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This is an announcement of Mourad Heddaya's MS Presentation<br class="">
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Candidate: Mourad Heddaya<br class="">
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Date: Thursday, February 15, 2024<br class="">
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Time: 3 pm CST<br class="">
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Remote Location: <a href="https://uchicago.zoom.us/j/9486973491?pwd=VUpDaTlKZ1RaL2dKSlpjMUV3VVk5dz09" class="">https://uchicago.zoom.us/j/9486973491?pwd=VUpDaTlKZ1RaL2dKSlpjMUV3VVk5dz09</a><br class="">
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Location: JCL 390<br class="">
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Title: Language of Bargaining<br class="">
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Abstract: Leveraging an established exercise in negotiation education, we build a novel dataset for studying how the use of language shapes bilateral bargaining. Our dataset extends existing work in two ways: 1) we recruit participants via behavioral labs instead
of crowdsourcing platforms and allow participants to negotiate through audio, enabling more naturalistic interactions; 2) we add a control setting where participants negotiate only through alternating, written numeric offers. Despite the two contrasting forms
of communication, we find that the average agreed prices of the two treatments are identical. But when subjects can talk, fewer offers are exchanged, negotiations finish faster, the likelihood of reaching agreement rises, and the variance of prices at which
subjects agree drops substantially. We further propose a taxonomy of speech acts in negotiation and enrich the dataset with annotated speech acts. We set up prediction tasks to predict negotiation success and find that being reactive to the arguments of the
other party is advantageous over driving the negotiation.<br class="">
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Advisors: Chenhao Tan<br class="">
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Committee Members: Chenhao Tan, Mina Lee, and Alexander Zentefis<br class="">
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