March 4th, 1999 : the recognition-primed decision hypothesis

On March 4, 1999, the newly appointed President of the Brazilian Central Bank, Mr Armínio Fraga, raised interest rates to a staggering 45% per annum. The objective of that decision was to keep foreign investors assets in Brazil, and prevent the country from default. At the time, Brazil suffered frem an enormously intense crisis of confidence, and fears of such default were widespread. Mr Fraga was walking a very fine line when making that decision, for it could bring forth unintended effects: the market, already concerned about Brazil's sustainability, could perceive the increased rate as an irreversible step towards the abyss inevitable default. Economic theory postulates the rational actor model as the driving force behind economic decision-making. The objective of this thesis is to present and discuss the hypothesis that this particular decision, and by extension many others, are better explained threugh the recognition-primed decision mode!.

A work domain analysis framework for modelling intensive care unit patients

Work domain analysis (WDA) has been applied to a range of complex work domains, but few WDAs have been undertaken in medical contexts. One pioneering effort suggested that clinical abstraction is not based on means-ends relations, whereas another effort downplayed the role of bio-regulatory mechanisms. In this paper it is argued that bio-regulatory mechanisms that govern physiological behaviour must be part of WDA models of patients as the systems at the core of intensive care units. Furthermore it is argued that because the inner functioning of patients is not completely known, clinical abstraction is based on hypothetico-deductive abstract reasoning. This paper presents an alternative modelling framework that conforms to the broader aspirations of WDA. A modified version of the viable systems model is used to represent the patient system as a nested dissipative structure while aspects of the recognition primed decision model are used to represent the information resources available to clinicians in ways that support lsquoif...thenrsquo conceptual relations. These two frameworks come together to form the recursive diagnostic framework, which may provide a more appropriate foundation for information display design in the intensive care unit.

Robust iris recognition using decision fusion and degradation modelling

This thesis investigates the use of fusion techniques and mathematical modelling to increase the robustness of iris recognition systems against iris image quality degradation, pupil size changes and partial occlusion. The proposed techniques improve recognition accuracy and enhance security. They can be further developed for better iris recognition in less constrained environments that do not require user cooperation. A framework to analyse the consistency of different regions of the iris is also developed. This can be applied to improve recognition systems using partial iris images, and cancelable biometric signatures or biometric based cryptography for privacy protection.

Using a Prediction and Option Generation Paradigm to Understand Decision Making

In many complex and dynamic domains, the ability to generate and then select the appropriate course of action is based on the decision maker's "reading" of the situation--in other words, their ability to assess the situation and predict how it will evolve over the next few seconds. Current theories regarding option generation during the situation assessment and response phases of decision making offer contrasting views on the cognitive mechanisms that support superior performance. The Recognition-Primed Decision-making model (RPD; Klein, 1989) and Take-The-First heuristic (TTF; Johnson & Raab, 2003) suggest that superior decisions are made by generating few options, and then selecting the first option as the final one. Long-Term Working Memory theory (LTWM; Ericsson & Kintsch, 1995), on the other hand, posits that skilled decision makers construct rich, detailed situation models, and that as a result, skilled performers should have the ability to generate more of the available task-relevant options. The main goal of this dissertation was to use these theories about option generation as a way to further the understanding of how police officers anticipate a perpetrator's actions, and make decisions about how to respond, during dynamic law enforcement situations. An additional goal was to gather information that can be used, in the future, to design training based on the anticipation skills, decision strategies, and processes of experienced officers. Two studies were conducted to achieve these goals. Study 1 identified video-based law enforcement scenarios that could be used to discriminate between experienced and less-experienced police officers, in terms of their ability to anticipate the outcome. The discriminating scenarios were used as the stimuli in Study 2; 23 experienced and 26 less-experienced police officers observed temporally-occluded versions of the scenarios, and then completed assessment and response option-generation tasks. The results provided mixed support for the nature of option generation in these situations. Consistent with RPD and TTF, participants typically selected the first-generated option as their final one, and did so during both the assessment and response phases of decision making. Consistent with LTWM theory, participants--regardless of experience level--generated more task-relevant assessment options than task-irrelevant options. However, an expected interaction between experience level and option-relevance was not observed. Collectively, the two studies provide a deeper understanding of how police officers make decisions in dynamic situations. The methods developed and employed in the studies can be used to investigate anticipation and decision making in other critical domains (e.g., nursing, military). The results are discussed in relation to how they can inform future studies of option-generation performance, and how they could be applied to develop training for law enforcement officers.

