Abstract
Every investment decision by organizations is an implicit expression of a prediction on the future state of companies, demand trends, future challenges, or politics. Usually, decision makers can chose between different options to invest and also tend to diversify investments in order to reduce risk. Research on cognitive processing, however, showed that unconscious heuristics triggered by “heuristic cues” influence such decisions in an irrational manner. Partition dependence, the partition of the state space, is such a heuristic cue that leads to an irrational bias in the assessment of probabilities towards a uniform distribution. This work demonstrates that this bias is also persistent in group-based forecasting. We explain and predict the occurrence of the partition dependence bias by applying the Dual-Process Theory of cognitive processing. The results of our three consecutive experiments suggest that besides task complexity, individual expertise, and motivation are moderators for the occurrence of partition dependence. We contribute to the research of biases in forecasting by applying approaches of the Dual-process Theories to explain the occurrence of partition dependence for the first time and shed light on future pathways in this research field.
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Notes
Different Dual-Process Theories usually also use different wording for the systems. Sometimes they also use slightly different understandings of the interactions of the two, or more, types of processing.
For example, the favourite-longshot bias is attributed too low liquidity that leads to the situation that in the border areas orders do not get matched, which can again be explained using the prospect theory (Snowberg and Wolfers 2010). Other explanations argue with the composition of the field of participants (Feess et al. 2014; Restocchi et al. 2018), the motivation of the participants, and subsequently incentives (Servan-Schreiber et al. 2004).
The probability that the realization of the event will fall into this interval.
In the unlikely event that two or more participants would have entered the experiment at the same time, participants would have seen each other trading. This, however, did not happen in current case.
Participants were assigned to the treatments using round-robin according to the time of first entry into the experiment. Due to a higher rate of drop-outs in the market treatments, the poll treatment was closed and the rest of the participants were only distributed among the market groups.
See Table 7 for the complete list of items.
Boerseninitiative e.V. Karlsruhe
Significance codes for all analyses: 0.000 *** 0.005 ** 0.01 * 0.05 . 0.1 n.s. 1.
A one-tailed test was applied, as the theory behind partition dependence already induces the direction of the effect.
“Rational” and “attributional thinking” in the wording of Brakel et al. (2002).
In the lmsr2 treatment the estimation of a trader was influenced by the estimation of the previous trader. This effect is reduced in the lmsr3 setting, as the first numbers shown to the trader always equals the uniform distribution.
Own translation.
The REI-10 measures the two reflective constructs “need for cognition” and “faith in intuition” by five items on a 5 point Likert-scale from completely false to completely true. The five items were selected from a longer version of the test (REI-42), selected by the highest factor loading each.
See Table 8 for the complete list of items (in German).
See Table 9 for the complete list of items (in German).
See Table 10 for the complete list of items (in German).
In the German election system usually no party is able to receive more than 50% of the votes. Hence, several parties have to form coalitions in order to gain a majority in the parliament to be able to govern.
A two-sample test for “equalitiy of proportions” (prop.test()) shows no significant inequality (\(p=.250\)) regarding the gender.
We extensively discussed to utilize incentive compatible fees, as this is usually the case in experimental economics. However, there are several reasons, why we decided to use a flat fee instead: (1) We need to incite the true assessment of the likelihood for intervals. If one assesses 80% to the correct interval, another 60%, should then both receive the “full” payment? If yes, how much need they to assess to an interval to receive the payment in order to qualify? This would be therefore not incentive compatible. If one would pay 80% of the full payment if the correct interval is assesses 80%, it would be game theoretically optimal to assess 100% the the interval that is perceived to be the most likely one. Therefore, this would be also not incentive compatible. (2) We aimed an similar incentive structure for the poll and the lmsr treatment. In the lmsr treatment it would be possible to pay out based on their outcome after the stocks paid out. However, this is not possible for the poll treatment. This would necessarily result in different incentive structures. (3) We aimed to perform the experiment on Amazon Mechanical Turk. To ensure that the exact time of completion of the survey does not have an advantage, the event of forecasting needs to be sufficiently in the future. We decided for approx. 90 days. However, it is not possible (or reasonable) to hold back the payment for the crowd workers for such a long period. Therefore, a flat fee, that is high enough to incite a truthful participation is most suitable in our context.
The experiment was conducted in english, therefore we used the original item formulations, as can be found in the corresponding papers.
This needs to be provided in order that the participants can successfully close the task on Amazon Mechanical Turk and redeem their payoff.
As several workers just started the task in order reserve it for a later completion, which resulted in a “long-tail” at the right end, the median provides a more realistic picture of the completion time than the average.
A two-sample test for “equalitiy of proportions” (prop.test()) shows no significant inequality (p=.380) regarding the gender.
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Acknowledgements
We thank the Deutsche Forschungsgemeinschaft (DFG) for generously funding this research (WE 1436/12-1), as well as Saskia Bluhm, Marie Reger, and Maximilian Schaedlich for their help in collecting the underlying data.
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Kloker, S., Straub, T. & Weinhardt, C. Moderators for Partition Dependence in Prediction Markets. Group Decis Negot 28, 723–756 (2019). https://doi.org/10.1007/s10726-019-09622-9
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DOI: https://doi.org/10.1007/s10726-019-09622-9