[1] A key component of the dynamics in a JAS is the differentiation between the two roles in that while the advisor provides input to the decision, the ultimate decision-making authority resides solely with the judge.
Though examples of JASs are prevalent in real-world settings, they are studied most frequently in laboratory experiments in which judge/advisor roles are randomly assigned and situations/variables are manipulated at a between-subjects level.
[5][6] Decision-making style refers to differences in the ways individuals approach decision tasks and respond to situations.
Weaknesses in these different areas make judges more susceptible to particular errors in judgment and may influence the way advisor input is received and acted upon.
These situations come up frequently in life and are part of almost every consumer decision about the kind of music to buy, clothes to wear, or restaurants to visit.
Advisor characteristics commonly associated with superior knowledge such as being older, more educated or more experienced also have been shown to decrease egocentric discounting in decision-making situations.
Decision-making outcomes in a JAS (or other advice-giving structures) have been widely shown to be more accurate than those from situations with isolated decision makers.
[3] Lastly, it was found that judges could actually become overconfident in their decisions when having to rely almost completely on advisor recommendations (due to not possessing nearly enough task-specific information themselves).
A recent example of an important JAS situation was that of the controversy around the federal loan guarantees to the now-bankrupt Solyndra.
For example, both the director of the National Economic Council and the Treasury secretary advised the president that they believed the selection guidelines were not thorough enough and might allow for funding of unnecessary, risky companies.
However, the Energy Secretary, under pressure from Congress, advised the president to actually speed up loans and decrease scrutiny on the selection process.
For example, an individual with diabetes might receive specific advice about better controlling their blood sugar after a situation that required that they go to the hospital.
An example of such application is seen in the work by Wilkins et al. (1999) on the development of the Raven and CoRaven decision-making aids used by the military to filter and represent massive amounts of battlefield data for strategic planning.
[30] Using principles derived from JAS research, the authors were able to analyze and better understand the aids, with the result being a more effective system that makes battlefield decision-making less of a risky process.
This utilization of JAS research is an example of one of the most promising and direct applications of the paradigm – collaborative technology, which can facilitate decision-making processes that are too complex for human cognition alone.
Judge advisor systems research can also be applied to business, finance, education, and many other fields in which hierarchical group-decision making is common.
Applications of such research could be used to make time-sensitive decisions in high-impact situations such as emergency rooms more efficient and accurate, potentially saving the lives of patients in need.
The JAS framework could be effectively applied in public affairs to increase the speed at which new policies are created and enacted.
[13] In the real world, decision-makers frequently have many motives beyond making the most accurate and informed decision, often due to social influences.
Some additional motives that have already been cited include attempting to diffuse responsibility for a decision,[5] minimizing the amount of effort on behalf of the decision-maker,[31] and maintaining good rapport with the advisor(s).
With the growing prevalence of machine learning systems, another emerging stream of JAS research focuses on unpacking contexts in which advice is provided by machine learning systems rather than human advisors, increasing the relevance of considering socio-technical factors in JAS scenarios.