{"id":1109,"date":"2019-02-15T10:31:58","date_gmt":"2019-02-15T10:31:58","guid":{"rendered":"http:\/\/kusuaks7\/?p=714"},"modified":"2023-07-28T05:14:38","modified_gmt":"2023-07-28T05:14:38","slug":"predicting-2016-presidential-election","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/predicting-2016-presidential-election\/","title":{"rendered":"Predicting the 2016 Presidential Election"},"content":{"rendered":"

A Predictive-Descriptive Artificial Intelligence-based expert computer system predicts a Democratic landslide victory in the 2016 presidential\u00a0race, regardless of the nominated candidate.\u00a0The GOP would require a dramatic shift in the electorate to win the White House.<\/em><\/p>\n

To win the 2016 U.S. presidential election, 270 electoral votes are required out of a total of 538 votes. The Artificial Intelligence (AI)-based, automated expert computer model predicts a potential Democrat win with 324 electoral votes, versus 214 for the Republicans. The AI-based<\/a> computer model process the prediction in two phases. In phase one, the model predicts that 257 electoral votes can be confidently assigned to the Democratic candidate, while 201 electoral votes can be confidently assigned to the Republican candidate. In phase two of the prediction, the remaining 80 electoral votes in five swing states are assigned based on the underlying patterns and statistics learned by the computer and the probabilities captured by the model.<\/p>\n

 <\/p>\n

Without using the AI-based computer model, the current political alignment of the country predicts that 251 electoral votes can be confidently assigned to the Democratic candidate vs. 109 electoral votes to the Republican candidate.\u00a0The remaining 181 electoral votes are assigned to a bigger pool of the so-called swing states. Alternatively, our AI-based computer system with human expert intervention and interaction predicts a Democrat win with 315 electoral votes, to 223 votes for the Republicans.<\/p>\n

In July 2015, Moody\u2019s Analytics predicted a Democratic win with 270 electoral votes, to 268 for the Republicans, regardless of who wins either party’s nomination.\u00a0However, in their revised model in August, Moody\u2019s suggested a small change in the forecast data that has swung the outcome from a\u00a0statistical tie to a Democratic\u00a0landslide win. Moody\u2019s model, which has\u00a0successfully predicted\u00a0every election dating back to 1980 (including a perfect electoral vote prediction in the 2012 election)\u00a0predicts that a Democratic candidate will win in 2016 with 326 electoral votes (against\u00a0only 212 electoral votes for the Republicans). Moody\u2019s will update its prediction every month running up\u00a0to November 2016. The updated Moody\u2019s Analytics presidential election forecast in October\u00a0predicts the Democrats having a distinct advantage,\u00a0with a main focus on\u00a0five swing states; Florida, Ohio, Colorado, New Hampshire and Virginia.<\/p>\n

 <\/p>\n

While Moody’s Analytics election model predicts a Democratic electoral landslide in the 2016 presidential vote, a computer model built by Reuters predicts a Republican victory instead. The Reuters\u00a0data model takes into account historical trends as an important factor, while largely ignoring the electoral system as a crucial variable. In addition to the Reuters\u00a0computer system and Moody\u2019s Analytics, the PredictWise project\u00a0from Microsoft Research predicts a Democrat win in\u00a0the 2016 presidential election with 57% of the\u00a0electoral votes (306 electoral votes), to 43% (232 electoral votes) for the Republicans.\u00a0In contrast to Reuters,\u00a0both Moody\u2019s and PredictiveWise predict a Democratic victory in 2016 and a quiet \u201crough tough hard Rodeo ride\u201d for the Republican candidate.<\/p>\n

We have simulated all the possible statistical scenarios \u2013\u00a0of the six potential Democratic nominees (Clinton, Sanders, Biden*, Webb, O\u2019Malley, and Chafee) and the nine potential Republican nominees (Trump, Carson, Rubio, Bush, Fiorina, Cruz, Huckabee, Paul, and Kasich). As an average, our model predicts a Democrat win with 296 electoral votes, to 242 for the Republicans, with a potential Democrat landslide win, as high as 337 electoral votes for the Democrats and 201 electoral votes for the Republicans.\u00a0In 2012, President Barack Obama won the 2012 presidential election with 332 electoral votes while Mitt Romney, his Republican opponent, garnered\u00a0only 206 electoral votes.<\/p>\n

