The Neuroeconomics of Learning and Information Processing; Applying Markov Decision Process

The Neuroeconomics of Learning and Information Processing; Applying

Markov Decision Process

Chatterjee, Sidharta

Andhra University

14 February 2011

Online at https://mpra.ub.uni-muenchen.de/28993/

MPRA Paper No. 28993, posted 20 Feb 2011 20:23 UTC

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Total Initial Value of the system before the program: 85

Total value unlocked or rewards derived by the agents: 921

Total No. of Policies: 22

Mean (avg.) Reward per agent: 41.86

Efficiency Rate: 49%

Reward gained by following PP’s: (excluding. Top 3) 563

Reward Value of Top three Policies: 35.72 (329)

Reward Value of Top three Policies as a %: 35.72%

Reward Value gained by top 14 patterns per agent: 40.21

Efficiency of patterned policy choices: 61.12%

Mean reward gained by following random policies: 6.25

Total rewards gained following non-patterned policies as a percentage: 4.3%

Total Reward Value gained by following PP’s as a %: 96.85

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FIELD N MEAN STD SEM MIN MAX SUM (Policy Groups)

A (a1+a2) 2 43.00 43.84 31.00 12 74 86 B (b1+b2+b3) 3 12.00 51.86 29.94 -36 67 36 D (d1+d2+d3) 3 28.00 41.58 24.01 -4 75 84 E (e1+e2+e3) 3 18.67 40.82 23.57 -12 65 56 F (f1+f2+f3) 3 28.67 41.77 24.11 -3 76 86 G (g1+g2+g3) 3 109.50 6.36 4.50 103 114 329