Modeling decision-making biases

McShane, Marjorie; Ниренбург, Сергей; Jarrell, Bruce E.

doi:10.1016/j.bica.2012.09.001

Cited by 19 publications

(19 citation statements)

References 9 publications

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“…When faced with the task of contextualizing incoherent information, the human mind often has a tendency to “fill in the blanks” with subjective inputs that may be influenced by cognitive biases inherent in human decision makers (McShane, Nirenburg, & Jarrell, 2013). In contrast, an AI-based decision system will be free of any such cognitive predispositions, and it will therefore be better at letting the data tell its own story, thus producing a visualization that is a truer representation of the data rather than a mere cognitive perception, and further, any data that are missing in the analysis of the leader will be made more explicit in examining the output of an AI-based decision-making system.…”

Section: The “Positives” To Be Expected From Automated Leadership Decmentioning

confidence: 99%

Rise of the Machines

Parry

Cohen

Bhattacharya

2016

Group & Organization Management

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Machines are increasingly becoming a substitute for human skills and intelligence in a number of fields where decisions that are crucial to group performance have to be taken under stringent constraints—for example, when an army contingent has to devise battlefield tactics or when a medical team has to diagnose and treat a life-threatening condition or illness. We hypothesize a scenario where similar machine-based intelligent technology is available to support, and even substitute human decision making in an organizational leadership context. We do not engage in any metaphysical debate on the plausibility of such a scenario. Rather, we contend that given what we observe in several other fields of human decision making, such a scenario may very well eventuate in the near future. We argue a number of “positives” that can be expected to emerge out of automated group and organizational leadership decision making. We also posit several anti-theses—“negatives” that can also potentially emerge from the hypothesized scenario and critically consider their implications. We aim to bring leadership and organization theorists, as well as researchers in machine intelligence, together at the discussion table for the first time and postulate that while leadership decision making in a group/organizational context could be effectively delegated to an artificial-intelligence (AI)-based decision system, this would need to be subject to the devising of crucial safeguarding conditions.

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Section: The “Positives” To Be Expected From Automated Leadership Decmentioning

confidence: 99%

Rise of the Machines

Parry

Cohen

Bhattacharya

2016

Group & Organization Management

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“…There is a limited number of studies in the literature, in which handling biased DMs in a specific case and context is addressed. For instance, McShane et al (2013) presented the OntoAgent model for automatic detection of decision-making biases, using clinical medicine as a sample application area. In particular, the intelligent agent was able to follow doctorpatient interaction and warn the doctor if the patient's decisions were being affected by biased reasoning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In some of these frameworks, such as Liu et al, 2018, the DMs have the freedom of changing their opinion after giving their initial opinion. Another research avenue related to bias is identifying the biased DMs (outliers) in the GDM process (see McShane, Nirenburg, & Jarrell (2013)). Even though this problem has been addressed in a range of contexts such as business in Lee (2014), NBA draft season in Ichniowski and Preston (2017), there is a shortage of efforts to present a general framework for identifying and managing outliers in GDM.…”

Section: Introductionmentioning

confidence: 99%

A decision support system for detecting and handling biased decision-makers in multi criteria group decision-making problems

Rabiee

Aslani

Rezaei

2021

Expert Systems with Applications

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Detecting and handling biased decision-makers in the group decision-making process is overlooked in the literature. This paper aims to develop an anti-biased statistical approach, including extreme, moderate, and soft versions, as a decision support system for group decision-making (GDM) to detect and handle the bias. The extreme version starts with eliminating the biased decision-makers (DMs). For this purpose, the DMs with a lower Biasedness Index value than a predefined threshold are removed from the process. Next, it continues with a procedure to mitigate the effect of partially biased DMs by assigning different weights to DMs with respect to their biasedness level. To do so, two ratios for the remaining DMs are calculated: (i) Overlap Ratio, which shows the relative value of overlap between the confidence interval (CI) of each DM and the maximum possible overlap value. (ii) Relative confidence interval CI which reflects the relative value of CI for each DM compared to the confidence interval CI of all DMs. The final step is assigning weight to each DM, considering the two values Overlap Ratio and Relative confidence interval. DMs with closer opinions to the aggregated opinion of all DMs, or those with an adequate level of discrimination in their judgments gain more weight. The framework addresses and prescribes possible actions for all possible cases in GDM including without outliers, cases with partial outliers, and extreme cases with complete disagreement among DMs, or when none of the DMs show an adequate level of discrimination power. The moderate version preassigns a minimum weight to all unbiased DMs and then follows the weighting step for the remaining total weight. However, the soft version follows the preassignmnet of weights to all DMs in the initial pool, meaning there is no elimination in this setting. The proposed approach is tested for several scenarios with different sizes. Four performance measures are introduced to evaluate the effectiveness of the proposed method. The resulted performance measures show the reliability of the outcomes.

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“…Increased standardization in mechanistic structures reduces the job's complexity by lowering the skill variety needed, reducing its ability to fulfill an employee. The power and responsibility of an employee to complete the job from start to finish encompasses task identity [60]; the more significant the task identity, the more likely the employee will be responsible for completing the work in its entirety to reach the objective [36]. The perceived relative importance of the job and its overall impact on the employee, co-workers, and external stakeholders [60] defines task significance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The power and responsibility of an employee to complete the job from start to finish encompasses task identity [60]; the more significant the task identity, the more likely the employee will be responsible for completing the work in its entirety to reach the objective [36]. The perceived relative importance of the job and its overall impact on the employee, co-workers, and external stakeholders [60] defines task significance. The more critical the job is to its overall success, the more significant it is to the employee and the more satisfaction derived from it.…”

Section: Literature Reviewmentioning

confidence: 99%

Teacher's Organizational Commitment in Mechanistic School Structures: A Study Conducted on the Job Characteristics of a Private School in Abu Dhabi

Masnour¹,

David²

2021

AJEMR

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Organizational structures are fundamental requirements of schools; the structure determines the components of the organization, which in turn contribute to the formation of job characteristics. Job characteristics affect labor motivation and satisfaction while feeding into the organizational commitment of the staff. The study investigates the effect of mechanistic structures on job characteristics and their combined impact on job satisfaction and organizational commitment. The Job Diagnostic Survey (JDS) and Motivational Potential Score (MPS) investigate the perceptions of an international school's teaching staff in Abu Dubai, United Arab Emirates (UAE). The findings indicate teachers possess affective commitment towards the school yet perceive their roles as severely curtailed in terms of authority and autonomy and limited in their ability to utilize diverse skill sets due to the extreme specialization inherent in their roles. Teachers view the organization as highly centralized due to the bureaucracy with high degrees of specialization and formalization. Still, they are motivated due to their perception of the importance of their roles. The number of cases is the limitation of the study, although it helped to focus on depth. Future studies may consider more cases for a broader understanding.

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Modeling decision-making biases

Cited by 19 publications

References 9 publications

Rise of the Machines

Rise of the Machines

A decision support system for detecting and handling biased decision-makers in multi criteria group decision-making problems

Teacher's Organizational Commitment in Mechanistic School Structures: A Study Conducted on the Job Characteristics of a Private School in Abu Dhabi

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