The initial aims of the project were to identify characteristics of the command process at Higher Command levels in the Royal Navy so that systems design could include decision aids at this important focal point"in the overall system (a naval Task Force). A prerequisite for formulating recommendations is an understanding "of the Task Force Commander's role and decision process. Consequently, an attempt was made early on to structure the Task Force command task. It became apparent that, without special measures, such a description could not be acquired. This thesis, therefore, is the story of a project about collecting data and informa tion, using it and then interpret ting it for the aboye purposes. Since there was little known about how to achieve an understanding of senior decision making, especially in potentially unstructured areas like the naval Higher Command function in a conflict environment, appropriate measures were developed to do so. The overall methodology designed consisted of: a) basic research; b) interview techniques; c) scientific gaming procedure; and d) a data collection-in-action regime. The methodology sought to use 'laboratory' techniques initially to acquire enough insight to then mount a study of clients "in action". It was considered necessary to supplement the former types of methods (interviews and gaming activities) with versions actually involving the clients when performing their role (in the form of a structured self-report study). The overall methodology was rooted in the systems ideas of Checkland (1981) and Bowen's views on the OR process (1981 and 1984) . Both authors react against the traditional prescriptive, normative approach of text book OR practitioners. The latter pursui t ignores messy, human aspects of organisational life and requires that a problem situation is well understood so that applied mathematical techniques can be used to formulate and then , solve' a problem. Most high-level decision problems, though, cannot be so formulated. This thesis is an attempt to formulate and understand high-level decision problems in a different way, using different techniques, but with a similar aim of arriving at useful and meaningful decision support recommendations. The methods to be described should start to fill the gap that exists at the moment in the OR repertoire of methodology catering for such needs. It was not possible to implement the overall methodology in the naval context. The industrial phase of the research was therefore initiated so that all of the phases could be tested in an albeit limited programme. Some preliminary insights and findings emerged in both contexts. Essentially, high-level decision makers approach their tasks differently. They have differing priorities also - as revealed by the interview and gaming phases. However, the data collection-in-action study (implemented in an industrial context only) revealed that what happens in reality (when they are at work) does not fully reflect the pattern of priorities revealed in the laboratory studies. Consequently, it is necessary to use other means to arrive at a complete picture of their decision making process. The overall methodology includes the interview and gaming phases because they are necessary to acquire enough insights to mount a data collection-in-action study later on. Also, they have other research and training uses. The ~hesis also includes the use of an analysis technique (based on Hogberg 1985) which assists with the appreciation of high-level decision making problems. The technique forms the basis of a proposed decision support system for both military and industrial contexts.

Information systems and in particular decision support systems have been developed to supplement human information processing and to assist with decision-making. Human decision-making is facilitated by the often unconscious use of heuristics or rules of thumb in situations where it may not be possible or feasible to search for the best decision. Judgemental heuristics have previously been found to lead to biases in decision-making. When information systems are used as decision aids, they may have an influence on biases. This study investigates the possible role of information systems in introducing, reinforcing or reducing biases of decision-making. It has been found that information systems have the ability to introduce new biases and to reinforce biases. Information systems can also reduce biases, but this requires innovate thinking on the way information is represented and the way human decision-making processes are supported. It has also been found that in the real world, other than the laboratories where biases are usually measured, other constraints on rational decision-making, such as politics or data errors, can overshadow the effects of biases. / Dissertation (MPhil)--University of Pretoria, 2003. / Informatics / unrestricted

No description available.

Agent-based models (ABM) are an increasingly important research tool for describing and predicting interactions among humans and their environment. A key challenge for such models is the ability to faithfully represent human decision making with respect to observed behaviour. This thesis aims to address this challenge by developing a methodology for empirical measurement and simulation of decision making in humanenvironment systems. The methodology employs the Beliefs-Desires-Intentions (BDI) model of human reasoning to directly translate empirically measured decision data into artificial agents, based on sound theoretical principles. A common simulated decision environment is used for both eliciting human decision making behaviour, and validating artificial agents. Using this approach facilitates the collection of decision making narratives by way of participatory simulation, and promotes a fair comparison of real and modelled decision making. The methodology is applied in two case studies: One to carry out a trial involving human subjects solving an abstract land-use problem, and another to examine the feasibility of up-scaling the methodology to a real agricultural scenario—dairy farming. Results from the experiments indicate that the BDI-based methodology achieved reasonably direct encoding of decision making behaviour from elicited human narratives. The main limitations found with the technique are: (1) the significant use of subjects’ time required to elicit their decision making behaviour; (2) the significant programming effort required; and (3) the challenge of aggregating behaviour from multiple subjects into a generalised decision making model. In spite of its limitations, BDI has shown its strengths as a tool for empirical analysis and simulation of decision making in research of human-environment systems.

