Participation and e-democracy how to utilize web 2.0 for policy decision-making

This paper investigates in how to utilize ICT and Web 2.0 technologies and e-democracy software for policy decision-making. It introduces a cutting edge decision-making system that integrates the practice of e-petitions, e-consultation, e-rulemaking, e-voting, and proxy voting. The paper demonstrates how under precondition of direct democracy through the use this system the collective intelligence (CI) of a population would be gathered and used throughout the policy process.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

A grouping model for distributed pipeline assets maintenance decision

Distributed pipeline assets systems are crucial to society. The deterioration of these assets and the optimal allocation of limited budget for their maintenance correspond to crucial challenges for water utility managers. Decision makers should be assisted with optimal solutions to select the best maintenance plan concerning available resources and management strategies. Much research effort has been dedicated to the development of optimal strategies for maintenance of water pipes. Most of the maintenance strategies are intended for scheduling individual water pipe. Consideration of optimal group scheduling replacement jobs for groups of pipes or other linear assets has so far not received much attention in literature. It is a common practice that replacement planners select two or three pipes manually with ambiguous criteria to group into one replacement job. This is obviously not the best solution for job grouping and may not be cost effective, especially when total cost can be up to multiple million dollars. In this paper, an optimal group scheduling scheme with three decision criteria for distributed pipeline assets maintenance decision is proposed. A Maintenance Grouping Optimization (MGO) model with multiple criteria is developed. An immediate challenge of such modeling is to deal with scalability of vast combinatorial solution space. To address this issue, a modified genetic algorithm is developed together with a Judgment Matrix. This Judgment Matrix is corresponding to various combinations of pipe replacement schedules. An industrial case study based on a section of a real water distribution network was conducted to test the new model. The results of the case study show that new schedule generated a significant cost reduction compared with the schedule without grouping pipes.

Attribute exclusion strategies in airline choice : accounting for exogenous information on decision maker processing strategies in models of discrete choice

We examine the impact of individual-specific information processing strategies (IPSs) on the inclusion/exclusion of attributes on the parameter estimates and behavioural outputs of models of discrete choice. Current practice assumes that individuals employ a homogenous IPS with regards to how they process attributes of stated choice (SC) experiments. We show how information collected exogenous of the SC experiment on whether respondents either ignored or considered each attribute may be used in the estimation process, and how such information provides outputs that are IPS segment specific. We contend that accounting the inclusion/exclusion of attributes will result in behaviourally richer population parameter estimates.

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.

Developing an IS-Impact decision tool : a literature based design science roadmap

This paper derives from research-in-progress intending both Design Research (DR) and Design Science (DS) outputs; the former a management decision tool based in IS-Impact (Gable et al. 2008) kernel theory; the latter being methodological learnings deriving from synthesis of the literature and reflection on the DR ‘case study’ experience. The paper introduces a generic, detailed and pragmatic DS ‘Research Roadmap’ or methodology, deriving at this stage primarily from synthesis and harmonization of relevant concepts identified through systematic archival analysis of related literature. The scope of the Roadmap too has been influenced by the parallel study aim to undertake DR applying and further evolving the Roadmap. The Roadmap is presented in attention to the dearth of detailed guidance available to novice Researchers in Design Science Research (DSR), and though preliminary, is expected to evolve and gradually be substantiated through experience of its application. A key distinction of the Roadmap from other DSR methods is its breadth of coverage of published DSR concepts and activities; its detail and scope. It represents a useful synthesis and integration of otherwise highly disparate DSR-related concepts.

Developing a multidimensional scale for ethical decision making

This article reports on the development of the managerial ethical profile (MEP) scale. The MEP scale is a multilevel, self-reporting scale measuring the perceived influence that different dimensions of common ethical frameworks have on managerial decision making. The MEP scale measures on eight subscales: economic egoism, reputational egoism, act utilitarianism, rule utilitarianism, self-virtue of self, virtue of others, act deontology, and rule deontology. Confirmatory factor analysis (CFA) was used to provide evidence of scale validity. Future research needs and the value of this measure for business ethics are discussed.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Developing a research method to test a new first-order decision making model for the procurement of public sector major infrastructure

