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Virtual Teams Need Face Time Too

The February issue of Harvard Business Review includes a “breakthrough idea” from Sandy Pentland of MIT, How Social Networks Work Best, that confirms what we have learned in years of managing virtual innovation teams: Web 2.0 tools are very useful when teams are gathering ideas and information but when the time comes to synthesize that information and decide where and how to proceed, teams benefit tremendously from face-to-face interaction.

The article describes the decision process of bees in determining where to locate a new hive. More…

Innovation Prediction Market Results

The prediction market we ran as part of our innovation program for high potential young employees at a large financial services firm turned out a great success on a number of levels. Most important, it generated excitement around the proposals, motivating a high percentage of participants to actively trade and provide a new type of feedback to one another. And, though forecasting wasn’t the primary goal of this market, the market results served well to predict project outcomes.

We invited all 52 participants to invest in the “stocks” of…

Launching a Prediction Market

A few years ago, when James Surowieki’s book, The Wisdom of Crowds, came out, I became interested in prediction markets as a way to tap the collective wisdom of the participants in our innovation programs. While we provide the innovation teams with frequent feedback from senior management, program faculty, and other industry experts as they generate and develop their ideas, I have always felt we could improve getting the participants to support one another. They are typically so wrapped up in developing their own projects, as…

Notes from Wolfers and Zitzewitz paper on Prediction Markets

Here are my notes on the paper, Prediction Markets, by Justin Wolfers and Eric Zitzewitz

Accuracy
Wolfers and Zitzewitz recently published Interpreting Prediction Market Prices as Probabilities that claims that “prediction market prices are usually close to the mean beliefs of traders” and concludes…

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Notes on Democratizing Innovation by Eric Von Hippel

Notes on DEMOCRATIZING INNOVATION- by Eric Von Hippel

Why users innovate for themselves

Users do it themselves rather than hiring a customizer because of agency costs (i.e., cost of monitoring the agent), because their needs are unique and they want to get precisely what they want, and also becuase they enjoy innovating.

Because innovation by users is widely distributed, they need a way to leverage and combine their efforts and avoid more than one user developing the same thing independently (market failure). This problem can be avoided

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How To Set Up a Prediction Market: HP Example

Here’s the closest thing I’ve found to an explanation of how to set up and conduct a prediction market. This paper, INFORMATION AGGREGATION MECHANISMS: CONCEPT, DESIGN AND IMPLEMENTATION FOR A SALES FORECASTING PROBLEM, by Charles R. Plott of CalTech and Kay-Yut Chen of Hewlett Packard Laboratories, describes how they set up a prediction market for sales forecasts at HP with the following results:

·        In 6 out of 8 events for which official forecasts were available the market predictions were closer to the…

Corporate Prediction Markets, Part 2

Here’s the second part of my notes from the paper, INFORMATION AGGREGATION MECHANISMS: CONCEPT, DESIGN AND IMPLEMENTATION FOR A SALES FORECASTING PROBLEM, by Charles R. Plott of CalTech and Kay-Yut Chen of Hewlett Packard Laboratories,which describes how they set up a prediction market for sales forecasts at HP.

Advantages of Prediction Market Over Other Forecasting Methods

·        The methodology is flexible. It can be used to aggregate any type of information possessed by different people. It involves a natural methodology for quantifying subjective,…

Background on Prediction Markets

Art Hutchinson pointed me to his blog, Mapping Strategy, and the collection of articles he’s been writing about prediction markets since last September. Here are my notes:”The strong consensus – supported by a compelling body of academic research – is that these mechanisms deliver uncannily accurate forecasts across a wide range of topics, time horizons, and approaches to participation. Even more interesting is that they appear to do so at a fraction of the cost of conventional techniques for generating business foresight, (e.g., trend extrapolation, market research,…

Notes on The Wisdom of Crowds

Here are my notes on The Wisdom of Crowds by James Surowiecki

Overview

Under the right circumstances, groups are smarter than the smartest people in them, even if the group doesn’t contain expert members.

Experts are as likely to disagree as agree. Experts’ individual consistency is also 0.5. Experts overestimate the likelihood that they are correct – little correlation between self-assessment and performance. Therefore, however well informed and sophisticated and expert is, his advice should be pooled with that of others and the larger…