A Dynamic Strategy Coach for Effective Negotiation

Zhou, Yiheng; He, He; Black, Alan W.; Tsvetkov, Yulia

doi:10.48550/arxiv.1909.13426

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…This literature explores conversations when an agent tries to manipulate her adversary (Lewis et al 2017). Such a setting has been barely explored but is particularly relevant for gauging information proactively, persuading, negotiating against by hiding information (Cuayá huitl et al, 2015) or learning and thus playing tactics (Zhou et al, 2019), and even deceiving other players (Lewis et al, 2017). A growing interest in the studies of strategic conversation also explores reasoning together with strategy generation (Lewis et al, 2017).…”

Section: Conversation Analytics: Literature Foundationmentioning

confidence: 99%

The Prompt-a-thon: Designing a Format for Value Co-Creation with Generative AI for Research and Practice

Kučević,

Von Brackel-Schmidt,

Lewandowski

et al. 2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Strategic conversations involve one party with an informational advantage and the other with an interest in the information. This paper proposes machinelearning based measures to quantify the degrees of evasiveness and incoherence of the informed party during real-time strategic conversations. The specific empirical context is the questions and answers (Q&A) part of earnings conference calls during which managers endure high pressure as they face analysts' scrutinizing questions. Being reluctant to disclose adverse information, managers may resort to evasive answers and sometimes respond less coherently due to increased cognitive load. Using data from the earnings calls of the S&P 500 companies from 2006 to 2018, we show that the proposed measures predict worse nextquarter earnings. Moreover, the stock market perceives incoherence as a negative signal. This paper contributes methodologically by developing two novel machinepowered measures to automatically evaluate behavioral cues during real-time strategic conversations. The proposed analytical tools are particularly beneficial to resource-constrained and informationally disadvantaged parties such as retail investors who may not be able to effectively trade on signals buried deep in unstructured conversational data.

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Section: Conversation Analytics: Literature Foundationmentioning

confidence: 99%

The Prompt-a-thon: Designing a Format for Value Co-Creation with Generative AI for Research and Practice

Kučević,

Von Brackel-Schmidt,

Lewandowski

et al. 2024

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Selecting the appropriate dialogue act and negotiating approach is essential for generating appropriate responses. The majority of earlier research on negotiating dialogues really concentrated on improving conversation techniques, ranging from high-level task-specific strategies (Lewis et al, 2017) to more precise task execution planning (He et al, 2018), to fine-grained planning of language outputs given strategic options (Zhou et al, 2019a). Joshi et al (2021) proposed a framework for modeling intricate negotiation strategies that makes use of intermediate structures and Graph Attention Networks (GAT) to make the model understandable.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Aided Graph Attentive Contextualization for Task Oriented Negotiation Dialogue Generation

Raut,

Saha,

Maitra

et al. 2023

Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacifi

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Over the past several years, the demand and popularity of using virtual assistants to finish jobs like service scheduling and online shopping have increased. While keeping the user's request in mind, an effective task-oriented virtual agent must strive to improve the seller's profit. Therefore, in order to achieve the best possible trade-off between the parties, this form of virtual agent has to have strong negotiating abilities. Although current conversational agents are quite good at making fluent sentences, they are still unable to use strategic thinking. In order to more effectively contextualize the choice of the next set of negotiation methods while producing answers, we develop Nego-GAT, an end-to-end negotiation system that includes sentiment information and graph attention embedding into GPT-2. Our selfsupervised model outperforms earlier cuttingedge negotiation models in terms of both the precision of strategy/dialogue act prediction and the caliber of the generated dialogue responses 1 .

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A Dynamic Strategy Coach for Effective Negotiation

Cited by 2 publications

References 16 publications

The Prompt-a-thon: Designing a Format for Value Co-Creation with Generative AI for Research and Practice

The Prompt-a-thon: Designing a Format for Value Co-Creation with Generative AI for Research and Practice

Sentiment Aided Graph Attentive Contextualization for Task Oriented Negotiation Dialogue Generation

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