31,096 research outputs found
Some Remarks about the Complexity of Epidemics Management
Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that
the assumptions underlying the established theory of epidemics management are
too idealistic. For an improvement of procedures and organizations involved in
fighting epidemics, extended models of epidemics management are required. The
necessary extensions consist in a representation of the management loop and the
potential frictions influencing the loop. The effects of the non-deterministic
frictions can be taken into account by including the measures of robustness and
risk in the assessment of management options. Thus, besides of the increased
structural complexity resulting from the model extensions, the computational
complexity of the task of epidemics management - interpreted as an optimization
problem - is increased as well. This is a serious obstacle for analyzing the
model and may require an additional pre-processing enabling a simplification of
the analysis process. The paper closes with an outlook discussing some
forthcoming problems
Fragility of the Commons under Prospect-Theoretic Risk Attitudes
We study a common-pool resource game where the resource experiences failure
with a probability that grows with the aggregate investment in the resource. To
capture decision making under such uncertainty, we model each player's risk
preference according to the value function from prospect theory. We show the
existence and uniqueness of a pure Nash equilibrium when the players have
heterogeneous risk preferences and under certain assumptions on the rate of
return and failure probability of the resource. Greater competition, vis-a-vis
the number of players, increases the failure probability at the Nash
equilibrium; we quantify this effect by obtaining bounds on the ratio of the
failure probability at the Nash equilibrium to the failure probability under
investment by a single user. We further show that heterogeneity in attitudes
towards loss aversion leads to higher failure probability of the resource at
the equilibrium.Comment: Accepted for publication in Games and Economic Behavior, 201
Control theoretic models of pointing
This article presents an empirical comparison of four models from manual control theory on their ability to model targeting behaviour by human users using a mouse: McRuer’s Crossover, Costello’s Surge, second-order lag (2OL), and the Bang-bang model. Such dynamic models are generative, estimating not only movement time, but also pointer position, velocity, and acceleration on a moment-to-moment basis. We describe an experimental framework for acquiring pointing actions and automatically fitting the parameters of mathematical models to the empirical data. We present the use of time-series, phase space, and Hooke plot visualisations of the experimental data, to gain insight into human pointing dynamics. We find that the identified control models can generate a range of dynamic behaviours that captures aspects of human pointing behaviour to varying degrees. Conditions with a low index of difficulty (ID) showed poorer fit because their unconstrained nature leads naturally to more behavioural variability. We report on characteristics of human surge behaviour (the initial, ballistic sub-movement) in pointing, as well as differences in a number of controller performance measures, including overshoot, settling time, peak time, and rise time. We describe trade-offs among the models. We conclude that control theory offers a promising complement to Fitts’ law based approaches in HCI, with models providing representations and predictions of human pointing dynamics, which can improve our understanding of pointing and inform design
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