Ron Sun and Isaac Naveh (2004)
Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model
Journal of Artificial Societies and Social Simulation
vol. 7, no. 3
To cite articles published in the Journal of Artificial Societies and Social Simulation, reference the above information and include paragraph numbers if necessary
Received: 14-Nov-2003 Accepted: 01-Apr-2004 Published: 30-Jun-2004
|Figure 1. The CLARION architecture|
where x is the current state, a is one of the actions, r is the immediate feedback, and γmaxb Q(y,b) is set to zero for the organizational design task that was tackled in this paper, because we rely on immediate feedback in this particular task (details below). Δ Q(x, a) provides the error signal needed by the backpropagation algorithm and then backpropagation takes place. That is, learning is based on minimizing the following error at each step:
where i is the index for an output node representing the action ai. Based on the above error measure, the backpropagation algorithm is applied to adjust internal weights of the network.
where A and B are two different rule conditions that lead to the same action a, and c1 and c2 are two constants representing the prior (by default, c1 = 1, c2 = 2). Essentially, the measure compares the percentages of positive matches under different conditions A and B.
where C is the current condition of a rule (matching the current state and action), all refers to the corresponding match-all rule (with the same action as specified by the original rule but an input condition that matches any state), and C' is a modified condition equal to C plus one input value. If the above holds, the new rule will have the condition C' with the highest IG measure. The generalization threshold (denoted thresholdGEN above) determines how readily an agent will generalize a rule.
where x is the current state, a is an action, and t controls the degree of randomness (temperature) of the process. (This method is also known as Luce's choice axiom (Watkins 1989). It is found to match psychological data in many domains.)
|Table 1: Human and simulation data for the organizational design task. D indicates distributed information access, while B indicates blocked information access. All numbers are percent correct|
|Agent/Org.||Team (B)||Team (D)||Hierarchy (B)||Hierarchy (D)|
|Table 2: Simulation data for agents running for 4,000 cycles. The human data from Carley et al (1998) are reproduced here for ease of comparison. Performance for CLARION is computed as percentage correct over the last 1,000 cycles|
|Agent/Org.||Team (B)||Team (D)||Hierarchy (B)||Hierarchy (D)|
|Figure 2. Training curves for different combinations of organizational structure and data access|
The rule should be read as follows: if input #4 is equal to 1, 2 or 3, and the other inputs are equal to 3, then select action 3 (hostile aircraft).
|Figure 3. Training curve (team organization, distributed access)|
|Figure 4. Training curve (team organization, blocked access)|
|Figure 5. Training curve (hierarchal organization, distributed access)|
|Figure 6. Training curve (hierarchal organization, blocked access)|
|Figure 7. A comparison of performance under different combinations of structure and organization after 100, 4,000 and 20,000 training cycles|
|Figure 8. The effect of organization on performance over time|
|Figure 9. The effect of information access on performance over time|
|Figure 10. The effect of probability of using the bottom level on performance over time|
|Figure 11. The effect of learning rate on performance over time|
|Figure 12. The effect of generalization threshold on the final performance|
|Figure 13. The interaction of generalization threshold and density with respect to the final performance|
|Figure 14. The interaction of generalization threshold and organization with respect to initial performance|
|Table 3: Simulation results for general parameters of the model. Only statistically significant interactions are shown (for main effects, NS = not significant). Time is computed as a repeated-measures variable at 4,000 and 20,000 cycles|
|Effect of probability of bottom level usage (PROB_BL)||11.73||2, 24||< 0.001|
|Effect of learning rate||32.47||2, 24||< 0.001|
|Effect of temperature||2.89||1, 24||NS|
|Interaction of PROB_BL and time||12.37||2, 24||< 0.001|
|Table 4: Simulation results for parameters related to RER learning|
|Effect of RER positivity threshold||.229||1, 24||NS|
|Effect of RER density||.094||2, 24||NS|
|Effect of RER generalization threshold||15.91||1, 24||< 0.001|
|Interaction of density and generalization threshold||2.93||2, 24||< 0.05|
|Interaction of generalization threshold and organization after 4,000 cycles||5.93||1, 24||< 0.05|
2 The following parameters were used for all agents: Temperature = 0.05; Learning Rate = 0.5; Probability of Using Bottom Level = 0.75; RER Positivity Criterion = 0.0; Density = 0.01; Generalization Threshold = 4.0. See Section 2 for a description of the cognitive parameters.
3 If we raise the threshold above a certain point, performance dips and an overall U-shaped curve is observed. The same is true for other parameters.
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