shapley-based feature importance setting. The main idea is that we can speed-up the computations of the relative impact of features on the predictions of a machine learning model. We do this by training models to approximate Shapley values of features, and examine their performance on previously unobserved instances.
3.1 Weighted Voting Games
We consider two types of models: fixed-size models that always predict a fixed number of outputs for games with a fixed number of players, and variable-size
ones that predict a variable number of outcomes and can handle variable numbers of players. We now describe our data generation process, models, training procedure, and evaluation metrics used in weighted voting games.
3.1.1 Data and Models: Fixed-Size
For each player game, we generate independent and identically distributed games. The training dataset is given by where denotes the number of outputs of interest. The features are obtained in two steps. First, we sample a weight vector on the interval from 1 to , and a quota . To create games where players are dependent on each other to achieve the task at hand, the quota distribution is parameterized such that the average drawn quota is half of the sum of the players’ weights. We then normalize the weights by the quota to get the feature vector . In other words, we have features which are the weights normalized as the respective proportion of the quota. Thus, a value indicates that player is a winning coalition by itself, and needs other players to meet the quota otherwise (Figure E.2).
We train models to predict the three solutions: the Least-Core, the Shapley values, and the Banzhaf indices. For the Shapey values and the Banzhaf indices, the model predicts the payoff allocation so . For the Least-Core solution, we train the model to not only predict the payoff allocation , but also to predict the Least Core Value (so in this case the model has outputs). For each player game, we produce a model , where are the model parameters. We use deep feedforward networks for . Appendix D.1 contains the full details about the experimental setup.
3.1.2 Data and Models: Variable-size
For the variable-size case, we consider a maximal number of possible players
and pad the inputs with zeros for games with less thanplayers. Hence, we generate a single dataset . Similarly to the fixed-size dataset, the feature matrix contains the normalized weights and the corresponding solutions with either or , with the ground truth output vector again padded with zeros when there are fewer than players. Hence, we allow for the prediction of up to players, and we shuffle the data so that players are located at random positions. The Least Core Value (LCV) is not shuffled but stored at the last element of each row (Figure E.1).
We produce a single model that can be used for different number of players , where are the model parameters. The model learns to allocate the joint payoff among at most players. During prediction time we pad the input with zeros when there are fewer than players, and we redistribute the payoffs allocated to non-player entries among the players according to their original share of the joint payoff.
3.1.3 Training and Evaluation
During training we minimize the Mean Square Error (MSE) between the true and predicted solutions. For the variable-size models we also include the padded locations so that the model learns not to allocate value to non-player entries.
Given a predicted payoff vector , we consider multiple metrics for assessing the models’ performance. First, we quantify the models’ predictive performance via the Mean Absolute Error (MAE), defined for each game as , where is the number of players. For the Least-Core there is another natural game theoretic metric. The goal of the Least-Core is to minimize the incentives of any subset to abandon the current team and form its own sub-team. Given a suggested payoff vector , the maximal excess over all possible coalitions measures the incentive to abandon the team, and serves as a good measure for the quality of the model.
3.2 Feature Importance Games
We perform an experiment to show that neural networks can provide a faster alternative for measuring feature importance at scale. We select a dataset (details in Appendix D), train a model, and use those to construct a dataset from features to Shapley values using the SHAP KernelExplainer Lundberg and Lee (2017)
Following, we partition our dataset into a train and test, and incrementally change the proportions between the two. For each increment, we train a model for 100 epochs and test it on the remainder of the unseen instances.
We present experimental results that allow us to assess how well neural models are able to learn a representation of the various solution concepts. We first describe the predictive performance of neural networks in the WVG setting, and consider properties of these solutions that make them hard to learn. We then consider the explainable AI domain, and study the performance and sample complexity of Shapley feature importance prediction.
4.1 Weighted Voting Games: Evaluation
For our WVG analysis we train a selection of fixed-size neural networks for each number of players 222A direct computation of the Shapley value requires enumerating through a large list of permutations, which becomes computationally very costly when there are many players. Hence, for games with players or more, we use Monte-Carlo approximations for the Shapley value to obtain the ground-truth solution. See full details in Appendix D.1) on games each. We also train a single variable-size model that is trained on one dataset containing games in total, consisting of games for each number of players games. The data is padded with zeros to allow for payoff allocation up to players (see details in Section 3.1).
