Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability

S. B. White; M. H. Bazerman; M. A. Neale

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Chapter | Research on Negotiation in Organizations | 1995

Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability

by S. B. White, M. H. Bazerman and M. A. Neale

Keywords: Negotiation; Outcome or Result; Forecasting and Prediction;

Format: Print

Citation:

White, S. B., M. H. Bazerman, and M. A. Neale. "Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability." In Research on Negotiation in Organizations, edited by R. J. Bies, R. Lewicki, and B. Sheppard. Greenwich, CT: JAI Press, 1995.

About the Author

Max H. Bazerman

Jesse Isidor Straus Professor of Business Administration

Negotiation, Organizations & Markets

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Article | MIT Sloan Management Review | Summer 2020

Want to Make Better Decisions? Start Experimenting

Michael Luca and Max Bazerman

Four lessons for using randomized controlled experiments to create value for your company and customers

Keywords: randomized controlled experiments; Value Creation; Decision Making;

Citation:
Luca, Michael, and Max Bazerman. "Want to Make Better Decisions? Start Experimenting." MIT Sloan Management Review 61, no. 4 (Summer 2020). View Details

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Article | Harvard Business Review Digital Articles | April 29, 2020

How Should We Allocate Scarce Medical Resources?

Max Bazerman, Regan Bernhard, Joshua D. Greene, Karen Huang and Netta Barak-Corren

Who should get a ventilator if there aren’t enough to go around? Research on decision making leads to three concrete guidelines that policy-makers and physicians can use to make fair choices when allocating scarce, life-saving resources. The key to making fair and ethical choices is to assure that decisions are made through an impartial process.

Keywords: COVID-19; Health Pandemics; Resource Allocation; Decision Making; Policy; Fairness; Ethics;

Citation:
Bazerman, Max, Regan Bernhard, Joshua D. Greene, Karen Huang, and Netta Barak-Corren. "How Should We Allocate Scarce Medical Resources?" Harvard Business Review Digital Articles (April 29, 2020). View Details

Cite View Details Find at Harvard Register to Read Related
Article | Proceedings of the National Academy of Sciences

Signing at the Beginning vs at the End Does Not Decrease Dishonesty

Ariella Kristal, A.V. Whillans, Max Bazerman, Francesca Gino, Lisa Shu, Nina Mazar and Dan Ariely

Honest reporting is essential for society to function well. However, people frequently lie when asked to provide information, such as misrepresenting their income to save money on taxes. A landmark finding published in PNAS (Shu, Mazar, Gino, Ariely, and Bazerman, 2012, “Signing at the Beginning Makes Ethics Salient and Decreases Dishonest Self-reports in Comparison to Signing at the End”) provided evidence for a simple way of encouraging honest reporting: asking people to sign a veracity statement at the beginning instead of at the end of a self-report form. Since this finding was published, various government agencies have adopted this practice. However, in this project, we failed to replicate this result. Across five conceptual replications (N = 4,559) and one highly powered, pre-registered, direct replication (N = 1,235) conducted with the authors of the original paper, we observed no effect of signing first on honest reporting. Given the policy applications of this result, it is important to update the scientific record regarding the veracity of these results.

Keywords: Morality; nudge; policy-making; replication; honesty; Moral Sensibility; Behavior; Policy;

Citation:
Kristal, Ariella, A.V. Whillans, Max Bazerman, Francesca Gino, Lisa Shu, Nina Mazar, and Dan Ariely. "Signing at the Beginning vs at the End Does Not Decrease Dishonesty." Proceedings of the National Academy of Sciences 117, no. 13 (March 31, 2020). View Details

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Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability

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