---
title: "Predictive Models of Malicious Behavior in Human Negotiations"
authors:
  - "Zahra Nazari"
  - "Jonathan Gratch"
doi: ""
url: "https://ijcai.org/proceedings/2016/126"
pdf: "https://ijcai.org/proceedings/2016/0126.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "855-860"
track: ""
keywords:
---

# Predictive Models of Malicious Behavior in Human Negotiations

Zahra Nazari, Jonathan Gratch

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 855-860.

## Abstract

Human and artificial negotiators must exchange information to find efficient negotiated agreements, but malicious actors could use deception to gain unfair advantage. The misrepresentation game is a game-theoretic formulation of how deceptive actors could gain disproportionate rewards while seeming honest and fair. Previous research proposed a solution to this game but this required restrictive assumptions that might render it inapplicable to real-world settings. Here we evaluate the formalism against a large corpus of human face-to-face negotiations. We confirm that the model captures how dishonest human negotiators win while seeming fair, even in unstructured negotiations. We also show that deceptive negotiators give-off signals of their malicious behavior, providing the opportunity for algorithms to detect and defeat this malicious tactic.