scholarly journals Beyond Misperception–Two Types of Mental Model Errors in a Dynamic Decision Task

2021 ◽  
Author(s):  
Martin FG. Schaffernicht ◽  
Miguel López-Astorga ◽  
Cristian A. Rojas-Barahona ◽  
Ramón D. Castillo
Keyword(s):  
Mental Models ◽  
Goal Setting ◽  
Dynamic Systems ◽  
Behavioral Problems ◽  
Feedback Loop ◽  
Initial Conditions ◽  
Decision Task ◽  
Model Errors ◽  
Problematic Behaviors ◽  
Decision Policies

This article contributes to research on mental models and how they underpin decision policies. It proposes a framework for the joint use of mental models of dynamic systems and the theory of mental models initiated by Johnson–Laird and defines two types of errors: (1) misrepresentation of the system’s structure, and (2) failure to deploy relevant mental models of possibilities. We use a dynamic decision task based on Moxnes’ “reindeer experiment” to formulate three intuitive policies, their underlying mental models, and the reasoning, and evaluate the policies under varying initial conditions. Each of the policies generates problematic behaviors like dependance on initial conditions, underperformance because of flawed goal setting and oscillation due to leaving the delay in a feedback loop out of account. We identify errors of both types in the mental models and relate them to the behavioral problems. Limitations and questions for further research conclude the paper.

scholarly journals Trust motivation: The self-regulatory processes underlying trust decisions

2019 ◽  
Vol 9 (2-3) ◽  
pp. 99-123 ◽  
Author(s):  
Lisa van der Werff ◽  
Alison Legood ◽  
Finian Buckley ◽  
Antoinette Weibel ◽  
David de Cremer
Keyword(s):  
Goal Setting ◽  
Feedback Loop ◽  
Future Research ◽  
Two Phase ◽  
Cognitive Trust ◽  
Regulatory Processes ◽  
Dynamic View ◽  
Definition Of ◽  
And Behavior ◽  
And Control

Theorizing about trust has focused predominantly on cognitive trust cues such as trustworthiness, portraying the trustor as a relatively passive observer reacting to the attributes of the other party. Using self-determination and control theories of motivation, we propose a model of trust motivation that explores the intraindividual processes involved in the volitional aspects of trust decision-making implied by the definition of trust as a willingness to be vulnerable. We distinguish between intrinsic and extrinsic drivers of trust and propose a two-phase model of trust goal setting and trust regulation. Our model offers a dynamic view of the trusting process and a framework for understanding how trust cognition, affect and behavior interact over time. Furthermore, we discuss how trust goals may be altered or abandoned via a feedback loop during the trust regulation process. We conclude with a discussion of potential implications for existing theory and future research.

Motion Planning for Dynamic Systems Using First-Order Logic

2002 ◽  
Author(s):  
Lei Yan ◽  
K. Krishnamurthy
Keyword(s):  
Motion Planning ◽  
Dynamic Systems ◽  
Initial Conditions ◽  
Search Space ◽  
Order Logic ◽  
First Order Logic ◽  
Knowledge Representation And Reasoning ◽  
Know How ◽  
First Order ◽  
Knowledge Based

The problem of motion planning for a class of dynamic systems is considered in this study. A knowledge-based approach is used to determine the initial conditions that will yield a certain desired state of the dynamic system. The search space is limited by using a set of rules because reasoning about dynamic systems is basically searching an infinite space. In this study, first-order logic is used for knowledge representation and reasoning. The methodology is applied to playing a pool game. The dynamics of the motion of the balls are complicated and significant expertise is required to know how to strike the balls. Simulated results presented show how the rules help in finding the appropriate strategies for playing the game.

scholarly journals Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

2020 ◽  
Vol 142 (1-2) ◽  
pp. 393-406
Author(s):  
Zhongkai Bo ◽  
Xiangwen Liu ◽  
Weizong Gu ◽  
Anning Huang ◽  
Yongjie Fang ◽  
...  
Keyword(s):  
Indian Ocean ◽  
North Pacific ◽  
Western North Pacific ◽  
Initial Conditions ◽  
Intraseasonal Oscillation ◽  
Tropical Indian Ocean ◽  
Boreal Summer ◽  
Model Errors ◽  
Maritime Continent ◽  
Boreal Summer Intraseasonal Oscillation

