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Much recent research in GIScience is focused on developing deep comprehension of the underlying nature of uncertainty in order to design effective uncertainty representations that support informed decision-making. As it is impossible to eliminate all uncertainty from an abstracted representation, it is important to understand the involvement of uncertainty in the geographic information life cycle, the many forms that uncertainty can take, and the influence these forms have on decision-making. This paper examines the involvement and influence of geographic information uncertainty during decision-making, using the case study domain of floodplain mapping. A set of focus groups composed of floodplain mapping experts was conducted to provide initial insight into the following research questions: (1) How is uncertainty involved in the creation, representation, and use of geographic information in the domain of floodplain mapping and how can this practice be improved? (2) Is the MacEachren et al. typology an appropriate categorization of the many geographic information uncertainties in the domain of floodplain mapping or are there categories that must be added, removed, or revised? (3) Which categories of uncertainty are the most influential on the decision-making process in the domain of floodplain mapping? Although the focus groups revealed that the current involvement of geographic information uncertainty is less than ideal, there was clear consensus that the MacEachren et al. typology is an appropriate categorization of geographic information uncertainty for the domain of floodplain mapping and that the categories accuracy/ error, precision/resolution, and currency are the most influential on the decision-making process. KEYWORDS: Geographic information uncertainty, uncertainty categorization, decision-making, MacEachren et al. typology, floodplain mapping, Introduction 'We actually made a map of the country on the scale of a mile to a mile!' said Mein Herr. 'Have you used it much ?' I enquired. 'It [...]

Developing reliable methods for representing and managing information uncertainty remains a persistent and relevant challenge to GIScience. Information uncertainty is an intricate idea, and recent examinations of this concept have generated many perspectives on its representation and visualization, with perspectives emerging from a wide range of disciplines and application contexts. In this paper, we review and assess progress toward visual tools and methods to help analysts manage and understand information uncertainty. Specifically, we report on efforts to conceptualize uncertainty, decision making with uncertainty, frameworks tot representing uncertainty, visual representation and user control of displays of information uncertainty, and evaluative efforts to assess the use and usability of visual displays of uncertainty. We conclude by identifying seven key research challenges in visualizing information uncertainty, particularly as it applies to decision making and analysis. KEYWORDS: Uncertainty, geovisualization, representation, decision making, usability, Introduction Information uncertainty is a complex concept with many interpretations across knowledge domains and application contexts. Efforts to develop visualization methods and tools that can help information analysts understand and [...]

This paper proposes a neural network embedded Monte Carlo (NNMC) approach to account for uncertainty in water quality modeling. The framework of the proposed method has three major parts: a numerical water quality model, a neural network technique, and Monte Carlo simulation. The numerical model is used to generate desirable output for training and testing sets, and the neural network is used as a universal functional mapping tool to approximate the input-output response of the numerical model. The Monte Carlo simulation then uses the neural network to generate numerical realizations based on a probabilistic distribution of parameters, thus obtaining a probabilistic distribution of the simulated state variables. By embedding a neural network into the conventional Monte Carlo simulation, the proposed approach significantly improves upon the conventional method in computational efficiency. The proposed approach has been applied to uncertainty and risk analyses of a phosphorus model for Triadelphia Reservoir in Maryland. The results of this research show that the NNMC approach has potential for efficient uncertainty analysis of water quality modeling. DOI: 10.1061/(ASCE)0887-3801(2002)16:2(135) CE Database keywords: Neural networks; Monte Carlo method; Water quality; Models.

Technological innovation within the firm can be modelled as a process of uncertainty reduction. The four major sources of uncertainty are user needs, technological environments, competitive environments, and organizationsl resources. Reducing these uncertainties is the reponsibility of the marketing and R&D functions within the firm. Because these functions are reciprocally interdependent, their success in reducing uncertainty requires integration and collaboration between them. A contingency framework is developed which shows the effect and the determinants of interfunctional information transfer. It is argued that the synergistic results of integration can best be understood as a within-role increase of uncertainty reduction, and a between-role convergence of functional uncertainty reduction. The implications of the model are discussed. (Reprinted by permission of the publisher.)

A simple simulation model is the best approach to describe the relationship between farming practices and water quality. The linked simulation-optimization Sediment Economic Simulation and Optimization (SEDEC) model is described, and the Monte Carlo Simulation and Generalized Sensitivity and Regret Analyses are used for accounting for uncertainty in SEDEC model inputs. A case-study based on the Highland Silver Lake Watershed in Illinois is discussed.

