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1. Understanding Miscommunication: Speech Act Recognition in Digital Contexts

   https://doi.org/10.1111/cogs.13023  

   Holtgraves, Thomas (October 2021, Cognitive Science)

   Abstract Successful language use requires accurate intention recognition. However, sometimes this can be undermined because communication occurs within an interpersonal context. In this research, I used a relatively large set of speech acts (n= 32) and explored how variability in their inherent face‐threat influences the extent to which they are successfully recognized by a recipient, as well as the confidence of senders and receivers in their communicative success. Participants in two experiments either created text messages (senders) designed to perform a specific speech act (e.g., agree) or interpreted those text messages (receivers) in terms of the specific speech act being performed. The speech acts were scaled in terms of their degree of face threat. In both experiments, speech acts that were more threatening were less likely to be correctly recognized than those that were less threatening. Additionally, the messages of the more threatening speech acts were longer and lower in clout than the less threatening speech acts. Senders displayed greater confidence in communicative success than receivers, but judgments of communicative success (for both senders and receivers) were unrelated to actual communicative success. The implications of these results for our understanding of actual communicative episodes are discussed. 

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2. Justified Communication Equilibrium

   https://doi.org/10.1257/aer.20201692  

   Clark, Daniel; Fudenberg, Drew (September 2021, American Economic Review)

   null (Ed.)

   Justified communication equilibrium (JCE) is an equilibrium refinement for signaling games with cheap-talk communication. A strategy profile must be a JCE to be a stable outcome of nonequilibrium learning when receivers are initially trusting and senders play many more times than receivers. In the learning model, the counterfactual “speeches” that have been informally used to motivate past refinements are messages that are actually sent. Stable profiles need not be perfect Bayesian equilibria, so JCE sometimes preserves equilibria that existing refinements eliminate. Despite this, it resembles the earlier refinements D1 and NWBR, and it coincides with them in co-monotonic signaling games. (JEL C70, D82, D83, J23, M51) 

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3. A virtual reality experiment on visual and auditory guidance for egress in road tunnel fires

   https://doi.org/10.1007/s10694-024-01668-9  

   Zhang, Yuxin; Kinateder, Max; Templeton, Anne; Warren, William H (November 2025, Fire Technology)

   Road tunnels are enclosed spaces that most occupants only experience while driving through them. In case of fire, however, occupants potentially need to evacuate on foot from a dangerous and unfamiliar environment. Clear and accurate guidance is important for an efficient and safe evacuation from tunnels. Common cues for evacuation guidance are a signage and audio messages that attract occupants to move on appropriate egress routes and avoid unsafe routes. This paper investigates how different types of visual and auditory signals influence occupants’ exit choices in a simulated tunnel evacuation. Common guidance cues were presented to participants in a mobile Head Mounted Display, and they were asked to choose between two possible exit doors in a simulated road tunnel. Two attracting cues (‘‘EXIT’’ signs, audio instructions), and two detracting cues (‘‘DO NOT ENTER’’ signs; traffic cones placed in front of an exit) were studied in three virtual reality (VR) experiments. In each experiment, the presence and direction of the cues were manipulated, and data from 20 participants were collected. Experiment 1 explored the effects of attracting cues, Experiment 2 detracting cues, and Experiment 3 the combination of attracting and detracting cues. Across all studies, participants tended to follow the guidance provided when there was only one cue. When several competing and even contradictory cues were present, participants were most likely to rely on audio instructions, followed by traffic cones and ‘‘DO NOT ENTER’’ signs, whereas ‘‘EXIT’’ signs were often disregarded. We conclude that participants tend to follow temporary cues that could carry current information, as opposed to permanently installed signage. Some corresponding suggestions are put forward on evacuation system design and strategic planning in a tunnel fire. 

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4. Multi-agent coordination via a shared wireless spectrum

   https://doi.org/10.1109/CDC.2017.8264671  

   Nowzari, Cameron (December 2017, IEEE Conference on Decision and Control)

   This paper considers a planar multi-agent coordination problem. Unlike other related works, we explicitly consider a globally shared wireless communication channel where individual agents must choose both a frequency and power to transmit their messages at. This problem is motivated by the pressing need for algorithms that are able to efficiently and reliably operate on overcrowded wireless networks or otherwise poor-performing RF environments. We develop a self-triggered coordination algorithm that guarantees convergence to the desired set of states with probability 1. The algorithm is developed by using ideas from event/self-triggered coordination and allows agents to autonomously decide for themselves when to broadcast information, at which frequency and power, and how to move based on information received from other agents in the network. Simulations illustrate our results. 

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5. Fairness Maximization among Offline Agents in Online-Matching Markets

   https://doi.org/10.1145/3569705  

   Ma, Will; Xu, Pan; Xu, Yifan (December 2022, ACM Transactions on Economics and Computation)

   Online matching markets (OMMs) are commonly used in today’s world to pair agents from two parties (whom we will call offline and online agents) for mutual benefit. However, studies have shown that the algorithms making decisions in these OMMs often leave disparities in matching rates, especially for offline agents. In this article, we propose online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fairness maximization. We derive further bounds based on fairness parameters, demonstrating conditions under which the competitive ratio can increase to 100%. There are two key ideas helping us break the barrier of 1-1/𝖾~ 63.2% for competitive ratio in online matching. One is boosting , which is to adaptively re-distribute all sampling probabilities among only the available neighbors for every arriving online agent. The other is attenuation , which aims to balance the matching probabilities among offline agents with different mass allocated by the benchmark LP. We conduct extensive numerical experiments and results show that our boosted version of sampling algorithms are not only conceptually easy to implement but also highly effective in practical instances of OMMs where fairness is a concern. 

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