Sequential decision problems are an almost universal problem class, spanning dynamic resource
allocation problems, control problems, optimal stopping/buy-sell problems, active learning problems, as well
as two-agent games and multiagent problems. Application settings span engineering, the sciences,
transportation, health services, medical decision making, energy, e-commerce and finance, but in this talk I will
emphasize applications in the rich area of energy. These problems have been addressed in the research
literature using a variety of modeling and algorithmic frameworks, including (but not limited to) dynamic
programming, stochastic programming, stochastic control, simulation optimization, stochastic search,
approximate dynamic programming, reinforcement learning, model predictive control, and even multiarmed
bandit problems. I will present a universal modeling framework that can be used for any sequential decision
problem in the presence of different sources of uncertainty. I use a “model first” strategy that optimizes over
policies for making decisions. I will present four (meta)classes of policies that are the foundation of any
solution approach that has ever been proposed for a sequential problem, either in the research literature or
used in practice (including policies that have not been invented yet). I will draw on over a decade of research
specifically in energy planning, where the increasing role of energy based on renewables has generated
considerable interest in designing and controlling systems in the presence of multiple and complex sources of
uncertainty. These problems provide nice applications for all four classes of policies. I will close by making the
case for teaching sequential decision analytics at both the undergraduate and graduate levels, including to
students in fields centered on applications as well as methodology.