Abstract In this paper, we evaluate the capability of the Beijing Climate Center Climate System Model (BCC-CSM) in simulating and forecasting the boreal summer intraseasonal oscillation (BSISO), using its simulation and sub-seasonal to seasonal (S2S) hindcast results. Results show that the model can generally simulate the spatial structure of the BSISO, but give relatively weaker strength, shorter period, and faster transition of BSISO phases when compared with the observations. This partially limits the model’s capability in forecasting the BSISO, with a useful skill of only 9 days. Two sets of hindcast experiments with improved atmospheric and atmosphere/ocean initial conditions (referred to as EXP1 and EXP2, respectively) are conducted to improve the BSISO forecast. The BSISO forecast skill is increased by 2 days with the optimization of atmospheric initial conditions only (EXP1), and is further increased by 1 day with the optimization of both atmospheric and oceanic initial conditions (EXP2). These changes lead to a final skill of 12 days, which is comparable to the skills of most models participated in the S2S Prediction Project. In EXP1 and EXP2, the BSISO forecast skills are improved for most initial phases, especially phases 1 and 2, denoting a better description for BSISO propagation from the tropical Indian Ocean to the western North Pacific. However, the skill is considerably low and insensitive to initial conditions for initial phase 6 and target phase 3, corresponding to the BSISO convection’s active-to-break transition over the western North Pacific and BSISO convection’s break-to-active transition over the tropical Indian Ocean and Maritime Continent. This prediction barrier also exists in many forecast models of the S2S Prediction Project. Our hindcast experiments with different initial conditions indicate that the remarkable model errors over the Maritime Continent and subtropical western North Pacific may largely account for the prediction barrier.

High Power Actuator Based on Magnetostrictive Composite Core with Temperature Drift Compensation

2014 ◽  
Vol 598 ◽  
pp. 69-74 ◽  
Author(s):  
Jerzy Kaleta ◽  
Krzysztof Kot ◽  
Rafał Mech ◽  
Przemyslaw Wiewiorski
Keyword(s):  
Magnetic Field ◽  
Pid Control ◽  
Feedback Loop ◽  
Initial Conditions ◽  
Signal Generation ◽  
Temperature Drift ◽  
Fiber Bragg Grating Sensors ◽  
The Core ◽  
Drift Compensation ◽  
Initial Settings

The paper presents an actuator based on a coil placed in the casing, with specially prepared connection rods. The construction allows installation of the fiber Bragg grating sensors inside the coil. It allows to measure deformation of the composite that is located in the core of the coil. Thanks to the signal generation with use of DASYLab software, it is possible to precisely control the frequency, value of amplitude excitation and to send the signal to the system with use of the measurement card. The main goal of the experiment is to keep constant value of deformation, by means of a feedback loop with use of PID control, and to change the initial conditions of the test by change of the external force. The system is designed to return to the initial settings by appropriate control of the intensity of magnetic field, and thus the deformation of the sample.

Receptor-Based Models with Diffusion-Driven Instability for Pattern Formation in Hydra

2003 ◽  
Vol 11 (03) ◽  
pp. 293-324 ◽  
Author(s):  
Anna Marciniak-Czochra
Keyword(s):  
Pattern Formation ◽  
Feedback Loop ◽  
De Novo ◽  
Initial Conditions ◽  
Reaction Diffusion ◽  
Reaction Diffusion Equations ◽  
Variable Model ◽  
Dissociated Cells ◽  
Cutting Experiments ◽  
Positional Value

The aim of this paper is to show under which conditions a receptor-based model can produce and regulate patterns. Such model is applied to the pattern formation and regulation in a fresh water polyp, hydra. The model is based on the idea that both head and foot formation could be controlled by receptor-ligand binding. Positional value is determined by the density of bound receptors. The model is defined in the form of reaction-diffusion equations coupled with ordinary differential equations. The objective is to check what minimal processes are sufficient to produce patterns in the framework of a diffusion-driven (Turing-type) instability. Three-variable (describing the dynamics of ligands, free and bound receptors) and four-variable models (including also an enzyme cleaving the ligand) are analyzed and compared. The minimal three-variable model takes into consideration the density of free receptors, bound receptors and ligands. In such model patterns can evolve only if self-enhancement of free receptors, i.e., a positive feedback loop between the production of new free receptors and their present density, is assumed. The final pattern strongly depends on initial conditions. In the four-variable model a diffusion-driven instability occurs without the assumption that free receptors stimulate their own synthesis. It is shown that gradient in the density of bound receptors occurs if there is also a second diffusible substance, an enzyme, which degrades ligands. Numerical simulations are done to illustrate the analysis. The four-variable model is able to capture some results from cutting experiments and reflects de novo pattern formation from dissociated cells.

scholarly journals Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

2013 ◽  
Vol 13 (19) ◽  
pp. 9917-9937 ◽  
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
F. Chevallier ◽  
A. Fortems-Cheney ◽  
S. Szopa ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Model Errors ◽  
Inverse Systems ◽  
The Impact

Abstract. A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.

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