Modeling User Perception of Interaction Opportunities for Effective Teamwork

This paper presents a model of collaborative decision-making for groups that involve people and computer agents. The model distinguishes between actions relating to participantspsila commitment to the group and actions relating to their individual tasks, uses this distinction to decompose group decision making into smaller problems that can be solved efficiently. It allows computer agents to reason about the benefits of their actions on a collaboration and the ways in which human participants perceive these benefits. The model was tested in a setting in which computer agents need to decide whether to interrupt people to obtain potentially valuable information. Results show that the magnitude of the benefit of interruption to the collaboration is a major factor influencing the likelihood that people will accept interruption requests. They further establish that peoplepsilas perceived type of their partners (whether humans or computers) significantly affected their perceptions of the usefulness of interruptions when the benefit of the interruption is not clear-cut. These results imply that system designers need to consider not only the possible benefits of interruptions to collaborative human-computer teams but also the way that such benefits are perceived by people.Engineering and Applied Science

Collective decision making by rational individuals

The patterns and mechanisms of collective decision making in humans and animals have attracted both empirical and theoretical attention. Of particular interest has been the variety of social feedback rules and the extent to which these behavioral rules can be explained and predicted from theories of rational estimation and decision making. However, models that aim to model the full range of social information use have incorporated ad hoc departures from rational decision-making theory to explain the apparent stochasticity and variability of behavior. In this paper I develop a model of social information use and collective decision making by fully rational agents that reveals how a wide range of apparently stochastic social decision rules emerge from fundamental information asymmetries both between individuals and between the decision makers and the observer of those decisions. As well as showing that rational decision making is consistent with empirical observations of collective behavior, this model makes several testable predictions about how individuals make decisions in groups and offers a valuable perspective on how we view sources of variability in animal, and human, behavior

Cerebellar tDCS Dissociates the Timing of Perceptual Decisions from Perceptual Change in Speech

Neuroimaging studies suggest that the cerebellum might play a role in both speech perception and speech perceptual learning. However, it remains unclear what this role is: does the cerebellum directly contribute to the perceptual decision? Or does it contribute to the timing of perceptual decisions? To test this, we applied transcranial direct current stimulation (tDCS) to the right cerebellum during a speech perception task. Participants experienced a series of speech perceptual tests designed to measure and then manipulate their perception of a phonetic contrast. One group received cerebellar tDCS during speech perceptual learning and a different group received "sham" tDCS during the same task. Both groups showed similar learning-related changes in speech perception that transferred to a different phonetic contrast. For both trained and untrained speech perceptual decisions, cerebellar tDCS significantly increased the time it took participants to indicate their decisions with a keyboard press. The results suggest that cerebellar tDCS disrupted the timing of perceptual decisions, while leaving the eventual decision unaltered. In support of this conclusion, we use the drift diffusion model to decompose the data into processes that determine the outcome of perceptual decision-making and those that do not. The modeling suggests that cerebellar tDCS disrupted processes unrelated to decision-making. Taken together, the empirical data and modeling demonstrate that right cerebellar tDCS dissociates the timing of perceptual decisions from perceptual change. The results provide initial evidence in healthy humans that the cerebellum critically contributes to speech timing in the perceptual domain

The Interactive Decision-Making Model Developed through Integrating Three Christian Spiritualities

ABSTRACT The Interactive Decision-making Model Developed through Integrating Three Christian Spiritualities Jinchang Chen Doctor of Ministry School of Theology, Fuller Theological Seminary 2018 As a generation of Millennials enters adulthood, it faces a more rapidly changing society than any previous generation. Often, Millennials have to make decisions among many complex choices. Through offering a decision-making model, this project hopes to bring divine reality into people’s routine and empower believers to discern God’s presence in their lives. Therefore, the purpose of this doctoral project is to develop a three-step decision-making model emphasizing the interaction between humans and the divine to promote an intimate walking with God among young professionals in Southern California. To accomplish what is intended, this paper examines community needs in terms of ministry context, describes current issues and debates on divine guidance, and evaluates some of the pitfalls in contemporary decision-making practices. In addition, there is a literature review on three decision-making models: The Blueprint Model, The Wisdom Model, and The Relationship Model, presents the Evangelical views on this subject. Moreover, the spirituality of desert fathers, Ignatian spirituality, and the thoughts of Dallas Willard are studied for their contributions to divine guidance. Therefore, based on the integration of three Christian traditions, an alternative decision-making approach: The Interactive Model is proposed. This model lays out seven theological principles, provides practical guidelines, and explores different ways that God can be experienced in the decision-making process. To implement this model into a decision-making practice, this project provides a six-week teaching seminar as well as a two-day retreat to help participants discern God’s guidance. During the retreat, each is guided through a three-step practice: preparation, reflection, and confirmation. A group of ten-to-fifteen people is recruited from the Hill Church at Irvine for a pilot study, and the findings are reported in this paper. In the end, this model is also evaluated to see if it is applicable to other Christian groups

