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Confounding dynamic risk taking propensity with a momentum prognostic strategy: the case of the Columbia Card Task (CCT).

Markiewicz Ł, Kubińska E, Tyszka T - Front Psychol (2015)

Bottom Line: We argue that the HOT version confounds an individual's willingness to accept risk with their beliefs in trend continuation vs. trend reversal in a prognostic task.However, this is not the case in the COLD version.We speculate that other dynamic risk taking measures can also be biased by a momentum strategy.

View Article: PubMed Central - PubMed

Affiliation: Centre for Economic Psychology and Decision Sciences, Kozminski University Warsaw, Poland.

ABSTRACT
Figner et al. (2009) developed the Columbia Card Task (CCT) to measure risk-taking attitudes. This tool consists of two versions: in the COLD version the decision maker needs to state in advance how many cards (out of 32) they want to turn over (so called static risk taking), in the HOT version they have the possibility of turning over all 32 cards one-by-one until they decide to finish (dynamic risk taking). We argue that the HOT version confounds an individual's willingness to accept risk with their beliefs in trend continuation vs. trend reversal in a prognostic task. In two experimental studies we show that people believing in trend continuation (momentum subjects) turn over more cards than those believing in trend reversal (contrarians) in the HOT version of the task. However, this is not the case in the COLD version. Thus, we provide evidence that, when considered as a dynamic risk propensity measure, the number of turned over cards in the HOT version of the CCT is a contaminated measure and reflects two phenomena: (1) risk preference and (2) the decision-maker's belief in trend continuation. We speculate that other dynamic risk taking measures can also be biased by a momentum strategy.

No MeSH data available.


The sequence presented to participants. Tails are coded as -1 and heads as +1 (Studies 1 and 2). Participants saw separate events one after another, and not the whole history as presented in the chart. Red circles highlight the critical decisions for the longest sequences determining prognostic strategy.
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Figure 1: The sequence presented to participants. Tails are coded as -1 and heads as +1 (Studies 1 and 2). Participants saw separate events one after another, and not the whole history as presented in the chart. Red circles highlight the critical decisions for the longest sequences determining prognostic strategy.

Mentions: Prognostic strategy use was measured by a procedure used by Tyszka et al. (2008), often referred to as a recency test. Ps were asked to make predictions based on observation of a randomly generated sequence of binomial events (a two-point distribution with equally probable values). The task instructions informed Ps that a sequence of fair coin tosses (with equal probabilities of head and tails) was going to be presented on the screen. The participants were asked to observe 20 series of 10 coin tosses (200 tosses in total). At every 10th event, Ps had to make a prediction about the next event. Participants’ aims were to make as many correct predictions as possible. The sequence of binomial events for Study 1 with circled predicted events is presented in Figure 1.


Confounding dynamic risk taking propensity with a momentum prognostic strategy: the case of the Columbia Card Task (CCT).

Markiewicz Ł, Kubińska E, Tyszka T - Front Psychol (2015)

The sequence presented to participants. Tails are coded as -1 and heads as +1 (Studies 1 and 2). Participants saw separate events one after another, and not the whole history as presented in the chart. Red circles highlight the critical decisions for the longest sequences determining prognostic strategy.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528171&req=5

Figure 1: The sequence presented to participants. Tails are coded as -1 and heads as +1 (Studies 1 and 2). Participants saw separate events one after another, and not the whole history as presented in the chart. Red circles highlight the critical decisions for the longest sequences determining prognostic strategy.
Mentions: Prognostic strategy use was measured by a procedure used by Tyszka et al. (2008), often referred to as a recency test. Ps were asked to make predictions based on observation of a randomly generated sequence of binomial events (a two-point distribution with equally probable values). The task instructions informed Ps that a sequence of fair coin tosses (with equal probabilities of head and tails) was going to be presented on the screen. The participants were asked to observe 20 series of 10 coin tosses (200 tosses in total). At every 10th event, Ps had to make a prediction about the next event. Participants’ aims were to make as many correct predictions as possible. The sequence of binomial events for Study 1 with circled predicted events is presented in Figure 1.

Bottom Line: We argue that the HOT version confounds an individual's willingness to accept risk with their beliefs in trend continuation vs. trend reversal in a prognostic task.However, this is not the case in the COLD version.We speculate that other dynamic risk taking measures can also be biased by a momentum strategy.

View Article: PubMed Central - PubMed

Affiliation: Centre for Economic Psychology and Decision Sciences, Kozminski University Warsaw, Poland.

ABSTRACT
Figner et al. (2009) developed the Columbia Card Task (CCT) to measure risk-taking attitudes. This tool consists of two versions: in the COLD version the decision maker needs to state in advance how many cards (out of 32) they want to turn over (so called static risk taking), in the HOT version they have the possibility of turning over all 32 cards one-by-one until they decide to finish (dynamic risk taking). We argue that the HOT version confounds an individual's willingness to accept risk with their beliefs in trend continuation vs. trend reversal in a prognostic task. In two experimental studies we show that people believing in trend continuation (momentum subjects) turn over more cards than those believing in trend reversal (contrarians) in the HOT version of the task. However, this is not the case in the COLD version. Thus, we provide evidence that, when considered as a dynamic risk propensity measure, the number of turned over cards in the HOT version of the CCT is a contaminated measure and reflects two phenomena: (1) risk preference and (2) the decision-maker's belief in trend continuation. We speculate that other dynamic risk taking measures can also be biased by a momentum strategy.

No MeSH data available.