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Modelling election dynamics and the impact of disinformation

Abstract of Article:

Complex dynamical systems driven by the unravelling of information can be modelled effectively by treating the underlying flow of information as the model input. Complicated dynamical behaviour of the system is then derived as an output. Such an information-based approach is in sharp contrast to the conventional mathematical modelling of information-driven systems whereby one attempts to come up with essentially ad hoc models for the outputs. Here, dynamics of electoral competition is modelled by the specification of the flow of information relevant to election. The seemingly random evolution of the election poll statistics are then derived as model outputs, which in turn are used to study election prediction, impact of disinformation, and the optimal strategy for information management in an election campaign.

Information Geometry

https://doi.org/10.1007/s41884-019-00021-2

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ACS [ acs.org ] Spring 2025 in San Diego, March 23-27 CINF Trusted Scientific Information for Public Policy Reliable information is crucial for effective public policy discussion and decision-making. Policymakers need accurate, transparent, and trustworthy information to make good decisions and allocate resources that effectively address societal issues. Public trust and accountability are enhanced when evidence is used, not assumptions, bias, or speculation. Problem identification and framing can define issues and avoid or mitigate unintended consequences. When all stakeholders have access to the same reliable information and a voice in the discussion, the results are easier accepted by all. This symposium has speakers who will share their best practices in providing reliable information to the public.

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ACS [ acs.org ] Fall 2024 in Denver, August 18-22

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ACS [ acs.org ] Fall 2024 in Denver, August 18-22

Division of Energy & Fuels (ENFL)

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