Simulation of the PCS-Network Model of Decision Making

This GUI allows you to simulate the Parallel-Constraint Satisfaction network model of decision making (Gloeckner & Betsch, 2008). You can set the sensitivity parameter P, the weights for all experts (cues), and the votes for all experts (cue-pattern). You can vary the number of experts between 2 and 8. You can also change the default values for the properties of the PCS-DM model. Under the tab 'Prediction', predictions for the choice, the decision time, and the confidence rating are displayed; under the tab 'Final Iterations' ('All Iterations'), energy, activations for all nodes for the final (all) iteration(s), and the number of iterations are displayed; under the tab 'Plot', activations for all nodes over iterations are plotted; under the tab 'Download', you can download the entire prediction matrix as a csv-file. For help and contact information visit www.coherence-based-reasoning-and-rationality.de Set the sensitivity P:

Set the weights for all experts:

Weight Expert A:

Weight Expert B:

Weight Expert C:

Weight Expert D:

Weight Expert E:

Weight Expert F:

Weight Expert G:

Weight Expert H:

Set the cue-pattern for all experts:

Expert A votes

For Stock 1

For Stock 2

For Both Stocks

Against Both Stocks

Expert B votes

For Stock 1

For Stock 2

For Both Stocks

Against Both Stocks

Expert C votes

For Stock 1

For Stock 2

For Both Stocks

Against Both Stocks

EXCLUDE Expert C

Expert D votes

For Stock 1

For Stock 2

For Both Stocks

Against Both Stocks

EXCLUDE Expert D

Expert E votes

For Stock 1

For Stock 2

For Both Stocks

Against Both Stocks

EXCLUDE Expert E

Expert F votes

For Stock 1

For Stock 2

For Both Stocks

Against Both Stocks

EXCLUDE Expert F

Expert G votes

For Stock 1

For Stock 2

For Both Stocks

Against Both Stocks

EXCLUDE Expert G

Expert H votes

For Stock 1

For Stock 2

For Both Stocks

Against Both Stocks

EXCLUDE Expert H

Change default values of PCS-DM parameters:

Decay of activations:

Inhibition weight between stocks:

Factor for links between nodes of cues and options:

Maximum activation of nodes (i.e., ceiling):

Minimum activation of nodes (i.e., floor):

Constant activation of driver node:

Prediction
Final Iteration
All Iterations
Plot
Download

Download Prediction