Byline: David P. Biros (1), Mark Daly (2), Gregg Gunsch (3) Abstract: The purpose of this research was to investigate the effects that user task load level has on the relationship between an individual's trust in and subsequent use of a system's automation. Military decision-makers trust and use information system automation to make many tactical judgments and decisions. In situations of information uncertainty (information warfare environments), decision-makers must remain aware of information reliability issues and temperate their use of system automation if necessary. An individual's task load may have an effect on his use of a system's automation in environments of information uncertainty. It was hypothesized that user task load will have a moderating effect on the positive relationship between system automation trust and use of system automation. Specifically, in situations of information uncertainty (low trust), high task load will have a negative effect on the relationship. To test this hypothesis, an experiment in a simulated command and control micro-world was conducted in which system automation trust and individual task load were manipulated. The findings from the experiment support the positive relationship between automation trust and automation use found in previous research and suggest that task load does have a negative effect on the positive relationship between automation trust and automation use. Experiment participant who incurred a higher task load exhibited an over-reliance on their automated information systems to assist them in their decision-making activities. Such an over-reliance can lead to vulnerabilities of deception and suggests the need for automated deception detection capabilities. Author Affiliation: (1) Department of Systems and Engineering Management, Air Force Institute of Technology, 2950 Hobson Boulevard, Wright-Patterson AFB, OH, 45433, USA E-mail (2) 33rd Information Operation Squadron, Lackland AFB, TX, 78243, USA E-mail (3) Department of Engineering and Computer Science, 2950 Hobson Boulevard, Wright-Patterson AFB, OH o45433, USA E-mail Article History: Registration Date: 01/10/2004

This study provides novel evidence that cash-flow news quantitatively explains the investment effect in the cross section of stock returns. The negative return predictability of asset growth, investment growth, and accruals is evident only through the cash-flow news component of returns. The cash-flow news returns associated with investment-sorted portfolios exhibit a reversal from the preformation period to the postformation period. Such a return reversal is in line with reversals in firm fundamentals and becomes stronger for stocks with higher information uncertainty. Our findings are consistent with the expectational errors hypothesis and fail to support the risk-based explanation for the investment effect. Keywords: investment effect; q-theory; cash-flow news; return decomposition History: Received July 25, 2014; accepted April 19, 2015, by Neng Wang, finance. Published online in Articles in Advance November 23, 2015., 1. Introduction It is well documented that corporate investment is negatively associated with future stock returns, which is referred to as the investment effect or the asset growth effect (Titman [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jeem.2008.12.002 Byline: John K. Stranlund (a), Carlos A. Chavez (b), Mauricio G. Villena (c) Keywords: Compliance; Enforcement; Emissions taxes; Monitoring; Asymmetric information; Uncertainty Abstract: We consider the pricing of a uniformly mixed pollutant with a model of optimal, possibly firm-specific, emissions taxes and their enforcement under incomplete information about firms' abatement costs, enforcement costs, and pollution damage. We argue that optimality requires an enforcement strategy that induces full compliance by every firm, except possibly when a regulator can base the probabilities of detecting individual violations on observable correlates of violators' actual emissions. Moreover, optimality requires discriminatory taxes, except when a regulator is unable to use observable firm-level characteristics to gain some information about the variation in firms' abatement costs or monitoring costs. Author Affiliation: (a) Department of Resource Economics, University of Massachusetts-Amherst, 214 Stockbridge Hall, 80 Campus Center Way, Amherst, MA 01003, USA (b) Departamento de Economia, Universidad de Concepcion, Concepcion, Chile (c) Escuela de Negocios, Universidad Adolfo IbaA[+ or -]ez, Santiago, Chile Article History: Received 4 February 2007

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jet.2008.05.006 Byline: Atsushi Kajii (a), Takashi Ui (b) Keywords: Multiple priors; Interim efficiency; No trade; Dynamic consistency; Rectangular prior set Abstract: This paper considers an exchange economy under uncertainty with asymmetric information. Uncertainty is represented by multiple priors and posteriors of agents who have either Bewley's incomplete preferences or Gilboa-Schmeidler's maximin expected utility preferences. The main results characterize interim efficient allocations under uncertainty; that is, they provide conditions on the sets of posteriors, thus implicitly on the way how agents update the sets of priors, for non-existence of a trade which makes all agents better off at any realization of private information. For agents with the incomplete preferences, the condition is necessary and sufficient, but for agents with the maximin expected utility preferences, the condition is sufficient only. A couple of necessary conditions for the latter case are provided. Author Affiliation: (a) Institute of Economic Research, Kyoto University, Japan (b) Faculty of Economics, Yokohama National University, Japan Article History: Received 1 February 2006; Revised 12 September 2007; Accepted 14 May 2008 Article Note: (footnote) [star] This paper is an extended version of the paper entitled 'Trade with Heterogeneous Multiple Priors' which is circulated as CAEA Discussion Paper No. 096.

