Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed to generating visualizations that illustrate various measures of risk associated with a litagion® agent in the context of entire industries or specific to a single party that produces or uses the litagion® agent. A Quindrex™ visualization can illustrate catastrophic risk presented by a risk agent, such as a litagion® agent. For example, the risk agent may be a chemical such as bisphenol A (BPA) or benzene. The visualization can include a plurality of portions, each corresponding to a metric of catastrophic risk associated with the risk agent visualized. A dartboard visualization can illustrate catastrophic risk presented by risk agents produced or used by a party, such as a company. The visualization can include a plurality of portions, such as wedges in a dartboard, each corresponding to a risk agent produced or used by the party.

Abstract: Embodiments of the disclosure are directed toward a latent risk assessment user interface, including generating one or more curves that indicate expected future losses due to one or more agents. Embodiments of the disclosure can visualize such losses by mapping estimated litigation losses over time in a loss-time curve and/or generating an exceedance probability curve that indicates total losses at various probabilities. Further, the latent risk assessment user interface can allow a user to select multiple agents and/or multiple companies for aggregation/comparison in a single user interface. Such a user interface can be useful for an insurance company or reinsurer with a portfolio of many companies across diverse industries that utilize the same agents. For example, the latent risk assessment user interface can display estimated future litigation losses over time due to use of BPA and carbon nanotubes for a portfolio of multiple companies, displayed in a single aggregated visualization.

Abstract: Embodiments of the disclosure are directed toward a latent risk assessment user interface, including generating one or more curves that indicate expected future losses due to one or more agents. Embodiments of the disclosure can visualize such losses by mapping estimated litigation losses over time in a loss-time curve and/or generating an exceedance probability curve that indicates total losses at various probabilities. Further, the latent risk assessment user interface can allow a user to select multiple agents and/or multiple companies for aggregation/comparison in a single user interface. Such a user interface can be useful for an insurance company or reinsurer with a portfolio of many companies across diverse industries that utilize the same agents. For example, the latent risk assessment user interface can display estimated future litigation losses over time due to use of BPA and carbon nanotubes for a portfolio of multiple companies, displayed in a single aggregated visualization.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed to methods of generating an event set of prospective litigation scenarios and further generating a visualization of a subset of the event set that constitutes a worst-case mass litigation associated with a common agent. A prospective litigation scenario may describe a claim that a particular plaintiff or class of plaintiffs suffered a harm caused by an agent in an exposure setting, and a particular defendant or class of defendants are identified as potentially liable. Further, a prospective litigation scenario may be paired with a liability risk score indicating the likelihood that a particular defendant or class of defendant might be held financially responsible given the claim.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed to generating visualizations that illustrate various measures of risk associated with a litagion® agent in the context of entire industries or specific to a single party that produces or uses the litagion® agent. A Quindrex™ visualization can illustrate catastrophic risk presented by a risk agent, such as a litagion® agent. For example, the risk agent may be a chemical such as bisphenol A (BPA) or benzene. The visualization can include a plurality of portions, each corresponding to a metric of catastrophic risk associated with the risk agent visualized. A dartboard visualization can illustrate catastrophic risk presented by risk agents produced or used by a party, such as a company. The visualization can include a plurality of portions, such as wedges in a dartboard, each corresponding to a risk agent produced or used by the party.

Abstract: A computer-implemented method, by a computer having a computer processor, of identifying emerging risks of agents causing harms to a particular system comprises accessing, via a computer network, an electronic document database comprising document data; inputting a set of criteria, which includes a selected set of agents and a selected set of harms to a particular system, specified by a user; extracting a subset of the document data that satisfies the set of criteria; generating, with the processor, an array containing agent-harm coincidences from the extracted subset of the document data; assessing, with the processor, statistical significance of each agent-harm coincidence relative to other agent-harm coincidences; compiling, with the processor, risk data, based on the statistical significance, of agents of the selected set of agents causing harms of the selected set of harms to the particular system; and outputting the risk data.

Abstract: A computer-implemented method, by a computer having a computer processor, of identifying emerging risks of agents causing harms to a particular system comprises accessing, via a computer network, an electronic document database comprising document data; inputting a set of criteria, which includes a selected set of agents and a selected set of harms to a particular system, specified by a user; extracting a subset of the document data that satisfies the set of criteria; generating, with the processor, an array containing agent-harm coincidences from the extracted subset of the document data; assessing, with the processor, statistical significance of each agent-harm coincidence relative to other agent-harm coincidences; compiling, with the processor, risk data, based on the statistical significance, of agents of the selected set of agents causing harms of the selected set of harms to the particular system; and outputting the risk data.