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Nov
17
2015
Inkling will freeze all questions and stop accepting forecasts on 12/20. We are no longer publishing new questions.

To continue forecasting, please visit AlphaCast.

Sep
02
2015
After graduating from Y Combinator’s second ever batch in the winter of 2006, then operating for close to 10 years, I have formed a new company with a new co-founder, Ben Roesch. That new company has acquired Inkling and all its assets, and we will be operating under a new name: Cultivate Labs

We have defined Cultivate’s new mission as improving how organizations work by using crowdsourcing to leverage the untapped knowledge, experience, and wisdom of their people to improve the decisions and judgments they’re making every day.

All of our current clients have been notified of this change and we are very excited for what is ahead, including an entirely rebuilt forecasting platform we will be rolling out over the next few months, new partnerships, a new crowdsourcing product to help companies prioritize new ideas using crowdfunding, and an expanded, highly talented team.

Here is our new website and Twitter account:
On a personal note, I wanted to thank all our current and former clients who have worked with us over the years. Prediction markets have been a new approach to forecasting at every single company we’ve worked with, and it was always a risk for someone to try this out. Whether the project ultimately succeeded or failed, someone had to take the chance to try something new in their organization, and for that I was always very thankful.

I also want to thank all the users of our public market at http://home.inklingmarkets.com. We’ll soon be announcing its future, but there are a hardcore group of people on that site who have been using it for years, spending hundreds, if not thousands of hours making thousands and thousands of forecasts. That site is one of the longest running collaborative forecasting exercises ever, and it’s thanks to users like wstritt, job, chelseaboys, mfwinalford, SneakyPete, and others that it has continued to thrive.

Finally, I wanted to say something about Nate Kontny, my co-founder at Inkling. Nate approached me in 2005 about starting this company while I was working at Accenture and he was at Digital River. We went on this crazy journey together until he left in 2011 to work on some new things. 

During that time we became close friends. It’s like everyone says – you’re married to your co-founder. In 2012 Nate joined the fabled Obama election technology team and soon thereafter built the successful writing tool Draft, ultimately landing at Highrise as the new CEO after 37Signals spun it out.

As these things go, Nate and I don’t talk anymore, and I miss that. I always learned something when I spent time with him, and laughed a lot too. I think the timing was right for us to part ways back in 2011, but that doesn’t lessen the fondness I’ll always have for working with him for 5 years and the respect I have for his talents as a programmer, writer, and entrepreneur. He’s one of the smartest, most creative, productive, and honest people I know. I’m a better person today having worked side by side with him on Inkling.

Upward and onward,

Adam
CEO, Co-Founder
Inkling Markets


Someone asked me the other day what my favorite memories were of running Inkling. I have many, but I’ll share 2 early ones now.

First was the journey of getting in to Y Combinator at the inception of our company. Nate and I travelled to Cambridge, MA to meet with Paul, Jessica, Trevor, and Robert. Y Combinator was still in its infancy and had only done one other “batch.” Our batch was going to be the first one they had done in California. The 20 minute interview seemed to hinge not on anything related to our idea, but on Nate’s chops as a developer, and the design of a logo on a political blog I had been writing called “Outrage of the Day.” We walked out dazed and wondering if we were even making the right move trying to do this. Who the hell were these people who didn’t even care about our business idea?

The rest of the day we wandered around Harvard and even went to see a movie. We were walking around some random neighborhood in Cambridge when we finally got the call. They wanted us. We had just a few minutes to call them back or the offer would be off. We took it. 18k investment. The next day we came home to Chicago, quit our corporate jobs, and 2 months later we were living in a rented house in Cupertino building our product, Inkling Markets.

Celebrating quitting our job at Dublins Bar in Chicago in 2005.


Another favorite memory was the night we launched a branded prediction market for CNN for the 2008 elections. At the time we had our own dedicated hardware at a datacenter and we had spent weeks white labeling our site and optimizing as much as we could in preparation for 100x the number of people who had ever used Inkling before.

