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Decision Making
Published in Christopher D. Wickens, Justin G. Hollands, Simon. Banbury, Raja. Parasuraman, Engineering Psychology and Human Performance, 2015
Christopher D. Wickens, Justin G. Hollands, Simon. Banbury, Raja. Parasuraman
A third influence on decision making over time is known as decision fatigue (Tierney, 2011). Repeated decisions can often lead to decreased effort invested in accuracy and analysis. This phenomenon was illustrated dramatically in an analysis of parole board decisions carried out by Danzigera, Levav, and Pesso (2011), who observed that the probability of granting parole declined from 75 percent early in the morning, down to approximately 25 percent later in the day. Stated simply, the effort or cognitive resources required to make careful decision analysis was depleted over time, such that the “effort-lite” default strategy of denying parole (essentially deciding not to decide) begins to dominate.
Conventional and Unconventional Data Mining for Better Decision-Making
Published in Seweryn Spalek, Data Analytics in Project Management, 2018
In some cases, even when the right data is there, and the correct decision seems obvious, the decision may still never be made. This could be indecisiveness of the decision-makers, or the lack of energy due to “decision fatigue” as decisions require willpower to be made.*
Prelude to Artificial Intelligence: Decision-Making Techniques
Published in Rodgers Waymond, Artificial Intelligence in a Throughput Model, 2020
The advantages of providing Artificial Intelligence a role to play in organizational decision-making are numerous: Speedier decision-making: The tempo of organizations has hastened and displayed no sign of decelerating in the global community. Further, the ability to speed up the decision-making process is imperative. For example, petroleum companies, retailers, travel sites, and other services routinely use dynamic pricing to optimize their margins.Improve management of multiple inputs: Machines are far better than humans at handling many distinctive factors at once when making multifaceted decisions, can process much more data at once, and use probability to imply or implement the best possible decision choice.Reduction of decision fatigue: Typically, people are compelled to make multiple decisions over a short period. Due to time pressures, the quality of those decisions may depreciate over time. Dissipated by all the decisions made during a shopping trip, consumers find it much difficult to defy the enticement of a sugar rush at the point of sale (perhaps this is why supermarkets place candy and snacks at the cash counters). Algorithms have no such weaknesses and can assist decision makers’ escape from making inadequate decisions borne of exhaustion.More innovative thinking and non-intuitive predictions: Artificial Intelligence assists decision makers notice configurations that may not be readily ostensible to human analysis. For instance, a notable pharmacy ascertained through Artificial Intelligence that people who bought beer also tended to buy diapers at the same time (https://www.theregister.co.uk/2006/08/15/beer_diapers/). This type of unique discernment can have immediate influence on an organization.
Actionable cognitive twins for decision making in manufacturing
Published in International Journal of Production Research, 2022
Jože M. Rožanec, Jinzhi Lu, Jan Rupnik, Maja Škrjanc, Dunja Mladenić, Blaž Fortuna, Xiaochen Zheng, Dimitris Kiritsis
In Section 6 we have shown the ACT provides means to link different types of knowledge and means to associate them to a specific use case by Knowledge Graph. In Table 5 we provide summary statistics of our Knowledge Graph implementation to show that such functionality is supported for a complex scenario of two use cases (Production Planning and Demand Forecasting). From the use cases, we find our approach reduces complexity regarding to the production planning and demand forecasting process, by automating the creation and update of production plans and demand forecasts, which are currently manually created in different documents by the employees. Such automation saves time and prevents biased estimates, reducing a great amount of time invested by the employees on data collection and manual computation, while achieves more accurate results. Furthermore, through the decision-making options recommendations, we expect to reduce operators' decision fatigue, by proactively recommending possible actions based on the ACT's analysis and forecast outcomes.