4.1.1 Predictive performance
Table 1 shows that the ability to handle games of variable numbers of players comes at the cost of having a lower accuracy. However, even for variable-size models, and even under a significant distribution shift, the errors in predicting all solution concepts are low. We further note that the error in predicting Least-Core based payoffs are generally larger than for the Shapley and Banzhaf power indices. One possible reason is that for these solutions, there are many cases where a small perturbation in the weights or quota results in a large perturbation of the Least-Core solution Elkind and Pasechnik (2009); Zuckerman et al. (2012).
|Least core payoffs||Least core excess||Shapley values||Banzhaf indices|
|Dataset||Mean MAE||MAE||Mean MAE||Mean MAE|
We now highlight several conclusions from our analysis of the results.
Fixed-size models display stable performance across solution concepts.
Our first observation is that the fixed-size neural networks are adept at estimating solutions across the three considered concepts. The average error per player ranges from to (Least Core), to (Shapley values) and to (Banzhaf indices). Table 5 provides a complete summary of our models’ in-sample performance.
Fixed-size models are robust to shifts in the weight distribution.
As shown in Figure 2, the predictive performance (Mean MAE) of the fixed-size models is consistent across test datasets. To account for the natural decrease in the MAE as increases, we also display the average payoff per player (). As expected, the error scales approximately with the average payoff per player.
player each. Figures show the MAE and 95 % confidence intervals for each solution concept. The black dashed line indicates the average payoff per player as a function of.
Variable-size models are robust to shifts in the weight distribution.
Figure 3 shows that the variable-size models are also able to generalize outside the training distribution. Across test sets, we observe a stable performance that decays with the number of players, as is expected. For the Least Core, we see that there is an abrupt decrease in performance for the excess beyond 8 players.
Variable-size models generalize to a larger number of players.
Our main objective is to investigate how to leverage machine learning to perform scalable value estimation in large multi-agent settings. To test this, we train the variable-size models on games up players, and evaluate on players (full details in section 3.1.2). Figure 3 demonstrates that there is no significant decrease in performance for games with more than ten players across solution concepts: variable-size models are able to extrapolate beyond the number of players seen during training. This suggests that there is be valuable information in the small player games such that games of larger sizes can be inferred.
Fixed-size models outperform variable-size models.
. From these, we conclude that in almost all settings the fixed-size networks outperform the variable-size networks. Variable-sized networks tend to have larger errors for all predicted variables and display more variance in their predictions.
4.1.2 Discontinuities in the Solution Space
The function mapping from game parameters to solutions contains discontinuities. Discontinuous jumps emerge from the players’ interdependence and the effect of the quota and are difficult for a model to learn. We analyze two examples that demonstrate how our models respond to such transitions.
Solution concepts are step-wise constant functions which are difficult to capture.
Consider taking a WVG and changing the weight of a player. This only changes the game theoretic solutions when it restructures the subset of winning coalitions. Thus, the function outputs the same value until the weight reaches a certain threshold where the structures of winning coalitions change, at which point the solution can change drastically. Learning these kind of functions is difficult, as the error around the discontinuity point is often large.
Our analysis examines an player game with a fixed weight vector and considers an array of quotas , where . We solve the game for each combination of the fixed weights and the changing quota in to obtain a matrix . Figure 5 shows the fixed-size model predictions for two selected WVGs. The models capture the overall effect of the changes to the quota, but do poorly close to the discontinuity point (where the ground truth solution incurs a large change).
4.2 Speeding Up Feature Importance Calculations By Predicting Shapley Values
We examine the sample efficiency and performance of neural networks trained to predict Shapley values on sets of features. We consider two well-known datasets: the classical UCI Bank Marketing dataset Moro et al. (2014) (17 features, 11,162 observations) and the Melbourne Housing dataset Pino (2018) (21 feature, 34,857 observations). Full details in Appendix D.2). Overall, we find that neural networks achieve high performance on the test set with very few training samples. Figure 6 displays the models’ performance (in RMSE) as a function of samples available for training. We observe that the error decays approximately exponentially with the proportion of data used for training on both the Banking and Melbourne datasets. A model trained on just 1 percent of the Melbourne dataset has an RMSE of 19.72 on the resultant test set. Increasing the number of training samples to 3 percent results in a RMSE of less than 0.07, a 99.6 percent decrease in error. We observe a similar trend for the Banking dataset: the RMSE decreases with 93.8 percent (from 4.10 to 0.25) when the available training data increases from 0.5 to 4 percent. These results highlight the strength of this approach: the computational cost of training the Shapley prediction network is very small as compared to the speedup obtained on the vast majority of the data (and any subsequent instances analyzed later). See Appendix I for a more extensive analysis.
