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Negotiation requires dynamically balancing self-interest and cooperation within the flow of conversation to maximize one’s own utility. Yet, existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. To address this, we introduce principle-driven negotiation agents, powered by ASTRA, a novel framework for turn-level offer optimization grounded in two core principles: opponent modeling and Tit-for-Tat reciprocity. ASTRA operates in three stages: (1) interpreting counterpart behavior, (2) optimizing counteroffers via a tool-integrated action with a linear programming (LP) solver, and (3) selecting offers based on strategy assessment and the partner’s acceptance probability. Through simulations and human evaluations, our agent effectively adapts to an opponent’s shifting stance and achieves favorable outcomes through enhanced adaptability and strategic reasoning. Beyond enhancing negotiation performance, it also serves as a powerful coaching tool, offering interpretable strategic feedback and optimal offer recommendations beyond human bounded rationality, with its potential further validated through human evaluation. Code is available at https://github.com/DSincerity/ASTRA-NegoAgent. 

In conflict, people use emotional expressions to shape their counterparts’ thoughts, feelings, and actions. This paper explores whether automatic text emotion recognition offers insight into this influence in the context of dispute resolution. Prior work has shown the promise of such methods in negotiations; however, disputes evoke stronger emotions and different social processes. We use a large corpus of buyer-seller dispute dialogues to investigate how emotional expressions shape subjective and objective outcomes. We further demonstrate that large-language models yield considerably greater explanatory power than previous methods for emotion intensity annotation and better match the decisions of human annotators. Findings support existing theoretical models for how emotional expressions contribute to conflict escalation and resolution and suggest that agent-based systems could be useful in managing disputes by recognizing and potentially mitigating emotional escalation. 

We present KODIS, a dyadic dispute resolution corpus containing thousands of dialogues from over 75 countries. Motivated by a theoretical model of culture and conflict, participants engage in a typical customer service dispute designed by experts to evoke strong emotions and conflict. The corpus contains a rich set of dispositional, process, and outcome measures. The initial analysis supports theories of how anger expressions lead to escalatory spirals and highlights cultural differences in emotional expression. We make this corpus and data collection framework available to the community, 

Social touch is a common method of communication between individuals, but touch cues alone provide only a glimpse of the entire interaction. Visual and auditory cues are also present in these interactions, and increase the expressiveness and recognition of the conveyed information. However, most mediated touch interactions have focused on providing only haptic cues to the user. Our research addresses this gap by adding visual cues to a mediated social touch interaction through an array of LEDs attached to a wearable device. This device consists of an array of voice-coil actuators that present normal force to the user’s forearm to recreate the sensation of social touch gestures. We conducted a human subject study (N = 20) to determine the relative importance of the touch and visual cues. Our results demonstrate that visual cues, particularly color and pattern, significantly enhance perceived realism, as well as alter perceived touch intensity, valence, and dominance of the mediated social touch. These results illustrate the importance of closely integrating multisensory cues to create more expressive and realistic virtual interactions. 

The intelligent virtual agent community often works from the assumption that embodiment confers clear benefits to human-machine interaction. However, embodiment has potential drawbacks in highlighting the salience of social stereotypes such as those around race and gender. Indeed, theories of computer-mediated communication highlight that visual anonymity can sometimes enhance team outcomes. Negotiation is one domain where social perceptions can impact outcomes. For example, research suggests women perform worse in negotiations and find them more aversive, particularly when interacting with men opponents. Research with human participants makes it challenging to unpack whether these negative consequences stem from women’s perceptions of their partner or greater toughness on the part of these men opponents. We use a socially intelligent AI negotiation agent to begin to unpack these processes. We manipulate the perceived toughness of the AI by whether or not it expresses anger — a common tactic to extract concessions. Independently, we manipulate the activation of stereotypes by randomly setting whether the interaction has embodiment (as a male opponent) or has only text (where we obscure gender cues). We find a clear interaction between gender and embodiment. Specifically, women perform worse, and men perform better against an apparently male opponent compared to a disembodied agent – as measured by the subjective value they assign to their outcome. This highlights the potential disadvantages of embodiment in negotiation, though future research must rule out alternative mechanisms that might explain these results. 

Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner’s dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing. 

What should we do with emotion AI? Should we regulate, ban, promote, or re-imagine it? Emotion AI, a class of affective computing technologies used in personal and social computing, comprises emergent and controversial techniques aiming to classify human emotion and other affective phenomena. Industry, policy, and scientific actors debate potential benefits and harms, arguing for polarized futures ranging from panoptic expansion to complete bans. Emotion AI is proposed, deployed, and sometimes withdrawn in collaborative contexts such as education, hiring, healthcare, and service work. Proponents expound these technologies’ benefits for well-being and security, while critics decry privacy harms, civil liberties risks, bias, and shaky scientific foundations, and gaps between technologies’ capabilities and how they are marketed and legitimized. This panel brings diverse disciplinary perspectives into discussion about the history of emotions—as an example of ’intimate’ data—in computing, how emotion AI is legitimized, people’s experiences with and perceptions of emotion AI in social and collaborative settings, emotion AI’s development practices, and using design research to re-imagine emotion AI. These issues are relevant to the CSCW community in designing, evaluating, and regulating algorithmic sensing technologies including and beyond emotion-sensing.