This research addresses the impact of expertise on the use of uncertain geographic information, using the case study domain of floodplain mapping. An online survey was developed to gain insight into the possible differences between experts and novices. Fifty-six participants completed the survey in which they were required to assess the risk at an identified location in the landscape relative to three floodplain delineations, each carrying a different degree of certainty, and then report their perceived difficulty in making the assessment and their confidence in the assessment. The responses were examined across three realms of expertise (education/training, work experience, and self-reported) and for two classes of expertise (domain and map-use). The analysis found that user confidence is affected by both domain and map-use expertise, but it is domain expertise that most impacts risk assessment itself and map-use expertise that most impacts the perceived difficulty of the risk assessment. KEYWORDS: Geographic information uncertainty, expertise, risk assessment, decision making, user-centered design, floodplain mapping, Introduction This research speaks to two current trends within GIScience. One is demonstrated in the recent advances in geospatial technologies, particularly web-based applications and location-based services, which have improved the [...]

In this paper, the relation between quantum covariances and quantum Fisher informations is studied. This study is applied to generalize a recently proved uncertainty relation based on quantum Fisher information. The proof given here considerably simplifies the previously proposed proofs and leads to more general inequalities. Index Terms--Generalized variance, operator monotone functions, quantum covariance, quantum Fisher information, uncertainty principle.

This paper addresses the important and somewhat contentious matter of how knowledge accrues in a system. The matter has at its heart the establishment of a scaling function for knowledge (as distinct from the scaling used for information) which is related to the density of the knowledge structure at any point in the system. We commence with a discussion of whether it is possible at all to scale knowledge, dispensing with any concepts of knowledge as a simple finite resource and making a distinction between the establishment of a metric and the act of measurement itself. First, we draw on the Shannon-Weaver (H) measure to establish how knowledge can be seen as contributing to the partitioning of message sets under the H-measure. This establishes how knowledge contributes to the quantity of information held within a system when viewed as a meta-structure for that information. Second, we build on the idea of knowledge as an endemic property of a structure of interconnections between concepts. We observe that knowledge content can be dense both in structures that are highly interconnected deploying a modest number of concepts and in those where the interconnections are more sparse but where the number of concepts deployed is high. A scaling function exhibiting appropriate properties is then proposed. It can be seen that the scaling associated with knowledge as meta-information and the scaling deriving from the interconnectivity point of view are connected. This scaling function is particularly useful in three ways. Firstly, it outlines the properties of knowledge itself which can be used as criteria for future knowledge-based research. Its application in practice creates the ability to identify areas of knowledge concentration within a system. Finally, this identification of knowledge 'hotspots' can be used to direct the investment of resources for the management of knowledge and it provides an indication of the appropriate approach for the management of this knowledge. We make some observations on the limitations of the approach, on its potential as a basis for managerial action (particularly in Knowledge Management) and on its relevance and applicability to OR practice (particularly in respect of systems approaches to knowledge mapping). Lastly, we offer a view on the likely line of research which may result from this work. Keywords: knowledge; scaling functions; entropy; information; uncertainty

This paper develops a framework to analyze the value of information in the context of health plan choice. We use a Bayesian learning model to estimate the impact and value of information using data from a large employer, which started distributing health plan ratings to its employees in 1997. We estimate the parameters of the model with simulated maximum likelihood, and use the estimates to quantify the value of the report card information. We model both continuous specifications with Gaussian priors and signals, and discrete specifications with Beta priors and Binomial signals. We find that the release of information had a statistically significant effect on health plan choices. Consumers were willing to pay about $330 per year per below expected performance rating avoided, and the average value of the report card per employee was about $20 per year. We find large variation in valuations across different performance domains, but no significant evidence of heterogeneity based on observable employee characteristics or unobservable dimensions. JEL classification: 111: D83; D12 Keywords: Information; Uncertainty; Bayesian learning models; Health plan choice; Health plan quality; Managed care; Report cards

We relax the standard assumption in the strategic trade policy literature that governments possess complete information about the economy. Assuming instead that governments must obtain information from firms, we examine firms' incentive to disclose information to the governments in the Brander-Spencer setting. With quantity competition, we find firms disclosing both demand and cost information, thereby justifying the literature's omniscient-government assumption. With price competition, however, firms have no incentives to disclose demand or cost information, so governments remain uninformed. Further, with quantity competition and unknown demand, governments are caught in an informational prisoner's dilemma. Keywords: Information; Uncertainty; Learning; Prisoner's dilemma; Strategic trade JEL classification: F12; F13

