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The purpose of this paper is twofold: first, to suggest a modified sales comparison model that is scalable and adaptable to value under conditions of certainty and uncertainty. The model can potentially be applied to residential property, non-residential property and large item plant and machinery in determining the value, rental or capitalisation rate. The second purpose is to address practitioner and end user bias, which if unaddressed can lead to potentially inconsistent valuation results.

Literature was reviewed on decision theory, specifically cognitive limitations, heuristics and biases. A qualitative approach is followed in the paper although the output of the proposed model itself is quantitative.

The paper argues that practitioners and end users alike tend to avoid advanced statistical techniques when valuing under conditions of certainty, while advanced statistical techniques would not be possible under conditions of uncertainty. In addition, practitioners can, due to the representative heuristic, be over-confident in their ability, skill or knowledge when performing valuations under conditions of certainty. When valuing under conditions of uncertainty, practitioners tend to avoid simple rule models as they consider the process too unique to be standardised. The combined effect is inconsistent valuation results unless it can potentially be addressed through an integrated and modified sales comparison model that takes into account varying degrees of certainty and uncertainty.

The proposed modified sales comparison model is an integrated model that can be adopted by practitioners in valuing residential, non-residential and large plant and machinery. It can potentially be used to value under conditions of certainty and uncertainty and improve valuation consistency. End users such as mortgage lenders and investors can benefit from the adoption of this model.

The aim of this paper is to propose an integrated and modified sales comparison model for valuing under conditions of certainty, normal uncertainty and abnormal uncertainty. The integrated model can value based on direct comparison under conditions of certainty and uncertainty while addressing the in practice avoidance of advanced statistical techniques and the implications of the representative heuristic and halo effect as cognitive biases on valuation consistency.

This chapter develops a return forecasting methodology that allows for instability in the relationship between stock returns and predictor variables, model uncertainty, and parameter estimation uncertainty. The predictive regression specification that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, uncertainty about the inclusion of forecasting variables, and uncertainty about parameter values by employing Bayesian model averaging. The implications of these three sources of uncertainty and their relative importance are investigated from an active investment management perspective. It is found that the economic value of incorporating all three sources of uncertainty is considerable. A typical investor would be willing to pay up to several hundreds of basis points annually to switch from a passive buy-and-hold strategy to an active strategy based on a return forecasting model that allows for model and parameter uncertainty as well as structural breaks in the regression parameters.

This paper discusses the experimental work in modelling uncertainty under a multi‐echelon enterprise resource planning (ERP)‐controlled manufacturing system. A new method known as part tagging (Ptag) is successfully implemented in a material requirements planning (MRP) planning architecture, which is used to generate a planned order release (POR) schedule for controlling purchase and manufacture operations in a batch manufacturing system using simulation. One of the most important findings is that parts tardy delivery (PTD) is a more responsive performance measure compared with finished products tardy delivery (FPTD); therefore it is recommended that PTD should be measured to reveal the unmasked effects of uncertainty. The main conclusion and implication from this experiment are that an ERP‐controlled manufacturing enterprise should diagnose for uncertainty in a way that produces significant effects on delivery tardiness, so that reduction of their levels will significantly minimise tardy delivery.

This study aims to develop a model which incorporates the impact of both aleatory and epistemic uncertainties into construction duration predictions, in a manner that is consistent with the nature/quality of information available about various factors which bring about uncertainties.

Data relating to 178 completed Tertiary Education Trust Fund (TETfund) building construction projects were obtained from construction firms via questionnaire survey. Using 90% of the data, the model was developed in the form of a hybrid-based algorithm implemented through a suitable user-friendly graphical user interface (GUI) using MATLAB programming language. Bayesian model averaging, Monte Carlo simulation and fuzzy logic were the statistical methods used for the algorithm development, prior to its GUI implementation in MATLAB. Using the remaining 10% data, the model's predictive accuracy was assessed via the independent samples t-test and the mean absolute percentage error (MAPE).

The developed model's predictions were found not statistically different from those of actual duration estimates in the 10% test data, with a MAPE of just 2%. This suggests that the model's ability to incorporate both aleatory and epistemic uncertainties improves accuracy of duration predictions made using it.

The model was developed using a particular type of building projects (TETfund building construction projects), and so its use is limited to projects with characteristics similar to those used for its development.

The developed model's predictions are expected to serve as a useful basis for consultancy firms and contractor organisations to make more realistic schedules and benchmark measures of construction period, thereby facilitating effective planning and successful execution of construction projects.

The study presented a model which permits combined manipulation of aleatory and epistemic uncertainties, hence ensuring a more realistic incorporation of uncertainty into construction duration predictions.

This paper aims to develop a comprehensive uncertainty quantification method using evidence theory for Park–Ang damage index-based performance design in which epistemic uncertainties are considered. Various sources of uncertainty emanating from the database of the cyclic test results of RC members provided by the Pacific Earthquake Engineering Research Center are taken into account.

