scholarly journals Temporal oscillations in preference strength provide evidence for an open system model of constructed preference

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter D. Kvam ◽  
Jerome R. Busemeyer ◽  
Timothy J. Pleskac

AbstractThe decision process is often conceptualized as a constructive process in which a decision maker accumulates information to form preferences about the choice options and ultimately make a response. Here we examine how these constructive processes unfold by tracking dynamic changes in preference strength. Across two experiments, we observed that mean preference strength systematically oscillated over time and found that eliciting a choice early in time strongly affected the pattern of preference oscillation later in time. Preferences following choices oscillated between being stronger than those without prior choice and being weaker than those without choice. To account for these phenomena, we develop an open system dynamic model which merges the dynamics of Markov random walk processes with those of quantum walk processes. This model incorporates two sources of uncertainty: epistemic uncertainty about what preference state a decision maker has at a particular point in time; and ontic uncertainty about what decision or judgment will be observed when a person has some preference state. Representing these two sources of uncertainty allows the model to account for the oscillations in preference as well as the effect of choice on preference formation.

2020 ◽  
Author(s):  
Peter D. Kvam ◽  
Jerome R Busemeyer ◽  
Timothy Joseph Pleskac

Contemporary theories of choice posit that decision making is a constructive process in which a decision maker uses information about the choice options to generate support for various decisions and judgments, then uses these decisions and judgments to reduce their uncertainty about their own preferences. Here we examine how these constructive processes unfold by tracking dynamic changes in preference strength. Across two experiments, we observed that mean preference strength oscillated over time and found that eliciting a choice strongly affected the pattern of oscillation. Preferences following choices oscillated between being stronger than those without prior choice (bolstering) and being weaker than those without choice (suppression). An open system model, merging epistemic uncertainty about how a person reacts to options and ontic uncertainty about how their preference is affected by choice, accounts for the oscillations resulting in both bolstering and suppression effects.


Author(s):  
Jiangzhong Cao ◽  
Bingo Wing-Kuen Ling ◽  
Wai-Lok Woo ◽  
Zhijing Yang

1998 ◽  
Vol 17 (3-4) ◽  
pp. 267-277
Author(s):  
Su Yeongtzay ◽  
Wang Chitshung

2016 ◽  
Vol 15 (3) ◽  
pp. 333-361 ◽  
Author(s):  
Muneer Shaik ◽  
S. Maheswaran

We document the presence of the random walk effect in stock indices and, at the same time, find that the constituent stocks of the indices are excessively volatile. This gives rise to a paradox in stock markets between the behaviour of the stock index and its constituent stocks. We address this phenomenon in this article and reconcile the seemingly contradictory inferences by extending the Binomial Markov Random Walk (BMRW) model. JEL Classification: C15, C58, G15


Author(s):  
Seyed Mohsen Hoseyni ◽  
Mohammad Pourgol-Mohammad ◽  
Ali Abbaspour Tehranifard ◽  
Faramarz Yousefpour

This paper describes a systematic framework for quantifying the degree of contribution of each parameter to the uncertainty of the output in severe accident assessment. This research is an extension of the recent work of the authors on uncertainty assessment of severe accident calculations where the main sources of uncertainty are identified through the so-called modified PIRT approach. The proposed methodology here utilizes uncertainty importance measures for the quantification of the effect of each input parameter on the output uncertainty. A response surface fitting approach is proposed for estimating the associated uncertainties with less calculation cost. The quantitative results are used to plan in reducing epistemic uncertainty in the input variable(s). The application of the proposed methodology is demonstrated for the ACRR MP-2 severe accident test facility.


2014 ◽  
Vol 21 (3) ◽  
pp. 970-977
Author(s):  
Hong Li ◽  
Xiao-yan Lu ◽  
Wei-wen Liu ◽  
Clement K. Kirui

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