Prediction Markets at Google

Peter A. Coles; Karim R. Lakhani; Andrew P. McAfee

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Case | HBS Case Collection | May 2007 (Revised August 2007)

Prediction Markets at Google

by Peter A. Coles, Karim R. Lakhani and Andrew P. McAfee

Abstract

In its eight quarters of operation, Google's internally developed prediction market has delivered accurate and decisive predictions about future events of interest to the company. Google must now determine how to increase participation in the market, and how to best use its predictions.

Keywords: Forecasting and Prediction; Knowledge Sharing; Knowledge Use and Leverage; Market Participation; Information Technology;

Format: Print

21 pages

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Citation:

Coles, Peter A., Karim R. Lakhani, and Andrew P. McAfee. "Prediction Markets at Google." Harvard Business School Case 607-088, May 2007. (Revised August 2007.)

About the Author

Karim R. Lakhani

Charles Edward Wilson Professor of Business Administration

Technology and Operations Management

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Case | HBS Case Collection | September 2019 (Revised August 2020)

Obsidian: Product or Platform?

Karim Lakhani, Peter Barrett, Julia Kelley and Kerry Herman

Citation:
Lakhani, Karim, Peter Barrett, Julia Kelley, and Kerry Herman. "Obsidian: Product or Platform?" Harvard Business School Case 620-018, September 2019. (Revised August 2020.) View Details

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Teaching Plan | HBS Case Collection | August 2020

Obsidian: Product or Platform?

Karim R. Lakhani, Peter Barrett and Julia Kelley

Teaching Plan for HBS Case No. 620-018.

Citation:
Lakhani, Karim R., Peter Barrett, and Julia Kelley. "Obsidian: Product or Platform?" Harvard Business School Teaching Plan 621-017, August 2020. View Details

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Working Paper | HBS Working Paper Series | 2020

When Do Experts Listen to Other Experts? The Role of Negative Information in Expert Evaluations for Novel Projects

Jacqueline N. Lane, Misha Teplitskiy, Gary Gray, Hardeep Ranu, Michael Menietti, Eva C. Guinan and Karim R. Lakhani

The evaluation of novel projects lies at the heart of scientific and technological innovation, and yet literature suggests that this process is subject to inconsistency and potential biases. This paper investigates the role of information sharing among experts as the driver of evaluation decisions. We designed and executed two field experiments in two separate grant funding opportunities at a leading research university to explore evaluators’ receptivity to assessments from other evaluators. Collectively, our experiments mobilized 369 evaluators from seven universities to evaluate 97 projects resulting in 760 proposal-evaluation pairs and over $300,000 in awards. We exogenously varied two key aspects of information sharing: 1) the intellectual distance between each focal evaluator and the other evaluators and 2) the relative valence (positive and negative) of others’ scores, to determine how these treatments affect the focal evaluator’s propensity to change the initial score. Although the intellectual similarity treatment did not yield a measurable effect, we found causal evidence of negativity bias, where evaluators are more likely to lower their scores after seeing critical scores than raise them after seeing better scores. Qualitative coding and topic modeling of the evaluators’ justifications for score changes reveal that exposures to low scores prompted greater attention to uncovering weaknesses, whereas exposures to neutral or high scores were associated with strengths, along with greater emphasis on non-evaluation criteria, such as confidence in one’s judgment. Overall, information sharing among expert evaluators can lead to more conservative allocation decisions that favors protecting against failure than maximizing success.

Keywords: Project evaluation; innovation; knowledge frontier; negativity bias; Projects; Innovation and Invention; Information; Diversity; Judgments;

Citation:
Lane, Jacqueline N., Misha Teplitskiy, Gary Gray, Hardeep Ranu, Michael Menietti, Eva C. Guinan, and Karim R. Lakhani. "When Do Experts Listen to Other Experts? The Role of Negative Information in Expert Evaluations for Novel Projects." Harvard Business School Working Paper, No. 21-007, July 2020. View Details

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Prediction Markets at Google

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