Advancing the state of the art in the modelling and simulation of information systems evaluation

It is widely accepted that Information Systems Evaluation (ISE) is a powerful and useful technique that can be used to assess IT/IS investments in an a-priori or a-posteriori sense. Traditional approaches to ISE have tended to centre upon financial and management accounting frameworks, seeking to reconcile tangible and intangible costs, benefits, risks and value factors. Such techniques, however, do not provide the IS researcher or practitioner with further insight or appreciation of any inherent and implicit inter-relationships, in the investment justification process. Thus, this paper outlines and discusses via a taxonomy and resulting classification, alternative and complementary approaches that can be applied to ISE from the fields of Artificial Intelligence (AI), Operational Research (OR) and Management Science (MS). The paper subsequently concludes that such approaches can be potentially used by researchers and practitioners in the field, as a basis for carrying out further research in the field of applied ISE

Quantifying uncertainty in pest risk maps and assessments : adopting a risk-averse decision maker’s perspective

Pest risk maps are important decision support tools when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision-makers tend to follow a more certain (i.e., risk-averse) course of action. We presented a new mapping technique that assesses pest invasion risk from the perspective of a risk-averse decision maker. We demonstrated the method by evaluating the likelihood that an invasive forest pest will be transported to one of the U.S. states or Canadian provinces in infested firewood by visitors to U.S. federal campgrounds. We tested the impact of the risk aversion assumption using distributions of plausible pest arrival scenarios generated with a geographically explicit model developed from data documenting camper travel across the study area. Next, we prioritized regions of high and low pest arrival risk via application of two stochastic ordering techniques that employed, respectively, first- and second-degree stochastic dominance rules, the latter of which incorporated the notion of risk aversion. We then identified regions in the study area where the pest risk value changed considerably after incorporating risk aversion. While both methods identified similar areas of highest and lowest risk, they differed in how they demarcated moderate-risk areas. In general, the second-order stochastic dominance method assigned lower risk rankings to moderate-risk areas. Overall, this new method offers a better strategy to deal with the uncertainty typically associated with risk assessments and provides a tractable way to incorporate decisionmaking preferences into final risk estimates, and thus helps to better align these estimates with particular decision-making scenarios about a pest organism of concern. Incorporation of risk aversion also helps prioritize the set of locations to target for inspections and outreach activities, which can be costly. Our results are especially important and useful given the huge number of camping trips that occur each year in the United States and Canada

Citation Statistics

This is a report about the use and misuse of citation data in the assessment of scientific research. The idea that research assessment must be done using ``simple and objective'' methods is increasingly prevalent today. The ``simple and objective'' methods are broadly interpreted as bibliometrics, that is, citation data and the statistics derived from them. There is a belief that citation statistics are inherently more accurate because they substitute simple numbers for complex judgments, and hence overcome the possible subjectivity of peer review. But this belief is unfounded.Comment: This paper commented in: [arXiv:0910.3532], [arXiv:0910.3537], [arXiv:0910.3543], [arXiv:0910.3546]. Rejoinder in [arXiv:0910.3548]. Published in at http://dx.doi.org/10.1214/09-STS285 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Quantifying uncertainty in pest risk maps and assessments : adopting a risk-averse decision maker’s perspective

Pest risk maps are important decision support tools when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision-makers tend to follow a more certain (i.e., risk-averse) course of action. We presented a new mapping technique that assesses pest invasion risk from the perspective of a risk-averse decision maker. We demonstrated the method by evaluating the likelihood that an invasive forest pest will be transported to one of the U.S. states or Canadian provinces in infested firewood by visitors to U.S. federal campgrounds. We tested the impact of the risk aversion assumption using distributions of plausible pest arrival scenarios generated with a geographically explicit model developed from data documenting camper travel across the study area. Next, we prioritized regions of high and low pest arrival risk via application of two stochastic ordering techniques that employed, respectively, first- and second-degree stochastic dominance rules, the latter of which incorporated the notion of risk aversion. We then identified regions in the study area where the pest risk value changed considerably after incorporating risk aversion. While both methods identified similar areas of highest and lowest risk, they differed in how they demarcated moderate-risk areas. In general, the second-order stochastic dominance method assigned lower risk rankings to moderate-risk areas. Overall, this new method offers a better strategy to deal with the uncertainty typically associated with risk assessments and provides a tractable way to incorporate decisionmaking preferences into final risk estimates, and thus helps to better align these estimates with particular decision-making scenarios about a pest organism of concern. Incorporation of risk aversion also helps prioritize the set of locations to target for inspections and outreach activities, which can be costly. Our results are especially important and useful given the huge number of camping trips that occur each year in the United States and Canada