CNN wasn’t great at communicating with us, and without warning, they put a link to the prediction market right on the front page of the election site. Suddenly it was like a fire hose had been turned on and we were getting crushed with sign ups and trading activity. It was still just me and Nate in the company at the time, so dealing with whatever happened was on us, exclusively. 

Amazingly things seemed to be going fairly smoothly until I got an email from my Dad late in the evening. Apparently he was repeatedly getting email alerts from our site for the exact same event. 13 so far, within an hour. It turned out there was a bug in our mail queue that only appeared under this level of duress. I’ll never forget the chat sessions I had with Nate as he was doing play by play of trying to both fix the bug and prevent even more repeat emails from going out by directly deleting thousands of records in the database representing queued mails that were supposed to go out. 

At around 2am I wrote to our contact at CNN to let them know what happened, thinking we were surely going to get canned after this, but fortunately they couldn’t care less. What are a few thousand pissed off people in the grand scheme of things when you’re (at the time) one of the top 10 most visited web sites on the Internet? We even got a commercial made about the prediction market featuring Wolf Blitzer: “Good luck kicking some assets!”







Aug
06
2015
On December 7 of last year, the Carolina Panthers were 3-8-1 and I spent about 1100 Inkles forecasting that they would make the playoffs.  Even at long odds, this may seem like wasted Inkles—I’m pretty sure that a 3-8-1 had never gone on to make the playoffs.  But there were a couple other important factors.  The Panthers play in the NFC South; the winner of each division must make the NFL playoffs; and at the time the NFC South was led by the 5-7 Falcons and Saints (even the 2-8 Buccaneers had a chance).  The Panthers were an underdog, but they had a fighting chance.  As it turned out, the Panthers won their last four games to make the playoffs, and my 1100 Inkles turned into two hundred thousand.

I remember this scenario whenever I think about the Republican primary field.  None of the competitors seems all that compelling (in my personal opinion,) and it’s easy to find arguments against each of them.  But someone will be the GOP nominee, and I’m trying to figure out which candidates are the favorites, which are legitimate underdogs, and which are best ignored.

Our market suggests that the top three candidates—Bush, Rubio, and Walker—have a combined 93% chance to win the nomination, which is higher but not that far from PredictWise’s 72%.  So that’s something to keep in mind when watching tonight’s debate; of the ten candidates in the main event, prediction markets consider only three of them to be real contenders.  But there’s also a chance another candidate emerges from the pack.  Donald Trump leads the polls with 23% support, while Ben Carson and Mike Huckabee are at 6.6%, ahead of Rubio’s 5.2%.

One interesting feature of prediction markets is that they can react very quickly to new information (much more quickly than polls, which take days to execute).  Our markets will be open during tonight’s debate, so if a candidate delivers a powerful comment (or a deadly gaffe), you can expect their forecasted likelihood of winning to move fairly quickly.

I’ll continue to post updates over the course of the primary season, and might even devulge my sleeper picks to be the GOP nominee (hint: neither of them qualified for tonight’s main debate.)

Ben Golden is a software developer, economist, and evangelist for Inkling Markets.  You can find him on Inkling Markets as benthinkin, and on Twitter, @BenGoldN.  Email him: ben at inklingmarkets dotcom
Jul
29
2015
In growing my Inkling score from five thousand to ten million Inkles, one of the most important questions was related to the number of points each team would score in the most recent NBA season.  The question asked about the difference between each team’s points and the average of all teams.  As I watched the question play out, I noticed that a couple power users were projecting that teams close to the current average would remain close (their values were low), but there also seemed to be a lot of volatility—teams’ projections were constantly changing.  As a result I started forecasting upwards any time a team approached the average.  I didn’t know what the forecast should be, but I felt pretty good saying it should be higher than where it was.  Eventually, I created a little Excel file to try to quantify my forecasts, assuming some volatility in their scoring, but mostly I was forecasting based on observation and hunch.