5 Discussion and Conclusion
We considered “neural payoff machines”, models that take in a representation of a cooperative game and solve it to produce a distribution of the joint gains of a team amongst its members. Our analysis focused on two concise representations of a characteristic function: Weighted voting games, and feature importance games for explainable AI. Our analysis shows that neural models capture cooperative game solutions well, and can generalize well outside the training distribution, even extrapolating to more players than in the training set.
We saw that the Least core is a harder solution concept to learn than the Shapley value and Banzhaf index. Potentially, the fact that the Least core is complex and can only be computed by solving multiple Linear Programs makes a hard concept to grasp from examples.
The methods we discussed can drive better analysis of decision making bodies and for approximating feature importance, but such methods can also be applied to other classes of cooperative games, so long as one can generate data consisting of game samples and their solutions. For instance, the same approach can be used for other cooperative representations, such as graph-based games Deng and Papadimitriou (1994); Aziz et al. (2009); Bachrach et al. (2008) or set-based representations Ohta et al. (2008). The direct applications that we have considered in this paper are also intrinsically valuable on their own, as we allow speeding up explainable AI analysis and political influence estimation. Some questions remain open for future work. Can similar techniques be used for non-cooperative games or for other types of cooperative games? Can this approach be applied to other solutions such as the Kernel or Nucleolous Chalkiadakis et al. (2011)? Finally, are there better neural network designs, such as graph neural networks or transformers, that can exploit invariances or equivariances in the game’s representation or be better equipped to deal with sharp discontinuities in the solutions?
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Appendix A Notation
The following notation is used throughout this paper:
the number of players in the game
the number of games
the maximum sequence length in variable feedforward networks
the quota, threshold that determines when a coalition can complete the goal or task
the weight of a player
, the normalized weight of a player
the grand coalition
a coalition (subset of players)
the minimal set of winning coalitions: the coalitions where each player is pivotal
the allocated reward or payoff to a player
the least core value (LCV), i.e. joint payoff to sacrifice such that there exists a feasible payoff vector in the least core
Appendix B Motivating The Use Of The Shapley Value To Analyze Influence In Weighted Voting Games
We provide an intuitive example motivating the use of Shapley values for measuring the true influence of participants, in weighted voting games or political power in decision making bodies.
Consider a parliament with 100 seats, and imagine that following the elections we have two large parties of 49 seats each, and one tiny party who has only managed to get 2 seats. However, note that having a majority means controlling a majority of the seats, i.e. more than 50 seats. This means that neither of the two big parties has a majority on its own (and of course the small party hasn’t got a majority on its own).
Hence, to achieve a required majority and form a government or pass a bill, the parties have to work in teams. Clearly, a coalition consisting of the two big parties has a majority, as together they have 49+49=98 seats out of the total of 100. However, a coalition consisting of any of the two big parties and the one small party also has 49+2=51 seats, which is also a majority.
Hence, in the setting discussed above, any two parties form a winning coalition that has a majority and is able to form a government or pass a bill. This can be modelled as a weighted voting game, where the weights are and the quota is .
Intuitively, as any two parties or more are a winning team and as any single party is a losing team, we might say that the weights are irrelevant. Although the big parties each have almost 25 times the seats as the small party, they all are symmetric in their opportunities to form a winning coalition, and one could claim they actually have equal political power. Clearly, the small party is in a very strong negotiation position, and could demand for instance to control a significant proportion of the joint budget.
In the above weighted voting game, the Shapley value of all agents would be equal (each getting a value of ), as it considers the marginal contributions of the parties over all permutations of players. Due to the symmetry between the parties, they have identical marginal contributions.
The above specific example illustrates some of the principles behind the fairness axioms that the Shapley value exhibits, and illustrates its importance for measuring influence in decision making bodies.
Appendix C Algorithm For Generating Least Core Solutions
The least core solutions can be generated by considering all possible coalitions. A simple improvement, however, is to use the minimal set of winning coalitions Deegan and Packel  instead. In this section, we describe the idea behind this approach and the algorithm used in this paper.
Let denote the set of all players, let denote the quota and let denote the weights for player , for all . The characteristic function is given by , for coalitions .
The naive approach to find the least core solutions is by considering all winning coalitions defined as
and to find a least core solution by solving LP-problem 0(a).
