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"Human behavior Mathematical models."
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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
Book
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.
eBook
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.
Book
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.
Journal Article
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.
eBook
Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions
Understanding pedestrian proxemic utility and trust will help autonomous vehicles to plan and control interactions with pedestrians more safely and efficiently. When pedestrians cross the road in front of human-driven vehicles, the two agents use knowledge of each other’s preferences to negotiate and to determine who will yield to the other. Autonomous vehicles will require similar understandings, but previous work has shown a need for them to be provided in the form of
continuous
proxemic utility functions, which are not available from previous proxemics studies based on Hall’s
discrete
zones. To fill this gap, a new Bayesian method to infer continuous pedestrian proxemic utility functions is proposed, and related to a new definition of ‘physical trust requirement’ (PTR) for road-crossing scenarios. The method is validated on simulation data then its parameters are inferred empirically from two public datasets. Results show that pedestrian proxemic utility is best described by a hyperbolic function, and that trust by the pedestrian is required in a discrete ‘trust zone’ which emerges naturally from simple physics. The PTR concept is then shown to be capable of generating and explaining the empirically observed zone sizes of Hall’s discrete theory of proxemics.
Journal Article
Mathematical Assessment of the Role of Human Behavior Changes on SARS-CoV-2 Transmission Dynamics in the United States
The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020–June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. This study suggests that, as more newly-infected individuals become asymptomatically-infectious, the overall level of positive behavior change can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).
Journal Article
SOCRATES-CoMix: a platform for timely and open-source contact mixing data during and in between COVID-19 surges and interventions in over 20 European countries
Background
SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections.
Methods
Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries.
We provide these timely, repeated observations using an online platform: SOCRATES-CoMix. In addition to providing cleaned datasets to researchers, the platform allows users to extract contact matrices that can be stratified by age, type of day, intensity of the contact and gender. These observations provide insights on the relative impact of recommended or imposed social distance measures on contacts and can inform mathematical models on epidemic spread.
Conclusion
These data provide essential information for policymakers to balance non-pharmaceutical interventions, economic activity, mental health and wellbeing, during vaccine rollout.
Journal Article
Dengue virus infection modifies mosquito blood-feeding behavior to increase transmission to the host
Mosquito blood-feeding behavior is a key determinant of the epidemiology of dengue viruses (DENV), the most-prevalent mosquitoborne viruses. However, despite its importance, how DENV infection influences mosquito blood-feeding and, consequently, transmission remains unclear. Here, we developed a high-resolution, video-based assay to observe the blood-feeding behavior of Aedes aegypti mosquitoes on mice. We then applied multivariate analysis on the high-throughput, unbiased data generated from the assay to ordinate behavioral parameters into complex behaviors. We showed that DENV infection increases mosquito attraction to the host and hinders its biting efficiency, the latter resulting in the infected mosquitoes biting more to reach similar blood repletion as uninfected mosquitoes. To examine how increased biting influences DENV transmission to the host, we established an in vivo transmission model with immuno-competent mice and demonstrated that successive short probes result in multiple transmissions. Finally, to determine how DENV-induced alterations of host-seeking and biting behaviors influence dengue epidemiology, we integrated the behavioral data within a mathematical model. We calculated that the number of infected hosts per infected mosquito, as determined by the reproduction rate, tripled when mosquito behavior was influenced by DENV infection. Taken together, this multidisciplinary study details how DENV infection modulates mosquito blood-feeding behavior to increase vector capacity, proportionally aggravating DENV epidemiology. By elucidating the contribution of mosquito behavioral alterations on DENV transmission to the host, these results will inform epidemiological modeling to tailor improved interventions against dengue.
Journal Article
Modelling the scaling properties of human mobility
Individual human trajectories are characterized by fat-tailed distributions of jump sizes and waiting times, suggesting the relevance of continuous-time random-walk (CTRW) models for human mobility. However, human traces are barely random. Given the importance of human mobility, from epidemic modelling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile-phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model accounts for the empirically observed scaling laws, but also allows us to analytically predict most of the pertinent scaling exponents.
Extensive data sets of trajectories of mobile-phone users provide a new basis for modelling human mobility. Random-walk models can capture some aspects, but go only so far. Now, two governing principles for human mobility are proposed, exploration and preferential return, paving the way to a more appropriate microscopic model for individual human motion.
Journal Article