Note the shift in approach between intuition (fast, emotional, human) and analysis (rigorous, logical, quantitative).  Both intuition and analysis are really valuable when forecasting in prediction markets, and one of the key advantages of implementing a prediction market is that it leverages both types of thinking.  We tend to think of analytical approaches as being more accurate, especially when making important decisions, and in many cases they are.  I felt more confident in my Excel projections than my initial hunch.  But the accuracy gain was minimal and it took much longer to generate.  There was a lot of predictive value in my initial hunch, and ultimately the forecasts I made before doing any analysis did more for my score those I made after.

In other cases, analysis may actually lead you astray.  For instance, consider Donald Trump’s presidential campaign.  When forecasting primary elections, the best analyses typically rely on candidates’ polling average, and Trump is currently leading the polls.  Does that make The Donald The Front-Runner?  Prediction Markets say no, both ours and others, and I’m pretty confident they’re right.  I also don’t think it’s rigorous analysis as much as people’s intuition guiding the markets.

As a forecaster, it’s important to learn when to rely on intuition versus analysis.  But as a prediction market administrator / consumer, what matters is that the market incorporates both intuitive and analytical approaches.  As a result forecasts and be fast, emotional, and human, while also being rigorous, logical, and quantitative.  Most importantly, they’re highly accurate.

Ben Golden is a software developer, economist, and evangelist for Inkling Markets.  You can find him on Inkling Markets as benthinkin, and on Twitter, @BenGoldN.  Email him: ben at inklingmarkets dotcom
Jul
23
2015
As I’ve become more involved with prediction markets, I’ve grown increasingly frustrated with journalists who make predictions (aka pundits) without linking to prediction market questions.  This is, in my opinion, lousy journalism, and insulting to readers.

Linking to a prediction market question has a number benefits, including:

  • requiring questions to be resolvable, which forces pundits to clarify what they’re actually predicting
  • tracking performance, revealing over time whether a pundit’s claims are insightful or nonsense
  • allowing readers to respond by forecasting on the prediction market, creating a more engaging user experience

Anyone who believes what they’re saying should be willing to stake their reputation on it by creating/linking to a prediction market and disclosing their forecasts.  It also creates a richer audience response; forecasting in prediction markets involves clear expressions of individuals’ views, which many readers might prefer over the chaos of comment forums.  My hope is that eventually, articles that contain predictions will always link to a prediction market—or embed it within the media platform itself—similar to how finance articles display tickers for any stocks they mention.

In the meantime, readers can also create prediction markets.  Lately, whenever I come across something that looks like a prediction, I’ll create a market myself—here are some recent examples.  Creating new questions on Inkling is easy, and anyone who signs up can do so.  (note: we do require approval, but we almost never reject a question.)  It typically takes just a few days for our users to start moving markets to a reasonable forecast.

The media’s job is to provide readers with accurate information, and when it comes to forecasting, prediction markets are an invaluable source of information.

Ben Golden is a software developer, economist, and evangelist for Inkling Markets.  You can find him on Inkling Markets as benthinkin, and on Twitter, @BenGoldN.  Email him: ben at inklingmarkets dotcom
Jul
17
2015
When my grandmother immigrated to the United States, she couldn’t afford to call her family on the telephone.  That was about 70 years ago.  Today, I have a friend whose brother moved to Sri Lanka to become a Buddhist monk and literally lives in a cave.  He and his family Skype.  This is the power of the Internet—for a significant portion of the planet, it’s now possible for any two people to communicate from anywhere, in real-time, basically for free.

This technology has improved our lives in many ways, but it’s also brought a lot of unexpected change, and will continue to do so.  Almost every human institution that exists today—companies, governments, academic institutions, systems of government and economics—were created at times when free real-time communication was unfathomable.  When you hear technology companies talk about ‘disruption’, they’re usually talking about changing an institution whose methods assume that people can’t communicate in real-time for free.

Enterprise Crowdsourcing isn’t about changing a specific industry, but rather the very nature of how businesses and other enterprises operate.  We’re challenging the command-and-control model of businesses, which says that tasks should be done by specialized departments, with information flowing up to decision-makers at appropriate time intervals and then back to departments at the discretion of their managers.  Command-and-control actually makes a lot of sense when communication is expensive, but it’s becoming increasingly inappropriate as a way of doing business now that communication is free.