In the LP-problem (0(a)), all winning coalitions are considered, Inside those winning coalitions there exists a subset, that could branch out and form a smaller coalition. This yields a higher payoff for the players in the sub-coalition and is hence a Pareto improvement for the ”decision-makers”. Therefore, we can instead only consider the subset of minimal winning coalitions, given by
and solve the more efficient LP-problem 0(b).
Appendix D Experimental details
In this section, we elaborate on the experimental procedures, model architectures and hyperparameters used in our experiments.
Code and notebook.
Following the guidelines, we provide python code to reproduce our results, a pedagogical notebook (called CooperativeGameTheoryPrimer.ipynb) that introduces the most important game theory formalisms and the necessary data in the accompanied zip file.
d.1 Weighted Voting Games
Optimization procedure fixed-size feedforward networks.
For each -player dataset we perform independent runs with a maximum of epochs and an early-stopping criteria which breaks a run if, after a baseline of 500 epochs, the validation loss does not improve for 75 consecutive epochs. For each run, 70 percent is allocated for training and 30 percent for validation. After training, we select the model with the best validation loss. The selected models are stored and used for evaluation. In all experiments we use the Adam Kingma and Ba
optimizer, ReLUNair and Hinton activation functions for the hidden layers, Softmax functions for output layer of the payoffs and a Sigmoid for the output layer of epsilon.
Optimization procedure var-size feedforward network.
We perform independent runs for one variable dataset with a maximum of number of epochs and an early-stopping criteria which breaks a run if, after a baseline of 500 epochs, the validation loss does not improve for 75 consecutive epochs. For each run, 70 percent is allocated for training and 30 percent for validation. After training, we select the model with the best validation loss, which is stored and used for evaluation. In all experiments we use the Adam Kingma and Ba  optimizer, ReLU Nair and Hinton  activation functions for the hidden layers, Softmax functions for output layer of the payoffs and a Sigmoid for the output layer of epsilon.
Approximated ground-truth Shapley values.
To obtain the ground-truth solutions for Shapley values above nine players, we perform Monte-Carlo sampling to obtain a subset of all possible permutations. For each of the resamples we randomly sample permutations and average across the resamples to obtain approximations of the ground-truth. To validate that the approximated labels are representative we compute the Mean MAE between the true and approximated Shapley values for up to players with 1000 games per -player game. Across all -player games the mean MAE between the true and approximated Shapley values was and never exceeded . Figure D.2 shows the distributions per player together with the true label (red dot) for 8 and 9 player games.
Parameterizations for test datasets.
|Test set name||location|
Test distributions are generated by the following parameterizations of the beta distribution
d.2 Explainable AI
We provide a step-wise explanation of how we performed the experiment in our explainable AI section, that is, train a neural network to predict the Shapley values for a dataset of features:
Select a dataset. The first step is to select a dataset of choice. In our example, we end up with a feature matrix where each row is an observation and each column a feature, and a target vector or depending on the task.
Feature to target. Following, we estimate a model that predicts one output for each set of features.
Feature to Shapley. Finally, we create an array of linearly spaced train-test splits . Each element in indicates the ratio of samples that are used for training the neural network, with the remaining samples used for evaluation. For each train-test partition in
then, we train a feed forward neural network onsamples by minimizing the MSE between the predicted and actual Shapley values (full details in Table 3) for 100 epochs, and evaluate on the remainder (unseen) samples.
Datasets and preprocessing.
summarizes the considered datasets. The UCI Banking dataset contains 15 features and a binary target (whether the client has subscribed a term deposit), whereas the Melbourne housing dataset contains 21 features and a regression target (the price of a house). We exclude the “duration” feature in the Banking dataset to prevent data leakage, as is recommended in the documentation. Moreover, we exclude the “Rooms” feature as there is a lot of overlap in this and the “Bedroom2” feature. The numerical values in both datasets are z-scored, and categorical features are encoded as numbers. Missing values are encoded as zeros, so that the complete dataset can be used for our experiment.
Feature to target.
We partition the dataset into train and test and train a Random Forest Classification model on the UCI Banking set, obtaining a final test accuracy of 72 %. Moreover, we train a decision tree regression model to predict the price given the set of house features and obtain a final MSE of 1.21 on the test dataset.
Compute Shapley values.
We use the trained models to obtain Shapley values for each instance in the feature dataset using the SHAP kernel explainer object Lundberg and Lee .
Feature to Shapley: procedure.