Modeling comprehensive human decision behaviors in a unified and extensible framework is quite challenging. In this research, an integrated Belief-Desire-Intention (BDI) modeling framework is proposed to represent the human decision behavior, whose submodules (Belief, Desire, Decision-Making, and Emotion modules) are based on a Bayesian belief network (BBN), Decision-Field-Theory (DFT), a probabilistic depth first search (PDFS) technique, and a BBN-reinforcement (Q-Learning) hybrid learning algorithm. A key novelty of the proposed model is its ability to represent various human decision behaviors such as decision-making, decision-planning, and learning in a unified framework.To this end, first, we extend DFT (a widely known psychological model for preference evolution) to cope with dynamic environments. The extended DFT (EDFT) updates the subjective evaluation for the alternatives and the attention weights on the attributes via BBN under the dynamic environment. To illustrate and validate the proposed EDFT, a human-in-the-loop experiment is conducted for a virtual stock market. Second, a new approach to represent learning (a dynamic evolution process of underlying modules) in the human decision behavior is proposed under the context of the BDI framework. Our research focuses on how a human adjusts his perception process (involving BBN) dynamically against his performance (depicted via a confidence index) in predicting the environment as part of his decision-planning. To this end, Q-learning is employed and further developed.To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE). The proposed modeling framework is demonstrated for a human's evacuation behaviors in response to a terrorist bomb attack. The constructed simulation has been used to test the impact of several factors (e.g., demographics, number of police officers, information sharing via speakers) on evacuation performance (e.g., average evacuation time, percentage of casualties).In addition, the proposed human decision behavior model is extended for decisions of many stakeholders that form a complex social network in the community-based development of software systems.To the best of our knowledge, the proposed human decision behavior modeling framework is one of the first efforts to represent various human decision behaviors (e.g., decision-making, decision-planning, dynamic learning) in a unified BDI framework.

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Dianne Speake, Florida State University, School of Nursing. Title and description from dissertation home page (viewed Sept. 28, 2004). Includes bibliographical references.

Achieving sustainable development to meet the needs of current and future generations is currently on top of the global agenda, both in scientific research as well as global politics. However, achieving sustainable development is still a grand challenge, not least because it is embedded in the context of global change that affects most resource use systems worldwide in multiple ways. Even though many approaches to sustainable management do consider the connection between human activity and environmental dynamics, the role of human behavior as a main driver of system dynamics in coupled human and natural systems is often only poorly addressed. In this thesis, we aim to contribute to an improved understanding under which conditions human resource use decisions lead to sustainable outcomes, with regard to global change. For this, we will take the perspective of human decision-making and its social, ecological and economic consequences in two different resource use contexts, namely a) pastoralism in drylands and b) disaster risk management with respect to floods. We explicitly consider individual human decision-making as driver of social-ecological system dynamics, investigate the feedbacks between system components, as well as the impact of global change on resource use. To analyze such complex system dynamics, simulation models have proven to be helpful analysis tools. Particularly agent-based modeling represents a flexible and powerful analysis tool, as it allows us to model the decisions and interactions of individual agents at the micro level, while at the same time observing the outcome of their behavior on a system level. Within three case studies, we develop agent-based simulation models that capture the dynamics and feedbacks of the social-ecological system under consideration in a spatially explicit way. The first study analyzes the performance of disaster management organizations under change. In the second study, we aim to detect the drivers for polarization in a pastoral system in Morocco. The last study investigates behavioral change of pastoralist households and its impact on social, ecological and economic outcome measures. By analyzing a range of scenarios in each study, we determine both the long-term impact of different decision regimes on the state of the social-ecological system as well as the dimensions of change that have the most profound impact on the system dynamics and the sustainability of resource use. Main results that could be obtained from the modeling experiments include the identification of key resources that have a high influence on the long-term system dynamics. We are also able to show that under the influence of global change, access to certain resources gains in importance, as resources can act as buffer mechanisms to mitigate the adverse effects of global change. Through the operationalization of behavioral theories in model rules and the explicit representation of heterogeneous agent decision making, we could determine under which conditions a more refined representation of human decision making matters, and when a change in behavioral strategies leads to different social-ecological outcomes. Furthermore, all three modeling studies demonstrate the usefulness of stylized agent-based models to gain insights into complex systems. Overall, this thesis contributes to social-ecological systems research by developing appropriate simulation models to address the problem of sustainable resource use under global change.

