Assessing Common Errors Students Make When Negotiating (bibtex)
by Emmanuel Johnson, Sarah Roediger, Gale Lucas, Jonathan Gratch
Abstract:
Research has shown that virtual agents can be effective tools for teaching negotiation. Virtual agents provide an opportunity for students to practice their negotiation skills which leads to better outcomes. However, these negotiation training agents often lack the ability to understand the errors students make when negotiating, thus limiting their effectiveness as training tools. In this article, we argue that automated opponent-modeling techniques serve as effective methods for diagnosing important negotiation mistakes. To demonstrate this, we analyze a large number of participant traces generated while negotiating with a set of automated opponents. We show that negotiators’ performance is closely tied to their understanding of an opponent’s preferences. We further show that opponent modeling techniques can diagnose specific errors including: failure to elicit diagnostic information from an opponent, failure to utilize the information that was elicited, and failure to understand the transparency of an opponent. These results show that opponent modeling techniques can be effective methods for diagnosing and potentially correcting crucial negotiation errors.
Reference:
Assessing Common Errors Students Make When Negotiating (Emmanuel Johnson, Sarah Roediger, Gale Lucas, Jonathan Gratch), In Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents - IVA '19, ACM Press, 2019.
Bibtex Entry:
@inproceedings{johnson_assessing_2019,
	address = {Paris, France},
	title = {Assessing {Common} {Errors} {Students} {Make} {When} {Negotiating}},
	isbn = {978-1-4503-6672-4},
	url = {http://dl.acm.org/citation.cfm?doid=3308532.3329470},
	doi = {10.1145/3308532.3329470},
	abstract = {Research has shown that virtual agents can be effective tools for teaching negotiation. Virtual agents provide an opportunity for students to practice their negotiation skills which leads to better outcomes. However, these negotiation training agents often lack the ability to understand the errors students make when negotiating, thus limiting their effectiveness as training tools. In this article, we argue that automated opponent-modeling techniques serve as effective methods for diagnosing important negotiation mistakes. To demonstrate this, we analyze a large number of participant traces generated while negotiating with a set of automated opponents. We show that negotiators’ performance is closely tied to their understanding of an opponent’s preferences. We further show that opponent modeling techniques can diagnose specific errors including: failure to elicit diagnostic information from an opponent, failure to utilize the information that was elicited, and failure to understand the transparency of an opponent. These results show that opponent modeling techniques can be effective methods for diagnosing and potentially correcting crucial negotiation errors.},
	booktitle = {Proceedings of the 19th {ACM} {International} {Conference} on {Intelligent} {Virtual} {Agents}  - {IVA} '19},
	publisher = {ACM Press},
	author = {Johnson, Emmanuel and Roediger, Sarah and Lucas, Gale and Gratch, Jonathan},
	month = jul,
	year = {2019},
	keywords = {Virtual Humans, UARC},
	pages = {30--37}
}
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