Election forecasting can be complicated and tricky, however there are a couple of people with proven track records with a decade or more of 100% accuracy predicting who will be the next U.S. President.
Allan Lichtman
In September of 2016, American University history professor Allan Lichtman predicted that Donald Trump would win the U.S. Election based on his protocol of reviewing a system that involves 13 true and false statements. His prediction record has been 100% perfect since 1984.
His published book titled: White Protestant Nation: The Rise of the American Conservative Movement was considered a finalist for the National Book Critics Circle Award in non-fiction. His prediction system is called The Keys to the White House and was released by Allan Lichtman in 1996.
Helmut Norpoth
Helmut Norpoth is a professor from Stony Brook University in New York and is the publisher of the primarymodel.com website. His system has correctly predicted the outcomes of the last five presidential elections with 100% accuracy (as of 2016).
Norpoth uses two "models" to make his prediction:
The first is the "primary" model that compares a candidate&�39;s strength in their respective primaries.
The second is based on the Mackerras pendulum method, devised by Australian psephologist Malcolm Mackerras. This is a general method of predicting the winner of large elections contested between two major parties based on the single-member electorates and includes uses a preferential voting system such as a Condorcet method or IRV. The pendulum functions by lining up all seats held in government, the opposition and its related parties, according to the percentage-point margin they are held by on a two-party-preferred basis. This method is also called "the swing" required for the seat to change hands. A uniform swing to the opposition or government parties, causes the number of seats that change hands to predict the outcome.
Helmut Norpoth&�39;s system has correctly predicted the winner of the popular vote in all five of the United States presidential elections since it was first published in 1996.
Arie Kapteyn
Kapteyn used his method to accurately forecast President Obama’s reelection. His system is based on a survey called the Daybreak Survey, which asks respondents to rate, on a scale from 0 to 100, their chance of voting for Trump, Clinton or some other candidate. The poll also asked people to use the same 0-100 scale to rate their likelihood of voting. By using this probability method, the poll avoided voters being forced into making their decision before they were really ready. This may have more accurately captured the ambiguity people felt about their choice for president.
Professor Ray Fair
Also having predicted Obama&�39;s re-election, Professor Fair of Yale University uses an algorithm he developed which forecasts the two-party popular vote of U.S. presidential races based on the state of the U.S. economy. His webpage also gives a monthly outlook of the U.S. Economy.
Alan Abramowitz
Alan Abramowitz, Professor of Political Science, at Emory University in Atlanta has correctly predicted every winner of the popular vote since 1988.
Abramowitz’s model analysis 3 simple points
1 - presidential approval rating
2 - GDP economic growth
3 - how long the incumbent’s party has been in power
Why Large Scale Events are so Easy to Forecast
The reason it is so easy to use a system to find the winner of a U.S. Election or the winner of a popular Football Game is because it is a major event. Major events can be predicted with great accuracy as long as a good source of historical data is available and in some cases recent user input is used. As we shall see later in this article, with Allan Lichtman and Helmut Norpoth, they have found a system that is able to sort through the data and predict a successful outcome to predict future U.S. presidents. First, let&�39;s get to the good stuff. Games.
Video Games Predicting the Winning Outcome
A large event is the U.S. super bowl. The video game maker EA Sports studios in Orlando, Florida makes a video game called Madden NFL. The video game is used to predict the winner of U.S. Super Bowls and has correctly predicted the winner in nine of the last 12 Super Bowls. It also predicted the exact score of Super Bowl XLIX.
Swarm Intelligence
Next there is the artificial intelligence system named UNU (short for Unanimous A.I.), which predicted other large scale events such as the Oscars and super bowl. Recently it has started predicting the future of the Kentucky Derby and picked the top finishers of the 2016 Kentucky Derby. UNU uses the "Swam Intelligence" technique. Swarm intelligence (also known as SI) is a collective behavior of decentralized and self-organized systems which are natural or artificial. The expression was introduced by Jing Wang and Gerardo Beni in 1989, in the context of cellular robotic systems.
Additional Highlights of UNU during the last couple years -
Accurately predicted the Cubs to win their very first pennant since the year 1945. UNU also happened to pick the Cubs&�39; opponent, the Cleveland Indians.