When you need to forecast your company’s sales for the next quarter, the old model said you should hire an analyst or consultant and assign them the task of sales forecasting.  The new model says you should ask everyone at your company, perhaps using a prediction market.  This communication is reasonably cheap (compared to paying analysts / consultants) and your employees have access to the insight needed to make highly accurate forecasts.

Forecasting is one example of a business function where the crowd outperforms an individual/department, but there are many others, some of which I’ll outline in upcoming posts.

Ben Golden is a software developer, economist, and evangelist for Inkling Markets.  You can find him on Inkling Markets as benthinkin, and on Twitter, @BenGoldN.
Jul
16
2015
Barry Ritholtz has written a curious column titled The ‘Wisdom of Crowds’ Is Not That Wise for Bloomberg View, which criticizes prediction markets.  This is not a new view for Ritholtz, as he reminds us by linking to six blog posts critical of prediction markets each written by…Barry Ritholtz.  Indeed Ritholtz has made it his mission to find instances of prediction markets ‘failing’, and has found six of them.  These include:

  • For about two months in 2003, a prediction market expressed there was a 50%-75% that Howard Dean would win the Iowa Caucus…Dean did not win the nomination.
  • For most of 2007, a prediction market thought there was a 10%-30% Barack Obama would win the New Hampshire Primary.  Then for about a week before the election, it thought the chance was in the 50%-95% range…Obama lost the New Hampshire Primary.
  • In 2005, a prediction market did a poor job forecasting the result of the Michael Jackson trial.  (And so did everyone else.)

Ritholtz’s expectation of prediction markets seems to be that they should be perfectly accurate, and when they fail to meet that standard, he dismisses prediction markets as unwise.  But prediction markets aren’t trying to be perfect; they generate probabilistic forecasts, meaning they expect to be ‘wrong’ sometimes.  When a prediction market assigns an 80% likelihoods to events, it expects that roughly one in five won’t happen.  So over time, of course there will be some instances where a market leans in one direction and the opposite result occurs.  If you can only find six of these cases over a twelve-year timeframe, you’re not looking very hard.

The value of prediction markets is that they’re more accurate than other forecasting methodologies, including surveys, data modeling, and gut instinct.  (They’re also often cheaper to implement, too.)  Ritholtz utterly fails to demonstrate that any alternative approach is better.  He concludes the following:
I remain unconvinced you can call prices “wise,” no matter what market sets them. Perhaps the most constructive comment one can make about the crowd in market prices is that there are no better alternatives yet invented for determining the price of any item to be bought or sold. “The best we’ve got” hardly rises to the level of “wisdom.” (emphasis added)
Here Ritholtz admits that prediction markets are the best tool for forecasting, but nonetheless argues semantics over whether markets are ‘wise’.  In my book, using the best tool available is a wise thing to do, so even if the markets aren’t wise themselves, people who use them to generate accurate forecasts are.

Ben Golden is a software developer, economist, and evangelist for Inkling Markets.  You can find him on Inkling Markets as benthinkin, and on Twitter, @BenGoldN.
Jul
02
2015
When I joined Inkling Markets in Sep 2014, I started forecasting on our public-facing forecasting site.  I already had some experience using prediction markets, having been actively involved with SciCast and its predecessor DAGGRE, and was determined to show my new employer that in addition to being a pretty decent software developer, I could forecast with the best of them. 

The challenge was daunting—Inkling users start with five thousand Inkles (our nominal currency) while the top forecasters have accrued hundreds of millions (in one case billions).  To reach the top ten, I would need to double my score more than thirteen times.  It was time to get to work.

One big difference between Inkling and my previous experiences is that I had some relevant domain experience.  Whereas SciCast focused on science and technology—areas where I have only limited knowledge—Inkling includes questions about sports and politics, where I’m much more comfortable.  Rather than relying entirely on technical forecasting tricks I’d developed and original research and analysis, I could often confidently place bets about future events by drawing insights from my understanding of how sports and politics work.

