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We present a computational model, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision-making. Current theories of moral decision-making extend beyond pure utilitarian models by relying on contextual factors that vary with culture. MoralDM uses a natural language system to produce formal representations from psychological stimuli, to reduce tailorability. The impacts of secular versus sacred values are modeled via qualitative reasoning, using an order of magnitude representation. MoralDM uses a combination of first-principles reasoning and analogical reasoning to determine consequences and utilities when making moral judgments. We describe how MoralDM works and show that it can model psychological results and improve its performance via accumulating examples.

Smoke plume height is important for modelling smoke transport and resulting effects on air quality. This study presents analyses of ceilometer measurements of smoke plume heights for twenty prescribed burns in the south-eastern United States. Measurements were conducted from mid-winter to early summer between 2009 and 2011. Approximately half of the burns were on tracts of land over 400ha (1000 acres) in area. Average smoke plume height was ~1km. Plume height trended upward from winter to summer. These results could be used as an empirical guideline for fire managers to estimate smoke plume height in the south-eastern US when modelling and measurement are not available. The average could be used as a first-order approximation, and a second-order approximation could be obtained by using the average for spring and autumn seasons, and decreasing or increasing by 0.2km the average for winter or summer. The concentrations of particulate matter with an aerodynamic diameter less than 2.5 or 10μm (PM2.5 and PM10) within smoke plumes calculated from ceilometer backscatter are ~80 and 90μgm–3, and trend downward from winter to summer. Large smoke concentrations are found in the lower portion of smoke plumes for many burns. Smoke plume height shows fast and uniform fluctuations at minute scales for almost all burns and slow and irregular fluctuations at scales from tens of minutes to hours for some burns.

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We present a cognitively motivated model of moral decisionmaking, MoralDM, which models psychological findings about utilitarian and deontological modes of reasoning. Current theories of moral decision-making extend beyond pure utilitarian models by including contextual factors that vary culturally. Our model employs both first-principles reasoning and analogical reasoning to implement rules of moral decision-making and compare previously solved cases to novel situations. The different impacts of secular versus sacred values are modeled via qualitative reasoning, using an order of magnitude representation. We evaluate MoralDM on stimuli taken from two psychology experiments.