Les clients des missions de drones sont demandeurs de systèmes d'information et de décision dans lesquels l'humain joue un rôle clé avec sa capacité unique d'analyse. Nous avons présenté comme première contribution un référentiel des niveaux d'automatisation qui interroge systématiquement sur le rôle de l'humain, et ce en rupture avec les référentiels précédents. Nous avons proposé comme seconde contribution un système décisionnel qui laisse les systèmes automatisés être incertains quant à l'ordre de certaines options de telle sorte que l'opérateur/superviseur puisse trancher. Afin d'augmenter le ratio nombre de drones / nombre d'humains, nous avons restreint l'usage de la décision humaine à l'arbitrage des conflits entre objectifs majeurs. Nous avons finalement expérimenté le sacrifice d'objectifs mineurs pour donner plus de temps à ces arbitrages. En conclusion nous rappellerons les mérites de nos deux contributions. LOA4 est un référentiel des niveaux d'autonomie centré sur l'humain, attentif à l'adaptation et compatible avec UML/PACT. Le système décisionnel que nous proposons est également centré sur l'humain et lui permet de jouer - même avec parcimonie et parfois au prix du sacrifice d'objectifs mineurs - un rôle clé basé sur sa capacité unique d'analyse. / Humans are still vital after years of automation and the clients of UAV mission systems want to preserve the human user’s key role because human knowledge, experience and judgment provide unique capability to analyze safety risks and to think ahead in uncertain and novel situations. Our first contribution is LOA4, a set of tree levels of automation in four dimensions to assess human involvement in partially automated systems. Previous sets focus too much attention on the computer rather than on the collaboration between the computer and its operator/supervisor. Unlike previous sets, our set systematically assess human involvement: is there none; some sometime; or some anytime. Its simplicity allow to recursively assess the situation of automation of a system based on the situation of automation of its parts. These information can be part of an IHM to increase the situation awareness in real time. Our second contribution is a mission system in which automated systems decide to delegate some decisions to humans. In order to increase the ratio number of “uavs / number of humans”, decisions that are untrusted to humans are mostly both ambivalent and critical. Some minor goals may be discarded to provide humans with more time to make their decisions. We implemented our approach and we report some results.

The purpose of this research was to determine whether changes in the presentation format of items in a computer display could be used to alter the impact of specific cognitive biases, and to add to the knowledge needed to construct theory-based guidelines for output design. The problem motivating this study is twofold. The first part of the problem is the sub-optimal decision making caused by the use of heuristics and their associated cognitive biases. The second part of the problem is the lack of a theoretical basis to guide the design of information presentation formats to counter the effects of such biases. An availability model of the impact of changes in presentation format on biases and heuristics was constructed based on the findings of a literature review. A six-part laboratory experiment was conducted utilizing a sample of 205 student subjects from the college of business. The independent variable was presentation format which was manipulated by altering the visual salience or visual recency of items of information in a visual computer display. The dependent variables included recall, perceived importance, and the subjects' responses to three judgment tasks. The results clearly demonstrate that changes in presentation format can be used to alter the impact of cognitive biases on human decision making. The results also provide support for the availability model, with the exception of the proposed influence of learning style. Learning style was found to have no significant impact on decision making whether alone or in combination with changes in presentation format. The results of this investigation demonstrate that by using our knowledge of cognitive processes (e.g., the visual salience effect, the visual recency effect, and the availability heuristic), presentation formats can be altered in order to moderate the effects of certain biases and heuristics in human decision making. An understanding of these results may be useful in improving DSS design.

L'objectif initial de cette thèse est d'apporter une solution à ce problème en proposant, premièrement, une approche cognitive basée sur le paradigme appelé Belief-Desire-Intention (BDI) pour représenter les processus de prise de décision des acteurs humains, et deuxièmement, une validation de cette approche dans le contexte d'un modèle complet de changement d'usage des sols dans lequel la plupart des facteurs cités ci-dessus sont également simulés. Le résultat de ce travail est une approche générique qui a été validée sur un modèle intégrant le changement d'usage des sols d'une région située dans le Delta du Mékong au Vietnam. Nos contributions principales sont les suivantes : Intégration d’une architecture BDI au sein d'une plateforme de modélisation à base d'agents (GAMA) ;Conception d’un cadre générique baptisé « Multi-Agent Based Land-Use Change » (MAB-LUC) permettant de modéliser et de simuler les changements d’usage des sols en prenant en compte les décisions des agriculteurs ;Proposition d’une solution permettant d’intégrer et d’évaluer les facteurs socio-économiques et environnementaux dans le cadre de la planification agraire et d’intégrer MAB-LUC dans le processus existant proposé par la FAO.Ce travail, au-delà du cas d’étude concernant le Delta du Mékong, a enfin été conçu de façon générique afin que la méthodologie utilisée puisse être généralisée à la modélisation de systèmes socio-écologiques où les facteurs humains doivent être représentés avec précision. / The initial goal of this thesis has been then to address this problem by proposing, on one hand, a cognitive approach based on the Belief-Desire-Intention (BDI) paradigm to represent the decision-making processes of human actors in agent-based models and, on the second hand, a validation of this approach in a complete land-use change model in which most of the factors cited above have also been simulated.The outcome of this work is a generic approach, which has been validated in a complex integrated land-use change model of a small region of the Vietnamese Mekong Delta. Our main contributions have been:The integration of the BDI architecture within an agent-based modeling platform (GAMA); The design of the Multi-Agent Based Land-Use Change (MAB-LUC) framework that can take into account the farmers’ decision-making in the land-use change processes;The proposal of a solution to assess the socio-economic and environmental factors in land-use planning and to integrate the MAB-LUC framework into the land-use planning process of. I conclude by showing that this work, designed in a generic fashion, can be reused and generalized for the modeling of complex socio-ecological systems where individual human factors need to be represented accurately.