In future posts I’ll provide a detailed look at how I designed, implemented and refined my strategy.  I’ll share tips and tricks, and more importantly how I think about prediction market questions.  I’ll talk about why I love prediction markets, what I get from them, and why you should get involved.  I’ll explore industries / businesses that benefit from greater use of prediction markets (spoiler alert: all of them).  And I’ll discuss the administration of prediction markets: how to pose questions to yield the best possible results, how to keep users happy, etc.

As for my progress forecasting on Inkling, I haven’t reached the top ten (yet), but I am in the top twenty.  In ten months, I’ve doubled my score only eleven times, for a score of roughly 11 million Inkles.  This is, I believe, a record for Inkling; I can’t find another user who reached 10 million Inkles within a year of joining the site, and only two cracked 5 million.  As I continue to forecast on Inkling Markets, and work with prediction markets, I look forward to writing about my adventures.

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Ben Golden is a software developer, economist, and evangelist for Inkling Markets.  You can find him on Inkling Markets as benthinkin, and on Twitter, @BenGoldN.
Jun
16
2015
The 2015 edition of the US Open will be played this week at Chambers Bay Golf Course in University Place, Washington. With big events like this (this is a major after all), we like to have groups of questions all forecasting different aspects of the event. Who’s going to make the cut? What score will the cut be? Is there going to be a playoff? Will everyone’s favorite player Tiger make the cut? All great prediction market questions, all tagged with “2015USOpen”. Check out all the 2015 US Open questions here, and if you want to create your own, make sure to use the same tag!
Jun
05
2015
A few weeks ago, we pushed a code release to our public market that incorporated a decent number of changes. Just as we always do, we were closely monitoring for any unforeseen errors and performance changes after the deployment. After a few hours, we noticed that the site was running a little slower than normal. Assuming our changes had led to this drop in performance, we set out to determine why.

Pulling up New Relic, the great tool that we use to monitor our apps’ performance, we found that we spent 90% of the time it took to process any of our requests rendering this single file that displays trade reasons for a question. And after watching for a few more minutes, that percentage was increasing. But we hadn’t changed anything relating to that piece of code, or at least that we expected would affect that piece of code.

When we looked at where the most recent trades were being placed, we realized that this was being caused by one question that had an extreme disproportionate amount of activity: “Who will win the 2014/2015 UEFA Champions League?” Turns out that one of the two of the semi-finals matches was being played right then, and a surge of new users, mostly located in Italy, were trading in this market.

It wasn’t just that the market was heavily traded though. The other part about this question was that traders were submitting reasons for their trades along with the trade itself, a really helpful option that gets discussions going in a question. So every time someone reloaded the page, we rendered all of the trade reasons submitted in that market, which ended up being a ton because of all the activity.

So in the end, it wasn’t our new code that was causing the slow down. It was just a bunch of soccer fans. After the game ended, we spent a little time only displaying only a few of the trade reasons initially, and if a user wanted to see more, we load them dynamically. A good lesson in only showing users what they need to know at the beginning, and having the option of getting all the information if they want.

The finals of the Champions League finals are coming up this weekend, and this time, we’ll be ready.
Check out that market here!
May
29
2015
Prediction markets are often used to forecast when an event will occur.  Our newest question type combines our standard option markets with an automated rolling function to streamline administration, and improve accuracy of these questions.

Before continuous date markets, you’d have to create stocks for each possible timeframe, with the name describing when that time frame began and ended. As time passed and a time frame ended, you would manually have to cash out an expired stocks. This limits the amount of information you’re able to get from a market, since each close out leaves the market with fewer stocks.

With continuous date markets, all the mechanics of running a market, that predicts a date, are taken care of for you. By specifying the time frame (daily, weekly, monthly) and number of time frames between date buckets, our backend takes care of stock name formatting, as well as keeping track of the actual date and time that the stocks begin and end.