UNU predicted 9 of 10 playoff teams that would make it to the MLB playoffs. It just barely missed a perfect 10/10, as the St. Louis Cardinals failed to qualify by only just one game.
UNU correctly predicted that the Cubs and Indians would make it through the LDS and LCS rounds to the World Series.
UNU correctly predicted the Cubs would win the Fall Classic, which hadn&�39;t happened in more than 107 years.
UNU achieved a 76% accuracy in predicting who would win the 2016 academy awards, out-performing most movie experts, including the LA Times and Rolling Stone Magazine.
UNU predicted the Kentucky Derby superfecta -- the first four horses in order. This turned a small �20 bet into �11,700.
UNU correctly predicted who would win the Stanley Cup. It also outperformed experts in predicting the number of games each series would go and correctly identified the majority of the postseason award winners.
UNU works by mixing together software algorithms and real-time human input by using the real time input from groups of people together online and creating a sort of "unified emergent intelligence", which expresses itself as a singular entity. So far more than 30,000 people have participated in swarming sessions answering over 140,000 questions.
The system proves that people working together as a unified dynamic system outperform the majority of individual research results when a large scale outcome is involved that involves solving problems or making decisions.
In 2014, researchers at Unanimous A.I. first discovered that people can form online swarms that amplify intelligence just like natural swarms. So Unanimous built the UNU platform to bring that experience to everyone.
Users can login for free, join an existing swarm, or form their own swarm on any topic. From sports and politics, to movies and music, online groups can form their own emergent intelligence and ask it questions about anything.
The reason these systems are so accurate is because it is only good at predicting large scale events, and not local events, such as a high school football team or local horse race. The large amount of data, when properly organized, can give a good idea of the outcome of a major event in the future.
Swarm Intelligence occurs most often in nature. The information that is received is a result of information coming from what Tesla termed the "solar rays", which are of a high frequency. Nature and groups are able to step down this frequency and decipher the information more clearly then a single person. What better way to observe swarm behavior then in ants. So if ants start exhibiting odd behavior that is not the norm, then that must mean a large scale future event is likely. Ants fear raindrops, not because of the actual impact (they are too light), but because the flowing water washes away their food and scented ant trails.
In a scientific research study published in January 2009 titled: Patterns of an Invasion by Argentine Ants (Linepithema humile) in a Riparian Corridor and its Effects on Ant Diversity and conducted by Theodore A. Kennedy of the Biological Sciences Department California Polytechnic State University in San Luis Obispo, California; the study found that a an association exists between ant invasions and stormy weather. The study also found that ant repellants and other any resistant strategies don&�39;t have any effect during this time.
The research study surveyed 69 California households between January 1998 and July 1999. The participants were asked to estimate the number of ants invading their home and if their store purchased pesticides were used. The study included temperature and rainfall data for comparison.
The study found that ants are extremely likely to enter homes wet and cold conditions and especially during winter in Northern California. Another small peak was shown to occur during hot and dry conditions during August and September. The study also found that the following ant killers were ineffective. These were
•Baits and traps, including Combat, Grant&�39;s and Ortho Ant Kill.
•Herbal and natural products, including hot pepper, chili oil, lemon and vinegar;
•Cleansers, such as bleach, ammonia, soap, Windex and Formula 409;
•Sprays, such as Raid, Black Flag and Hot Shot;
None of these products had any effect on the ant invasions. Some did show a reduction when infestation was high following a rainstorm or during periods of drought. The sprays proved to be only slightly more potent than the household cleansers or ant baits in getting rid of ants. Herbal and natural remedies showed the least effectiveness.
The study showed that Argentine ant behavior was tied to the weather and that ants seek dry areas to escape searing heat or excessive long term dampness and there is almost no 100% repellant that can stop them. The study also stated that spraying pesticides went ants are not visible is a waste. The cause of a decline in ant visits is most likely a change in weather. Ants come in because of the certain weather conditions, and they leave because of the right weather conditions.
So in conclusion, Swarm Intelligence is used in nature to overcome extreme weather approaching and maybe even being used to over come the powerful ant repellents. It also shows that people predict better and organize better under the right conditions, with the right information when in groups and the result of large scale outcome is sought.
So in conclusion the future is not made up of random bits of information, but instead may be the result of order manifesting itself out of large scale events.
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