Finally, we can train a neural network using the datasets . To investigate the sample complexity required to learn a representative mapping from features to Shapley values, we conduct the following experiment. First, we create an array of linearly spaced training splits . Each element in in indicates the ratio of samples that are used for training the neural network, leaving the remaining samples for evaluation. For each train/test partition in then, train a feed forward neural network on samples through minimizing the MSE between the predicted and actual Shapley values (architecture and hyperparameters specified in Table 3) for 100 epochs, and evaluate on the samples. Figure 6 depicts the resulting RMSE for both datasets as a function of the training fraction.
|Number of layers||3|
|Adam learning rate||1e-4|
Appendix E Additional Methods
Appendix F Additional Results
In-sample predictive performance.
For completeness, we provide the MAEs for each solution concepts on the in sample dataset up to players in Table 5.
|Least core payoffs||Least core excess||Shapley values||Banzhaf indices|
|Metric||Mean MAE||MAE||Mean MAE||Mean MAE|
Capturing step-wise jumps: full description.
We provide a more detailed description to Figure 5. In the Figure on the left, each player is a winning coalition by itself when . Up to , player 3 is more critical than players 1 and 2 and receives a larger share of the joint payoffs. All players obtain equal payoffs when the only winning coalition is the grand coalition. On the right side: each player is a winning coalition by itself when , which results in an equal payoff of for each player. Increasing the quota continuously alters the set of winning coalitions, thereby the relative importance of each player. At players 1, 2 and 3 have become less critical and obtain a payoff of each compared to for player 4 and 5. Finally, when , the only winning coalition that can be formed is the grand coalition: each player is required and the joint payoff is divided equally.
Appendix G Hardware details
We trained our neural networks on an internal compute cluster with 10 Quadro RTX 6000 GPUs and two Intel(R) Xeon(R) Gold 5218 CPUs, each of which has 16 physical cores (32 logical processors).
Appendix H Asset licensing
Two main assets were used in this work:
The UCI Banking dataset
The Melbourne Housing dataset
The Melbourne Housing dataset was released under the license CC BY-NC-SA 4.0. The UCI Banking dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Appendix I Evaluating the Speedup Achieved by Our Approach, Depending on the Size of the Dataset
Many domains involve datasets with a large number of features, sometimes with over thousands or millions of data instances. A prominent example is language modelling, where large bodies of text are generated, translated or processed in different manners Patel et al. . In such cases, computing the Shapley value for each data instance using sampling-based methods takes an extremely long time.
Our approach is targeted at speeding up feature importance computations of many instances. We perform a simple experiment to demonstrate the effectiveness of a model-based approach for explaining model decisions. We take the Melbourne Housing dataset and obtain 9000 instances by 13 features after preprocessing steps (encoding the categorical features and standardization). First, we generate a ground-truth dataset by setting the number of samples in the SHAP package (KernelExplainer) to 5000 samples. The number of samples in the KernelExplainer determines the number of times a model is re-evaluated for every prediction. A higher number of samples will result in more accurate (and lower variance in the) Shapley values, but require a longer compute time. Computing the Shapley values on our 9000 samples takes 4.13 hours. Figure I.1 (left) shows that the inference time scales approximately linearly with the number of re-evaluation samples.
To contrast the difference in computation time, we compute Shapley values on 10 % of the data (computation time is 29 minutes) train the neural network in 30 seconds, and predict Shapley values on the remaining 90% of the data (less than a second). All together, this procedure takes roughly 12 % of the time of it took to compute Shapley values for the entire dataset. This speeds-up the whole procedure by 8x while keeping a reasonable prediction quality on the remaining unseen data (Figure I.1 right).
Next, we use SHAP again to compute Shapley values for 90% of the data, instances, this time with the default number of model re-evaluations samples, yielding slightly accurate Shapley values (the default is ). We quantify the trade-off in prediction quality as the by comparing the model predicted Shapley values to the Shapley values obtained with the default number of samples.
Concretely, we measure the Mean Mean Absolute Error (MMSE) of our model predictions and the SHAP package to the ground-truth dataset. We note that speedup does come at a slightly reduced empirical performance: the model MSE on the test set is, , compared to the MSE with SHAP: . However, we expect that hyperparameter tuning and better preprocessing will improve the quality of the model predictions.
All together, it is easy to see how this win in computation time translates to larger datasets. Consider a similar dataset with 100,000 or a million entries. Computing Shapley values on the entire dataset with SHAP will take weeks (40 or 400 hours), while the time for our procedure remains relatively the same: building the train set and training the neural network only takes a fraction of the time required by the sampling-based approach: the analysis using our method would take a few hours.