Additionally, when a stock date bucket is about to expire, we “roll” the market in to the future. This means we resolve the closing stock to 0 (since it hasn’t happened yet) and we split the last stock of the market into two. This maintains the same granularity in forecasting until the answer is known. When we do this, we make sure to keep trader’s net worth the same, by giving them an equal position in the newly created stock as they did in the previous last stock.

Let us know what you think!
May
29
2015
Prediction markets are often used to forecast when an event will occur.  Our newest question type combines our standard option markets with an automated rolling function to streamline administration, and improve accuracy of these questions.

Before continuous date markets, you’d have to create stocks for each possible timeframe, with the name describing when that time frame began and ended. As time passed and a time frame ended, you would manually have to cash out an expired stocks. This limits the amount of information you’re able to get from a market, since each close out leaves the market with fewer stocks.

With continuous date markets, all the mechanics of running a market, that predicts a date, are taken care of for you. By specifying the time frame (daily, weekly, monthly) and number of time frames between date buckets, our backend takes care of stock name formatting, as well as keeping track of the actual date and time that the stocks begin and end.

Additionally, when a stock date bucket is about to expire, we “roll” the market in to the future. This means we resolve the closing stock to 0 (since it hasn’t happened yet) and we split the last stock of the market into two. This maintains the same granularity in forecasting until the answer is known. When we do this, we make sure to keep trader’s net worth the same, by giving them an equal position in the newly created stock as they did in the previous last stock.

Apr
02
2014
We are hiring a new Rails Developer. Information about the position is posted here:


If you’re interested in applying, we’d love to hear from you. If you know anyone that hates their current job and is looking for something way better, we would appreciate you passing this along to them. :)
Apr
02
2014
A government funded project we’re participating in received a nice write up and audio story on NPR this morning.

Read the article and hear NPR’s broadcast of the story here.

The Good Judgment Project originated out of IARPA’s ACE program which is an attempt to push the state of the art on crowdsourced forecasting techniques and also see if “crowds” without access to classified information would be better forecasters than those inside the intelligence apparatus.

From the article:
According to one report, the predictions made by the Good Judgment Project are often better even than intelligence analysts with access to classified information, and many of the people involved in the project have been astonished by its success at making accurate predictions.
But there are caveats to this as the IARPA project sponsor Jason Matheny points out:
Matheny doesn’t think there’s any risk that it will replace intelligence services as they exist. 
“I think it’s a complement to methods rather than a substitute,” he said. 
Matheny said that though Good Judgment predictions have been extremely accurate on the questions they’ve asked so far, it’s not clear that this process will work in every situation. 
“There are likely to be other types of questions for which open source information isn’t likely to be enough,” he added.

Mar
03
2014
The Oscars are over, the Hollywood elite are home in their mansions nursing their hangovers and taking an in-house spa day, and the rest of us are left to gossip about the highlights and lowlights of the show last night.

With that in mind, let’s see how Inkling did in its predictions.

Of the 18 categories we reported on last night before the show started, Inkling correctly predicted 17 of them for a 94% hit rate. The one miss? Original Song. Damn.

Kudos to all those who participated and made predictions. Given these are crowdsourced predictions, we’re nothing without you. :)
Mar
03
2014
The Oscars begin shortly and here’s Inkling’s predictions. Each winner Inkling predicted has the highest chance of winning among its fellow nominees in the category. Remember, we deal in probabilities, so all we’re telling you is the chance a nominee will win, like when the weather man tells you there’s a 70% chance of rain, there’s a 30% chance it won’t rain!

Read on after the predictions for a little more about how to interpret these results and how we calculate them.

