| NSF Org: |
SES Division of Social and Economic Sciences |
| Recipient: |
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| Initial Amendment Date: | August 9, 2006 |
| Latest Amendment Date: | August 26, 2008 |
| Award Number: | 0620008 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Robert O'Connor
roconnor@nsf.gov (703)292-7263 SES Division of Social and Economic Sciences SBE Directorate for Social, Behavioral and Economic Sciences |
| Start Date: | August 1, 2006 |
| End Date: | July 31, 2010 (Estimated) |
| Total Intended Award Amount: | $0.00 |
| Total Awarded Amount to Date: | $279,914.00 |
| Funds Obligated to Date: |
FY 2008 = $6,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
506 S WRIGHT ST URBANA IL US 61801-3620 (217)333-2187 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
506 S WRIGHT ST URBANA IL US 61801-3620 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Decision, Risk & Mgmt Sci, Methodology, Measuremt & Stats |
| Primary Program Source: |
01000809DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
Analogous to the three legs of a stool, the three fundamental bases for any decision are (1) The alternatives, or what we can do; (2) The information, or what we know; and (3) The preferences, or what we like. When faced with uncertainty, people may choose different alternatives based on their taste for risk and the information they have about that uncertain situation. This research focuses on the information element of the decision that is captured by joint probability distributions of several variables.
Incorporating dependence is a fundamental step for making inferences about uncertain events or for learning when we receive new information, but eliciting a representative probability distribution is a task that requires care if one is to minimize the effects of cognitive and motivational biases. When eliciting joint probability distributions, we are faced with added difficulties such as conditioning the probability assessments on several variables or assessing dependence parameters between them. These requirements make the assessment of joint probability distributions a difficult task to perform in practice.
The objectives of the proposed research are to develop and test a new method for constructing joint probability distributions of continuous random variables using isoprobability contours (contours of points with the same cumulative probability). We explore a new method for constructing isoprobability contours by eliciting pairwise preferences over binary gambles, without the need for numeric responses from the decision maker. This approach facilitates the joint probability assessment significantly. Once the isoprobability contours and at least one one-dimensional marginal probability distribution is determined, is it possible to construct the joint distribution of all the variables present. Thus, we also propose a new method for assessing dependence between the variables of the decision situation using isoprobability contours.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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