Appendix J Heuristics and Simpler Models
We now consider a common heuristic for payoff allocation in weighted voting games, theweight proportional payoff allocation. We use this as a benchmark to evaluate our models’ performance against. We find that our fixed models outperform this benchmark heuristic across solution concepts and test sets.
The WeightProportional (WP) heuristic divides the payoffs in a way that is directly proportional to the players’ weights, that is, the payoff of a player is:
for any player game. WP provides meaningful benchmark as it is the most naive method for allocating payoffs. The resulting payoffs (and thus errors) do not take into account the quota, which may have a major impact on the correct solutions by means of the emergent set of winning coalitions. As such, any improvement on this benchmark thus suggests a better understanding of the collaborative structures in the game at hand.
We also compare the predictive performance of our models against the predictive performance of multinomial regression models. These models have just one layer with inputs and outputs for each player game and a softmax nonlinearity at the end. Across all game sizes, our models outperform the linear models by 45 % to 95 % percent. Results are found in Table 6.
|Linear Core||Neural net Core||Linear Shapley||Neural net Shapley||Linear Banzhaf||Neural net Banzhaf|
Comparing neural network performance to multinomial logistic regression models.
Appendix K Hard cases
Across solution concepts (Shapley, Banzhaf and Least core), discontinuous jumps in the solution space yield large errors (see Section 4.1.2 and Figures 5, L.4). In this section, we highlight failure cases for the Least core. In contrast to the power indices, a solution in the least core is obtained by solving a Linear Program under several hard constraints. Thus, a solution to a weighted voting game is only feasible when, for each winning coalition , we have that
where is the payoff of player in coalition . In words, each winning coalition must obtain joint reward that is at least as good as what they would have obtained on their own. If this criteria is not met, the solution is infeasible: some players will have an incentive to deviate from the grand coalition.
Majority of the model predictions fail to meet hard constraints. Figure K.1 depicts the percentage of feasible solutions over the game sizes. For small player games (), between 50 and 5.5 % of the model predictions are feasible). The larger the number of players, the fewer model predictions satisfy the hard constraints. From eight player games and above, the number of feasible predictions ranges between 0.2 and 10 %. A preliminary analysis shows no relationship between infeasible solutions and larger errors in the payoffs nor the LCV.
The fact that the model is not able to predict many feasible solutions in these large games is unsurprising. The set of winning coalitions increases approximately exponentially in . As such, the more players in a game, the more hard constraints need to be satisfied in order for a solution to be feasible per game. All hard constraints need to be met in order for a solution to be feasible. We expect that including a penalty term for infeasible predictions during training will help to improve this metric and hope to address this in future work.
When the least core is a set, simplex based methods tend to find corner solutions, which leads to ambiguity in the solutions.
Further analysis of individual weighted voting games reveals that the most problematic games are cases where the least core contains multiple correct solutions, but the full joint payoff is allocated to a single player. In other words, payoffs are divided such that one player receives one, and all the others zero. To illustrate what is happening here, consider a simple four player game that produced the largest error (Figure K.2). We focus our analysis on the game on the left, but our insights apply to a wide range of -player games in the dataset. This particular game has the following weights and quota (rounded to one decimal place for simplicity)
where we can see that no player is a winning coalition by itself (all ’s q). In fact, the only winning coalition in this game is the grand coalition, that is,
The solution to this weighted voting game is:
where player one receives the full joint payoff, even while its’ weight is smaller than the weight of player three (). The reason why we obtain this solution comes down to two facts. First of all, the least core is a set, and can therefore contain multiple correct solutions. Secondly, simplex based methods return corner solutions. Thus, in cases where several players are required to form a winning coalition, the solver will allocate the joint payoff to an arbitrary player that belongs to this critical subset.
Figure K.3 depicts this visually for a simple two player game. Here, we have the WVG with , so the only winning coalition is the grand coalition. Solving this game results in either of the corner solutions: the payoff vector or with a LCV . However, if we define as the solution of a player one, any combination is a feasible solution in the least core.
Obtaining the full set of solutions for this game can be done empirically. In principle, we can take the payoff vector and LCV above, generate the set of permutations of length two (player pairs) and check whether the solution is still feasible if we give a small value from one player (whose payoff is not zero) to any other player. However, this approach does not scale to larger games. It would be more efficient to derive rules for acquiring the full least core set from a single solution.
We limited our analysis above to four-player games. However, we observe the above behavior irrespective of the number of players in the game. Therefore, addressing this issue is critical for improving the predictive performance of our models.
