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Maurice Schweitzer

Cecilia Yen Koo Professor @ The Wharton School

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Biography

Maurice E. Schweitzer is a behavioral scientist whose research investigates the conditions under which people deceive each other, the conditions under which they cooperate, and the role of trust as the connective tissue of organizational and economic life. He is the Cecilia Yen Koo Professor at The Wharton School of the University of Pennsylvania, where he is a member of the Operations, Information and Decisions Department and one of the most cited scholars in the field of negotiations and managerial decision-making.

Schweitzer earned his PhD from the Wharton School and has held faculty positions at Wharton since the early 1990s. His research uses laboratory experiments, field studies, and survey methods to examine how individuals make decisions in conditions of strategic interaction, including negotiations, competitive markets, and team settings. His scholarly contribution has been the experimental documentation of when and why people lie, and what the consequences of those lies are for trust and subsequent cooperation.

His 2015 book Friend & Foe, co-authored with Adam Galinsky, synthesizes research on the simultaneous demands of competition and cooperation in human relationships. The book argues that nearly every consequential interaction, in business, politics, and personal life, requires people to balance the impulse to cooperate against the impulse to compete. Its argument has been adopted in management and negotiation curricula in business schools worldwide.

His co-authored work with Rachel Croson on direct questioning and deception is among the most cited studies in the experimental literature on negotiations. Their 1999 paper "Curtailing Deception" demonstrated experimentally that direct questions reduce active lies but increase the use of misleading statements that technically contain no false claims, a finding that has shaped subsequent research on the boundary between deception and ordinary social communication. His broader research program on emotion, trust, and cooperation has produced over a hundred peer-reviewed articles. He has received teaching awards across multiple Wharton programs and is a frequent commentator in The New York Times, The Washington Post, and the Harvard Business Review.

Published Works

  • Friend & Foe: When to Cooperate, When to Compete, and How to Succeed at Both (Crown Business, 2015) — with Adam D. Galinsky
  • "Curtailing Deception: The Impact of Direct Questions on Lies and Omissions," International Journal of Conflict Management, vol. 10, no. 3, 1999, pp. 225-248 — with Rachel Croson
  • "The Dark Side of Self- and Social Perception: Black Uniforms and Aggression in Professional Sports," Journal of Personality and Social Psychology, 1988 — with Frank et al.
  • "Promises and Lies: Restoring Violated Trust," Organizational Behavior and Human Decision Processes, 2006 — with Hershey and Bradlow
  • "Trust and the Strength of Ties," Journal of Marketing Research, multiple co-authored studies
  • Over 100 peer-reviewed papers in journals including Journal of Personality and Social Psychology, Organizational Behavior and Human Decision Processes, and Academy of Management Journal

Contribution to AI First Principles

Maurice Schweitzer's research grounds Deception Destroys Trust. The treatise cites his co-authored 1999 paper with Rachel Croson to establish that "perceived dishonesty is one of the fastest and most potent destroyers of trust in any relationship."

Schweitzer's experimental research provides the empirical foundation for the principle's central claim. His work demonstrates that the boundary between deception and ordinary communication is finer than people assume, and that the trust costs of perceived dishonesty are immediate, lasting, and difficult to repair. Applied to AI, his findings imply that systems that obscure their nature, even by omission rather than by overt false statement, expose their operators to the same trust collapse documented in his experimental data on human deception. The principle's directive to make AI obvious, not hidden, has its experimental case in his body of work.

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