TY - BOOK

T1 - Mathematical models of human behavior

AU - Møllgaard, Anders Edsberg

PY - 2016

Y1 - 2016

N2 - During the last 15 years there has been an explosion in human behavioral data caused by theemergence of cheap electronics and online platforms. This has spawned a whole new researchfield called computational social science, which has a quantitative approach to the study ofhuman behavior. Most studies have considered data sets with just one behavioral variable suchas email communication. The Social Fabric interdisciplinary research project is an attemptto collect a more complete data set on human behavior by providing 1000 smartphones withpre-installed data collection software to students at the Technical University of Denmark.The data set includes face-to-face interaction (Bluetooth), communication (calls and texts),mobility (GPS), social network (Facebook), and general background information including apsychological profile (questionnaire).This thesis presents my work on the Social Fabric data set, along with work on otherbehavioral data. The overall goal is to contribute to a quantitative understanding of humanbehavior using big data and mathematical models. Central to the thesis is the determination ofthe predictability of different human activities. Upper limits are derived for communication,movement, and social proximity, along with upper limits on the predictability of the spatialdynamics. Furthermore, it is shown that different human activities have a strong impact oneach other and that human activity patterns follow a general linear principle throughout thepopulation.It is found that the type and strength of an interaction impacts the tendency for theinteraction to take place in a relation formed by two similar individuals. It is furthermoreshown that most relations are asymmetric in the sense that one part initiates communicationmuch more often than the other. Evidence is provided, which implies that the asymmetry iscaused by a self-enhancement in the initiation dynamics. These results have implications forthe formation of social networks and the dynamics of the links.It is shown that the Big Five Inventory (BFI) representing a psychological profile onlycorrelates weakly with the behavioral variables collected by the smartphone. In particular,the BFI almost does not impact our choice of friends, our interaction patterns, and our mobilitypatterns. The extraversion variable provides the only exception to this rule.The dynamics of online attention on Twitter is studied and the underlying stochasticdynamics is derived under a Markovian assumption. Finally, it is studied how the occurrenceof new aircraft incidents influence the memory of old aircraft incidents on Wikipedia. Newincidents are found to trigger a large flow of attention to old incidents, thereby implying thatinteractions between spreading processes are driving forces of attention dynamics.Overall, the thesis contributes to a quantitative understanding of a wide range of differenthuman behaviors by applying mathematical modeling to behavioral data. There can be nodoubt that such mathematical models can have great predictive power, but obviously thereare also limitations.

AB - During the last 15 years there has been an explosion in human behavioral data caused by theemergence of cheap electronics and online platforms. This has spawned a whole new researchfield called computational social science, which has a quantitative approach to the study ofhuman behavior. Most studies have considered data sets with just one behavioral variable suchas email communication. The Social Fabric interdisciplinary research project is an attemptto collect a more complete data set on human behavior by providing 1000 smartphones withpre-installed data collection software to students at the Technical University of Denmark.The data set includes face-to-face interaction (Bluetooth), communication (calls and texts),mobility (GPS), social network (Facebook), and general background information including apsychological profile (questionnaire).This thesis presents my work on the Social Fabric data set, along with work on otherbehavioral data. The overall goal is to contribute to a quantitative understanding of humanbehavior using big data and mathematical models. Central to the thesis is the determination ofthe predictability of different human activities. Upper limits are derived for communication,movement, and social proximity, along with upper limits on the predictability of the spatialdynamics. Furthermore, it is shown that different human activities have a strong impact oneach other and that human activity patterns follow a general linear principle throughout thepopulation.It is found that the type and strength of an interaction impacts the tendency for theinteraction to take place in a relation formed by two similar individuals. It is furthermoreshown that most relations are asymmetric in the sense that one part initiates communicationmuch more often than the other. Evidence is provided, which implies that the asymmetry iscaused by a self-enhancement in the initiation dynamics. These results have implications forthe formation of social networks and the dynamics of the links.It is shown that the Big Five Inventory (BFI) representing a psychological profile onlycorrelates weakly with the behavioral variables collected by the smartphone. In particular,the BFI almost does not impact our choice of friends, our interaction patterns, and our mobilitypatterns. The extraversion variable provides the only exception to this rule.The dynamics of online attention on Twitter is studied and the underlying stochasticdynamics is derived under a Markovian assumption. Finally, it is studied how the occurrenceof new aircraft incidents influence the memory of old aircraft incidents on Wikipedia. Newincidents are found to trigger a large flow of attention to old incidents, thereby implying thatinteractions between spreading processes are driving forces of attention dynamics.Overall, the thesis contributes to a quantitative understanding of a wide range of differenthuman behaviors by applying mathematical modeling to behavioral data. There can be nodoubt that such mathematical models can have great predictive power, but obviously thereare also limitations.

UR - https://soeg.kb.dk/permalink/45KBDK_KGL/fbp0ps/alma99122654362705763

M3 - Ph.D. thesis

BT - Mathematical models of human behavior

PB - The Niels Bohr Institute, Faculty of Science, University of Copenhagen

ER -