##
**Decline, adopt or compromise? A dual hurdle model for advice utilization.**
*(English)*
Zbl 1500.91055

Summary: Research on advice utilization often operationalizes the construct via judge advisor systems (JAS), where a judge’s belief is elicited, they are provided advice, and given an opportunity to revise their belief. Belief change, or weight of advice (WOA), is measured as the shift in the judge’s belief proportional to the difference between their original belief and the advice. Several JAS studies have found WOA typically takes on a trimodal distribution, with inflation at the boundary values of 0 (indicating a judge declined advice) and 1 (adoption of advice). A dual hurdle beta model is proposed to account for these inflations. In addition to being an innovative computational model to address this methodological challenge, it also serves as a descriptive theoretical model which posits that the decision process happens in two stages: an initial discrete “choosing” stage, where the judge opts to either decline, adopt, or compromise with advice; and a subsequent continuous “averaging” stage, which occurs only if the judge opts to compromise. The approach was assessed via reanalysis of three recent JAS studies reflective of popular topics in the literature, such as algorithmic advice utilization, egocentric discounting effects, and judgmental forecasting. In each case new results were uncovered about how different correlates of advice utilization influence the decision process at either or both of the discrete and continuous stages, often in quite different ways, providing support for the descriptive theoretical model. A Bayesian graphical analysis framework is provided that can be applied to future research on advice utilization.

### MSC:

91B06 | Decision theory |

### Keywords:

advice taking; judge advisor system; boundary inflation models; graphical analysis; Bayesian models
PDFBibTeX
XMLCite

\textit{M. Himmelstein}, J. Math. Psychol. 110, Article ID 102695, 15 p. (2022; Zbl 1500.91055)

Full Text:
DOI

### References:

[1] | Allahverdyan, A. E.; Galstyan, A., Opinion dynamics with confirmation bias, PLoS One, 9, 7, e99557 (2014) |

[2] | Atanasov, P.; Rescober, P.; Stone, E. R.; Swift, S. A.; Servan-Schreiber, E.; Tetlock, P., Distilling the wisdom of crowds: Prediction markets vs. prediction polls, Management Science, 63, 3, 691-706 (2017) |

[3] | Bonaccio, S.; Dalal, R. S., Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences, Organizational Behavior and Human Decision Processes, 101, 2, 127-151 (2006) |

[4] | Budescu, V. D.; Chen, E., Identifying expertise to extract the wisdom of crowds, Management Science, 61, 2, 267-280 (2014) |

[5] | Bürkner, P.-C., Brms: An R package for Bayesian multilevel models using Stan, Journal of Statistical Software, 80, 1, 1-28 (2017) |

[6] | Burton, J. W.; Stein, M.-K.; Jensen, T. B., A systematic review of algorithm aversion in augmented decision making, Journal of Behavioral Decision Making, 33, 2, 220-239 (2020) |

[7] | Castelo, N.; Bos, M. W.; Lehmann, D. R., Task-dependent algorithm aversion, Journal of Marketing Research, 56, 5, 809-825 (2019) |

[8] | Cragg, J. G., Some statistical models for limited dependent variables with application to the demand for durable goods, Econometrica, 39, 4, 829-844 (1971) · Zbl 0231.62040 |

[9] | Dietvorst, B. J.; Simmons, J. P.; Massey, C., Algorithm aversion: People erroneously avoid algorithms after seeing them err, Journal of Experimental Psychology: General, 144, 1, 114 (2015) |

[10] | Ecken, P.; Pibernik, R., Hit or miss: What leads experts to take advice for long-term judgments?, Management Science, 62, 7, 2002-2021 (2016) |

[11] | Feng, C. X., A comparison of zero-inflated and hurdle models for modeling zero-inflated count data, Journal of Statistical Distributions and Applications, 8, 1, 1-19 (2021) · Zbl 07512275 |

[12] | Gabry, J.; Simpson, D.; Vehtari, A.; Betancourt, M.; Gelman, A., Visualization in Bayesian workflow, Journal of the Royal Statistical Society: Series A (Statistics in Society), 182, 2, 389-402 (2019) |

[13] | Gino, F.; Moore, D. A., Effects of task difficulty on use of advice, Journal of Behavioral Decision Making, 20, 1, 21-35 (2007) |

