Algorithmic Decision Theory for solving complex decision problems

Today's decision makers in fields ranging from engineering to psychology, from medicine to economics and/or homeland security are faced with remarkable new technologies, huge amounts of information to help them in reaching good decisions, and the ability to share information at unprecedented speeds and quantities. These tools and resources should lead to better decisions. Yet, the tools bring with them daunting new problems: the massive amounts of data available are often incomplete, unreliable and/or distributed and there is great uncertainty in them; interoperating/distributed decision makers and decision making devices need to be coordinated; many sources of data need to be fused into a good decision; information sharing under new cooperation/competition arrangements raises security problems. When faced with such issues, there are few highly efficient algorithms available to support decision makers. The objective of Algorithmic Decision Theory (ADT) is to improve the ability of decision makers to perform well when facing these new challenges and problems through the use of methods from theoretical computer science, in particular algorithmic methods. The primary goal of ADT is hence to explore and develop algorithmic approaches for solving decision problems arising in a variety of applications areas. Examples include, but are not limited to:
- Computational tractability/intractability of social consensus and multiple criteria compromise functions;
- Improvement of decision support and recommender systems;
- Development of automatic decision devices including on-line decision procedures;
- Robust decision making;
- Learning for multi-agent systems and other on-line decision devices.
This presentation will focus more specifically on multiple criteria decision aiding methodology, the actual research field of the author.