<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>This paper describes a model of dynamic decision making in the <italic>Dynamic Stocks and Flows</italic> (DSF) task, developed using the ACT-R cognitive architecture. This task is a simple simulation of a water tank in which the water level must be kept constant whilst the inflow and outflow changes at varying rates. The basic functions of the model are based around three steps. Firstly, the model predicts the water level in the next cycle by adding the current water level to the predicted net inflow of water. Secondly, based on this projection, the net outflow of the water is adjusted to bring the water level back to the target. Thirdly, the predicted net inflow of water is adjusted to improve its accuracy in the future. If the prediction has overestimated net inflow then it is reduced, if it has underestimated net inflow it is increased. The model was entered into a model comparison competition—the <italic>Dynamic Stocks and Flows Challenge</italic>—to model human performance on four conditions of the DSF task and then subject the model to testing on five unseen transfer conditions. The model reproduced the main features of the development data reasonably well but did not reproduce human performance well under the transfer conditions. This suggests that the principles underlying human performance across the different conditions differ considerably despite their apparent similarity. Further lessons for the future development of our model and model comparison challenges are considered.</p></abstract>

This paper describes a model of dynamic decision making in the Dynamic Stocks and Flows (DSF) task, developed using the ACT-R cognitive architecture. This task is a simple simulation of a water tank in which the water level must be kept constant whilst the inflow and outflow changes at varying rates. The basic functions of the model are based around three steps. Firstly, the model predicts the water level in the next cycle by adding the current water level to the predicted net inflow of water. Secondly, based on this projection, the net outflow of the water is adjusted to bring the water level back to the target. Thirdly, the predicted net inflow of water is adjusted to improve its accuracy in the future. If the prediction has overestimated net inflow then it is reduced, if it has underestimated net inflow it is increased. The model was entered into a model comparison competition—the Dynamic Stocks and Flows Challenge—to model human performance on four conditions of the DSF task and then subject the model to testing on five unseen transfer conditions. The model reproduced the main features of the development data reasonably well but did not reproduce human performance well under the transfer conditions. This suggests that the principles underlying human performance across the different conditions differ considerably despite their apparent similarity. Further lessons for the future development of our model and model comparison challenges are considered.

Modelling Dynamic Decision Making with the ACT-R Cognitive Architecture

Department of Behavioural and Social Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK

Department of Psychology, Faculty of Arts and Human Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK

Journal of Artificial General Intelligence

{"article-title":"Modelling Dynamic Decision Making with the ACT-R Cognitive Architecture"}

This paper describes a model of dynamic decision making in the Dynamic Stocks and Flows (DSF) task, developed using the ACT-R cognitive architecture. This task...

Modelling Dynamic Decision Making with the ACT-R Cognitive Architecture

Online veröffentlicht: 23. Nov. 2011

Seitenbereich: 52 - 68

DOI: https://doi.org/10.2478/v10229-011-0009-1

Schlüsselwörter
ACT-R, Cognitive Architecture, DSF, Dynamic Decision Making

This content is open access.

Modelling Dynamic Decision Making with the ACT-R Cognitive Architecture

Online veröffentlicht: 23. Nov. 2011

Seitenbereich: 52 - 68

DOI: https://doi.org/10.2478/v10229-011-0009-1

SchlüsselwörterACT-R, Cognitive Architecture, DSF, Dynamic Decision Making

This content is open access.

Schlüsselwörter
ACT-R, Cognitive Architecture, DSF, Dynamic Decision Making