Análisis de los resultados de la validación

Supplementary Experimental Sessions

1. Overview

In Chapter Three, I examine the impact of client-specific experience on an individual’s risk-assessment decisions. As discussed in Section 2 of Chapter Three, to manipulate experience I create two data sets providing differing patterns of experience, and measure whether

participants choose no-error risk-assessment predictions to a greater extent after the long no- error-result series. By randomly generating the PATTERN and NO-PATTERN data series separately, I am unable to match results on a period-by-period basis. Therefore, I match periods between PATTERN and NO-PATTERN conditions based on risk cue and result attributes. There is no theoretical or statistical reason that this should impact my results; however, I question how long the bias results persist subsequent to the long no-error series. In a set of supplemental experimental sessions, I manipulate only the 25 no-error-pattern treatments (“25 treatment periods”). All other periods contain the same information between conditions, allowing better examination of the persistence of the experience bias.

Some have also questioned whether participants correctly understand that the result of each period is not a function of other periods. My theory assumes that subjects are biased by prior experience. Assuming subjects believe each period is a function of other periods, their reliance on experience during the risk-assessment process is rational and could provide an alternate explanation for my findings.

In this supplementary experiment, I address these two concerns. I combine the original PATTERN and NO-PATTERN data sets and manipulate the 25-period no-error pattern series

directly to create new conditions, allowing measurement of results without having to match periods. I also update the instructions to clarify that results in each period are not a function of other periods. The remainder of this chapter discusses these supplementary sessions in more detail, including their results.

2. Matched Periods

In Chapter Three, Section 2.3, I discuss the process of randomly drawing a series of values to generate the data sets for the PATTERN and NO-PATTERN conditions in my original experimental sessions. As discussed in that section, by generating each condition’s data set separately, the risk cues and results for each period between the PATTERN and NO-PATTERN conditions do not directly line up period-by-period. Because of this, I am required to match thirteen periods, subsequent to the 25 treatment periods, between the PATTERN condition and the NO-PATTERN condition based on cue and result attributes. As seen in Figure 8, my original results suggest that the bias effect is maintained for approximately the first six of the 13 matched periods. I have no statistical or theoretical reason to suspect this process leads to incorrect

inferences. While I take care to match periods on cue and result attributes, I am only using 13 out of the available 18 periods of data subsequent to the 25 treatment periods. It is possible,

however, that the matching process influences inferences regarding the persistence of the bias effect. Therefore, these additional sessions further explore the persistence of the bias effect.

In these additional sessions, I combine the current PATTERN and NO-PATTERN data sets into one 117 period (including 15 practice periods) data series. I combine both conditions in total to preserve the same number of periods as the original experiments in order to mitigate any possible effects associated with changing the length of the experiment. This permits direct, not

matched, comparisons between periods, affording a direct measure for how long subjects bias toward experience.

2.1 Data Series Discussion

This supplementary experiment includes four data series conditions. In the first two conditions, I combine the original PATTERN and NON-PATTERN conditions into a total of 117 periods. In the first condition, I maintain the original 25 sequential no-error periods from the PATTERN condition. For the second condition, I manipulate the 25 sequential no-error periods in the PATTERN set so that they are no longer a series of no-errors. In essence, the only

difference between conditions becomes the 25 treatment periods, a distinction shown in Figure 10. Because everything before and after the 25 treatment periods across these two different conditions is identical, I no longer need to match periods after the series.40

The third and fourth treatments are identical to conditions one and two, with the

exception that I combine the original PATTERN and NO-PATTERN data sets in reverse order (i.e., instead of original PATTERN after NO-PATTERN, I place original NO-PATTERN after PATTERN). With these treatments I explore whether the impact of having the 25 treatment periods early or late in the long series of random periods has any effect. This order change produces no statistical effect, so I do not mention this order condition again.

40

My focus with these additional sessions is to explore further implications of decisions based on experience manipulations. As such, I am not manipulating cue costs in these additional sessions.

FIGURE 10

No-Error Series Manipulation between PATTERN and NO-PATTERN

Figure 10 distinguishes manipulated periods between PATTERN and NO-PATTERN conditions. Highlighted rows differ from the other uniform rows in the 117 periods of PATTERN and NO- PATTERN conditions. Series Periods Error? Cue 1 Shows Cue 2 Shows Series Periods Error? Cue 1 Shows Cue 2 Shows 1 No Error No Error No Error 1 No Error No Error No Error

2 No Error No Error No Error 2 ERROR ERROR ERROR

3 No Error No Error ERROR 3 No Error No Error ERROR

4 No Error No Error No Error 4 No Error No Error No Error 5 No Error No Error No Error 5 No Error No Error No Error

6 No Error No Error ERROR 6 No Error No Error ERROR

7 No Error No Error No Error 7 No Error No Error No Error

8 No Error No Error No Error 8 ERROR ERROR ERROR

9 No Error No Error No Error 9 No Error No Error No Error 10 No Error No Error No Error 10 No Error No Error No Error 11 No Error No Error No Error 11 No Error No Error No Error 12 No Error ERROR No Error 12 No Error No Error No Error 13 No Error No Error No Error 13 No Error No Error No Error 14 No Error No Error ERROR 14 No Error No Error ERROR

15 No Error No Error No Error 15 ERROR ERROR ERROR

16 No Error No Error No Error 16 No Error ERROR No Error 17 No Error No Error No Error 17 No Error No Error No Error 18 No Error No Error No Error 18 No Error No Error No Error

19 No Error No Error No Error 19 ERROR ERROR ERROR

20 No Error No Error No Error 20 No Error No Error No Error 21 No Error No Error No Error 21 No Error No Error No Error 22 No Error No Error No Error 22 No Error No Error No Error

23 No Error ERROR No Error 23 No Error ERROR No Error

24 No Error No Error ERROR 24 No Error No Error ERROR 25 No Error No Error No Error 25 No Error No Error No Error

NO-PATTERN Data Set