Deception in Negotiations, Organizations, and Markets: Theoretical and Empirical Insights
Bibliographic record
Abstract
Deception pervades interpersonal and organizational life. In this symposium, we discuss our state-of-the-science theoretical and empirical research on the characteristics, antecedents, and consequences of self-interested deception. We focus on self- interested deception in negotiations, organizations, and markets – contexts that are “breeding grounds” for lies, misrepresentation, and fraud. Taken together, our papers offer important insights into deception and prompt the need for more theory and research on deception in the fields of business ethics, negotiation and conflict management, and organizational behavior. Why Dont Lies Pay? Deceiver Guilt Undermines Negotiator Subjective Value Presenter: Alex Bryant Van Zant; The Wharton School, U. of Pennsylvania Presenter: Laura Kray; U. of California, Berkeley Presenter: Jessica Alynn Kennedy; Vanderbilt U. Thanks for Nothing: Expressing Gratitude Invites Exploitation by Competitors Presenter: Jeremy A. Yip; McDonough School of Business Georgetown U. Presenter: Kelly Lee; Oklahoma State U. Presenter: Cindy Chan; U. of Toronto Presenter: Alison Wood Brooks; Harvard U. All Bark and No Bite: How the Profit Motive Shapes Deception Detection in Organizations Presenter: Danielle E. Warren; Rutgers U. Minority Report: A Modern Perspective on Reducing Unethical Behavior in Organizations Presenter: Oliver Hauser; Harvard Business School Presenter: Michael Greene; Deloitte Presenter: Katherine Ann DeCelles; U. of Toronto Presenter: Michael Norton; Harvard U. The Two Faces of Emotional Intelligence: Emotional Intelligence & Deception in Interactions Presenter: Joseph P. Gaspar; Quinnipiac U. Presenter: Redona Methasani; U. of Connecticut
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".