Prediction markets derive their predictive value from the crowd (a.k.a. wisdom of the crowd). An example given by Suroweicki (2004): “If one asks a large enough number of people to guess the number of jelly beans in a jar, the averaged answer is likely to be very close to the correct number.” But how’s this theory used in practice?

Prediction markets are very applicable to political elections, and an important election is coming up. On the 8th of November a new president will be chosen in the USA. Hillary Clinton running as the democratic candidate, and Donald Trump for the republicans. In the images below the prediction markets for the presidential candidates are shown. What can we infer from this?

Source: http://predictwise.com/politics/2016-president-winner

The upper graph shows the probability of Clinton and Trump winning the presidential elections. The market predicts that at this moment, Clinton has a probability of 87% of becoming the (first female) president. Her probability of becoming president increased sharply after the first televised debate, as well as today’s news about Trump’s opinion towards women. The changes in the probabilities for Clinton winning can be seen more clearly in the graph below. The question underlying these probabilities is as follows: how much are you willing to pay to receive one dollar if Hillary Clinton is elected president? And vice versa: how much are you willing to pay to receive one dollar if Donald Trump is elected president? The dollar amounts (willingness to pay) the market reflects is the probability the crowd assigns to an event to occur.

Source: http://predictwise.com/politics/2016-president-winner

According to the market, Trump only has a chance of 13% of becoming the next president of the USA. What would your advice be based on the information above? Should Trump suspend his campaign or is the crowd not getting it right this time?

References:

Surowiecki, R. (2004) Available at: http://www.diplomacy.edu/resources/books/reviews/wisdom-crowds-why-many-are-smarter-few

Personalized content is hot today and we like it. I can illustrate this using a example from my own experience. My washing machine broke down the other day. Naturally, I went online and googled: “buy washing machine”. I clicked through some websites to see what was available on the market. After my internet search advertisements of washing machine retailers started popping up on every website I visited. This has become the norm and it made my life easier as I could make a well-informed decision. “Thanks for growing investment in customer data, predictive analytics, and marketing cloud solutions – the majority of organizations report that today they are personalizing content in social media and owned web, email, and e-commerce channels.” (Diorio, 2016). So personalization is everywhere, however, is personalization always a good thing?

In the washing machine example the benefits of personalized content are clear. The advertiser does saves money by showing targeted ads. I get to see relevant ads which help me make an informed decision. The website owners get some money for showing the advertiser’s advertisement. Nice.

However, personalizing information is everywhere, also in the news industry. And this is where personalization becomes questionable. Is it a good idea to enable news viewers to personalize what they want to see? According to Donsbach & Traugott (2008) personalizing news content can lead to tunnel vision. The topics of the news they view are adjusted to what they want to see, instead of what needs to be seen. As a result of this people’s reference point change will start to shift. The news people don’t see is called the ‘information blind spot’. This gives way to polarization and demonization of others’ opinion. This is applicable to any information good around, the impact however, depends on what information good is considered. It doesn’t matter that you only listen to your favorite songs on Spotify. It doesn’t matter that you only follow your friends on Instagram. It doesn’t matter that you only follow certain politicians on Twitter, or does it? Do you think we all end up in our own personalized bubble? Is personalization in the news industry a bad thing (c.q. will it lead to polarization)?

References:

Diorio, S. (2016) – http://www.forbes.com/sites/forbesinsights/2016/02/11/how-marketers-are-driving-growth-through-personalized-content/#197c93e928cc

Donsbach, W. & Traugott, M. (2008) – The SAGE Handbook of Public Opinion Research