Appendix L Applying Our Models to Voting In The European Council
In this section we apply our trained models to predict voting power of member states in the EU Council. First, we take the weights from four countries in the EU voting council Leech [2002b], see Table 7, and define the quota as the majority vote (50 % of the total weights + 1). Our (weighted) voting game consists of the member states Hungary, Netherlands, Poland and Ireland with the weights , and quota .
|Member state||Population||Population Perc.||Weights||Weights Perc.|
Following, we divide the weights by the quota and feed the normalized weights (see Section 3.1). We contrast the model predictions against the ground-truth solutions and find that the Mean Mean Absolute Error (MMAE) between our model predictions and actual solutions per member state is 0.003 (Banzhaf), 0.002 (Shapley), 0.017 and 0.007 for the Least core payoffs and excess respectively. Figure L.1 compared the payoff allocations for the Shapley, Banzhaf and Core solution concepts. We observe that the contribution measured by the Power indices (Shapley and Banzhaf) is less than the weight, while the Core attributes a larger share of the joint payoff to this member state.
Next, we perform a sensitivity analysis. In such an analysis, we are interested in the interdependence of member states’ voting power. Holding all other game parameters constant, we increment the weight of Hungary () with values of one, until it exceeds the quota of 30,5. Figure L.2 depicts how the member states’ voting power (in Shapley value) changes as Hungary gains absolute power (as we increase the weight). The predicted payoff for Hungary is depicted by the red triangles, the model predictions for the other states in blue, and the actual solutions by the green dots. We observe that there are two transition points in the solution space. One transition point occurs when . Hungary can now form a winning coalition both with the Netherlands () and with Poland (), which raises its power in the voting and accordingly its’ share of the joint payoff. We notice that our model is able to predict accurate payoffs overall, with a spike in absolute error per player at this transition point.
The second transition point occurs when . Hungary is now also a winning coalition by itself, thereby obtaining a larger share of the joint payoff. We observe that its’ Shapley value increases to approximately 0.6, obtaining the more than half of the total reward. Our model is able to capture this transition almost perfectly (MMAE ).
We perform a similar analysis, this time perturbing the quota. Figure L.3 depicts the same game with the original weights fixed, increasing the value of the quota from small to the sum of the weights (notice that this is the same experiment we performed in Section 4.1.2). The value of the quota determines the set of winning coalitions, thereby dictating the payoff of each player. As such, perturbing in this game yields a large number of discontinuous jumps in the member states solutions compared to our previous analysis perturbing the weight of Hungary. Our model is able to capture the overall changes in the solutions but has difficulty with capturing large jumps in payoffs for small quota increments.
Our variable models models have been trained on games up to ten players and can be applied to instantly predict the payoffs of member states in larger councils. To illustrate this, we take all member states in Table 7 and use our variable models to predict the payoffs for each, again setting the quota as the majority vote. We report the prediction quality of our models with respect to the ground truth (in Mean Mean Absolute Error), which is 0.013 for Shapley, 0.004 for Banzhaf, and 0.05 for the least core. Figure L.4 compares the solutions across member states and solution concepts.
While there is room for improvement in terms of prediction quality, our approach provides a useful tool for obtaining quick estimates of the voting power in medium size games (). Furthermore, while approximation methods exist for Shapley and Banzhaf, there are currently no effective methods for estimating payoffs in the least core. This solution concept is more challenging as the solutions are obtained by solving a Linear Program with a hard constraints. Thus, solving a game with 15 or even 20 players contains on the order of or constraints, which slow down the solvers dramatically. Here, we can apply the same technique, solve for the small/medium size-games, then leverage our neural network approach to tackle a higher numbers of players. In other words, provided a solver that can handle up to players, we can train a neural network on this number of players, allowing us to extend the number of players even beyond , possibly up to .
Appendix M Real World Applications of Weighted Voting Games
Weighted voting games, and cooperative game theory in general, provide valuable tools for reasoning about the allocation of collective outcomes to individual agents. As such, their applicability span numerous domains. In this section, we highlight several real-world applications of our work. In all examples, our framework can be applied to both speed up and enhance the scalability. Dinar et al.  utilize solution concepts from cooperative game theory – including the Core, the Shapley Value, the Nucleolus and Nash – to allocate water resources to competing parties in the presence of scarcity. More recently, Mirzaei-Nodoushan et al.  also performed an extensive evaluation of both cooperative and non-cooperative game theoretic approach for water management in trans-boundary river basins.
