Narrow Results

Social determinants of health and overall socioeconomic disparities play important factors in determining the resilience of communities in the face of disasters and emergencies. The Emergency Management field is only beginning to address these special populations in their preparedness plans, even though these communities are the most vulnerable and thus the most important for overall resilience. Current response and recovery structures do not focus on addressing these disparities and rebuilding communities to be more resilient in the face of disasters, creating an endless cycle of human suffering and economic waste. Through collaborative partnerships, both federally and private, Emergency Managers can establish a response and recovery model, which informs future planning and mitigation, that addresses inequities building a more resilient nation.

The Frame Problem is the problem of how to design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use. I apply the generative methodology I have developed elsewhere for cognitive science and AI research to show how the Frame Problem can be appropriately updated.