Given global demand for new infrastructure, governments face substantial challenges in funding new infrastructure and simultaneously delivering Value for Money (VfM). The paper begins with an update on a key development in a new early/first-order procurement decision making model that deploys production cost/benefit theory and theories concerning transaction costs from the New Institutional Economics, in order to identify a procurement mode that is likely to deliver the best ratio of production costs and transaction costs to production benefits, and therefore deliver superior VfM relative to alternative procurement modes. In doing so, the new procurement model is also able to address the uncertainty concerning the relative merits of Public-Private Partnerships (PPP) and non-PPP procurement approaches. The main aim of the paper is to develop competition as a dependent variable/proxy for VfM and a hypothesis (overarching proposition), as well as developing a research method to test the new procurement model. Competition reflects both production costs and benefits (absolute level of competition) and transaction costs (level of realised competition) and is a key proxy for VfM. Using competition as a proxy for VfM, the overarching proposition is given as: When the actual procurement mode matches the predicted (theoretical) procurement mode (informed by the new procurement model), then actual competition is expected to match potential competition (based on actual capacity). To collect data to test this proposition, the research method that is developed in this paper combines a survey and case study approach. More specifically, data collection instruments for the surveys to collect data on actual procurement, actual competition and potential competition are outlined. Finally, plans for analysing this survey data are briefly mentioned, along with noting the planned use of analytical pattern matching in deploying the new procurement model and in order to develop the predicted (theoretical) procurement mode.

Manipulating task constraints to improve tactical knowledge and collective decision-making in rugby union

In team sports such as rugby union, a myriad of decisions and actions occur within the boundaries that compose the performance perceptual- motor workspace. The way that these performance boundaries constrain decision making and action has recently interested researchers and has involved developing an understanding of the concept of constraints. Considering team sports as complex dynamical systems, signifies that they are composed of multiple, independent agents (i.e. individual players) whose interactions are highly integrated. This level of complexity is characterized by the multiple ways that players in a rugby field can interact. It affords the emergence of rich patterns of behaviour, such as rucks, mauls, and collective tactical actions that emerge due to players’ adjustments to dynamically varying competition environments. During performance, the decisions and actions of each player are constrained by multiple causes (e.g. technical and tactical skills, emotional states, plans, thoughts, etc.) that generate multiple effects (e.g. to run or pass, to move forward to tackle or maintain position and drive the opponent to the line), a prime feature in a complex systems approach to team games performance (Bar- Yam, 2004). To establish a bridge between the complexity sciences and learning design in team sports like rugby union, the aim of practice sessions is to prepare players to pick up and explore the information available in the multiple constraints (i.e. the causes) that influence performance. Therefore, learning design in training sessions should be soundly based on the interactions amongst players (i.e.teammates and opponents) that will occur in rugby matches. To improve individual and collective decision making in rugby union, Passos and colleagues proposed in previous work a performer- environment interaction- based approach rather than a traditional performer- based approach (Passos, Araújo, Davids & Shuttleworth, 2008).

The ecological dynamics of decision-making in sailing

Competitive sailing is characterised by continuous interdependencies of decisions and actions. All actions imply a permanent monitoring of the environmental conditions, such as intensity and direction of the wind, sea characteristics, and the behaviour of the opponent sailors. These constraints on sailors’ behavior are in constant change implying continuous adjustments in sailors’ actions and decisions. Among the different parts of a regatta, tactics and strategy at the start are particularly relevant. Among coaches there is an adage that says that “the start is 50% of a regatta” (Houghton, 1984; Saltonstall, 1983/1986). Olympic sailing regattas are performed with boats of the same class, by one, two or three sailors, depending on the boat class. Normally before the start, sailors visit the racing venue and analyse wind and sea characteristics, in order to fine- tune their boats accordingly. Then, five minutes before the start, sailors initiate starting procedures in order to be in a favourable position at the starting line (at the “second zero”). This position is selected during the start period according to wind shifts tendencies and the actions of other boats (Figure 11.1). Only after the start signal can the boats cross the imaginary starting line between the race committee signal boat “A” and the pin end boat. The start takes place against the wind (upwind), and the boats start racing in the direction of mark 1. Based on the evaluation of the sea and wind characteristics (e.g. if the wind is stronger at a particular place on the course), sailors re- adjust their strategy for the regatta. This strategy may change during the regatta, according to wind changes and adversary actions. More to the point, strategic decisions constrain and are constrained by on- line decisions during the regatta.

