How people learn about causal influence when there are many possible causes: A model based on informative transitions

Derringer, Cory; Rottman, Benjamin M.

doi:10.1016/j.cogpsych.2018.01.002

Cited by 10 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within the field of causal learning, research has studied how people think that the causes combine together, how they learn the unique impact of each cause, and how they learn about many stable or unstable causes simultaneously (e.g., Derringer & Rottman, 2018;Lucas, Bridgers, Griffiths, & Gopnik, 2014;Spellman, 1996). Research on multiple cue learning (e.g., Speekenbrink & Shanks, 2010) is similar in that the participant needs to learn how the multiple cues together predict an outcome.…”

Section: Learning From Experiencementioning

confidence: 99%

Motivated Reasoning in an Explore‐Exploit Task

Caddick

Rottman

2021

Cognitive Science

View full text Add to dashboard Cite

The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country and inferred the influence of the policies on the economy. The task was challenging and ambiguous, allowing participants to interpret the relations between the policies and the economy in multiple ways. In three studies, we found evidence of motivated reasoning despite financial incentives for accuracy. For example, participants who believed that border security funding should be increased were more likely to conclude that increasing border security funding actually caused a better economy in the task. In Study 2, we hypothesized that having neutral preferences (e.g., preferring neither increased nor decreased spending on border security) would lead to more accurate assessments overall, compared to having a strong initial preference; however, we did not find evidence for such an effect. In Study 3, we tested whether providing participants with possible functional forms of the policies (e.g., the policy takes some time to work or initially has a negative influence but eventually a positive influence) would lead to a smaller influence of motivated reasoning but found little evidence for this effect. This research advances the field of causal learning by studying the role of prior preferences, and in doing so, integrates the fields of causal learning and motivated reasoning using a novel explore-exploit task.

show abstract

Section: Learning From Experiencementioning

confidence: 99%

Motivated Reasoning in an Explore‐Exploit Task

Caddick

Rottman

2021

Cognitive Science

View full text Add to dashboard Cite

show abstract

“…Some events have other consequences as a result of a chain reaction. In order to successfully eliminate all the consequences, the causes of the problem should be found based on the appropriate philosophy of cause and effect and their filtering [17]. In the event of a component failure, all corrective measures must be taken to establish the previous state of process availability within the required time period.…”

Section: Data Center Physical Infrastructurementioning

confidence: 99%

“…When designing a computer data center it is vital to ensure the necessary main and backup energy supplies, an uninterruptible power supply (UPS), energy support for the system and communication cabinets, and cooling device power supplies, because costs are an important factor when designing the support infrastructure [11,12]. Compared to other approaches [15,16], the proposed method quickly provides filtered information about individual events in nodes and fast main event identification, as well as comprehensive monitoring of the IT data center infrastructure [17,18].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring of Data Center Physical Infrastructure

Matko

Brezovec²,

Milanovič

2019

Applied Sciences

View full text Add to dashboard Cite

This paper presents a new intelligent monitoring and event management method for data center physical infrastructure based on multilayer node event processing. The priorities of highly classified data centers are not only the maintenance of the highest level of reliability and availability of the operation, but also fast, specific event identification and energy consumption monitoring, which altogether improve the level of energy efficiency. The new method, using a tree node for each device, improves information about the events in a specific node. Its major advantage is that in the case of a large number of nodes, it considerably reduces the number of identified events (alarms/alerts) that might be the main cause. Fast event processing lowers data center physical infrastructure operating costs, improving at the same time the level of energy efficiency. The experimental results show that the use of tree nodes significantly reduces the number of unexpected events, the time needed for the main event identification, and the maintenance response time to events. By using event entities processing, multilayer nodes have a significant impact on the efficient operation of data center physical infrastructure.

show abstract

“…Some events have other events as a consequence in a chain reaction. To remove all the consequences successfully, the cause of the problem has to be found based on a cause-effect philosophy [17]. In case of a component failure, all corrective actions have to be performed in order to establish the previous process availability state within the demanded period of time.…”

Section: Event Processingmentioning

confidence: 99%

Improved Data Center Energy Efficiency and Availability with Multilayer Node Event Processing

Matko

Brezovec²

2018

Energies

View full text Add to dashboard Cite

This article presents an overview of a new multilayer node event processing (MNEP) method for improved data center energy efficiency and event management monitoring in its physical infrastructure. Event management is essential for the provision and maintenance of the reliability and functionality of the physical infrastructure. For effective maintenance, action preventing any downtime accurate event information as well as appropriate energy support is of outmost importance. The novelty of this approach lies in the enhanced availability and reliability of the physical infrastructure, improved data center infrastructure energy efficiency, as well as lower costs when designing a new physical infrastructure system. The MNEP method presents a data model based on the node tree processing. The experimental results show that the number of all potential unexpected events is reduced significantly as a result of the accurate identification of the alarm root cause. The comparison of time parameters required to evaluate the maintenance factors shows that the time needed to detect, identify, and react to alarm events decreases and has a significant impact on maintaining a defined high level of availability and energy efficiency in a data center physical infrastructure.

show abstract

How people learn about causal influence when there are many possible causes: A model based on informative transitions

Cited by 10 publications

References 38 publications

Motivated Reasoning in an Explore‐Exploit Task

Motivated Reasoning in an Explore‐Exploit Task

Intelligent Monitoring of Data Center Physical Infrastructure

Improved Data Center Energy Efficiency and Availability with Multilayer Node Event Processing

Contact Info

Product

Resources

About