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Table 1 shows results from undergraduate
students at the Faculty of Management Sci-
ences at the University of Chieti - Pescara.
The same experiment was performed with
selected entrepreneurs whose result are re-
ported in figure 2. The first column of the
tables reports the subject's identification
number.
3
The following columns inform on
the scores achieved by the subject in con-
secutive trials. The black slots identify the
achievement of the maximum score. It is im-
portant to highlight that the score goes from
5% (when equity cover 100% of liabilities)
to 9% (when debt is 100%). It can be ob-
served that a significant share of the sub-
jects (both students and entrepreneurs) have
not yet taken advantage of the leverage effect
when they left the computers room.
The second exercise was carried out ac-
counting for a risky environment. We asked
the experimental subjects to take part to three
experimental sessions (labeled as B1a, B1b
and B1c) which are characterized by a de-
creasing “accuracy” of forecasts and a low
level of the volatility of demand. In this case,
the aim is to investigate the switching behav-
ior between a
forward looking
and a
back-
ward looking
conduct. Intuition suggests that when forecasts are precise, agents
should rely on them. In the case of imprecise forecast, good “guesses” on future
demand could still be obtained by looking at the past if the demand time series
is smooth (i.e. it has a low volatility). We present hereafter the results of linear
regressions performed in order to investigate the experimental subjects' switching
conduct. Since experiments with the entrepreneurs' subject pool is still at an early
stage, we report hereafter only students' results. The estimated equation is:
Ta b l e 1
Results from a class of stu-
dents at Management Sciences Faculty
01 6.97 7.44 8.69 8.93
03 8.33 8.34
9
06 7.84 8.21 8.02 8.49 8.07 8.63 8.6 8.4
9
07 8.24 8.51 8.69 8.83
08 8.45 8.77 8.82 8.9
8.99
09 8.9 8.97
9
10 7.34 7.77 6.79 8.59 8.11
11 8.69 8.82 8.96 8.98
9
12 6.75 8.97 8.94
13 8.45 7.96 8.59
14 8.8
9
8.97 8.88
9
15 8.35 8.65 8.81 8.15 8.77 8.79
16 8.96
9 9
17 7.68 8.85
9 9
18 8.38 8.57 8.85 8.93
9
19 7.05 8.78
9
20 7.58 8.98
9
21 8.45 8.88 8.92 8.92
22 7.17 7.6 8.95
23 8.74 8.78 8.69 8.82
Ta b l e 2
Results from entrepreneurs se-
lected from the Pescara entrepreneurs
union
01 8.35 8.81
9
02 7.71 7.04 6.76 7.4 8.5
03 8.66 8.54 8.84
04 6.44 8.66 8.82
9
05 8.09 8.26 8.35
05 8.82 8.98
9
07 8.5
8.93
9
y
j
,
e
,
t
=
α
j
,
e
+
β
j
,
e
y
∗
j
,
e
,
t
−
1
+
φ
j
,
e
y
j
,
e
,
t
+
ε
j
,
e
,
t
(1)
where
y
j
,
e
,
t
is the production capacity chosen by subject
j
in exercise
e
∈
at time
t
;
y
∗
j
,
e
,
t
−
1
and
y
j
,
e
,
t
are respectively the past demand re-
alization and the current level of forecast and
{
B
1
a
,
B
1
b
,
B
1
c
}
ε
j
,
e
,
t
is the error term.
We ran a regression for each subject in each of the three experiments. The results
show that subjects in general sets the optimal production capacity as a weighted
average of the previous period demand and the forecast. Surprisingly enough, the
3
In tables 1 and 3, the identification numbers 2, 4 and 5 are missing because the students
associated with them do now attended the session.
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