A conceptual decision-making framework for the delivery of innovative change in organisations

Research found that today’s organisations are increasingly aware of the potential barriers and perceived challenges associated with the successful delivery of change — including cultural and sub-cultural indifferences; financial constraints; restricted timelines; insufficient senior management support; fragmented key stakeholder commitment; and inadequate training. The delivery and application of Innovative Change (see glossary) within a construction industry organisation tends to require a certain level of ‘readiness’. This readiness is the combination of an organisation’s ability to part from undertakings that may be old, traditional, or inefficient; and then being able to readily adopt a procedure or initiative which is new, improved, or more efficient. Despite the construction industry’s awareness of the various threats and opportunities associated with the delivery of change, research found little attention is currently given to develop a ‘decision-making framework’ that comprises measurable elements (dynamics) that may assist in more accurately determining an organisation’s level of readiness or ability to deliver innovative change. To resolve this, an initial Background Literature Review in 2004 identified six such dynamics, those of Change, Innovation, Implementation, Culture, Leadership, and Training and Education, which were then hypothesised to be key components of a ‘Conceptual Decision-making Framework’ (CDF) for delivering innovative change within an organisation. To support this hypothesis, a second (more extensive) Literature Review was undertaken from late 2007 to mid 2009. A Delphi study was embarked on in June 2008, inviting fifteen building and construction industry members to form a panel and take part in a Delphi study. The selection criterion required panel members to have senior positions (manager and above) within a recognised field or occupation, and to have experience, understanding and / or knowledge in the process of delivering change within organisations. The final panel comprised nine representatives from private and public industry organisations and tertiary / research and development (R&D) universities. The Delphi study developed, distributed and collated two rounds of survey questionnaires over a four-month period, comprising open-ended and closed questions (referred to as factors). The first round of Delphi survey questionnaires were distributed to the panel in August 2008, asking them to rate the relevancy of the six hypothesised dynamics. In early September 2008, round-one responses were returned, analysed and documented. From this, an additional three dynamics were identified and confirmed by the panel as being highly relevant during the decision-making process when delivering innovative change within an organisation. The additional dynamics (‘Knowledge-sharing and Management’; ‘Business Process Requirements’; and ‘Life-cycle Costs’) were then added to the first six dynamics and used to populate the second (final) Delphi survey questionnaire. This was distributed to the same nine panel members in October 2008, this time asking them to rate the relevancy of all nine dynamics. In November 2008, round-two responses were returned, analysed, summarised and documented. Final results confirmed stability in responses and met Delphi study guidelines. The final contribution is twofold. Firstly, findings confirm all nine dynamics as key components of the proposed CDF for delivering innovative change within an organisation. Secondly, the future development and testing of an ‘Innovative Change Delivery Process’ (ICDP) is proposed, one that is underpinned by an ‘Innovative Change Decision-making Framework’ (ICDF), an ‘Innovative Change Delivery Analysis’ (ICDA) program, and an ‘Innovative Change Delivery Guide’ (ICDG).

Decision support to green technology implementation on Australian university campuses

As the societal awareness on sustainability is gaining momentum worldwide, the higher education sector is expected to take the lead in education, research and the promotion of sustainable development. Universities have the diversity of skills and knowledge to explore new concepts and issues, the academic freedom to offer unbiased observations, and the capacity to engage in experimentation for solutions. There is a global trend that universities have realized and responded to sustainability challenge. By adopting green technologies, buildings on university campuses have the potential to offer highly productive and green environments for a quality learning experience for students, while minimising environmental impacts. Despite the potential benefits and metaphorical link to sustainability, few universities have moved towards implementing Green Roof and Living Wall on campuses widely, which have had more successful applications in commercial and residential buildings. Few past research efforts have examined the fundamental barriers to the implementation of sustainable projects on campuses from organizational level. To address this deficiency, an on-going research project is undertaken by Queensland University of Technology in Australia. The research is aimed at developing a comprehensive framework to facilitate better decision making for the promotion of Green Roof and Living Wall application on campuses. It will explore and highlight organizational factors as well as investigate and emphasize project delivery issues. Also, the critical technical indicators for Green Roof and Living Wall implementation will be identified. The expected outcome of this research has the potential to enhance Green Roof and Living Wall delivery in Australian universities, as a vital step towards realizing sustainability in higher education sectors.