Recently on a Sunday during my weekly 2 hour break from all things family related, I sat at one of my favorite coffee houses and read the Sunday New York Times. An article in the Business section particularly caught my attention: The Trump Effect on CEO Pay, and it got me thinking: should a Board of Directors have a better check on their CEO than either termination or bonuses tied to stock price? Could there be something put in place better for the company's long-term prospects?
So, you asked a prediction market question, and the outcome is now known. The question has been resolved and winnings have been disbursed to the forecasters who held winning positions. Forecasters know how well they did based upon their profits in the question, and you know who your good forecasters were too. But how accurate was your organization at answering the question itself? There are several things to consider when thinking about accuracy of the prediction market:
A multinational energy company uses Cultivate Forecasts predict economic and geopolitical events that impact oil and gas prices. We find out why they decided to incorporate internal crowdsourcing within their business and how they have been so successful at engaging employee participation.
As a small bootstrapped company, we're constantly worrying about our next stage of growth. And of course start up orthodoxy says any size business should have a few key performance metrics they track to be aware of how you're doing in the things that influence revenue, profit, and the culture you want to cultivate. But as we've learned, it's not enough to just track your KPI's, you need to forecast how you're going to do on them weeks and months ahead of time.
With an election coming next month in the Netherlands, Marco van Schaardenburgh has successfully launched Prendid, a “voorspellingsmarkt” (prediction market) where players are predicting the outcome of the parliamentary elections. Joining is free and available to anyone globally.
Broadspectrum, whose parent organization is Ferrovial, is an Australian company that operates in the Defence, Property, Social, Infrastructure, Resources and Industrial sectors, and provide Logistics, Facilities Management, Consulting, Construction, Care, Welfare, Operations, Maintenance, Well Servicing, and Business Support services. We recently completed a pilot project in their Defence line of business...
Using crowdsourcing only at the front-stages of the product development cycle means organizations are missing out on a big opportunity to further tap the wisdom and knowledge of the organization. Here's how you can use internal crowdsourcing across your entire development lifecycle.
It is incredible that a company of Uber's size, with the experience they have entering new markets is still having the kinds of colossal failures they are having in Germany. But Uber is certainly not alone in costly missteps like this and billions are being lost every year. Yet a solution already exists: your employees.
I enjoyed John Horgan's piece on Bayes Theorem for Scientific American. Bayes Theorem and Bayesian reasoning are highly applicable when thinking about forecasting and prediction markets; indeed, one prediction market built a Bayes Net into its platform. In this post I'll explain what Bayesian reasoning is, why it matters to prediction markets, and give a concrete (but semi-fictitious) example of how it's applied.
Our sites use a popular prediction market algorithm called LMSR to determine how markets adjust when someone makes a forecast, and how user scores are affected by making correct and incorrect forecasts.
I've been trying to pick NFL game winners. I'm not using any complex analytical model; rather, I'm making decisions the way most sports bettors do--I watch some games, read the news, and use my judgment. I make each of my picks on SportsCast, which allows me to track my performance, interact with other forecasters, and track the performance of the prediction market--that is, the collective performance of all the forecasters on SportsCast.
One of the first and most important questions we get from clients, forecasters, and consumers of our data is: “How accurate are these forecasts?”. In order to answer this question, we have utilized and built upon a widely accepted proper scoring rule, i.e. a way to measure accuracy for a probabilistic forecast.
Joining a prediction market can be confusing and anxiety-inducing. It's easy to be overwhelmed by all the questions, to not understand the forecasting interface, or to have trouble forming opinions to base forecasts on. All of this is pretty natural--as a now-experienced forecaster, I can remember these feelings the first time I joined a prediction market. In this post I'll address a few specific emotional barriers that make it difficult to start forecasting.
On a recent podcast, Jack Schultz and I discussed two razor companies that are poised to become unicorn companies. Unicorns--startups that grow to billion-dollar valuations while remaining private--are somewhat mysterious and the subject of continuous speculation.
Wikipedia’s intro paragraph for prediction markets is the following:Prediction markets (also known as predictive markets, information markets, decision markets, idea futures, event derivatives, or virtual markets) are exchange-traded markets created for the purpose of trading the outcome of events.
One use of prediction markets I've been really excited about is forecasting individual players' performances in major sports. These predictions are incredibly useful when playing fantasy sports--both daily fantasy and season-long leagues--and the forecasts that currently exist tend to be, in my experience, pretty mediocre. Prediction markets present an opportunity for the wisdom of the crowds to intervene, and will likely lead to more accurate forecasts.
Two key take-aways from the emerging scandal surrounding Daily Fantasy sites: one, gambling data can be extremely valuable, and two: the only thing Americans love more than gambling is hating on gambling. Taken together, these findings illustrate why large-scale prediction markets present a path towards improving human knowledge in a wide range of topics.
Amongst the leadership teams of the portfolio companies at any medium to large investment firm, there is an incredible amount of experience, wisdom, and perspective that is not collectively being taken advantage of.
We recently started working with a Houston-based client in the Energy sector, who wanted to use a prediction market to help with internal operations, and to create greater transparency and communication within their company. We spent a couple months meeting with our client to learn about their business and objectives, and using test questions (e.g. asking about Houston sports teams) to help participants understand how prediction markets work. Our initial questions focused on specific operations
Have you ever been tasked with driving a project you’ve felt was going nowhere? Maybe you were a project manager or project owner, coordinating a team that was working on something you felt wasn’t gaining traction within the organization.