 <\/p>\n

According to Moody\u2019s, the key swing states for 2016 include Colorado, Florida, Ohio, Virginia, Iowa, New Hampshire, Nevada, Pennsylvania, and Wisconsin. While Moody\u2019s model predicts that three states account for the change in margin (with Ohio, Florida, and Colorado swinging from leaning Republican to leaning Democrat), the AI-based model predicts up to seven\u00a0potential swing states: Colorado, Florida, Nevada, Ohio, Virginia, West Virginia and possibly Arkansas (low probability).\u00a0In addition, Moody\u2019s model estimates that\u00a0three Republican candidates\u00a0will influence the election results in Ohio and\/or Florida,\u00a0potentially making\u00a0the outcome of these important states even more unpredictable.\u00a0In contrast, the AI-based model predicts that five Republican candidates and two Democratic candidates will influence the election in five potential swing states.<\/p>\n

 <\/p>\n

Furthermore, the AI-based computer model predicts three major factors for a Democratic landslide victory, 1) the President Obama effect, 2) the President Clinton effect, 3) and the economy and the decline in\u00a0unemployment rate.\u00a0Other potential factors that are\u00a0considered in the human expert intervention computer model are\u00a01) the current and former governors in each swing state, 2) the most recent U.S. Congress\u00a0election, 3) the unemployment rate trends,\u00a04) and the relationship between\u00a0a\u00a0potential presidential nominee (as well as his\/her views and background)\u00a0and the swing state. All of these factors were included into the AI-based expert computer model using an AI-based expert rule system, using data such as the\u00a0history of the presidential election and a few common sense rules extracted from basic general elections.<\/p>\n

As far as unemployment rates, we used the unemployment rates of\u00a0all U.S. counties to predict future\u00a0unemployment rates for each county, based on\u00a0the presidential nominee\u2019s party affiliation and\u00a0the most optimistic and pessimistic scenarios. The predictions are based on Machine Learning- and\u00a0AI-based Predictive Analytics. We could further refine the model by including\u00a0Governor, State Legislation, and Senate\/House elections at the\u00a0county and state levels.<\/p>\n

Follow me on\u00a0Twitter<\/a> and add me on\u00a0LinkedIn<\/a>\u00a0and\u00a0Facebook<\/a>\u00a0for more posts about AI-based Predictive-Descriptive models and predictions. Visit my blog<\/a> for further details on these\u00a0results and other similar studies.<\/p>\n","protected":false},"excerpt":{"rendered":"

A Predictive-Descriptive Artificial Intelligence-based expert computer system predicts a Democratic landslide victory in the race for the White House in 2016.<\/p>\n","protected":false},"author":515,"featured_media":4092,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[140],"ppma_author":[2435],"class_list":["post-1109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-predictive-analytics"],"authors":[{"term_id":2435,"user_id":515,"is_guest":0,"slug":"masoud-nikravesh","display_name":"Masoud Nikravesh","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Nikravesh","first_name":"Masoud","job_title":"","description":"Masoud has been a researcher in the Lawrence Berkeley National Laboratory since 1995 and is widely recognized as an international leader in scientific computing and artificial intelligence.\n\nCombining over 25 years domain expertise and technical know-how in the field of machine learning and advanced analytics with high-performance computing and big data with his leadership and entrepreneurship skills, Masoud is helping research organizations in healthcare, financial analytics and cyber security, government, universities and industrial labs to accelerate their research and engineering. He's currently acting as the Chief Data Scientist at Skry, a blockchain analytics and intelligence company based in Silicon Valley"}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/515"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1109"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1109\/revisions"}],"predecessor-version":[{"id":29682,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1109\/revisions\/29682"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/4092"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1109"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}