Developing an integrated decision model for the enhancement of sustainability deliverables for Australian road infrastructure projects

Road and highway infrastructure provides the backbone for a nation's economic growth. The versatile dispersion of population in Australia, from sparsely settled communities in remote areas to regenerated inner city suburbs with high density living in metropolitans, calls for continuing development and improvement on roads infrastructure under the current federal government policies and state governments' strategic plans. As road infrastructure projects involve large resources and mechanism, achieving sustainability not only in economic scales but also through environmental and social responsibility becomes a crucial issue. Current efforts are often impeded by different interpretation on sustainability agenda by stakeholders involved in these types of projects. As a result, sustainability deliverables at the project level is not often as transparent and measurable, compared to promises in project briefs and designs. This paper reviews the past studies on sustainable infrastructure construction, focusing on roads and highway projects. Through literature study and consultation with the industry, key sustainability indicators specific to road infrastructure projects have been identified. Based on these findings, this paper introduces an on-going research project aimed at identifying and integrating the different perceptions and priority needs of the stakeholders, and issues that impact on the gap between sustainability foci and its actual realization at project end level. The exploration helps generate an integrated decision-making model for sustainable road infrastructure projects. The research will promote to the industry more systematic and integrated approaches to decision-making on the implementation of sustainability strategies to achieve deliverable goals throughout the development and delivery process of road infrastructure projects in Australia.

A two-stage decision model for information filtering

Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems.

Modelling passenger flow at airport terminals : individual agent decision model for stochastic passenger behaviour

Airport system is complex. Passenger dynamics within it appear to be complicate as well. Passenger behaviours outside standard processes are regarded more significant in terms of public hazard and service rate issues. In this paper, we devised an individual agent decision model to simulate stochastic passenger behaviour in airport departure terminal. Bayesian networks are implemented into the decision making model to infer the probabilities that passengers choose to use any in-airport facilities. We aim to understand dynamics of the discretionary activities of passengers.

A preliminary decision model for shared services : insights from an Australian university context

Shared Services involves the convergence and streamlining of an organisation’s functions to ensure timely service delivery as effectively and efficiently as possible. This would result in lower cost, improved service delivery and economies of scale. The conventional wisdom of today is that the potential for Shared Services is increasing due to the increasing costs of changing systems and business requirements and also in implementing and running information systems (IS). However many organizations opt instead for an outsourcing arrangement as the alternative towards cost savings, due in essence to a lack of realization of this potential for Shared Services. This paper rationales turning from outsourcing (to looking within organisations) to leverage on Shared Services for similar cost savings and reaping other potential benefits. The paper’s objectives and contributions are three-fold: (1) distinguish between Shared Services and Outsourcing, (2) report on insights from a single Australian university case study through a transaction cost lens, and to demonstrate the potential for Shared Services and (3) develop a decision model to gauge the potential of implementing Shared Services across similar organisations.

Optimal maintenance and replacement via a semi-Markov decision model

Optimal bang-coast maintenance policies for a machine, subject to failure, are considered. The approach utilizes a semi-Markov model for the system. A simplified model for modifying the probability of machine failure with maintenance is employed. A numerical example is presented to illustrate the procedure and results.

Multirating decision model validation: the relevance of the quality of the securitization issues

Rating enables the information asymmetry existing in the issuer-investor relationship to be reduced, particularly for issues with a high degree of complexity, as is the case of securitizations. However, there may be a serious conflict of interest between the issuer’s choice and remuneration of the agency and the credit rating awarded, resulting in lower quality and information power of the published rating. In this paper, we propose an explicative model of the number of ratings requested, by analyzing the relevance of the number of ratings to measure the reliability, where multirating is shown to be associated to the quality, size, liquidity and the degree of information asymmetry relating to the issue. Thus, we consider that the regulatory changes that foster the widespread publication of simultaneous ratings could help to alleviate the problem of rating model arbitrage and the crisis of confidence in credit ratings in general and in the securitization issues, in particular.