Spotlight on Assistance Dogs-Legislation, Welfare and Research

Assistance dogs are a very diverse group of working dogs that are trained to assist humans with different types of disabilities in their daily lives. Despite these dogs' value for humankind, research on their welfare status, cognitive and behavioural capacities, selection criteria for the best fitting individuals, effective training and management practices, and genetic issues are so far lacking. This review highlights the need to address these topics and to promote progress in legal issues around assistance dogs. The topic of assistance dogs is approached comprehensively by outlining the current status of knowledge in three different dimensions: (1) the legal dimension, outlining important legal issues in the EU and Australia; (2) the welfare dimension; and (3) the dimension of research, covering assistance dog selection and training. For each of these three dimensions, we discuss potential approaches that can be implemented in the future in order to support assistance dog working performance, to protect the dogs' welfare, and to improve our knowledge about them. Additionally, there remain many legal issues, such as the presence of assistance dogs in public areas, the resolution of which would benefit both the assistance dog and the owner with disability

Behavioural decisions & policy

We study the public policy implications of a model in which agents do not fully internalize all the conscequences of their actions. Such a model uni…es seemingly disconected models with behavioral agents. We evaluate the scope of paternalistic and libertarian-parternalistic policies in the light of our model, and propose an alternative type of approach, called soft-libertarian, which guides the decision makers in the internalization of all the conscequences of their actions. Psychotherapy is one example of a soft-libertarian policy. Moreover, we show that in our behavioral framework, policies that increase the set of opportunities or provide more information to the agent may not longer be individual welfare improving

Judgements during information seeking: a naturalistic approach to understanding the assessment of enough information

In this article, theories of human judgement and decision making are reviewed and their use by library and information science researchers examined. A different perspective on judgement and decision making is offered by the field of naturalistic decision making (NDM) and the implications of this approach are considered for an expanded understanding of how judgements and decisions are made during information seeking. This discussion is illustrated by a case from a recent emperical investigation into how judgements of enough information are made in the workplace. The article concludes with a critical evaluation of the NDM approach. It is argued that NDM, a recent development in decision theory, offers a new perspective from which to investigate judgements and decisions during information seeking

Explaining the Policy Constraints of Anti-democratic Regimes by Means of Sequential OLS-Regressions

One of the key problems of many sociological regression models is their modest explanatory power. This has not only to do with the insufficient development of the underlying theories but also with the free will of the concerned social actors, which manifests itself in irrational, spontaneous, and sometimes even arbitrary decisions. The foreign and economic policy of the US government under Donald Trump is an excellent example of this source of indeterminacy. An alternative and more promising approach is an explanation of the constraints of social behaviour by the unequal distribution of power resources and the competing interests of the actors concerned. This approach requires, on the one hand, enough observational data which include cases that reached the analysed constraints. On the other hand, there is a need for statistical procedures which estimate and explain these constraints. Assuming that sufficient amounts of data are available, this paper proposes the use of sequential OLS regressions, which eliminate step by step non-critical observations in order to identify the cases that reached the mentioned constraints. For illustrative purposes, the author analyses the policy space of anti-democratic regimes with regard to their possibilities of curbing democracy. On the basis of the democracy scores of Freedom House, the author explores the governmental constraints set by (i) national civil societies and (ii) international NGOs for the promotion of political/civil rights. The related sequential regressions allow for an assessment of how effective the different constraints are and how far democracy may deteriorate in the worst case under given structural conditions