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45,273 result(s) for "Human behavior models."
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Modeling Human Behaviors in Psychology Using Engineering Methods
The main purpose of the work is to showcase the interdisciplinary engineering approaches in modeling and understanding human behaviors during interpersonal interactions those that could be typical, distressed, or atypical. The ability to measure human behaviors quantitatively has been a core component and a major research direction in both fields of engineering and psychology - though often with distinct approaches designed for different targeted applications. Engineering methods often strive to achieve high predictive accuracies using behavioral informatics techniques; these techniques employ a combination of behavior measures derived using automated signal based descriptors, and of statistical frameworks modeled using machine learning techniques. These approaches are often distinct from the observational approaches the gold standard for the past three decades in the study of psychology, even in clinical settings. The observational approaches are largely based on human subjective judgments.
Mind force
Connections between genes and molecules, neurons and hormones, thinking and language, people and organizations create a continuous flow of synchronized interactions. These intermingled interactions form dynamical networks across many scales, from molecular, to biological, to cognitive and social. In a sequence of cycles, the reader is guided in this heterogeneous hypernetwork to discover the fields and landscapes of Mind Force. Mind, brain, body and society emerge from the same stream through the complexity of nature: the energy of Mind Force and human attractions.
Outnumbered : from Facebook and Google to fake news and filter-bubbles - the algorithms that control our lives
\"In this book, David Sumpter takes an algorithm-strewn journey to the dark side of mathematics. He investigates the equations that analyse us., influence us and will (maybe) become like us, answering questions such as: Are Google algorithms racist and sexist? ; Why do election predictions fall so drastically? ; What does the future hold as we relinquish our decision-making to machines? Featuring interviews with those working at the cutting edge of algorithm research, along with a healthy dose of mathematical self-experiment, Outnumbered will explain how mathematics and statistics work in the real world, and what we should and shouldn't worry about.\"--from book cover
A new approach to identifying the drivers of regulation compliance using multivariate behavioural models
Compares seven multivariate behavioural models of fisher compliance decisions using structural equation modelling. Rests the ability of each model to best predict recreational fishers' compliance with two fishing regulations (daily and size limits). Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.
Algorithms to live by : the computer science of human decisions
Explores \"how the algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of memory, [this book] transforms the wisdom of computer science into strategies for human living\"--Amazon.com.
Planning for cars that coordinate with people: leveraging effects on human actions for planning and active information gathering over human internal state
Traditionally, autonomous cars treat human-driven vehicles like moving obstacles. They predict their future trajectories and plan to stay out of their way. While physically safe, this results in defensive and opaque behaviors. In reality, an autonomous car’s actions will actually affect what other cars will do in response, creating an opportunity for coordination. Our thesis is that we can leverage these responses to plan more efficient and communicative behaviors. We introduce a formulation of interaction with human-driven vehicles as an underactuated dynamical system, in which the robot’s actions have consequences on the state of the autonomous car, but also on the human actions and thus the state of the human-driven car. We model these consequences by approximating the human’s actions as (noisily) optimal with respect to some utility function. The robot uses the human actions as observations of her underlying utility function parameters. We first explore learning these parameters offline, and show that a robot planning in the resulting underactuated system is more efficient than when treating the person as a moving obstacle. We also show that the robot can target specific desired effects, like getting the person to switch lanes or to proceed first through an intersection. We then explore estimating these parameters online, and enable the robot to perform active information gathering: generating actions that purposefully probe the human in order to clarify their underlying utility parameters, like driving style or attention level. We show that this significantly outperforms passive estimation and improves efficiency. Planning in our model results in coordination behaviors: the robot inches forward at an intersection to see if can go through, or it reverses to make the other car proceed first. These behaviors result from the optimization, without relying on hand-coded signaling strategies. Our user studies support the utility of our model when interacting with real users.
Human Factors Methods and Accident Analysis
Human Factors Methods and Accident Analysis is the first book to offer a practical guide for investigators, practitioners and researchers wishing to apply accident analysis methods. It is also unique in presenting a series of novel applications of accident analysis methods, including HF methods not previously used for these purposes (e.g. EAST, critical path analysis), as well as applications of methods in new domains.