Decision making with incomplete information

Canellas, Marc Christopher

Title:

Decision making with incomplete information

dc.contributor.advisor	Feigh, Karen M.
dc.contributor.author	Canellas, Marc Christopher
dc.contributor.committeeMember	German, Brian
dc.contributor.committeeMember	Pritchett, Amy
dc.contributor.committeeMember	Cross, Steve
dc.contributor.committeeMember	Rogers, Juan
dc.contributor.department	Aerospace Engineering
dc.date.accessioned	2017-08-17T18:58:15Z
dc.date.available	2017-08-17T18:58:15Z
dc.date.created	2017-08
dc.date.issued	2017-05-09
dc.date.submitted	August 2017
dc.date.updated	2017-08-17T18:58:15Z
dc.description.abstract	Decision makers are continuously required to make choices in environments with incomplete information. This dissertation sought to understand and, ultimately, support the wide range of decision making strategies used in environments with incomplete information. The results showed that the standard measure of incomplete information as total information, is insufficient for understanding and supporting decision makers faced with incomplete information. The distribution of information was shown to often be a more important determinant of decision making performance. Two new measures of the distribution of incomplete information were introduced (option imbalance and cue balance) and tested across three computer simulations of 18 variations of decision making strategies within hundreds of environments and millions of decision tasks with incomplete information, and one human-subjects study. The simulations were powered by a new general linear model of decision making which can efficiently and transparently model a wide range of strategies beyond the traditional set in the literature. Of the many potential mediators of the relationship between the distributions of incomplete information and performance, only the strategies' estimates of missing information were significant in the computational studies. Accurate estimates resulted in total information being the only meaningful determinant of accuracy while inaccurate estimates resulted in low option imbalance and high cue balance causing high accuracy. The simulation results were partially contradicted by a study in which human decision makers with accurate estimates were affected by option imbalance and cue balance in the same manner as inaccurate estimates – suggesting that some distributions might simply be difficult regardless of the estimates. These results argued that decision support should modify the presentation of information away from difficult distributions. These arguments were codified as heuristic information acquisition and restriction rules which, when tested, increased accuracy without probability and cue weight information.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/58631
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Decision making
dc.subject	Incomplete information
dc.subject	Computational cognitive modeling
dc.subject	Judgment
dc.subject	Mathematical models
dc.subject	Behavioral decision making
dc.subject	Decision analysis
dc.subject	Human-subject studies
dc.subject	Command and control
dc.subject	Degraded and denied information environments
dc.subject	Mathematical psychology
dc.title	Decision making with incomplete information
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Feigh, Karen M.
local.contributor.corporatename	College of Engineering
local.contributor.corporatename	Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries	Doctor of Philosophy with a Major in Aerospace Engineering
relation.isAdvisorOfPublication	43635977-32d3-4083-875f-9a9adff86a8f
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication	a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication	f6a932db-1cde-43b5-bcab-bf573da55ed6
thesis.degree.level	Doctoral