Schedule chicken is when someone is personally behind on their work, but doesn't say anything because they think someone else will, and take the heat for it, thus protecting their own ass. This apparently goes on at Apple, and is completely absurd. There's another way.
We're excited to announce the availability of a new product, Flashcast. Flashcast is an entirely new way to interact with your audience. Ask them to make predictions about a related topic and watch the results, live.
When we start projects with our clients, one of the first items we talk about is whether they want people to be anonymous in our prediction market or if they’ll use their real identities. The answer often reveals a lot, both about company culture and their personal fears of what will be made transparent. The spoiler alert is most don’t want anonymity.
After working with dozens of companies who have culture initiatives, I’m convinced their multi-million dollar investments in consultants, employee time, internal marketing, and the like will only see a partial return because a blocker is in their way: their culture of fear.
Our bread and butter working with clients is organizing their employees to participate in crowdsourcing exercises. Recently we have been approached more to help get forecasts from external crowds, either to support research projects, or to better understand what outside experts or customers think.
In the last month we’ve had two potential clients, after receiving proposals from us for projects, say they needed to step back and start the projects very differently than what we had originally discussed. They just weren't ready yet, they acknowledged...
I've been watching for the last few years the announcement, long delay, then recent launch of Augur, the decentralized, blockchain-based prediction market. First off, despite it taking a long time to launch, Augur finally did, and kudos to them for doing so. Building a prediction market from scratch is not easy, building a prediction market on top of immature technology is harder, and building a prediction market to be completely decentralized with smart contracts is downright scary.
We're pairing up with Lending Club to sponsor a monthly event where
cybersecurity startups pitch their companies, and the audience gets to make
predictions on our prediction market about how they're going to do in the
next year in terms of fundraising, hiring, etc.
Everyone who has worked in corporate America has seen or used it. The traffic light metaphor for status. Red, there’s significant problems with a project. Yellow, there are some issues, but we’re going to make it. Green, all is good. Unfortunately, this system is a disaster for decision making.
We're excited to be working with Idealantis, a startup based in Hyderabad, India on a prediction market pilot about the upcoming Gujarat state elections. Working with research organization People's Pulse, Idealantis has launched this test as a precursor to a country-wide effort for the general elections coming in 2019.
Cultivate Labs CEO, Adam Siegel, recently spoke at the Institute for Business Forecasting and Planning and spent time hearing directly form attendees about the challenges they face. He shares lessons learned about the challenges sales and operations planners face in their roles, and how crowdsourced forecasting can alleviate them and improve their forecast accuracy and efficiency.
Real-money prediction markets are illegal in the U.S., but blockchain technology is giving rise to several digital currency based prediction markets based on Ethereum. Cultivate Labs defines common terminology associated with blockchain-based prediction markets.
Joining a prediction market or prediction pool can be intimidating. You may feel like you've plunged into a world of impossible questions, ongoing arguments, and have no idea where to start. You might think there's no way you could possibly add anything, or that your forecasts couldn't possible be better than anyone else's. But prediction markets don't have to be intimidating - here are four tips to get started.
The relationship between employers and employees is changing as the drivers of employee satisfaction are changing. Employees are no longer driven solely by a paycheck - they want their work to have a purpose and to believe in what their organization offers to the world. Organizations must shift their focus from paychecks and soft benefits like casual dress codes and office ping pong tables to better engage employees by ensuring opportunities for purpose, learning, and flexibility in their jobs.
In more than 10 years of working with large companies on building prediction markets and other crowdsourced forecasting mechanisms, we’ve seen one common thread with the projects that are unsuccessful. Project owners overestimate the technology, and underestimate what it takes to engage people to make the technology successful. Avoid these 5 pitfalls to improve the success of your Cultivate Forecasts prediction market.
An open door policy isn't an effective feedback tool for leadership if you haven't created an organizational culture that's open to feedback. Your team can provide valuable insight and feedback about decisions, so actively work to create an environment in which employees are comfortable speaking up to leadership.
The ousting of Uber CEO Travis Kalanick on Tuesday is hardly surprising, though perhaps a bit out of step for both the revered ‘boys club’ who traditionally protect their own and the Silicon Valley culture that worships individuals who can, at all costs, create 10x returns on investment. But he should’ve seen it coming a long time ago, he brought it on himself after all...
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: