Area:
Marketing Business Administration, Management Business Administration, Social Psychology
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High-probability grants
According to our matching algorithm, Joshua Klayman is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 1983 — 1985 |
Klayman, Joshua |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Processes of Learning in Probabilistic Environments @ National Opinion Research Center |
0.901 |
| 1986 — 1989 |
Klayman, Joshua |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Knowledge and Inference in Probabilistic Environments @ National Opinion Research Center |
0.901 |
| 1987 — 1990 |
Klayman, Joshua |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Knowledge and Inference in Probabilistic Environments Ii: Learning and Experience in Real-World Contexts @ National Opinion Research Center
How does an experienced neurologist learn to determine the cause of a patient's chronic headaches, a venture capitalist learn to spot a good prospect, or a teacher learn to motivate students? In all these cases, successful judgment is part talent, part training, and part luck. But the development of judgmental abilities over time also requires learning from experience. Because we live in an inherently probabilistic world, we must try to extract information from incomplete and imperfect feedback. The research proposed here explores the processes by which learning takes place in probabilistic environments, and the strategies by which people try to accomplish that task. The goal of this research is to provide information useful for both understanding and improving these learning processes. The results of this work have important potential applications in practical decision contexts. A great many major decisions are made on the basis of intuitive judgment, particularly when the situation is ill-defined, unstable, or novel, or when time, information, and resources are limited. It would be especially useful to have a better understanding of the limitations and possibilities of human learning in such situations. A better understanding of human learning can also guide the development and use of statistical methods and other decision aids. Such understanding can help clarify the costs and benefits of reliance on human vs. mechanical judgment, aid in the design and implementation of interactive man-machine systems, and identify means by which human learning processes themselves can be improved and facilitated.
|
0.901 |
| 1994 — 1997 |
Klayman, Joshua
Gonzalez-Vallejo, Claudia |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Confidence and Accuracy: the Roles of Random Error, Bias, and Learning @ National Opinion Research Center
The PI proposes to investigate the way in which people assign confidence to their beliefs and estimates. One hypothesis is that noise, i.e., error variance within the confidence judgment, may result in systematic overconfidence. This problem would be most pronounced as a person attempted to answer difficult questions, a finding which has previously been found in the literature. A new study is proposed to test this hypothesis. The PI also will investigate the role of practice in order to determine if greater experience within a topic domain enhances one's ability to assign more appropriate confidence levels to answers within that domain. Finally, the investigator will attempt to determine if accuracy and confidence are influenced by the same types of feedback.
|
0.901 |