Moreover, cooperative game theory has proven useful in analyzing the costs of joint activities Young . Vázquez-Brage et al.  combine a model of airport games with the Shapley value to fairly distribute the aircraft landing fees among airlines. When an airport is build, the size of the runway is chosen to match the largest designed airplaine. The authors address how to fairly divide costs between airlines provided the different designs and sizes of their planes.
Bistaffa et al.  applied cooperative game theory to analyze the social ridesharing problem. Mobility platforms such as Lyft allow their users to share their trip with nearby users in real-time, providing a more environmentally friendly and cost effective alternative to commuting alone. They study how the costs of the ride should be divided among passengers. Another application is in supply chain management, where Fiestras-Janeiro et al.  study how to coordinate actions between suppliers, manufactorers, retailers and customers.


text:
Stamps of note celebrate Irish Music
Dublin, November 7, 2006: The Clancy Brothers and Tommy Makem; The Dubliners; The Chieftains and Altan, four Irish groups who have succeeded in bringing Irish Music to a worldwide audience over the past four decades – and who continue to delight contemporary audiences – are featured on four new Irish postage stamps issued by An Post today.
The new stamps feature ‘snapshots in time’ photographs of each band and are now on sale online at www.irishstamps.ie; by phone at (01) 705 7400, at main Post Offices and the GPO, Dublin.
An Post Chairperson, Margaret McGinley said that the new stamps speak volumes about the fortitude and brilliance of the featured musicians and about the vibrant, living, forward-looking entity that is Irish Music - “Each stamp comprises a snapshot in time and depicts the group at a particular point in their own unique history. The fact that these groups continue to develop their music and to dazzle and delight their fans – as an ensemble or as individual musicians - is testament to their musical brilliance.”
The An Post chairperson also paid tribute to those featured musicians who have passed away: “We remember today those shining stars of Irish Music no longer with us but who have left a rich and lasting legacy of music and lyrics. In particular we remember Derek Bell of the Chieftains; Ciaran Bourke and Luke Kelly of the Dubliners; Brothers Paddy, Tom and Bobby Clancy and Frankie Kennedy of Altan.
The Clancy Brothers and Tommy Makem (75c) emerged as a potent force on the American folk scene in 1955. Their natural exuberance and boisterous style firmly established them as the top Irish music group of the time. Their first Irish tour in 1963 was rapturously received and they blazed a trail for the scores of Irish music groups who followed in their footsteps. The late Paddy, Tom and Bobby Clancy are survived by their brother Liam who continues to perform as a solo artist and who celebrated the stamp launch with Tommy Makem who travelled from the U.S. for the occasion.
The Dubliners (48c) formed in 1962 out of the backroom sessions in Dublin’s O’Donoghue’s bar and their arrival on the folk scene heralded an irreverent and robust new dimension to the genre. This talented and colourful group featured the raw impassioned vocal power of Ronnie Drew and Luke Kelly, the virtuoso banjo-playing of Barney McKenna and the multi-instrumental talent of John Sheahan.
The Chieftains (48c) had their genesis in the early 1960s at the Ceoltóirí Chualann sessions organised by composer Seán Ó Riada. Signed by the Claddagh record label, the band has built an international reputation as the most remarkable example of Irish traditional music in the world. Multiple Grammy Award winners, the Chieftains have collaborated with contemporary musicians such as Frank Zappa, Jackson Browne, Emmylou Harris and Van Morrison.
The founding members of Altan (75c) were the Belfast fluteplayer, Frankie Kennedy and Gweedore singer and fiddler, Máiread Ní Mhaonaigh. Their award-winning music, ranging from melancholic old Irish songs to excitable, hectic reels and jigs, was a combination of old Donegal fiddle music and Northern flute tunes. Signed in 1996 to Virgin Records, they gained gold and platinum albums in Ireland and continue to tour all over the world.
A specially produced limited-edition booklet containing four of each of the four stamps as well as detailed background information about the development of Irish music and the unique role played by each of the featured groups is also available. Priced just €12, it makes and ideal Christmas stocking filler for musically-inclined friends at home and abroad.

domain: artistic


text: I am also looking for Clute's that are part of my family. The earliest member that I have is William Clute who died in 1901. Married Ellen Hurley about 1881 in Oneida, NY. Children were Mary, Martha and George T.. George had several children (Fred, Bertha, Grace, Dorothy, Howard, and it is a question if Helen married into the family or was one of Geroge's children)have not found any other children. Please contact me if you think we have a tie to the family.

domain: history