Why Does Advice Discounting Occur? The Combined Roles of Confidence and Trust

Wang, Xiuxin; Du, Xiufang

doi:10.3389/fpsyg.2018.02381

Cited by 22 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, clear evidence for egocentric discounting has been found in both groups. Egocentric discounting was documented in Japanese [60,61] and Chinese participants [20,36,62], and in a group of executives from 24 different nationalities [28]. While some studies find stronger rates of egocentric discounting in East Asian participants as opposed to Western ones [61], others do not [20,60].…”

Section: Proximate Explanations For Egocentric Discountingmentioning

confidence: 99%

Social information use and social information waste

Morin

Jacquet

Vaesen

et al. 2021

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

Social information is immensely valuable. Yet we waste it. The information we get from observing other humans and from communicating with them is a cheap and reliable informational resource. It is considered the backbone of human cultural evolution. Theories and models focused on the evolution of social learning show the great adaptive benefits of evolving cognitive tools to process it. In spite of this, human adults in the experimental literature use social information quite inefficiently: they do not take it sufficiently into account. A comprehensive review of the literature on five experimental tasks documented 45 studies showing social information waste, and four studies showing social information being over-used. These studies cover ‘egocentric discounting’ phenomena as studied by social psychology, but also include experimental social learning studies. Social information waste means that human adults fail to give social information its optimal weight. Both proximal explanations and accounts derived from evolutionary theory leave crucial aspects of the phenomenon unaccounted for: egocentric discounting is a pervasive effect that no single unifying explanation fully captures. Cultural evolutionary theory's insistence on the power and benefits of social influence is to be balanced against this phenomenon. This article is part of the theme issue ‘Foundations of cultural evolution’.

show abstract

Section: Proximate Explanations For Egocentric Discountingmentioning

confidence: 99%

Social information use and social information waste

Morin

Jacquet

Vaesen

et al. 2021

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

show abstract

“…Situational factors play a role in whether individual trust and use advice. The decision to use a piece of advice relies on both confidence in one's beliefs and trust in the advice 33 . Generally, individuals overweight their own information and underweight the advice of others 34 .…”

Section: Advice Takingmentioning

confidence: 99%

Quantifying machine influence over human forecasters

Abeliuk

Benjamin

Galstyan

2020

Sci Rep

View full text Add to dashboard Cite

Crowdsourcing human forecasts and machine learning models each show promise in predicting future geopolitical outcomes. Crowdsourcing increases accuracy by pooling knowledge, which mitigates individual errors. On the other hand, advances in machine learning have led to machine models that increase accuracy due to their ability to parameterize and adapt to changing environments. To capitalize on the unique advantages of each method, recent efforts have shown improvements by “hybridizing” forecasts—pairing human forecasters with machine models. This study analyzes the effectiveness of such a hybrid system. In a perfect world, independent reasoning by the forecasters combined with the analytic capabilities of the machine models should complement each other to arrive at an ultimately more accurate forecast. However, well-documented biases describe how humans often mistrust and under-utilize such models in their forecasts. In this work, we present a model that can be used to estimate the trust that humans assign to a machine. We use forecasts made in the absence of machine models as prior beliefs to quantify the weights placed on the models. Our model can be used to uncover other aspects of forecasters’ decision-making processes. We find that forecasters trust the model rarely, in a pattern that suggests they treat machine models similarly to expert advisors, but only the best forecasters trust the models when they can be expected to perform well. We also find that forecasters tend to choose models that conform to their prior beliefs as opposed to anchoring on the model forecast. Our results suggest machine models can improve the judgment of a human pool but highlight the importance of accounting for trust and cognitive biases involved in the human judgment process.

show abstract

“…Data preparation. Following Wang and Du (2018), trials were removed if the initial estimate matched the advice (novice advice: young ϭ 2.7%, older ϭ 2.3% of trials, t(110) ϭ 0.76, p ϭ .448, d ϭ .14; expert advice: young ϭ 3.4%, older ϭ 3.3% of trials, t(110) ϭ 0.08, p ϭ .939, d ϭ .01). This was because such trials cannot establish the degree to which participants used advice.…”

Section: Materials and Proceduresmentioning

confidence: 99%

“…Taking advice involves cognitive processes such as initial valuation of the advice followed by the process of combining advice with individual judgment (Meshi, Biele, Korn, & Heekeren, 2012). Research involving young adults consistently shows that individuals adjust their independent estimates more toward (i.e., give more weight to) advice from experts than novices (e.g., Meshi et al, 2012; Sniezek & Buckley, 1995; Wang & Du, 2018). It is suggested that people value expert advice more than novice advice, and this valuation process takes place even before a decision is made and the outcome known.…”

mentioning

confidence: 99%