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sum of the two slopes parameters is aprroximately equal to one for a large number
of the subjects. Taking this in mind, we report in table 3 the estimates for the
j , e s.
The comparison of the coefficients for each subject in the three experiments shows
how, except in a number of occasions, with rising standard deviation of forecast
subjects set the production choice closer and closer to the past demand realization
(
β
progressively increases going trough B 1 a , B 1 b and B 1 c ). Subject 10 and subject
13 represents two exception because they heavly rely on forecasting service when
chosing the production capacity in all the three experiments. 4 Furthermore, subjects
14, 16, 19, 20 and 22 are those who set the production choice according to both
backward and forward looking. These early results show a degree of heterogeneity
agents' learning how to manage risky and uncetain environments.
β
Ta b l e 3 Estimates of the β j , e parameter present in equation (1) by using OLS regression.
The subjects pool is the class of students at Management Sciences Faculty.
e
j
=
1
j
=
3
j
=
6
j
=
7
j
=
8
j
=
9
j
=
10
j
=
11
j
=
12
j
=
13
B 1 a 0.182
0.156 ∗∗ 0.600 ∗∗∗ 0.389 ∗∗∗ 0.011
0.237 0.002
0.195 ∗∗∗ 0.018
0.228 ∗∗∗
(0.090)
(0.075)
(0.060)
(0.084)
(0.044)
(0.134)
(0.016)
(0.052)
(0.032)
(0.077)
B 1 b 0.240 ∗∗∗ 0.469 ∗∗∗
0.364 ∗∗∗ 0.427 ∗∗∗ 0.115 ∗∗
0.896 ∗∗∗ 0.089
0.423 ∗∗∗ 0.441 ∗∗∗ 0.071
(0.067)
(0.058)
(0.063)
(0.057)
(0.065)
(0.045)
(0.092)
(0.081)
(0.081)
(0.056)
B 1 c 0.999 ∗∗∗ 0.816 ∗∗∗
0.726 ∗∗∗ 0.791 ∗∗∗ 0.807 ∗∗∗ 0.769 ∗∗∗
0.070 0.767 ∗∗∗ 0.816 ∗∗∗ 0.079
(0.075)
(0.055)
(0.032)
(0.055)
(0.098)
(0.083)
(0.044)
(0.045)
(0.067)
(0.071)
e
j = 14
j = 15
j = 16
j = 17
j = 18
j = 19
j = 20
j = 21
j = 22
j = 23
B 1 a 0.533 ∗∗∗ 0.355 ∗∗∗
0.120 ∗∗
0.350 ∗∗∗ 0.107 0.006
0.910 ∗∗∗ 0.298 ∗∗∗ 0.050
-0.033
(0.052)
(0.060)
(0.046)
(0.052)
(0.054)
(0.040)
(0.102)
(0.071)
(0.062)
(0.055)
B 1 b 0.295 ∗∗∗ 0.613 ∗∗∗
0.389 ∗∗∗ 0.596 ∗∗∗ 0.239
0.265 ∗∗∗ 0.395 ∗∗∗ 0.958 ∗∗∗ 0.386 ∗∗∗ 0.491 ∗∗∗
(0.042)
(0.064)
(0.070)
(0.067)
(0.011)
(0.140)
(0.088)
(0.067)
(0.039)
(0.064)
B 1 c 0.638 ∗∗∗ 0.758 ∗∗∗
0.578 ∗∗∗ 0.779 ∗∗∗ 0.801 ∗∗∗ 0.345 ∗∗∗ 0.586 ∗∗∗ 1.009 ∗∗∗ 0.644 ∗∗∗ 0.838 ∗∗∗
(0.078)
(0.055)
(0.041)
(0.068)
(0.056)
(0.109)
(0.049)
(0.039)
(0.045)
(0.049)
Notes : ∗∗∗ , ∗∗ , , denote significance at p = 0.01, 0.05, and 0.10, respectively (twotailed
tests). Standard errors are in parentheses.
4
Conclusions
This paper describes a web-based software model which is a small scale abstraction
of the real world characterized by risk and uncertainty.
The results of our experiments with two different subject pools, i.e. entrepreneurs
and students, showed that the model could offer a suitable framework for the explo-
ration of agents' behaviors in a stochastic and dynamic context. Our software allows
in particular to observe how agents learn to forecast and manage the capital structure
of their firms.
We made use of Experimental Economics which has been drawing a growing
attention among researchers since it allows for the observation of individuals' be-
haviors. In particular our software showed that the experimental method could be
4
One can imagine that they they have very low scores especially in experiment B 1 c because
of the large number of bailouts they activate.
 
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