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1. Too Many Cooks: Bayesian Inference for Coordinating Multi‐Agent Collaboration

   https://doi.org/10.1111/tops.12525  

   Wu, Sarah A. ; Wang, Rose E. ; Evans, James A. ; Tenenbaum, Joshua B. ; Parkes, David C. ; Kleiman‐Weiner, Max ( April 2021 , Topics in Cognitive Science)

   Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm, Bayesian Delegation outperforms alternatives. Bayesian Delegation is also a capable ad hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results argue for the centrality of ToM for successful decentralized multi‐agent collaboration.

    

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2. Showing versus doing: Teaching by demonstration

   Ho, M. K. ; Littman, M. L. ; MacGlashan, J. ; Cushman, F. ; Austerweil, J. L. ( January 2016 , NeurIPS)

   People often learn from others’ demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration has been less well studied computationally. Here, we develop a Bayesian model for teaching by demonstration. Stark differences arise when demonstrators are intentionally teaching (i.e. showing) a task versus simply performing (i.e. doing) a task. In two experiments, we show that human participants modify their teaching behavior consistent with the predictions of our model. Further, we show that even standard IRL algorithms benefit when learning from showing versus doing. 

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3. Motivating prosocial behavior by leveraging positive self‐regard through values affirmation

   https://doi.org/10.1111/jasp.12841  

   Schneider, Claudia R. ; Weber, Elke U. ( November 2021 , Journal of Applied Social Psychology)

   Prosocial behavior is critical to address global social, environmental, and economic challenges. Yet humans often do not act with the benefit of others in mind, especially when those others are distant and unknown. We suggest that a failure to act prosocially may stem in part from cognitive and emotional capacity limitations. Hence an intervention that reduces worry about the self and thereby increases available resources may foster prosociality. Starting from self‐affirmation theory which posits that affirming the self can establish self‐integrity, we propose that a values affirmation intervention can motivate prosociality through fostering positive self‐regard. Across two studies we find that, compared to control participants, affirmed participants display greater willingness to volunteer time and exhibit increased actual prosocial behavior by completing an unpaid study and donating real money to charity. As hypothesized, increases in positive self‐regard mediate the effect of values affirmation on prosocial behavioral intentions as well as behavior.

    

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4. Don’t Let Me Be Misunderstood:Comparing Intentions and Perceptions in Online Discussions

   https://doi.org/10.1145/3366423.3380273  

   Chang, Jonathan P. ; Cheng, Justin ; Danescu-Niculescu-Mizil, Cristian ( January 2020 , Proceedings of WWW)

   Discourse involves two perspectives: a person’s intention in making an utterance and others’ perception of that utterance. The misalignment between these perspectives can lead to undesirable outcomes, such as misunderstandings, low productivity and even overt strife. In this work, we present a computational framework for exploring and comparing both perspectives in online public discussions. We combine logged data about public comments on Facebook with a survey of over 16,000 people about their intentions in writing these comments or about their perceptions of comments that others had written. Unlike previous studies of online discussions that have largely relied on third-party labels to quantify properties such as sentiment and subjectivity, our approach also directly captures what the speakers actually intended when writing their comments. In particular, our analysis focuses on judgments of whether a comment is stating a fact or an opinion, since these concepts were shown to be often confused. We show that intentions and perceptions diverge in consequential ways. People are more likely to perceive opinions than to intend them, and linguistic cues that signal how an utterance is intended can differ from those that signal how it will be perceived. Further, this misalignment between intentions and perceptions can be linked to the future health of a conversation: when a comment whose author intended to share a fact is misperceived as sharing an opinion, the subsequent conversation is more likely to derail into uncivil behavior than when the comment is perceived as intended. Altogether, these findings may inform the design of discussion platforms that better promote positive interactions. 

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5. What Could Go Wrong: Adults and Children Calibrate Predictions and Explanations of Others' Actions Based on Relative Reward and Danger

   https://doi.org/10.1111/cogs.13163  

   Gjata, Nensi N. ; Ullman, Tomer D. ; Spelke, Elizabeth S. ; Liu, Shari ( June 2022 , Cognitive Science)

   When human adults make decisions (e.g., wearing a seat belt), we often consider the negative consequences that would ensue if our actions were to fail, even if we have never experienced such a failure. Do the same considerations guide our understanding of other people's decisions? In this paper, we investigated whether adults, who have many years of experience making such decisions, and 6‐ and 7‐year‐old children, who have less experience and are demonstrably worse at judging the consequences of their own actions, conceive others' actions as motivated both by reward (how good reaching one's intended goal would be), and by what we call “danger” (how badly one's action could end). In two pre‐registered experiments, we tested whether adults and 6‐ and 7‐year‐old children tailor their predictions and explanations of an agent's action choices to the specific degree of danger and reward entailed by each action. Across four different tasks, we found that children and adults expected others to negatively appraise dangerous situations and minimize the danger of their actions. Children's and adults' judgments varied systematically in accord with both the degree of danger the agent faced and the value the agent placed on the goal state it aimed to achieve. However, children did not calibrate their inferences abouthow muchan agent valued the goal state of a successful action in accord with the degree of danger the action entailed, and adults calibrated these inferences more weakly than inferences concerning the agent's future action choices. These results suggest that from childhood, people use a degree of danger and reward to make quantitative, fine‐grained explanations and predictions about other people's behavior, consistent with computational models on theory of mind that contain continuous representations of other agents' action plans.

    

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