Abstract
General agreement exists effective disaster management faces constraints related to knowledge sharing and a need for real-time research responses. Extreme case examples of disasters especially vulnerable to these challenges are global pandemics, or disease outbreaks, in which data required for research response are only available after the start of an outbreak. This paper argues the developing field of probabilistic innovation (innovation increasing probability of solving societal problems through radically increasing coordination of volumes of problem-solving inputs and analysis), and its methodologies, such as those drawing from crowdsourced R&D and social media, may offer useful insights into enabling real time research capabilities, with important implications for disaster and crisis management. Three paradigms of disaster research are differentiated, as literature is related to theory offered by post normal science, Kuhnian 'normal science' and Lakatosian 'structural science,' and the goal of achieving real time research problem solving capacity in disaster crisis situations. Global collaborative innovation platforms and large-scale investments in emerging crowdsourced R&D and social media technologies together with synthesis of appropriate theory may contribute to improved real time disaster response and resilience across contexts, particularly in instances where data required to manage response is only available after disasters unfold.