[14] | Hatfield, L. A.; Boye, M. E.; Hackshaw, M. D.; Carlin, B. P., Multilevel Bayesian models for survival times and longitudinal patient-reported outcomes with many zeros, Journal of the American Statistical Association, 107, 499, 875-885 (2012) · Zbl 1443.62382 |

[15] | Himmelstein, M.; Atanasov, P.; Budescu, D. V., Forecasting forecaster accuracy: Contributions of past performance and individual differences, Judgment and Decision Making, 16, 2, 323-362 (2021) |

[16] | Himmelstein, M.; Budescu, D. V., Preference for human or algorithmic forecasting advice does not predict if and how it is used, Journal of Behavioral Decision Making (2022) |

[17] | Himmelstein, M.; Budescu, D. V.; Han, Y., The wisdom of timely crowds, (Seifert, M., Judgment in predictive analytics (2022), Springer), (in press) |

[18] | Kaufmann, E.; Budescu, D. V., Do teachers consider advice? On the acceptance of computerized expert models, Journal of Educational Measurement, 57, 2, 311-342 (2020) |

[19] | Liu, F.; Kong, Y., Zoib: An R package for Bayesian inference for beta regression and zero/one inflated beta regression, R Journal, 7, 2, 34-51 (2015) |

[20] | Logg, J. M.; Minson, J. A.; Moore, D. A., Algorithm appreciation: People prefer algorithmic to human judgment, Organizational Behavior and Human Decision Processes, 151, 90-103 (2019) |

[21] | McElreath, R., Statistical rethinking: a bayesian course with examples in R and Stan (2018), Chapman and Hall/CRC |

[22] | Morstatter, F., Galstyan, A., Satyukov, G., Benjamin, D., Abeliuk, A., & Mirtaheri, M., et al. (2019). SAGE: a hybrid geopolitical event forecasting system. In Proceedings of the 28th international joint conference on artificial intelligence (pp. 6557-6559). |

[23] | Ospina, R.; Ferrari, S. L.P., A general class of zero-or-one inflated beta regression models, Computational Statistics & Data Analysis, 56, 6, 1609-1623 (2012) · Zbl 1243.62099 |

[24] | Prahl, A.; Van Swol, L., Understanding algorithm aversion: When is advice from automation discounted?, Journal of Forecasting, 36, 6, 691-702 (2017) |

[25] | Schultze, T.; Rakotoarisoa, A.-F.; Schulz-Hardt, S., Effects of distance between initial estimates and advice on advice utilization, Judgment and Decision Making, 10, 2, 144-171 (2015) |

[26] | Sherif, M.; Hovland, C. I., Social judgment: assimilation and contrast effects in communication and attitude change (1961), Yale University Press |

[27] | Smithson, M.; Verkuilen, J., A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables, Psychological Methods, 11, 1, 54-71 (2006) |

[28] | Sniezek, J. A.; Van Swol, L. M., Trust, confidence, and expertise in a judge-advisor system, Organizational Behavior and Human Decision Processes, 84, 2, 288-307 (2001) |

[29] | Soll, J. B.; Larrick, R. P., Strategies for revising judgment: How (and how well) people use others’ opinions, Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 3, 780-805 (2009) |

[30] | Soll, J. B.; Mannes, A. E., Judgmental aggregation strategies depend on whether the self is involved, International Journal of Forecasting, 27, 1, 81-102 (2011) |

[31] | RStan: The R interface to Stan (2022), R package version 2.21.2 |

[32] | Stan modeling language users guide and reference manual (2022) |

[33] | Surowiecki, J., The wisdom of crowds (2005), Anchor: Anchor New York |

[34] | Verkuilen, J.; Smithson, M., Mixed and mixture regression models for continuous bounded responses using the beta distribution, Journal of Educational and Behavioral Statistics, 37, 1, 82-113 (2012) |

[35] | Wang, X.; Du, X., Why does advice discounting occur? The combined roles of confidence and trust, Frontiers in Psychology, 9, 2381 (2018) |

[36] | Yaniv, I., Weighting and trimming: Heuristics for aggregating judgments under uncertainty, Organizational Behavior and Human Decision Processes, 69, 3, 237-249 (1997) |

[37] | Yaniv, I., Receiving other people’s advice: Influence and benefit, Organizational Behavior and Human Decision Processes, 93, 1, 1-13 (2004) |

[38] | Yaniv, I.; Milyavsky, M., Using advice from multiple sources to revise and improve judgments, Organizational Behavior and Human Decision Processes, 103, 1, 104-120 (2007) |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.