One important function of Intelligent Transportation System (ITS) applied in tourist cities is to improve visitors' mobility by releasing real-time transportation information and then shifting tourists from individual vehicles to intelligent public transit. The objective of this research is to quantify visitors' psychological and behavioral responses to tourism-related ITS. Designed with a Mixed Ranked Logit Model (MRLM) with random coefficients that was capable of evaluating potential effects from information uncertainty and other relevant factors on tourists' transport choices, an on-site and a subsequent web-based stated preference survey were conducted in a representative tourist city (Chengde, China). Simulated maximum-likelihood procedure was used to estimate random coefficients. Results indicate that tourists generally perceive longer travel time and longer wait time if real-time information is not available. ITS information is able to reduce tourists' perceived uncertainty and stimulating transport modal shifts. This novel MRLM contributes a new derivation model to logit model family and for the first time proposes an applicable methodology to assess useful features of ITS for tourists., 1. Introduction Tourist cities in China have witnessed an impressive growth in tourism industry over the last decade. In particular, this growth should considerably owe to the surge of the [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jhealeco.2006.06.002 Byline: Alan Brennan, Samer A. Kharroubi Keywords: Bayesian decision theory; Value of information; Uncertainty; Sensitivity analysis; Sample size; Clinical trial design; Stochastic CEA; Approximate Bayesian inference Abstract: We describe a novel process for transforming the efficiency of partial expected value of sample information (EVSI) computation in decision models. Traditional EVSI computation begins with Monte Carlo sampling to produce new simulated data-sets with a specified sample size. Each data-set is synthesised with prior information to give posterior distributions for model parameters, either via analytic formulae or a further Markov Chain Monte Carlo (MCMC) simulation. A further 'inner level' Monte Carlo sampling then quantifies the effect of the simulated data on the decision. This paper describes a novel form of Bayesian Laplace approximation, which can be replace both the Bayesian updating and the inner Monte Carlo sampling to compute the posterior expectation of a function. We compare the accuracy of EVSI estimates in two case study cost-effectiveness models using 1st and 2nd order versions of our approximation formula, the approximation of Tierney and Kadane, and traditional Monte Carlo. Computational efficiency gains depend on the complexity of the net benefit functions, the number of inner level Monte Carlo samples used, and the requirement or otherwise for MCMC methods to produce the posterior distributions. This methodology provides a new and valuable approach for EVSI computation in health economic decision models and potential wider benefits in many fields requiring Bayesian approximation. Author Affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, South Yorkshire S1 4DA, UK Article History: Received 17 September 2005; Revised 16 June 2006; Accepted 16 June 2006

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.apergo.2006.04.021 Byline: Dick Lenior (a), Wiel Janssen (b), Mark Neerincx (c), Kirsten Schreibers (a) Keywords: Traffic control; Decision support; Supervision; Adaptive systems; Cognitive engineering Abstract: The theme Smart Transport can be described as adequate human-system symbiosis to realize effective, efficient and human-friendly transport of goods and information. This paper addresses how to attune automation to human (cognitive) capacities (e.g. to take care of information uncertainty, operator trust and mutual man-machine adaptations). An introduction to smart transport is presented, including examples of best practice for engineering human factors in the vehicle ergonomics and train traffic control domain. The examples are representative of an ongoing trend in automation and they show how the human role changes from controller to supervisor. Section 2 focuses on the car driver and systems that support, or sometimes even take over, critical parts of the driving task. Due to the diversity of driver ability, driving context and dependence between driver and context factors, there is a need for personalised, adaptive and integrated support. Systematic research is needed to establish sound systems. Section 3 focuses on the train dispatcher support systems that predict train movements, detect potential conflicts and show the dispatcher the possibilities available to solve the detected problems. Via thorough analysis of both the process to be controlled and the dispatcher's tasks and cognitive needs, support functions were developed as part of an already very complex supervision and control system. The two examples, although from a different field, both show the need for further development in cognitive modelling as well as for the value of sound ergonomics task analysis in design practice. Author Affiliation: (a) ErgoS Engineering and Ergonomics, PO Box 267, Enschede, The Netherlands (b) TNO HF, PO Box 23, Soesterberg, The Netherlands (c) Delft University of Technology/TNO HF, PO Box 23, Soesterberg, The Netherlands Article Note: (footnote) [star] This paper covers the two keynotes in the symposium 'Ergonomics on the move -- Smart Transport', an integral part of the IEA2006 conference, by Wiel Janssen -- section 2 and Dick Lenior -- section 3.