In this paper, an uncertainty quantification methodology based on evidence theory is presented for the whole process of performance-based seismic design (PBSD), while considering uncertainty in the Park–Ang damage model. To alleviate the burden of high computational cost in propagating uncertainty, the differential evolution interval optimization strategy is used for efficiently finding the propagated belief structure throughout the whole design process.

The investigation results of this paper demonstrate that the uncertainty rooted in Park–Ang damage model have a significant influence on PBSD design and evaluation. It might be worth noting that the epistemic uncertainty present in the Park–Ang damage model needs to be considered to avoid underestimating the true uncertainty.

This paper presents an evidence theory-based uncertainty quantification framework for the whole process of PBSD.

We develop a theoretical model to compare forecast uncertainty estimated from time-series models to those available from survey density forecasts. The sum of the average variance of individual densities and the disagreement is shown to approximate the predictive uncertainty from well-specified time-series models when the variance of the aggregate shocks is relatively small compared to that of the idiosyncratic shocks. Due to grouping error problems and compositional heterogeneity in the panel, individual densities are used to estimate aggregate forecast uncertainty. During periods of regime change and structural break, ARCH estimates tend to diverge from survey measures.

This study aims to design a proper supply chain network for the vaccine industry in Iran, which considers several features such as uncertainties in demands and cost, perishability of vaccines, wastages in storage, limited capacity and different priorities for demands.

This study presents a mixed-integer linear programming (MILP) model and using a robust counterpart approach for coping with uncertainties of model.

The presented robust model in comparison with the deterministic model has a better performance and is more reliable for network design of vaccine supply chain.

This study considers uncertainty in the network design of vaccine supply chain for the first time in the vaccine context It presents an MILP model where strategic decisions for each echelon and tactical decisions among different echelons of supply chain are determined. Further, it models the difference between high- and low-priority demands for vaccine.

The purpose of this paper is to theoretically using two new models to analyze the effect of respondents’ uncertainty about their stated willingness to pay (WTP) on welfare estimates in the contingent valuation method (CVM) theoretically using two new models, and empirically to reveal consumers’ WTP to improve drinking water supply safety (WSS) in China.

In this paper, two alternative preference uncertainty treatment approaches are proposed to estimate consumers’ WTP theoretically and they are applied to China’s WSS improvement program from a payment card method, which depends on how consumers’ certainty level about their valuation is. Furthermore, four regression models are presented to investigate the determinants of consumers’ WTP.

Theoretically, the alternative approaches that proposed in this research can remove overestimation bias from traditional CVM method but with lower estimation efficiency. In addition, the empirical results of the uncertainty adjusted models show that the expected WTP to improve drinking WSS is from 0.55 to 0.56 Renminbi yuan/ton, which are lower than the estimates from the conventional standard CVM models. Consumers’ preferences for their concerns about WSS, attitudes toward WSS improvement programs, trusts in implement authorities and their knowledge of WSS have significant effects on the WTP for improving drinking WSS and on respondents’ uncertainty too.

Theoretically to the authors’ knowledge, it is the first attempt to compare alternative approaches to treat respondent uncertainty using numerical certainty scale combined with payment card format valuation questions in CVM. Empirically it is the first study at this large scale that investigates consumers’ WTP for improving drinking WSS incorporating with respondent uncertainty in China. In addition, to assess consumer preferences for improved drinking water safety and the sources of uncertainty, information on consumers’ attitudes toward WSS are considered at the first time.

This paper aims to focus on the assessment of the ability of computer models with imperfect functional forms and uncertain input parameters to represent reality.

In this assessment, both the agreement between a model’s predictions and available experiments and the robustness of this agreement to uncertainty have been evaluated. The concept of satisfying boundaries to represent input parameter sets that yield model predictions with acceptable fidelity to observed experiments has been introduced.

Satisfying boundaries provide several useful indicators for model assessment, and when calculated for varying fidelity thresholds and input parameter uncertainties, reveal the trade-off between the robustness to uncertainty in model parameters, the threshold for satisfactory fidelity and the probability of satisfying the given fidelity threshold. Using a controlled case-study example, important modeling decisions such as acceptable level of uncertainty, fidelity requirements and resource allocation for additional experiments are shown.

Traditional methods of model assessment are solely based on fidelity to experiments, leading to a single parameter set that is considered fidelity-optimal, which essentially represents the values which yield the optimal compensation between various sources of errors and uncertainties. Rather than maximizing fidelity, this study advocates for basing model assessment on the model’s ability to satisfy a required fidelity (or error tolerance). Evaluating the trade-off between error tolerance, parameter uncertainty and probability of satisfying this predefined error threshold provides us with a powerful tool for model assessment and resource allocation.