2 edition of Effect of social interaction on human probabilistic inference found in the catalog.
Effect of social interaction on human probabilistic inference
by Krannert Graduate School of Industrial Administration, Purdue University in West Lafayette, Ind
Written in English
Bibliography: p. 19-21.
|Statement||by Herbert Moskowitz and Willibrord T. Silva.|
|Series||Institute for Research in the Behavioral, Economic, and Management Sciences. Paper no. 397|
|Contributions||Silva, Willibrord T., joint author.|
|LC Classifications||HD6483 .P8 no. 397, HD69.D4 .P8 no. 397|
|The Physical Object|
|Pagination||28, 9 p.|
|Number of Pages||28|
|LC Control Number||73621973|
Mobile Human Network Management and Recommendation by Probabilistic Social Mining Jun-Ki Min and Sung-Bae Cho Abstract—Recently, inferring or sharing of mobile contexts has been actively investigated as cell phones have become more than a communication device. However, most of . Probabilistic Inference of Unknown Locations Exploiting Collective Behavior when Individual Data is Scarce of mobile phone and social media data, among other sources, has concerned with modeling and predicting human movement . More recently, the increasing availability of .
have been proposed for inference, none provides correct results. These packages fail to address the new conceptual as well as analytic challenges induced by the dimension of human interaction. Take, for example, the social network density, a popular index of connections between subjects in a social network Wasserman & Faust . Human performance in probabilistic inference. Subjects integrated probabilistic information from both prior and cue in our task, but rarely exhibited the signature of full ‘synergistic integration’, i.e. a performance above that which could be obtained by using either the prior or the cue alone (see Figure 5). However, unlike most studies Cited by:
Abstract. Robots’ increased autonomous capabilities necessitate human-robot communication. Exploration of this communication, including the pattern of and the content of such, is relevant to the development of these robots and the understanding of how Author: Shan G. Lakhmani, Julia L. Wright, Michael Schwartz, Daniel Barber. we are in an interactive social setting, our brains perform Bayesian inference of the intentions and cooperativeness of others using probabilistic representations. We em-ploy the framework of partially observable Markov decision processes (POMDPs) to model human decision making in a social context, focusing speciﬁcally on theCited by: 4.
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Intelligent Human-Machine Interaction Based on Dynamic Bayesian Networks Probabilistic Intention Recognition Article (PDF Available) in Journal of Intelligent and Robotic Systems 45(1) Author: Karim Tahboub.
Interaction (HRI), since the robot’s reactions ideally depend on the underlying inten-tionof the human’s action, including the others’ goal, target, desire, and plan (Simon, ). Human beings rely heavily on the skill of intention inference (for example, in sports, games, and social interaction) and can improve the ability of intent.
This paper addresses NSense, a tool that has been developed to capture and to infer social interaction patterns aiming to assist in the promotion of social well-being.
In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive).
Although commonly thought of in terms of causal relationships, the concept of an interaction can. This raises the question whether in a probabilistic inference task, in which people often need to deal with social and non-social information, denoting information as social will increase the probability that people search for this information and give it a greater weight in the decision by: 1.
tion to the target variable for indirect inference. Thus, it is neither local nor direct. These two features also change the third and fourth aspects of a local MM. Probabilistic inference is part of the cognitive process, and uncertainty is part of the outcome. Reference Class We use Brunswik's (, p.
The book considers the implications of this work, and the wider “probabilistic turn” in cognitive science and artiﬁcial intelligence, for understanding human rationality. Keywords:Bayes’theorem,conditional inference,logic,non-monotonic reasoning,probability, rationalanalysis,rationality,reasoning, selection task.
The book serves as a model for social scientific research. The authors first develop a probabilistic behavioral social choice theory that generalizes deterministic social choice theory, which has dominated thinking in the field.
This alone represents a major contribution. Regenwetter et al. then creatively use survey data to test their by: If, however, the correct model is considered in which there are the required interaction effects, the relationship will produce a perfect fit.
20 Thus, a misspecification of a deterministic relationship can easily lead a researcher to think that there is a probabilistic relationship between the cause and by: Graphical Models for Recognizing Human Interactions respond to the probability of state St at time t in one chain given that the other chain -denoted hereafter by superscript I - was in state S~_l at time t - 1.
These new probabilities express the causal influence (coupling) of one chain to the other. The Many Facets of the Cause-Effect Relation Christopher Khoo, Syin Chan & Yun Niu Researchers in social psychology have found that the human In view of the problem that a cause need not be necessary or sufficient for its effect, the concept of probabilistic causation has gained popularity.
In this probabilistic view, an event of. On the Origins of Suboptimality in Human Probabilistic Inference Luigi Acerbi1,2*, Sethu Vijayakumar1, Daniel M. Wolpert3 1Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom, 2Doctoral Training Centre in.
Statistical inference Infant learning Probabilistic reasoning Single-event probability Action methodology abstract Reasoning under uncertainty is the bread and butter of everyday life. Many areas of psy-chology, from cognitive, developmental, social, to clinical, are interested in how individuals make inferences and decisions with incomplete.
SA5Q: Guidelines for Human-AI Interaction Besmira Nushi, Dan Weld, Saleema Amershi and Adam Fourney. Considerable research attention has focused on improving the raw performance of AI and ML systems, but much less on the best ways to facilitate effective human-AI interaction.
Schaffner offers four reasons for his conclusion: most of our behavior appears to be influenced by thousands of genes of very small effect; their action is conditioned by the environment; genes work through “partially probabilistic neural networks”; 1 and free will accords with our individual : David Lahti.
Abstract—Inference of human intention may be an essential step towards understanding human actions and is hence impor-tant for realizing efﬁcient human-robot interaction. In this paper, we propose the Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions.
"Bayesian Rationality: the probabilistic approach to human reasoning" () is a well laid out book, carefully and extensively referenced. This adds to the frustration in that I am left with a sense that Bayesianism, like phenomenology, makes lots of promises that fall short no matter how enthusiastically they are by: A social experiment is a kind of psychological or sociological research for testing a people’s reaction to certain situations or events.
The experiment relies on a particular social approach, when a main source of information is people with their own knowledge and point of view. To carry out a social experiment, specialists usually divide participants into two groups — active participants.
In fact, it may be the case that human learners, along with other animals (Behrend and Bitterman,Yang and Shadlen, ) have an innate sensitivity for probabilities and the capacity for probabilistic inference.
Though many dual-process frameworks of human cognition suggest that heuristic reasoning should be more prevalent early in Cited by: A Bayesian network is a probabilistic representation for uncertain relationships, which has proven to be useful for modeling real-world problems.
When there are many potential causes of a given effect, however, both probability assessment and inference using a Bayesian network can be difficult. In this paper, we describe causal independence, a collection of conditional independence [ ]Cited by:.
International Journal of Humanities and Social Science Vol. 1 No. 11 [Special Issue – August ] As soon as human understanding observes an event, it moves to the idea of another thing that precedes or follows it because it presumes events in a cause-effect.
Neither the controlled direct effect (i.e. the effect of T on Z if M is set to the same value for everybody) nor the natural direct effect (i.e.
the effect of T on Z if M is set to the value it would naturally take in the absence of intervention on T) is generally a function of the average effects you would learn through separately.Abstract: In this book, we explore the probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models.
In particular, we examine how a broad range of empirical phenomena in cognitive science (including intuitive physics, concept learning, causal reasoning, social cognition, and.