Alright, on to the predictions:

Which film will win the most Oscars?
Gravity (75% chance)

Best Picture
12 Years a Slave (76% chance)

Best Actress 
Cate Blanchett (82% chance)

Best Actor
Matthew McConaughey (55% chance)

Supporting Actress
Lupita Nyong’o (68% chance)

Supporting Actor
Jared Leto (76% chance)

Original Screenplay
Her (48% chance)

Adapted Screenplay
12 Years a Slave (80% chance)

Visual Effects
Gravity (80% chance)

Sound Mixing
Gravity (39% chance)

Sound Editing
Gravity (38% chance)

Original Song
Happy (53% chance)

Original Score
Gravity (31% chance)

Makeup and Hair Styling
Dallas Buyers Club (51% chance)

Foreign Language Film
The Great Beauty (57% chance)

Best Director
Gravity (53% chance)

Costume Design
The Great Gatsby (34% chance)

Cinematography 
Gravity (63% chance)

Animated Feature Film
Frozen (92% chance)


To see details about each prediction, like what chances the other nominees have, go here: http://home.inklingmarkets.com/markets?term=oscar

As you can see, sometimes there is a strong signal from people making predictions when they think they know who the winner will be, i.e. Animated Feature Film (Frozen 92%) or Best Actress (Cate Blanchett 82%) – but other times there isn’t much of a signal at all (costume design, original score, sound editing, etc.) This lack of a signal can be for two reasons. First, sometimes there just aren’t that many predictions made so the chances don’t move very much from where they started (evenly split up among however many nominees there are, i.e. 5 nominees = 20% chance each). But other time no strong signal can mean disagreement among people about what the outcome will be. For Original Screenplay, for example, there’s a 48% chance “Her” will win. But this was after 36 predictions. In contrast, there were only 18 predictions made in the Adapted Screenplay category, but those 18 predictions amounted to an 80% chance “12 Years a Slave” would win, meaning there was almost unanimous opinion among those who made the predictions. “Her” was a more controversial pick in that category.

If you have more questions about how this all works, find us on twitter at @inklinghq.

Enjoy the show tonight!







Jan
22
2014
As someone running a small company with a handful of employees and several contractors, I’ve had to learn the limits of how hard I can push people and what I can ask them to do. I suspect other founders or managers in larger companies could stand to examine this aspect of their leadership style as well.

A few months ago we took on a very large consulting project helping George Mason University and IARPA launch and grow a new prediction market focused on Science and Technology forecasting called SciCast (it’s launched, you should check it out!). While the project has gone well thus far, it has also presented our small company with a significant set of challenges: namely that we still have product development, customer support, consulting, and sales activities for our own product line, Inkling Markets! 

Where as before our interaction with our clients was controlled and routine, the SciCast project is a traditional consulting engagement with deliverables, timelines, a demanding client, multiple collaborators, and metrics we’ve been asked to meet. As anyone who has been a consultant knows, this level of accountability can be a serious amount of work. And with that amount of work inevitably comes adversity and additional pressure to succeed.

Unfortunately, this is where things tend to get dangerous. 

Doing well on this project, trying to grow our revenue, building our company for long term success – these are all goals that are of primary importance to me as the founder, but only secondary importance to any employee. An employee may partially share my goals for the business and see its growth as beneficial to their sustained employment, but they have other incentives at work as well. They want to make a good salary, they want to maximize bonuses, and they want to build experience for gaining more responsibility in the company or for their next job outside the company.  For us to have the most productive working relationship, we must reach a balance. That balance is lost if I’m making excess demands on people’s time and well-being.

Before working at Inkling I worked at a large consulting firm for many years. Consulting firms are famous for the “grind” – working 6 or 7 days a week on a client project, excessive travel, 16+ hour days. If you talk to anyone in the middle of one of those projects, you hear words like “death march,” “fire drill,” and “insanity.” Managers are hard charging and tend to focus more on making sure short-term tasks are getting done on time vs. the long view of burning people out. One not need to look far then to understand why attrition rates at consulting firms are high and why a disproportionate number of people leave after only a few years. 

That model may work (despite itself) when a large business can afford to have a human resources machine drumming up interest at universities around the world for new employees to indoctrinate, but it doesn’t work so well for a small business whose switching cost at losing even a single employee can be quite burdensome.

