Thanks to the Illinois Technology Association (of which we are a member) for publishing our guest blog post on toxic corporate culture. We make the argument that before you can heavily invest in shiny new technology as you prepare for the #futureofwork, you should look to make positive changes to your culture first to take maximum advantage of your investment.
We recently had an article run in the Huffington Post about the future of work and what skills will be the most valuable given the new ways organizations will be structured and their desires to be more agile...
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.
For years, companies of all shapes and sizes have utilized the power of the crowd to research, test, and drum up support for their products or service offerings. It makes sense — tapping into the external crowd can not only power idea generation at scale and in real time, but it can also drive engagement among your most important brand ambassadors.Traditionally, market research has dictated that customers (or people like them) are always the best sources of information. But this is limiting...
One of our developers left Cultivate recently to go work for a much larger company - an experience he has never had before. They are throwing more money at him than we ever could, and he will work on a team larger than our entire company...
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.
While I’ll grant the naysayers that some networking events end up being a waste of time, I’ve been pleasantly surprised to find one aspect universally helpful: the ability to have short, 5 minute conversations with everyone I meet about our business.
Over at Grantland, Zach Lowe has published an article featuring 35 crazy prediction for the upcoming NBA season. Writes Lowe: For this to be fun, we have to find the sweet spot between bat-crap crazy and probable. Let’s all be wrong together!
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