Influences upon judgment revision are issues of both theoretical and applied interest. Many studies in the extant literature have been categorized as Judge Advisor Systems (JAS) research, and algorithmic decompositions of estimation problems. JAS researchers acknowledge the differentiated social roles of advisor(s) and decision-makers; and seek to isolate the influence of advice from advisor(s), upon the deliberations of decision-makers or judges. JAS research commonly operationalizes advice in solely numeric terms, which undermines the JAS paradigm’s claims of ecological validity. Algorithmic decompositions of estimation problems provides judges with knowledge of the process of advice generation, and differs from advice provided by advisors in JAS studies, as advice is self-generated by users of algorithmic decompositions. The current work sets out why both the JAS paradigm, and algorithmic decompositions are limited (particularly in terms of single judge-advisor information exchange episodes), as means to aid beneficial judgment revision. Six studies are reported that frame, and operationalize research questions that extend understanding of potentially beneficial judgment revision. ‘Conformity to advice’ emerges as an important explanatory factor in judgment revision. Chapter 4 examines participants’ preferences for solely numeric or reasons-based advice, and explores process measures of depth of information search. Participants report an overwhelming preference for reasons-based advice. Chapter 5 investigates the cognitive weighting strategies participants utilize when considering reasons-based or solely numeric advice. Here, participants are insensitive to the type of advice, and discount advice to the same extent - irrespective of type. Chapter 6 investigates the influence of algorithmic decomposition upon beneficial judgment revision. Here, participants were provided with a step-by-step process for solving seemingly intractable estimation problems, or given advice constituted as a testimonial assertion. Results highlight conformity to advice, and the limitations of experimenter generated algorithmic decompositions of estimation problems of unknown effectiveness. Chapter 7 and 8 sought to develop the idea that algorithmic decompositions should influence judgment revision (both for extremely large, and small numerical quantities). Results show that algorithmic decompositions did not facilitate beneficial judgment revision. Instead conformity to advice, irrespective of the quality of advice, was observed only for estimates of large numerical quantities. Chapter 9 was framed as a final attempt to establish if people are able to successfully distinguish between objectively beneficial, and spurious advice. Results indicate that people are unsuccessful in doing so, and find such a task cognitively demanding. Methodological limitations of both the current state of the JAS paradigm, and research involving algorithmic decompositions of estimation problems are identified, in addition to the limitations of the work presented here. Ultimately, methodological suggestions are formulated that may improve understanding of advice giving and taking, in the context of JAS research.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:529129 |
| Date | January 2010 |
| Creators | McColl, Andrew Franklin |
| Publisher | Durham University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.dur.ac.uk/632/ |
Page generated in 0.0023 seconds