Several Consequences of Optimality

Rationality is frequently associated with making the best possible decisions. It's widely acknowledged that humans, as rational beings, have limitations in their decision-making capabilities. Nevertheless, recent advancements in fields, such as, computing, science and technology, combined with the availability of vast amounts of data, have sparked optimism that these developments could potentially expand the boundaries of human bounded rationality through the augmentation of machine intelligence. In this paper, findings from a computational model demonstrated that when an increasing number of agents independently strive to achieve global optimality, facilitated by improved computing power, etc., they indirectly accelerated the occurrence of the "tragedy of the commons" by depleting shared resources at a faster rate. Further, as agents achieve optimality, there is a drop in information entropy among the solutions of the agents. Also, clear economic divide emerges among agents. Considering, two groups, one as producer and the other (the group agents searching for optimality) as consumer of the highest consumed resource, the consumers seem to gain more than the producers. Thus, bounded rationality could be seen as boon to sustainability

Cognitive And Sensorimotor Interactions In Human Decision-Making Within A Virtual Environment

There is a long tradition of studying economic decision making, where humans often fail to maximise expected gain. More recently, attention has been directed towards decision making in mathematically equivalent sensorimotor tasks, where humans often approach maximum expected gain. But numerous everyday tasks have ‘cognitive’ and ‘sensorimotor’ costs. This raises a fundamental, but hitherto neglected research question about the factors that influence decision making when an economic choice has sensorimotor risks. We created a ‘game’ in virtual reality where participants needed to hit targets in order to win points. The game required participants to choose between two targets where one was easier to hit (closer and on permanent display) and the alternative was a harder-to-hit ‘risky’ target worth more points (further away and programmed to time-out). The time allowed to hit the ‘risky’ target was the median of the individual’s baseline trials. Participants deceased their movement time during the baseline trials so the risky targets were more likely to be hit than not regardless of their distance (this resulted in the risky targets having a higher expected gain with respect to the extrinsic reward). In Experiment 1, we found participants (n = 40) were motivated by the reward (so frequently selected the higher value target). Nevertheless, the behaviour was also influenced by the sensorimotor costs, such that participants were more likely to choose the safe option (despite this decreasing expected gain) when the high reward target (worth twice as many points) was further. We found gender differences whereby women were less likely to reach for the high reward target when it was further away. Subsequently, the same selection frequencies were found in two separate groups (both n = 40) despite the high reward target having three and five times more points than the safe option, suggesting that a sensorimotor cost threshold acts as an upper bound on the selection choice process. In Experiment 2, we added motor noise whilst keeping the expected gain constant and found that this manipulation did not affect decision making (i.e. we found same selection frequencies as in Experiment 1). In Experiment 3, we added perceptual noise and again found that this did not affect the decision making. Experiments 2 and 3 suggest that adults are well tuned to the costs of their sensorimotor actions. The data from all 200 participants showed a bias to: (i) select a risky target after a safe trial; (ii) select a risky target after a high reward target was hit (compared to when it was missed). These behavioural phenomena are well captured by a partially observable Markov decision process (pom-dp), and a pom-dp model was able to capture the behaviour by integrating extrinsic rewards and sensorimotor costs in a choice selection process. The pom-dp predicted that participants should increasingly select the risky target across multiple sessions, with the result that males and females should converge on similar selection rates across the different target distances. Experiment 4 tested this prediction with participants repeating the task across multiple sessions over three days. This resulted in an increased probability of the high reward target being selected, and by the end of the sessions the gender differences were not observed. The first four experiments always contained a known ‘safe’ target so Experiment 5 introduced a selection task where the choices needed to be made in a more dynamic fashion and there was not always an obvious ‘safe’ target. Experiment 5 confirmed that participants rapidly combine extrinsic rewards and sensorimotor costs in order to choose between targets on a trial-by-trial basis. Experiment 6 investigated decision making in younger children and showed that the combination of extrinsic rewards and sensorimotor costs occurs in even 7-8 year old children (though there was greater evidence of sub-optimal selections occurring on some trials when the age of the group was younger)

Analysis of hate speech detection in social media

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Maria Salamó Llorente[en] The presence of social networks has increased in our daily lives and have become platforms for sharing information. But, it also can be used for sending hate messages or for propagating false news. Users can take advantage of their anonymity to provide these toxic interactions. Furthermore, some groups of people (minorities) get disproportionately more targeted than the rest. This raises the problem of how to detect if a message contains hate speech. A solution could be the use of machine learning models that would be in charge of this decision. In addition, it could handle the enormous amount of texts interchanged daily. However, there are many approaches to tackle the problem, which are divided mainly into two groups. The first one is through the use of classical algorithms to extract information from the text. The other one is through the use of deep learning models that can understand some context that allows for better predictions. The main objectives of the project are the exploration and comparison of different types of models and techniques. The diverse models are trained with three distinct toxicity datasets, of two natural language processing competitions. Generally, the best performing model is BERT or SBERT, both models based on the deep learning approach, with metric scores much higher than any model based on the traditional methods. The results show the vast potential of Natural Language Processing for the detection of hate speech. Although the best models did not have a very high perplexity, a more reliable model could be trained with more training data or new architectures. Even at the current state, the models could be used as an external font for helping humans in the decision-making process. Moreover, these models could filter the most confident predictions while leaving the rest for the reviewer team