A Risk Scenario for Small Businesses in Hurricane Sandy Type Disasters

This research uses a series of surveys followed by mathematical modeling to help discover risk factors, mitigating actions, and the highest return scenarios as a basis for a low-cost business continuity/disaster recovery plan. The surveys use a Delphi study format in order to rank a base list of risks and mitigating actions and to supplement those lists with ones added by the participants. Survey results are analyzed and presented back to the group for a second round of ranking and supplementing the risk/action categories. This paper describes the top ten risks and high value scenario for small business interruptions as determined by a Delphi survey of small businesses affected by Hurricane Sandy. The highest ranked risk is loss of business reputation. The research then uses Cross Impact Analysis and Interpretive Structural Modeling to determine the risk interactions and the highest valued scenario for which to prepare

Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective

The emergence of online user-generated content has raised numerous questions about discourse gender differences as compared to face-to-face interactions. The intended gender-free equality of Internet has been challenged by numerous studies, and significant differences have been found in online communications. This paper proposes the application of text mining techniques to online gender discourse through the analysis of shared reviews in electronic word-of-mouth communities (eWOM), which is a form of user-generated content. More specifically, linguistic issues, sentiment analysis and content analysis were applied to online reviews from a gender perspective. The methodological approach includes gathering online reviews, pre-processing collected reviews and a statistical analysis of documents features to extract the differences between male and female discourses in a specific product category. Findings reveal not only the discourse differences between women and men but also their different preferences and the feasibility of predicting gender using a set of frequent key terms. These findings are interesting both for retailers so they can adapt their offer to the gender of customers, and for online recommender systems, as the proposed methodology can be used to predict the gender of users in those cases where the gender is not explicitly stated

Collaborative development of a small business emergency planning model

Small businesses, which are defined by the US Small Business Administration as entities with less than 500 employees, suffer interruptions from diverse risks such as financial events, legal situations, or severe storms exemplified by Hurricane Sandy. Proper preparations can help lessen the length of the interruption and put employees and owners back to work. Large corporations generally have large budgets available for planning, business continuity, and disaster recovery. Small businesses must decide which risks are the most important and how best to mitigate those risks using minimal resources. This research uses a series of surveys followed by mathematical modeling to help discover risk factors, mitigating actions, and the highest return scenarios as a basis for a low-cost business continuity/disaster recovery plan. The surveys use a Delphi study format in order to rank a base list of risks and mitigating actions and to supplement those lists with ones added by the participants. Survey results are analyzed and presented back to the group for a second round of ranking and supplementing the risk/action categories. After two rounds of surveys the data is presented to an expert panel to investigate how the risks interrelate. Quantifying the interrelationships is the basis for the Cross Impact Analysis model that is able to show the relative impact of one event upon another. Once the impacts are known, a series of high valued scenarios are developed using Interpretive Structural Modeling. These high valued scenarios can be used by the small businesses as a basis for a business continuity/disaster recovery plan

Probabilistic Logics in Foresight

A prudent decision-maker facing a complicated strategic decision considers the factors relevant to the decision, gathers information about the identified factors, and attempts to formulate the best course of action based on the available information. Careful consideration of any alternative course of action might reveal that in addition to the desirable intended consequences, a number of less desirable outcomes are likely to follow as well. Facing a complicatedly entangled net of considerations, entwined positive and negative outcomes, and uncertainty, the decision-maker will attempt to organize the available information and make the decision by using some strategy of reasoning on the information. A logic is away of reasoning adherent to rules, based on structured knowledge. A modeling language and inference rules comprise a logic. The language of a logic is formal, consisting of a defined set of building blocks having well defined meanings. The decision-maker can use a modeling language to describe the information pertinent to the decision-making problem, and organize the information by giving it a structure, which specifies the relationships between the individual considerations. While reasoning about the extensive amount of information in its disorganized form may be overwhelming, in a structured form the information becomes much more useful for the decision-maker, as nowit can be analyzed in a systematic fashion. Inference is systematic reasoning about structured information. As the information is described in a formal and structured way and the process of reasoning about it is systematic, the inference may be automated. Computational inference permits reasoning that would not be possible by intuition in cases where the amount of considerations and their interdependencies exceeds human cognitive capacity. The decision-maker may direct the efforts to describing the decision factors and knowledge with the formal language, with a narrower and more manageable frame of attention, and perform the inference with a computer. Probabilistic language gives room for haziness in knowledge description, and is thus suitable for describing knowledge originating from humans, conveyed to the decision-maker in a non-formal format, such as viewpoints and opinions. Many domains of decision-making and planning use human sourced knowledge, especially if the informants are knowledgeable people or experts with relevant, developed understanding on the domain issues. The expert views can augment the knowledge bases in cases where other forms of information, such as empirical or statistical data, are lacking or completely absent, or do not capture or represent considerations important for the decision-making. This is a typical setting for strategic decision-making, long range planning, and foresight, which have to account for developments and phenomena that do not yet exist in the form they might in the future, or at all. This work discusses approaches for decision support and foresight oriented modeling of expert knowledge bases and inference based on such knowledge bases. Two novel approaches developed by the author are presented and positioned against previous work on cross-impact analysis, structural and morphological analysis, and Bayesian networks. The proposed approaches are called EXIT and AXIOM. EXIT is a conceptually simple approach for structural analysis, based on a previously unutilized computational process for discovery of higher-order influences in a structural model. The analytical output is, in relation to comparable approaches, easier to interpret considering the causal information content of the structural model. AXIOM is a versatile probabilistic logic, combining ideas of structural analysis, morphological analysis, cross-impact analysis and Bayesian belief networks. It provides outputs comparable to Bayesian networks, but has higher fitness for full model parameterization through expert elicitation. A guiding idea of the methodological development work has been that the slightly aged toolset of cross-impact analysis can be updated, improved and extended, and brought to be more interoperable with the Bayesian approach

Quantitative cross impact analysis with latent semantic indexing

Cross impact analysis (CIA) consists of a set of related methodologies that predict the occurrence probability of a specific event and that also predict the conditional probability of a first event given a second event. The conditional probability can be interpreted as the impact of the second event on the first. Most of the CIA methodologies are qualitative that means the occurrence and conditional probabilities are calculated based on estimations of human experts. In recent years, an increased number of quantitative methodologies can be seen that use a large number of data from databases and the internet. Nearly 80% of all data available in the internet are textual information and thus, knowledge structure based approaches on textual information for calculating the conditional probabilities are proposed in literature. In contrast to related methodologies, this work proposes a new quantitative CIA methodology to predict the conditional probability based on the semantic structure of given textual information. Latent semantic indexing is used to identify the hidden semantic patterns standing behind an event and to calculate the impact of the patterns on other semantic textual patterns representing a different event. This enables to calculate the conditional probabilities semantically. A case study shows that this semantic approach can be used to predict the conditional probability of a technology on a different technology