Bias, Information, Noise: The BIN Model of Forecasting

Satopää, Ville A.; Salikhov, Marat; Mellers, Barb

doi:10.2139/ssrn.3540864

Cited by 5 publications

(12 citation statements)

References 56 publications

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“…First, this will reduce the cognitive effort for senior managers, which will have access to the group decision so as to themselves combine the individual decisions. Second, and as shown by research on the wisdom of crowds (see, e.g., Satopää et al, 2021) and also the current project, groups often make better, more accurate judgments than individuals-even expert individuals. Second, the team decision is used as a group feedback, which improve the efficiency and effectiveness of the individual and team decision making, as shown by several studies (e.g., Adomavicius et al, 2009;Bordetsky & Mark, 2000;Robert Jr et al, 2018).…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 69%

“…The identification and measurement of bias errors require the availability of the actual answers (here, scores provided by the expert) but, and as remarked by Kahneman et al ( 2016 ), the actual answers will only be known at the end of the mission, if ever. At this level, we recall that historical decisions, when available, can be used to estimate and correct bias errors, as advocated by Satopää et al ( 2021 ). In the conducted experiment, the actual threat levels had already been determined by the authors based on input from the scenario designer.…”

Section: Resultsmentioning

confidence: 99%

“…Such inconsistency is mainly due to two types of errors: noise and bias. Here, “noise” is defined as the variability of judgments or inconsistent decisions, whereas “bias” is defined as consistent diversion from the target (Adame, 2016 ; Hammond et al, 2006 ; Kahneman et al, 2016 , 2021 ; Satopää et al, 2021 ). These errors complicate the process of intelligence analysis and can result in key pieces of data being misclassified or overlooked with potential implications for the recognition of security threats (Heuer Jr., 1999 ; Reyna et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

“…Here, we generally need to know the actual answer in order to measure bias (Kahneman et al, 2016 ). However, Satopää et al ( 2021 ) argue we can rely on historical decisions to estimate and correct bias errors. The automatic correction of bias errors is difficult and may complicate the situation (adding more bias or noise).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of noise and bias errors in intelligence information systems

Labib

Chakhar

Hope

et al. 2022

Asso for Info Science & Tech

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An intelligence information system (IIS) is a particular kind of information systems (IS) devoted to the analysis of intelligence relevant to national security. Professional and military intelligence analysts play a key role in this, but their judgments can be inconsistent, mainly due to noise and bias. The team‐oriented aspects of the intelligence analysis process complicates the situation further. To enable analysts to achieve better judgments, the authors designed, implemented, and validated an innovative IIS for analyzing UK Military Signals Intelligence (SIGINT) data. The developed tool, the Team Information Decision Engine (TIDE), relies on an innovative preference learning method along with an aggregation procedure that permits combining scores by individual analysts into aggregated scores. This paper reports on a series of validation trials in which the performance of individual and team‐oriented analysts was accessed with respect to their effectiveness and efficiency. Results show that the use of the developed tool enhanced the effectiveness and efficiency of intelligence analysis process at both individual and team levels.

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Section: Theoretical and Practical Implicationsmentioning

confidence: 69%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of noise and bias errors in intelligence information systems

Labib

Chakhar

Hope

et al. 2022

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…To investigate how the three interventions—probability training, teamwork, and allowing the best forecasters to work together—improved performance accuracy, Satopää et al (2021) developed a Bayesian BIN model (Bias, Information, Noise) that disentangled three processes underlying forecasts. Improvement can come from reducing systematic bias, decreasing noise (random errors), and amplifying valid signals.…”

Section: Improving Human Predictionsmentioning

confidence: 99%

Predicting the future with humans and AI

Mellers

McCoy

2022

Consumer Psychology Review

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We review the classic clinical versus statistical prediction debate as well as related modern work on humans versus. algorithms. Despite the successes of statistical prediction over clinical prediction, there is still widespread resistance to algorithms. We discuss recent attempts to understand that resistance. Current research focuses on when people use algorithmic predictions, how people perceive algorithms, and how algorithms can be made more appealing. We also examine attempts to boost human forecasting accuracy, either by spotting talent, cultivating talent via training, or developing algorithms that aggregate individual forecasts. We hypothesize that hybrid models with both human and algorithmic predictions may encounter less resistance than algorithms alone, especially when the algorithm is "humanized" (with anthropomorphic features) and the human is "algorithmized" (by reducing nose, decreasing bias and increasing signal).

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Noise in Expectations: Evidence from Analyst Forecasts

de Silva,

Thesmar

2023

The Review of Financial Studies

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Analyst forecasts outperform econometric forecasts in the short run but underperform in the long run. We decompose these differences in forecasting accuracy into analysts’ information advantage, forecast bias, and forecast noise. We find that noise and bias strongly increase with forecast horizon, while analysts’ information advantage decays rapidly. A noise increase with horizon generates a mechanical reversal in the sign of the error-revision (Coibion-Gorodnichenko) regression coefficient at longer horizons, independently of over-/underreaction. A parsimonious model with bounded rationality and a noisy cognitive default matches the term structures of noise and bias jointly.

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Bias, Information, Noise: The BIN Model of Forecasting

Cited by 5 publications

References 56 publications

Analysis of noise and bias errors in intelligence information systems

Analysis of noise and bias errors in intelligence information systems

Predicting the future with humans and AI

Noise in Expectations: Evidence from Analyst Forecasts

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