With that in mind, I’ve tried to catalog some warning signs that you may be pushing your team a little too hard and need to ease up:
  • People start becoming unresponsive or orders of magnitude slower responding to requests;
  • Quality of work diminishes – just doing tasks for the sake of doing them;
  • People are quiet in discussions and don’t proactively offer up opinions or solutions; and most obviously
  • Openly complaining about the amount of work or the tedium of completing it.

“But we still have to meet our deadlines and how do we do it without working people hard,” you ask?

Sometimes the problem may not be quantity, but quality. Is everyone working as effectively as they can? Is someone spinning their wheels on something unnecessarily? Sometimes you may just have to bite the bullet and tell your client you simply cannot get something done on time. My experience more often than not is if you are transparent about the likelihood of missing a deadline as early as possible and give valid reasons for why it can’t get done, people are understanding and will work with you to narrow the scope of your work or agree to an extension. If there is no other option left than to put yourself through a “fire drill,” try to keep the length as short as possible and ease off when it’s over for a few days. I strongly believe in the concept of vacationing while not actually on vacation.

As a founder, no one is going to be as passionate about your business as you will be. If you want your team to work hard, they need to share in some of that passion and be incentivized correctly. If they are not, their shelf life will be short. Eventually, people break and simply decide it’s not worth it anymore. When that happens, you’re on your own with no team at all, and that’s the worst outcome of all.

Like this post and want to know when I write others every once in a great while? Follow me on Twitter






Dec
04
2013
The latest version of the Inkling iOS app is now available on the app store. New features include:

  • Native alerts for new questions that have been published and new comments made in questions you’re participating in
  • Support for our “advanced” trading interface
  • Lots of bug fixes and layout improvements
Please be sure to update and download the latest version and as always let us know if you have any feedback, suggestions for new features or issues with something not working.
Oct
31
2013
Reggae Skeleton Dance – Happy Halloween!


Oct
30
2013
We were late to the party in updating our app for iOS7 but finally have a version out that is compatible and doesn’t crash. Anyone who has a current version and has upgraded to iOS7 should run an update and everything will work again.

We’re also close to doing another release that will add a bunch of new features including advanced trading, native alerts, setting your own price alerts, and more. It should be an nice upgrade and will be available in about a month.
Sep
10
2013
A few application improvements we’ve made recently:

  • On your dashboard the data was always stale. It was automatically refreshed every hour, but this could get confusing in active questions where your profit/loss had gone up or down quickly, but you couldn’t tell unless you manually clicked “refresh” for that position. Now all your data on the dashboard is updated in real time.
  • We’re taking a look at each email sent by Inkling to make sure it has valuable information in it and re-building them if they don’t. For example we just re-did the emails you get when a question ends. Now it should be much clearer exactly how you did and what happened overall in the question. 
  • Stats are updating now much more frequently
  • We’re slowly migrating to a new look for the application. And if you haven’t noticed, we updated our logo too.
  • We’ve made a ton of “under the hood” improvements which should make the application feel snappier. 
For our enterprise customers, we’ve introduced several new capabilities including:

  • Enhanced reporting capabilities allowing you to fine tune what data you want to pull down from what time periods.
  • In addition to collecting people’s predictions, you can now ask for a specific probability value as another input.
  • Lots of enhancements to the API


Jul
02
2013
When it comes to economics versus psychology, score one for psychology. Economists argue that markets usually reflect rational behavior—that is, the dominant players in a market, such as the hedge-fund managers who make billions of dollars’ worth of trades, almost always make well-informed and objective decisions. Psychologists, on the other hand, say that markets are not immune from human irrationality, whether that irrationality is due to optimism, fear, greed, or other forces.
Now, a new analysis published in the XX issue of the Proceedings of the National Academy of Sciences (PNAS) supports the latter case, showing that markets are indeed susceptible to psychological phenomena.

Apr
13
2007
A long-running “criticism” of our rudimentary email alerts was they didn’t include the question the market was asking so they were of minimal value. Now those of you who subscribe to the market alerts will notice the question is now being included.

Another related request has been for RSS feeds. It’s been a long time coming but we’re close to rolling those out as well after we get through our final stages of testing.